1_3_pandas_1.ipynb

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        "# 자료형: 데이터프레임\n",
        "\n",
        "1. 넘파이 2차원 리스트/어레이를 입력변수로 하여 생성\n",
        "2. 칼럼이름과 1차원 리스트/어레이으로 구성된 딕셔너리를 입력변수로 하여 생성"
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              "pandas.core.frame.DataFrame"
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      "source": [
        "- 데이터프레임 통계량"
      ]
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      "source": [
        "df.mean()"
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          "data": {
            "text/plain": [
              "KOR    3.0\n",
              "USA    4.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
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      ]
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      "source": [
        "df.mean().to_frame('mean')"
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              "      <th>KOR</th>\n",
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              "     mean\n",
              "KOR   3.0\n",
              "USA   4.0"
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      "source": [
        "df.apply(['mean', 'median', 'sum', 'std'])  # prod"
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              "      <th>USA</th>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
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              "        KOR   USA\n",
              "mean    3.0   4.0\n",
              "median  3.0   4.0\n",
              "sum     9.0  12.0\n",
              "std     2.0   2.0"
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      "source": [
        "## 슬라이스"
      ]
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              "    KOR  USA\n",
              "17    1    2\n",
              "18    3    4\n",
              "19    5    6"
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      },
      "source": [
        "df.loc[18:, :]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    KOR  USA\n",
              "18    3    4\n",
              "19    5    6"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQZN7ISXFb6G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb61f2d8-e919-4f4b-9a0a-7caa746a2160"
      },
      "source": [
        "df.loc[17, :]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KOR    1\n",
              "USA    2\n",
              "Name: 17, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0xVGoqFFb6G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "f1ad0eae-d7a7-4dbd-cccd-ab7720a4bbaa"
      },
      "source": [
        "df.loc[[17]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "    KOR  USA\n",
              "17    1    2"
            ]
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          "metadata": {},
          "execution_count": 19
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Jni6I5OFb6G"
      },
      "source": [
        "df = df.loc[18:, :].copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A41vwQa8Fb6H"
      },
      "source": [
        "- 값 지정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMxrIhwkFb6H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "f137bc46-4caa-466d-8ca8-c092a4caaa47"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "    KOR  USA\n",
              "18    3    4\n",
              "19    5    6"
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          },
          "metadata": {},
          "execution_count": 21
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0bN1bOaFb6H"
      },
      "source": [
        "df.loc[18, 'KOR'] = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQENYkKOFb6H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "90654740-c22e-4087-ff4d-aeb10c2b4ed0"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    KOR  USA\n",
              "18    0    4\n",
              "19    5    6"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqspKdNaFb6H"
      },
      "source": [
        "df.loc[:, 'USA'] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdrGp4lLFb6H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "d1e54ce4-e56a-4fc6-d1ee-7b6b51352eba"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    KOR  USA\n",
              "18    0    1\n",
              "19    5    1"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6L_-qYKYFb6I"
      },
      "source": [
        "df.loc[:, :] = [[1, 2], [3, 4]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZiRi6yLFb6I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "a248dd27-2114-45dc-d12c-1f5817deb74e"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    KOR  USA\n",
              "18    1    2\n",
              "19    3    4"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEL4erYjFb6I"
      },
      "source": [
        "df = [[1, 2], [3, 4]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8i_pNlvmFb6I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eddd3c76-d3ac-49ca-d1da-e2f49f3ae26c"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[1, 2], [3, 4]]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvfXMKPVFb6I"
      },
      "source": [
        "## 멀티인덱스"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llFendVRFb6I"
      },
      "source": [
        "df = pd.DataFrame({'country':['USA', 'KOR', 'USA', 'KOR'],\n",
        "                   'pop':[1, 2, 3, 4],\n",
        "                   'gdp':[3, 4, 5, 6]},\n",
        "                  index = [16, 16, 17, 17])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRMikIq-Fb6J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "45ec767d-68f1-439a-c99b-ffcb285dc4a4"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>USA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KOR</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "16     KOR    2    4\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkeihnctFb6J"
      },
      "source": [
        "df = df.set_index('country', append=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rHSjfjfFb6J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "21c36ef8-dda7-4a6a-8ebd-a83b5e8ac91c"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
              "      <th>USA</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
              "      <th>USA</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            pop  gdp\n",
              "   country          \n",
              "16 USA        1    3\n",
              "   KOR        2    4\n",
              "17 USA        3    5\n",
              "   KOR        4    6"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf0QR9hHFb6J"
      },
      "source": [
        "from pandas import MultiIndex as MI"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siMrWviQFb6J"
      },
      "source": [
        "df.index = MI.from_product([['16', '17'], ['미국', '한국']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMrhzfKWFb6J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "7e13f01e-82c9-46fe-e6a7-2a79c4c1ddca"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
              "      <th>미국</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>한국</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
              "      <th>미국</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>한국</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pop  gdp\n",
              "16 미국    1    3\n",
              "   한국    2    4\n",
              "17 미국    3    5\n",
              "   한국    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VHxRRa2Fb6K"
      },
      "source": [
        "df.index = [':'.join(c).strip() for c in df.index.values]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqhnEpXCFb6K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "38a9ffd8-f39a-4cc2-fcc7-4b95ce43fc6a"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16:미국</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16:한국</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17:미국</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17:한국</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pop  gdp\n",
              "16:미국    1    3\n",
              "16:한국    2    4\n",
              "17:미국    3    5\n",
              "17:한국    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPjt_0TpFb6K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7c6ae3e-5a5a-4af4-9bbc-ba5d396bc1e7"
      },
      "source": [
        "df.index.str.split(':').tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['16', '미국'], ['16', '한국'], ['17', '미국'], ['17', '한국']]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OL6mTBMuXuEv"
      },
      "source": [
        "라벨이 주어진 형태에 따라 멀티인덱스 생성 함수를 선택\n",
        "\n",
        "\n",
        "```\n",
        "- from_tuples() : [['16', '미국'], ['16', '한국'], ['17', '미국'], ['17', '한국']]\n",
        "- from_product() : [['16', '17'], ['미국', '한국']]\n",
        "- from_arrays() : [['16', '16', '17', '17'], ['미국', '미국', '한국', '한국']]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8splM2pFb6K"
      },
      "source": [
        "df.index = MI.from_tuples(df.index.str.split(':').tolist())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CME_jUWeFb6K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "f037a06b-7f15-4097-91eb-086d98092e41"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
              "      <th>미국</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>한국</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
              "      <th>미국</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>한국</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       pop  gdp\n",
              "16 미국    1    3\n",
              "   한국    2    4\n",
              "17 미국    3    5\n",
              "   한국    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTSiA2zcFb6K"
      },
      "source": [
        "IndexSlice: 다양한 층위에서 데이터 선택"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUd5WGaFFb6K"
      },
      "source": [
        "df = pd.DataFrame({'country':['USA', 'KOR', 'USA', 'KOR'],\n",
        "                   'pop':[1, 2, 3, 4],\n",
        "                   'gdp':[3, 4, 5, 6]},\n",
        "                  index = [16, 16, 17, 17])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Qnx37FrFb6L"
      },
      "source": [
        "df = df.set_index('country', append=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AFbUmSnFb6L"
      },
      "source": [
        "idx = pd.IndexSlice"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKt3N6P7Fb6L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "8f55ff78-eb36-417e-e526-32663c7836bd"
      },
      "source": [
        "df.loc[idx[:, 'USA'], :]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <th>USA</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <th>USA</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            pop  gdp\n",
              "   country          \n",
              "16 USA        1    3\n",
              "17 USA        3    5"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6HeKk_DFb6L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45c6b5a1-c911-47fb-e7e7-69eefd67f031"
      },
      "source": [
        "df.loc[idx[16, 'USA'], :]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pop    1\n",
              "gdp    3\n",
              "Name: (16, USA), dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVwZilgIFb6L"
      },
      "source": [
        "df = pd.DataFrame({'country':['USA', 'KOR', 'USA', 'KOR'],\n",
        "                   'pop':[1, 2, 3, 4],\n",
        "                   'gdp':[3, 4, 5, 6]},\n",
        "                  index = [16, 16, 17, 17])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogWJ7-IHFb6L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "acf38d1d-fe00-4da8-dcbc-049b3c084cfe"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>USA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KOR</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "16     KOR    2    4\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbO_yKewFb6L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "29dc70f1-b7a9-45a4-a245-ec762638c8db"
      },
      "source": [
        "df.loc[df['pop'].ge(3)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "ee38f06f-cf3b-4ad6-ec7c-85f2fae5d1dc"
      },
      "source": [
        "df"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>USA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KOR</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "16     KOR    2    4\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkB6zT6TFb6M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "acb91f9c-7139-405b-c47a-00ac35673340"
      },
      "source": [
        "df.loc[df.country.isin(['USA', 'JPN'])]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>USA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "17     USA    3    5"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjyhnkPTFb6M"
      },
      "source": [
        "- droplevels\n",
        "- get_level_values\n",
        "- reorder_levels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZdVvSvAFb6M",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "source": [
        "df"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>USA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KOR</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "16     KOR    2    4\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nP0xVFvJFb6M"
      },
      "source": [
        "df = df.set_index('country', append=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57HNR0HLFb6M"
      },
      "source": [
        "df.index.names = ['yr', 'country']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hty55evqFb6N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "89da230b-a74c-43a0-8d9f-80b987569b98"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yr</th>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
              "      <th>USA</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
              "      <th>USA</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            pop  gdp\n",
              "yr country          \n",
              "16 USA        1    3\n",
              "   KOR        2    4\n",
              "17 USA        3    5\n",
              "   KOR        4    6"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyTNXzmvFb6N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e52f6260-d17c-41f5-b158-f4c860f9cef8"
      },
      "source": [
        "df.index.get_level_values(0).unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([16, 17], dtype='int64', name='yr')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKl-hA3xFb6N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75e35f70-b256-47af-da7e-b28eb1090fbb"
      },
      "source": [
        "df.index.get_level_values(1).unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['USA', 'KOR'], dtype='object', name='country')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JphVi29kFb6N"
      },
      "source": [
        "df = df.loc[idx[:, 'USA'], :]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlhDiRfiFb6N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "f78e2280-a0a2-41a7-cf66-ad324f65007d"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yr</th>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <th>USA</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <th>USA</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            pop  gdp\n",
              "yr country          \n",
              "16 USA        1    3\n",
              "17 USA        3    5"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2QWkTNXFb6N"
      },
      "source": [
        "df.index = df.index.droplevel(level = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwW-khqYFb6N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "b01dff8e-9687-42c3-d3ae-6d9bddc28837"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yr</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    pop  gdp\n",
              "yr          \n",
              "16    1    3\n",
              "17    3    5"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZXWDtRuFb6O"
      },
      "source": [
        "## 연습문제\n",
        "\n",
        "1.  연도(2017~2020), 국가별(kr, us, jp) 경제성장률과 물가상승률 데이터프레임을 생성하시오\n",
        "    - 인덱스 첫번째 레벨은 연도, 두번째 레벨은 국가명, 칼럼 라벨은 [gdp, inf]\n",
        "    - 경제성장률과 물가상승률은 넘파이 랜덤표본 이용\n",
        "\n",
        "3.  2018년 이후 국가별 경제성장률과 물가상승률을 보이시오"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZI5iU7wFb6O"
      },
      "source": [
        "# 연산"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1Riglx2Fb6O"
      },
      "source": [
        "# 데이터프레임을 병렬로 표시하기 위한 함수 정의\n",
        "from IPython.display import display_html\n",
        "def display_by_side(*args):\n",
        "    html_str=''\n",
        "    for df in args:\n",
        "        html_str+=df.to_html()\n",
        "    display_html(html_str.replace('table','table style=\"display:inline\"'),raw=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgS0MsnGFb6O"
      },
      "source": [
        "df = pd.DataFrame({'KOR':[1, 3], 'USA':[2, 4]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ui4W9sPFb6P"
      },
      "source": [
        "df1 = df + 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0SMErWnFb6P"
      },
      "source": [
        "df1.columns = ['USA', 'CHN']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_EoQTidFb6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "ad99f3d5-ec50-42a9-d44b-cf4764e5a538"
      },
      "source": [
        "display_by_side(df1, df + df1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USA</th>\n",
              "      <th>CHN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CHN</th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table style=\"display:inline\">"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9y3TvVHBFb6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "58902dd4-6291-4ef0-8494-edc1e5d9479e"
      },
      "source": [
        "display(df, df.T)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   KOR  USA\n",
              "0    1    2\n",
              "1    3    4"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0  1\n",
              "KOR  1  3\n",
              "USA  2  4"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gy-VVs5zFb6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "701b5f79-7437-46d4-c33d-7e869e66dd48"
      },
      "source": [
        "df.dot(df.T)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0   1\n",
              "0   5  11\n",
              "1  11  25"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiUXEJ29Fb6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "8cd2e9f2-18c6-491e-bf53-e1c6d8fa6f98"
      },
      "source": [
        "df.T.dot(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     KOR  USA\n",
              "KOR   10   14\n",
              "USA   14   20"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1XtL82SFb6Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "930a90cf-205f-429e-8e46-8b12f8365468"
      },
      "source": [
        "df.dot(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-89-23a34507af6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdot\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             \u001b[0mcommon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices are not aligned\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m             \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: matrices are not aligned"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsbUXKftFb6Q"
      },
      "source": [
        "df1 = pd.DataFrame({'USA':[1, 3], 'CHN':[2, 4]})\n",
        "df2 = pd.DataFrame({'JPN':[2, 4], 'KOR':[6, 8]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yT7WQIaeFb6Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "156dca58-0b6e-4d07-c88f-74b4d37abae4"
      },
      "source": [
        "display(df1, df2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USA</th>\n",
              "      <th>CHN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   USA  CHN\n",
              "0    1    2\n",
              "1    3    4"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>JPN</th>\n",
              "      <th>KOR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   JPN  KOR\n",
              "0    2    6\n",
              "1    4    8"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uOYUIFgFb6Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "fa00a57f-e77c-4a00-ed23-a569a347f85a"
      },
      "source": [
        "pd.concat([df1, df2], axis = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USA</th>\n",
              "      <th>CHN</th>\n",
              "      <th>JPN</th>\n",
              "      <th>KOR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   USA  CHN  JPN  KOR\n",
              "0    1    2    2    6\n",
              "1    3    4    4    8"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoE2i4Q7Fb6Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "b0a29c03-4813-47d5-afe8-fbfb002261cb"
      },
      "source": [
        "pd.concat([df1, df2], axis = 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USA</th>\n",
              "      <th>CHN</th>\n",
              "      <th>JPN</th>\n",
              "      <th>KOR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   USA  CHN  JPN  KOR\n",
              "0  1.0  2.0  NaN  NaN\n",
              "1  3.0  4.0  NaN  NaN\n",
              "0  NaN  NaN  2.0  6.0\n",
              "1  NaN  NaN  4.0  8.0"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1QanSsPDFb6Q"
      },
      "source": [
        "## merge, concat\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1)\n",
        "print(df2)"
      ],
      "metadata": {
        "id": "Y7QvZRLOKNF0",
        "outputId": "8859c882-0be7-4468-af0c-e532b30c7ec2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   USA  CHN\n",
            "0    1    2\n",
            "1    3    4\n",
            "   JPN  KOR\n",
            "0    2    6\n",
            "1    4    8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CguyYdjRFb6Q"
      },
      "source": [
        "df2.columns = ['CHN', 'KOR']"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1)\n",
        "print('')\n",
        "print(df2)"
      ],
      "metadata": {
        "id": "GJkoc5dBKVkg",
        "outputId": "007eed32-f15c-44fa-9d94-628896ea1664",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   USA  CHN\n",
            "0    1    2\n",
            "1    3    4\n",
            "\n",
            "   CHN  KOR\n",
            "0    2    6\n",
            "1    4    8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UP0uAmN3Fb6R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "f6f97929-9448-4884-dfdd-fdf3db7b5986"
      },
      "source": [
        "df1.merge(df2, on = 'CHN', how = 'left')"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   USA  CHN  KOR\n",
              "0    1    2    6\n",
              "1    3    4    8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2087988-67f2-4d75-8229-d565b9c09a1d\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-c2087988-67f2-4d75-8229-d565b9c09a1d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c2087988-67f2-4d75-8229-d565b9c09a1d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0e5a4ca0-ab20-45a5-afc8-e30e26149ce0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"USA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KOR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 6,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z83FoLYFFb6R"
      },
      "source": [
        "## add, sub, mul, div"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "l37XvMbiKqen",
        "outputId": "4a663dfc-09fc-476c-ebfd-2757ef6c9c04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   KOR  USA\n",
              "0    1    2\n",
              "1    3    4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0df9926-99d9-44d7-9d69-7b94255c459a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0df9926-99d9-44d7-9d69-7b94255c459a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f0df9926-99d9-44d7-9d69-7b94255c459a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f0df9926-99d9-44d7-9d69-7b94255c459a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-72236469-1d70-4d60-9db0-464e971f027a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-72236469-1d70-4d60-9db0-464e971f027a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-72236469-1d70-4d60-9db0-464e971f027a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_81d13e67-c7ae-4b32-8d97-edb8220b1067\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_81d13e67-c7ae-4b32-8d97-edb8220b1067 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"KOR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.sum(axis=0))\n",
        "print('')\n",
        "print(df.sum(axis=1))"
      ],
      "metadata": {
        "id": "Ose41ZpMKnGp",
        "outputId": "9a228217-9711-4baa-b56f-662ec19f412e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KOR    4\n",
            "USA    6\n",
            "dtype: int64\n",
            "\n",
            "0    3\n",
            "1    7\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total']"
      ],
      "metadata": {
        "id": "iig9sQlPK_Co",
        "outputId": "3594f718-b474-4ca5-b16d-cf46cc7115db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'Total'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3653\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Total'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-142-70cd383c7af7>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Total'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3761\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3762\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3763\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3655\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3656\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3657\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Total'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuXeYsyaFb6R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "3d8f6b2d-d04a-4e5f-e974-1d640499bd4b"
      },
      "source": [
        "df['Total'] = df.sum(axis = 1)\n",
        "df"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   KOR  USA  Total\n",
              "0    1    2      3\n",
              "1    3    4      7"
            ],
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_69b2a1bd-7755-4111-85b0-66d8fc78690b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"KOR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 3,\n        \"max\": 7,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6h2nFaPpFb6R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "bdae0c60-6251-41b7-b868-ceda5dae0537"
      },
      "source": [
        "df.div(df.Total, axis = 0)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        KOR       USA  Total\n",
              "0  0.333333  0.666667    1.0\n",
              "1  0.428571  0.571429    1.0"
            ],
            "text/html": [
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              "      <th>KOR</th>\n",
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              "      <th>1</th>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.571429</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d187013-657e-4935-aa5e-4a8a35b8be27');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
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              "\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08bf198c-a461-4424-93b0-151a7eb091b4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"KOR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06734350297014738,\n        \"min\": 0.3333333333333333,\n        \"max\": 0.42857142857142855,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.42857142857142855,\n          0.3333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06734350297014738,\n        \"min\": 0.5714285714285714,\n        \"max\": 0.6666666666666666,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5714285714285714,\n          0.6666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMO_Zgs1Fb6R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "af155acf-ce8a-4d82-9ed4-54c388abfcb3"
      },
      "source": [
        "df.div(df.Total, axis = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>KOR</th>\n",
              "      <th>Total</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0   1  KOR  Total  USA\n",
              "0 NaN NaN  NaN    NaN  NaN\n",
              "1 NaN NaN  NaN    NaN  NaN"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O46OqJEnFb6R"
      },
      "source": [
        "## cut, qcut\n",
        "\n",
        "- 숫자 데이터를 범주형 데이터로 변환"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vacW6ZpHFb6R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "824e17d0-45d1-452b-9025-ddda7309af29"
      },
      "source": [
        "from numpy.random import randint\n",
        "df = pd.DataFrame({'a':range(4), 'b':randint(1,10,4)})\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b\n",
              "0  0  9\n",
              "1  1  7\n",
              "2  2  5\n",
              "3  3  3"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eI2fE8V5Fb6S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d892a714-2ac2-449f-8026-32ea43f4e11f"
      },
      "source": [
        "pd.cut(df.b, [0, 5, 10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    (5, 10]\n",
              "1    (5, 10]\n",
              "2     (0, 5]\n",
              "3     (0, 5]\n",
              "Name: b, dtype: category\n",
              "Categories (2, interval[int64]): [(0, 5] < (5, 10]]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCRKd05rFb6S"
      },
      "source": [
        "df.loc[:, 'c'] = pd.cut(df.b, [0, 5, 10], labels=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xn7zvZKSFb6S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "eab18f54-b196-4199-9d32-772ebc905f02"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c\n",
              "0  0  9  1\n",
              "1  1  7  1\n",
              "2  2  5  0\n",
              "3  3  3  0"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08fMH40Fb6S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62432f12-73c4-46a5-8fdf-00a18baf03b1"
      },
      "source": [
        "pd.qcut(df.b, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      (6.0, 9.0]\n",
              "1      (6.0, 9.0]\n",
              "2    (2.999, 6.0]\n",
              "3    (2.999, 6.0]\n",
              "Name: b, dtype: category\n",
              "Categories (2, interval[float64]): [(2.999, 6.0] < (6.0, 9.0]]"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ob2Q1l0Fb6S"
      },
      "source": [
        "## groupby"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8C-x8ScFb6S"
      },
      "source": [
        "df = pd.DataFrame({'country':['USA', 'KOR', 'USA', 'KOR'],\n",
        "                   'pop':[1, 2, 3, 4],\n",
        "                   'gdp':[3, 4, 5, 6]},\n",
        "                  index = [16, 16, 17, 17])"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "O4oI-gZpLNaH",
        "outputId": "34a15d84-c662-485d-93a7-22130d03c024",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "16     KOR    2    4\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ],
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              "      <th>16</th>\n",
              "      <td>USA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KOR</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>USA</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
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              "\n",
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              "        document.querySelector('#df-1474d41a-7c99-4376-940e-1ff00638bcc6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1474d41a-7c99-4376-940e-1ff00638bcc6');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3778857-47c9-4f05-abde-a9663a922868 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_31b71f28-c30f-411c-9e9c-c842b9cab20b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_31b71f28-c30f-411c-9e9c-c842b9cab20b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"KOR\",\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goXsR01iFb6S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "c2a72873-9384-4a68-a174-fe67c4a021c4"
      },
      "source": [
        "df.groupby('country').mean()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         pop  gdp\n",
              "country          \n",
              "KOR      3.0  5.0\n",
              "USA      2.0  4.0"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47ea9da5-f30c-48da-842c-190a280c597e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-47ea9da5-f30c-48da-842c-190a280c597e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-47ea9da5-f30c-48da-842c-190a280c597e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-68b9c55a-dab4-4615-916d-6a3938497e25\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68b9c55a-dab4-4615-916d-6a3938497e25')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-68b9c55a-dab4-4615-916d-6a3938497e25 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"USA\",\n          \"KOR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 2.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 4.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqFZ7Hh8Fb6S"
      },
      "source": [
        "df = df[['pop', 'gdp']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9xWkOwvFb6T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40cda880-7d6c-4793-9db5-c8e3995275cb"
      },
      "source": [
        "df.groupby(pd.cut(df['pop'], [0, 2, 4]))['gdp'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pop\n",
              "(0, 2]    3.5\n",
              "(2, 4]    5.5\n",
              "Name: gdp, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMHEb2UbFb6T"
      },
      "source": [
        "## lambda 함수"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cal_square(x):\n",
        "  return x**2"
      ],
      "metadata": {
        "id": "8hMRuiDFLg0T"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7x6M5I2sFb6U"
      },
      "source": [
        "square = lambda x: x * x"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdS8A-5NFb6U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3777ef60-1606-4a80-fb07-bc941e414fbe"
      },
      "source": [
        "square(2)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cal_square(2)"
      ],
      "metadata": {
        "id": "wlJDm7ATLywO",
        "outputId": "482ef135-c7d3-4ff2-8e38-583c8c5a1b6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Texb31pkFb6U"
      },
      "source": [
        "## apply/applymap/map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RABzpkabFb6U"
      },
      "source": [
        "df = pd.DataFrame({'country':['USA', 'KOR', 'USA', 'KOR'],\n",
        "                   'pop':[1, 2, 3, 4],\n",
        "                   'gdp':[3, 4, 5, 6]},\n",
        "                  index = [16, 16, 17, 17])"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['pop', 'gdp']].apply(lambda x: x+100)"
      ],
      "metadata": {
        "id": "P8B0nWbLL-d7",
        "outputId": "caf9c30f-ea99-4c59-fdf0-365fd424a0d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    pop  gdp\n",
              "16  101  103\n",
              "16  102  104\n",
              "17  103  105\n",
              "17  104  106"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c78b67cb-df2c-4dc8-aba8-6a1b2bae3576\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>101</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>102</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>103</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>104</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c78b67cb-df2c-4dc8-aba8-6a1b2bae3576')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c78b67cb-df2c-4dc8-aba8-6a1b2bae3576 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c78b67cb-df2c-4dc8-aba8-6a1b2bae3576');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ab5d79e1-ff0c-41de-b69d-bf5b850ae05e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab5d79e1-ff0c-41de-b69d-bf5b850ae05e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab5d79e1-ff0c-41de-b69d-bf5b850ae05e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['pop', 'gdp']]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 101,\n        \"max\": 104,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          102,\n          104,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 103,\n        \"max\": 106,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          104,\n          106,\n          103\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['pop', 'gdp']].apply(lambda x: x.div(x.sum()))"
      ],
      "metadata": {
        "id": "4eslS_DSMJIi",
        "outputId": "e4244ce7-1551-44d3-b931-c129f6750b1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    pop       gdp\n",
              "16  0.1  0.166667\n",
              "16  0.2  0.222222\n",
              "17  0.3  0.277778\n",
              "17  0.4  0.333333"
            ],
            "text/html": [
              "\n",
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              "      <th>16</th>\n",
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              "      <td>0.166667</td>\n",
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              "      <th>17</th>\n",
              "      <td>0.3</td>\n",
              "      <td>0.277778</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.333333</td>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9e03ad8f-1432-42b3-871d-506521c73979 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9e03ad8f-1432-42b3-871d-506521c73979');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-782752c2-568a-473f-811b-bdf25277dbe9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-782752c2-568a-473f-811b-bdf25277dbe9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-782752c2-568a-473f-811b-bdf25277dbe9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['pop', 'gdp']]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12909944487358055,\n        \"min\": 0.1,\n        \"max\": 0.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2,\n          0.4,\n          0.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07172191381865586,\n        \"min\": 0.16666666666666666,\n        \"max\": 0.3333333333333333,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2222222222222222,\n          0.3333333333333333,\n          0.16666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy7ZDNGVFb6U",
        "outputId": "aa107cbf-ff8a-4c90-d4b4-ebd8260a1146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "df[['pop', 'gdp']].apply(lambda x: x.div(x.sum()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.2</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.3</td>\n",
              "      <td>0.277778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    pop       gdp\n",
              "16  0.1  0.166667\n",
              "16  0.2  0.222222\n",
              "17  0.3  0.277778\n",
              "17  0.4  0.333333"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-fk7mzNFb6V",
        "outputId": "600ab638-de7a-49b3-968a-0c7a47f6e45a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "df = df[['pop', 'gdp']].apply(lambda x: x.div(x.sum()))\n",
        "df.applymap(lambda x: '{:,.2f}'.format(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pop</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pop   gdp\n",
              "16  0.10  0.17\n",
              "16  0.20  0.22\n",
              "17  0.30  0.28\n",
              "17  0.40  0.33"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvgmlRAkFb6V"
      },
      "source": [
        "## stack/unstack"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsCMBSD5Fb6V"
      },
      "source": [
        "df = pd.DataFrame({'country':['USA', 'KOR', 'USA', 'KOR'],\n",
        "                   'pop':[1, 2, 3, 4],\n",
        "                   'gdp':[3, 4, 5, 6]},\n",
        "                  index = [16, 16, 17, 17])\n",
        "\n",
        "df = df.set_index('country', append=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nme-3ZupFb6V",
        "outputId": "d4373fd9-5f07-446e-b613-44a28787a0bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "df.unstack(level = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">pop</th>\n",
              "      <th colspan=\"2\" halign=\"left\">gdp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        pop     gdp    \n",
              "country KOR USA KOR USA\n",
              "16        2   1   4   3\n",
              "17        4   3   6   5"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0UGrRKaFb6V"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMs9Lko9Fb6V"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVXOvyFEFb6V"
      },
      "source": [
        "## 연습문제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoygnEUIFb6V"
      },
      "source": [
        "1.  앞에서 계산한 연도(2017~2020), 국가별(kr, us, jp) 경제성장률과 물가상승률 데이터프레임을 이용하여\n",
        "    - 2018년 이후 국가별 평균 경제성장률과 물가상승률을 계산하라\n",
        "    - 2018년 이후 한국과 미국의 연도별 평균경제성장률을 계산하라\n",
        "\n",
        "2.  같은 방식으로 중국(cn)의 경제성장률과 물가상승률 데이터프레임을 생성하여 결합하라\n",
        "\n",
        "3.  4개 국가 평균 경제성장률이 0보다 큰 연도를 찾아라"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIVC5jL6Fb6W"
      },
      "source": [
        "# 시계열"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.randint(1, 20, 24)\n",
        "print(a)\n",
        "print(np.shape(a))\n",
        "\n",
        "print(a.reshape(12, 2))\n",
        "print(np.shape(a.reshape(12, 2)))"
      ],
      "metadata": {
        "id": "SMEEgBwhMZVv",
        "outputId": "5645eb74-0afa-4e59-d4c8-86e0d503e7ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12 15 15  5  6 13 17  7  9 13 12 17 10  5  9 10  7  8  4 16  5 18 17  2]\n",
            "(24,)\n",
            "[[12 15]\n",
            " [15  5]\n",
            " [ 6 13]\n",
            " [17  7]\n",
            " [ 9 13]\n",
            " [12 17]\n",
            " [10  5]\n",
            " [ 9 10]\n",
            " [ 7  8]\n",
            " [ 4 16]\n",
            " [ 5 18]\n",
            " [17  2]]\n",
            "(12, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(np.random.randint(1, 20, 24).reshape(12, 2), columns=['con', 'gdp'])"
      ],
      "metadata": {
        "id": "llGpNcujM-kO",
        "outputId": "85e7ed4b-6aa3-4f74-e5dc-c060df4b2857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        }
      },
      "execution_count": 164,
      "outputs": [
        {
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          "data": {
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              "    con  gdp\n",
              "0    19   19\n",
              "1     6   13\n",
              "2    12   17\n",
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              "7    13    1\n",
              "8     9    3\n",
              "9     1    6\n",
              "10    6   18\n",
              "11   10   17"
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"con\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1,\n        \"max\": 19,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9,\n          6,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1,\n        \"max\": 19,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3,\n          13,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 164
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e421xR4yFb6W"
      },
      "source": [
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randint(1, 20, 24).reshape(12, 2), columns=['con', 'gdp'])"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
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        "id": "XqDVcDx2Fb6W",
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          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "2636d166-1cbb-4a2f-8ffb-a86658157ac7"
      },
      "source": [
        "df"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    con  gdp\n",
              "0     9    6\n",
              "1    10   15\n",
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              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"con\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1,\n        \"max\": 18,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          14,\n          10,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 2,\n        \"max\": 18,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          16,\n          15,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwgF96pWFb6W"
      },
      "source": [
        "df.index = pd.date_range('2017-3-31', '2019-12-31', freq='Q')"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lG9j_OCvFb6W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "ebb1e849-2457-4774-b4cf-085502daca82"
      },
      "source": [
        "df"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            con  gdp\n",
              "2017-03-31    9    6\n",
              "2017-06-30   10   15\n",
              "2017-09-30   18    6\n",
              "2017-12-31    2   18\n",
              "2018-03-31   10   14\n",
              "2018-06-30    2    2\n",
              "2018-09-30    1   14\n",
              "2018-12-31   16    9\n",
              "2019-03-31   13   12\n",
              "2019-06-30   14   16\n",
              "2019-09-30    3    5\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e3aaa658-74d0-4321-b756-f395c8a76ca0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e3aaa658-74d0-4321-b756-f395c8a76ca0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-8fafa5c6-7a02-4e12-90b7-110ede014a71\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fafa5c6-7a02-4e12-90b7-110ede014a71')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8fafa5c6-7a02-4e12-90b7-110ede014a71 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0ecf15b2-fc00-424d-9cdd-8a7e281d884d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0ecf15b2-fc00-424d-9cdd-8a7e281d884d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"con\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1,\n        \"max\": 18,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          14,\n          10,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 2,\n        \"max\": 18,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          16,\n          15,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=[12, 8])\n",
        "ax.plot(df['gdp'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8bVJD1YXNaEK",
        "outputId": "6af8aacc-f5e3-4ca9-9ae9-e84ec747841e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wywtDpnFb6W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd54182-6758-42ef-80b2-d65426dd998f"
      },
      "source": [
        "type(df.index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.indexes.datetimes.DatetimeIndex"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0ah28fmRHMb"
      },
      "source": [
        "## resample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeQtJkTeFb6X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "394b6a06-c3bb-49ac-e1d2-b8e9fbc10a07"
      },
      "source": [
        "df.resample('A').sum()"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            con  gdp\n",
              "2017-12-31   39   45\n",
              "2018-12-31   29   39\n",
              "2019-12-31   32   38"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7678f04-e3e3-4bbf-82e3-cf52d60c845a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>con</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>39</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>29</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>32</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7678f04-e3e3-4bbf-82e3-cf52d60c845a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7678f04-e3e3-4bbf-82e3-cf52d60c845a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7678f04-e3e3-4bbf-82e3-cf52d60c845a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6b4af489-b20d-4801-88c1-be9ee1f5a54a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b4af489-b20d-4801-88c1-be9ee1f5a54a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6b4af489-b20d-4801-88c1-be9ee1f5a54a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"con\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 29,\n        \"max\": 39,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39,\n          29,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 38,\n        \"max\": 45,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          45,\n          39,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0m0d_84Fb6X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "99b1e4f1-8eaa-4868-da08-94143ebc58ae"
      },
      "source": [
        "df.resample('A').last()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>con</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            con  gdp\n",
              "2017-12-31    3   13\n",
              "2018-12-31    1    8\n",
              "2019-12-31    3   11"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shkBcxDFFb6X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "4e62dd27-df92-4a23-cedd-d8c87657ff34"
      },
      "source": [
        "df.resample('A').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>con</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>6.75</td>\n",
              "      <td>12.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>7.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>9.50</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             con    gdp\n",
              "2017-12-31  6.75  12.25\n",
              "2018-12-31  7.00   7.00\n",
              "2019-12-31  9.50   7.00"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.resample('A').agg({'con': 'mean', 'gdp': 'sum'})"
      ],
      "metadata": {
        "id": "yWpHt6L-OOOf",
        "outputId": "9c88400f-9194-43be-be32-c3d71b5e169a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             con  gdp\n",
              "2017-12-31  9.75   45\n",
              "2018-12-31  7.25   39\n",
              "2019-12-31  8.00   38"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62e29c04-fd5c-46b1-9185-7910e5548b11\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>con</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>9.75</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>7.25</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>8.00</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62e29c04-fd5c-46b1-9185-7910e5548b11')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-62e29c04-fd5c-46b1-9185-7910e5548b11 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-62e29c04-fd5c-46b1-9185-7910e5548b11');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0d9fcbb3-a1da-41fb-be09-5be27c7594e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d9fcbb3-a1da-41fb-be09-5be27c7594e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0d9fcbb3-a1da-41fb-be09-5be27c7594e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"con\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.282900359861721,\n        \"min\": 7.25,\n        \"max\": 9.75,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.75,\n          7.25,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 38,\n        \"max\": 45,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          45,\n          39,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQvJHYdUPHbd"
      },
      "source": [
        "## simple graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUTUDC33Fb6X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "f2e10993-bba7-4c90-8bf6-3c503bc48bd5"
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b989b0a10>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mc217b1RFb6X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "fcd6638b-4ec0-4200-c551-a52cd72c9301"
      },
      "source": [
        "df.plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b988c91d0>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5ilVq7XFb6X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1a57a362-38cd-49a7-f917-1994306f1026"
      },
      "source": [
        "df.resample('M').interpolate(method='cubic').plot()\n",
        "# ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 177
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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oCcp8GIGe+SjdDV1t4Op/9yAIghBJEhMTe3l3tLS08K1vfYvPfOYzAOTk5PDiiy/2es0nPvGJfseZNGkSr7/+engX2wfJfFgITdPY5LdUP3/m0C22IyE482HqoXTtDUrsCZBvUOYjdQok5ICvqycLIwiCYCC7du3iySef5MSJE+zcuZPbb78dgBtuuMHglQ2PBB8W4kRVC2UN7bgddlZOSRv38WZkJeCw26hv7aKisSMEKwwT53YCGqRMNq7TxGYT3YcgCKbjRz/6EYsWLWLdunW0tLSwceNGMjLM35EnZRcLsemYKrksn5JKrNsx7uPFuBxMy4jnWGUzh8obyUken4YkbOj+HkZlPXQK18DBv4vuQxAEU7BkyZJe3Sxjwaist2Q+LERPySV0Ue1svfRSZmLRqR58GKX30NEzH8XbwBdlk4IFQRAiiAQfFqHL62PLSb+legj0HjpzzC461bQgsanBmY/s+eBOgI4GqAyv+58gCEI0I8GHRdhdXE9zRzdp8W7m5oZuoFqR2TMfDcXQUgl2J+QuNHYtDmdP6Ud0H4IQVfh8PqOXYAlCVaYRzYdF0Ftsz5uejt0+Nkv1gdDbbU9UNdPZ7cPtNFk8qpdcsuebo721cA2cfEc5na682+jVCIIwTtxuN3a7ndLSUjIzM3G73WMeWxHtaJpGVVUVNpsNl8s1rmNJ8GERNh4fv6X6QExKjiExxklTezcnqpoDZRjTYLS/R18KxWxMEKIJu93O1KlTKSsro7TUoJkuFsJms5Gfn4/DMb6mBwk+dLraoXSX+nIxWdTb0NbFnuJ6IDT+HsHYbDbm5CSx7XQth8sbzRd8mKXTRSdvOdgcqhxUXwwpBUavSBCEceJ2uyksLKS7uxuvV8TkQ+FyucYdeIAEHz28+p+w4/dw1f/A6s8avZpebD5Rg0+DaRnx5KWEvvRQlJuogo+yJlgS8sOPHW+XchQF48WmOp4EpT0p3aVKLxJ8CEJUoJcSxltOEEaGyQr8BtFWB3ueUtubfgLd5jLc2nQ8dJbqA6G32x4y24yXykPQ3QaeZEifYfRqeihcox5FdCoIgjAmJPgA2PtXNa4doLkc9v7F2PX0YdOx0FmqD4Rpp9sGJtkuMWaS7WCI7kMQBGFcmOgT3SA0TZVbQHVUALz3MzBJ21VxbSuna1px2G2snjZ+S/WB0DMflU0d1LZ0huUcY6JENxczSclFR59wW3EA2uoNXYogCIIVkeCjZDtUHgRnLHz8rxCTDDXH4MhLRq8MgI3+rMeSghQSY8JTi0zwOClMiwNMZjamZz7MIjbVScyG1KlAkAGaIAiCMGIk+NCzHvM/DMl5sOLT6t+bfqKyIgaj6z1Caak+EKYzG2tvhKojatssbbbBiO5DEARhzEzs4KOtHvY/q7aX3akeV/0rODzqrvvMe0atDACvT+O94zUAXBAmvYdOkdls1kt3ARokF0JCltGr6Y/oPgRBEMbMxA4+9j2tuimy5kL+CvW7hCxYcrva3vQTw5YGsP9cAw1tXSTGOFmUnxzWc83RMx9m6XgJlFxMmPWAnszHuQ+g20Q6GUEQBAswcYMPTYMPHlPby+7sbSx23ufAZofjb0D5fkOWBz1TbNdMS8fpCO//Kl10eqS8Ca/P+HKTacWmOhkzITZNdUmV7TF6NYIgCJZi4gYf53ZA5QFwxsDCj/Z+Lm0azL1Bbb/308ivzc+7R8Pr7xHM5PR4Ylx2Orp9nK5pCfv5hkTTzCs21bHZRPchCIIwRiZu8LHDn/WYdxPEpvZ/fu196nH/36DuTMSWpdPS0c3Os3VA+Pw9gnHYbczONonotPEcNFcoG/McgyfZDkXhKvUoug/BAHw+LWQTRgUh0kzM4KO9ob/QtC+TFsO0i0HzwuZHIrSwHradqqXLq5GfGsuU9LiInFM3GztitOhUb1/NngfuyPztY0LPfBRvMUVnlBD9NHd088KeUj735C4Wfut11v14Ay0d3UYvSxBGzcSc7bLvaehqhcwiKFg1+H5r71Pj03f+ES76T4hPj9QKA/4eF8zMiNh456Jck9ism73kopO7SJXtWmug5rjSgQhCiKlq6uDNQxW8fqCc947X0OntMUBsrurm1++e5IuXzzJwhYIweiZe8BHsaNpXaNqXaRerL5iyPbDt13DJAxFYoCLg7zEj/CUXnYDNutGZj3M71aNZxaY6To/yIDnzntJ9SPAhhIizNa28dqCc1w+W88GZul6JtakZ8VwxL5uUWDfff/Uwv373JB9fVUh2UoxxC44EJ9+BM+9D6hQ16yl9BsSFx/VZCD8TL/go3QXl+5SXx8Jbh97XZlPZj2c+Bdt+BWs/D+74sC+xorGdoxXN2Gxw3vTIZVt0o7Hi2jaa2rvC5qg6JN5uv8cH5jQX60vhan/wsQWWftLo1QgWRdM0DpY18toBleHo2/K+MD+ZK+flcMXcbGZkJWCz2dA0jTcPVbDjTB0/eu0IP/zIIoNWHwEay+CJjylrhGBiUnoCkfQZkD5NPaZNVxOoBdMy8YIPPesx78aRRc1zb1BW2nWnYOfjsPpfw7k6oKfksiAvmdR4d9jPp5Ma7yY7yUNFYwdHK5pYNtmAu4qqQ6ok5kmCDAukkqXjRRgHB0sbeWZHCa8fLKekrueL1WG3sWpqGlfOy+HyudlMSont91qbzcbXrp3Dhx99n2d2lnDn2inMmxRePyDD2PB9FXikToWUAqg5CY0l0F6vyrTnBhhzkJDjD0imq8eClT3mgILhTKzgo6MJ9j2jtgcTmvbF7lC+Hy/dD5t/DivuAkd4MwKbjukll/C32PalKCeJisYqDpUZFHzoYtNJJptkOxj5KwAb1J6E5kpzurEKpqS5o5sP/+I92ruUhiPGZefCmZlcOS+Hy+ZkkRI3/I3H0sJUrluYy4t7y/jvlw/xp7tWRUwjFjGqjyndHcCNv4DJ/oC/s1W972pPKM1VTdBja7WaUN5cDmc29RzrXzdBzoLI/w1CPyZW8LHvGehqUXfU+h3rSFj8cXjnIWgoVl0yi4Yp14wDTdPYFCFL9YEoyk1kw9EqjhglOj3nNxczu9hUJzZFOeRWHlCll7kfMnpFgkXYfbae9i4fGQkevnfTfC6cmUms2zHq4/znVUW8fqCC947X8M6RKi4pirIA+K3vqK7DWVf3BB6gOuFy5qufvrTVqeyIHpgcekENEN3zlAQfJsECt5YhZKRC0764YtXMF1CmY2Fsqzxc3kR1cwexLgdLJ6eE7TyDMcdo0akefFhB76Ejc16EMbDjjN/HZ0Y6V87LGVPgAVCQFsen1k4B4HsvH6I7qBvG8pTsgIP/AGxw2YMjf11sqhrNsPCjcMlX4ZKvqd8feA58UfTfx8JMnOCjdBeU7QaHGxZ+bPSvX3EXuBPUHe6xN0K+PJ1Nfr3HqmlpeJxj+zAaD3q77eGypsgbGHU0QeUhtW32TpdgRPchjIEdfhPBZZMHMDkcJf92yQxS41wcr2zmqe3F4z6eKdA0ePMbanvxxyF77tiPNfNy8CQrA0N5n5qCiRN87PiDepzzobH5dcSm9uhE3vtJqFbVj43+eS5G6D0ApmUk4HLYaOro5lx92/AvCCWBSbYFkJgd2XOPBz3zUbYHOg22phcsgdenscuf+VgaguAjOdbFFy5Trd4Pv3GUpvaucR/TcI6vh9MbVWfixeO0OXB6YM71anv/M+NfmzBuJkbw0dGsjMVg5ELTgVhzD9hdqrWyeHtIlhZMe5eXrSeN03sAuJ12pmeqFrWI26xbseQCSn2flK/q0iUDqO4FoQ/HKpto6ugm3u0IjDUYL7evnsy0jHhqWjr55YYTITmmYfh88OY31fbKu9V7bLwsuFk9Hvg7eKMgOLM4EyP42P836GxWvd9Tzh/7cZIm9XiDhCH7seNMHR3dPrISPczKNq5HXff7iLjuQ//itlrwAaL7EEaFrvdYXJgSsonVLoedr1xdBMD/bTxFaaQzl6Fk/zNQsU+13F/wpdAcc8qFEJ8JbbVw4u3QHFMYMxMj+Bir0HQg1n5ePR5+CaqOju9YfdD9Pc6PoKX6QBTlKtFpxG3WrdbpEkwg+JB6sjA8evCxrHD8JZdgLp+bzaqpaXR0+/jha0dCeuyI0d2hOlwAzr8vdC6mDifM+7DaltKL4UR/8FG2B0p3qnLJ4o+P/3iZs2H2tYAG7/90/McLQrdUv2CmMXoPHT3zEdF224Zz0FSmJtnmLo7ceUOFHnyUbFcurYIwBDtDqPcIxmaz8V/XKmHmc7vOsbekPqTHjwgfPAb1Z5VJ2KrPhvbYC25Rj4dfUj4hgmFEf/AREJpeD/Eh+lI//z71uOcv0FgakkPWtnRyoFSVOdYaJDbVmePPfJysaqa9yxuZk+pZj+y55p5kOxhZc1WKuLNZdUQJwiBUN3dwukZ98S0JceYDYEF+MjctyQPgey8dinzX2nhob4R3f6C2L/5K6D8L8ldASqF6nx57LbTHFkZFdAcfnS2w969qezxC074UrITC88DXBVseDckh3ztejaaprENWorEDorISPaTGufBpcLyyOTInPWdhvQcoJ9yClWpbdB/CEOhZj1nZCSTHhsct+d+vnI3HaWfrqVreOFgRlnOEhff/n5oSnT4Dlnwi9Me32WC+X3i6T0ovRhLdwcf+Z6GzCdKmwZQLQntsPfvxwe+hrX7ch9tooKV6X2w2W2DC7aGyCIlOS/ROFwvqPXRE9yGMgFD6ewzGpJRYPn3BVAD+55XDdFnBeKypAjY/orYve1BpNMLBfH/p5dgb0N4QnnMIwxLdwYcuNF16R+jnhMy8QqXaO5vgg9+O61CapgXMxS6YZUyLbV8CZmOR0H34vD2TbK0oNtUJmI1tCasLrmBtAnqPMJRcgvnsxTPISHBzsrqFP285E9ZzhYR3f6DGX+QtU35M4SJ7HmQWgbcDDr0YvvMIQzLqb+R3332X66+/nkmTJmGz2fj73//e6/k777wTm83W6+eqq64K1XpHTvk+lcq3u2Dx7aE/vs0Ga7+gtrf8Errax3yok9UtlDa043bYWTnFgGFuAxDRdtuqw+pDx51ojUm2gzFpqbremsqg3gIf9kLE6ez2sadE3W2HM/MBkOBx8sXL1fvpp+uP0dBmYm+LmhM9N4vrvjX+rsShsNl6sh+6/5MQcUYdfLS0tLBo0SIeeeSRQfe56qqrKCsrC/w8+eST41rkmNCFpkXXQkKYsgnzb1ZunC2VsOeJMR9Gz3osn5I65vkOoUYvu0TEaCwwyXax0k5YFXec+htAdB/CgBwobaCz20dqnIupGfFhP9+tywuYmZVAXWsXj759POznGzNvfRd83TDjcpga4hL5QMz3t9ye2qCmUQsRZ9TBx9VXX813v/tdbrrppkH38Xg85OTkBH5SU8Mb4fejsxX2/kVth1Jo2heHS7meghJK+cbWGRLs72EWZmUnYrNBTUsnVU0d4T2ZLja1cslFp2CVepTgQxiAgL/H5NSIePk4HXa+es0cAB577zTFtSZsLy3dBQeeBWyw7huROWf6dJWp1HzK8VSIOGHRfLzzzjtkZWUxe/ZsPvvZz1JTUzPovh0dHTQ2Nvb6GTcHnoOORkidAlMvGv/xhmLpJ9Xcl9qTcHj09cMur48tuqX6DHPoPQBi3Q6mpqs7s7CXXqJBbKoTrPsQhD7sPBsef4+huHh2JufPyKDT6+P7rx6O2HlHjG6jvvCjkR13v+Aj6lEMxwwh5MHHVVddxR//+EfWr1/P97//fTZs2MDVV1+N1ztwVuChhx4iOTk58FNQEAIP/3AKTfviju/RlBx/c9Qv311cT3NHN6lxLuZNSgrx4sZH8ITbsNHRDFX+SbbRkPnQO16qDkFrrbFrEUyFpmlhczYdCpvNxlevmYPNBi/uLQsEQKbgxFtw8h01bVwfex8p5t0E2KB4qzI1EyJKyL+ZP/axj/GhD32IBQsWcOONN/Liiy+yfft23nnnnQH3f+CBB2hoaAj8FBePcxx0xUEo2QZ2Z3iEpgMxea16LN426pfqeo+1MzKw242zVB+IQLttODMfZbtV6jMpDxJzwneeSBGfAelquuhYrgchejlX30ZFYwdOu42F+SkRPffcSUl8ZFk+AN998aA5jMeCh8ctvwtSJ0f2/Em5PbO+9v8tsucWwt9qO23aNDIyMjh+fGCxk8fjISkpqdfPuNjpF5rOviZyY9l1c6mqw9A2uruKfeeU8n3lVHN0uQQzOycCmQ8rD5MbDPH7EAZAz3rMy0s2RFj+pStmE+tysPNsPa/sL4/4+ftx4Fk1/sKdCBd+2Zg16Hbr+yT4iDRhDz5KSkqoqakhNzc33KeCrjbY4++sWXZH+M+nE5+hjMxg1CPVD/ot1c1WcgGY4898HK9sDp9JkZWHyQ2G6D6EAdhpQMklmOykGD5zofqc+p9XDtPRHaHRCQPR3ak6XEAN6wzV6IvRMudDqj2+Yh9UmlAPE8WMOvhobm5m9+7d7N69G4BTp06xe/duzp49S3NzM//+7//Oli1bOH36NOvXr+eGG25gxowZXHnllaFee38O/kM51iUXwrRLw3++YPQuh1Gk2muaOyhvbMdmg9k55gs+8lNjiXc76PT6OF3dEp6TnIsisamOnvko3Tku/xchuoiEs+lw/MtF08hK9HC2tpXHNxvoRbPzD1B3CuKzYPW/GbeOuDSYcZnaFuFpRBl18PHBBx+wZMkSlixZAsD999/PkiVLePDBB3E4HOzdu5cPfehDzJo1i7vuuotly5axceNGPB5PyBffjyMvq8clt4dfaNoXvfRSvHXELznoty6fkh5PgidMVsLjwG63BUovh8LhdNpYBo3nwGaH3EWhP75RpE2D+EzwdipNizDhaeno5pC/fLl0coph64hzO/nyFbMB+Nn6Y9S1dEZ+ER3NsOH7avui/wBPQuTXEEzAcOwZcSaOIKP+hr744ovRNK3fz+9//3tiY2N57bXXqKyspLOzk9OnT/PrX/+a7OwIaS8aStRj9vzInC8YPfNxbseIR6rrJZe5uebLeugU5epmY2EQner+Hllzjf8ACiU2m+g+hF7sKanH69PIS4klNznW0LXcvCyfopxEGtu7+dlbxyK/gM2PQEuVCtLD6cM0UmZfDc5YlYkp3Wn0aiYM0TXbpbFMPSZFQF/Sl8wiJZzqbIbKgyN6iZ75mGtCvYfOnJwwzngJlFyiSGyqI7oPIYjAPBcDSy46DruN/7p2LgCPbz7DyaoITa4GaK6C93+mti/9L2XUaDSeBBWAgAhPI0j0BB/ebmj2K7gTJ0X+/HZHj2iyZGS6j0Dmw8TBR1gzHyVR5Gzal0DmY4tqKRQmND3+HinGLsTP+TMzuLQoi26fxv+8EkGh5cYfqRu03MUwd3CX7IijG44deHbMTtXC6Iie4KOlUvlF2ByQkGXMGkYhOm3r9HLCf8cxz8Rll1nZKvNR2tBOQ2sIB1MFT7KNJrGpTs5CcMVBez1UHzF6NYKB+HwaO8/WA7Bssnla6r96TREOu43XD1YEXJbDSlsdfPCY2l73zcjr8oZixmUQk6yGQp553+jVTAhM9H9/nOgll4Rs44aTjUJ0eqSiCZ8GGQluMhMjIMYdI8mxLvJSVI06pDbrVUfUHZA7ATJnh+64ZsHh6snoiO5jQnOiqpmGti5iXY6Aa7AZmJGVyMdXFgLw3ZcO4vOFWWy5/29qjH3WXJh2cXjPNVqcHtV2C9L1EiGiJ/hoKlWPRug9dPKXAzaoOz3spES95DInNykiA6bGQ5Ff93GkIoS6D13vMWmJtSfZDkVA9zHyDigh+tBLLosKknE5zPWRe9+6mSR6nOw/18hzu86F92S7/qweF9+uRNlmQzccO/B35UMihBVzvRPGQ0BsaoDeQycmGbLUBMnhSi8Hy5Sz6bxJyeFe1bjR79YOhdLp9FwUOpv2JTDhVjIfE5ngSbZmIz3Bwz2XzgDgh68doa0zTHqHioOqk8TuhIW3jukQbZ1eSupaw5ehmXKBypy316uZM0JYMZ+5xFjRMx9GiE2DKVipul2Kt8Kc6wbdzQpiUx19xktIyy4lUehs2pf8FcrDpP4MNJYaGxgLhmEGc7GhuPO8KTy++Qzn6tv4zcaTfP6ymaE/yW5/1mPWVZAw+und5Q3t3PabLZyqbiHW5WBGVgIzsxOYlZ3IrOwEZmYlkpcSO775WHaHGja39Zeq9DL7qrEfSxiW6Ak+jGyzDaZglZqqO0Tmw+vTAlkEM3t86MzxZz6OlDfh82njH4DX2QKVB9R2NGc+YpKU50z5XtX1Mv/DRq9IiDC1LZ2crFLuwEsKzBl8xLgcfOXqIj735C5+ueEEH1tRQFZSTOhO4O2CvX9R22MY9lnZ1M7H/08FHgBtXV72nWsIzMXSiXM7mJmVwMyggGRmdgJ5KbEjL23Pv0UFH4dfhs5WcMeNer3CyIii4MNfrzQ685HvF52W7lJ1Q6e73y6na1po6/IS47IzNSM+wgscPVPS43E77bR2eimua2Vy+jjXXLZHdSYlTor+bEDhGgk+IoGmwaaHlVFUQrb/J0vZdydkqX8bYGS3y5/1mJ4ZT2p8/88Cs3Ddwlx+994pdp2t539fP8r3b1kYuoMfe12ZisVnwszLR/XSmuYO/un/tnKyqoW8lFj+/OlVdPs0jlU0cbSimWOVTRyraOZkdTOtnV72lDSwp6R3UBLvdjAjO5HV09L4jytVh8+g5C+HlMkqW3n0FZh/81j+YmEERE/w0WSSzEf6dIhNg7Za9aUzQFlBL7kU5SQN/UYwCU6HnZlZCRwobeRQWdP4g4+Av0cUZz10ClbCtl/1aFyE8FC6E9Z/a+h9XHE9gUh8ZlCQ4t/OXxHyNn0z6z2Csdls/Ne1c7j5F5v5645i7jhvSuhKwrrQdOGtozIVq2/t5BO/3cbRimaykzw8cfeqwGfPjKwErl7Qs2+X18eZmhaOVjRztKKJY5XNHKto4mRVCy2dXvYU17OnuJ6lhalcOS9n8JPabCrg2PRjZTgmwUfYiI7gQ9N6yi5GZz5sNlV6OfqK0n0MFHyUmXeS7WAU5SRxoLSRw+WNXDV/iDfvSAiITaNY76GT4/+ErDqizMbM5G0QTZzepB6z5sHkNdBcodw0myvUT1er+qk7rX4GIikfvrA7pK6bVgk+QHmQXLswl5f2lvHfLx/i8btWjr8Tr7kKjr2mtpf804hf1tjexSd/t42DZY1kJHh44u7VQ970uBx2ZmQlMiMrkWsW9NyAdvmHYj76zgme23WOf+w+N3TwAcpwbNOP4fgb0FYPsSkjXrcwcqIj+OhohC7/1FWjMx+g7nb14GPNPf2etpLYVCdY9zFuSqLYVr0vadPA4VaeJg3FkDrZ6BVFJ6ffU49Lbh/wPUdHszIibK70ByR9tk9vgsYSOPYGFF0TkiV1eX3sKakHrBF8AHzlqiLeOFDBpuPVvHOkikuKxpkJ2vsX8HXDpKU9nYDD0NzRzZ2/28bekgbS4t08cfcqpmeOrWTmctiZmZ3Ipy+YynO7zvHmoUoa27tIihkiwMyeq7xIKg/CoRdg6SfGdG5haKLjNkzPesQkg9sEGoqA2di2AackHrDAQLm+9HS8jDP4aCpXH/I2u/L4iHYcLkj3dw9UHjJ2LdGKz9vTzjx57cD7eBJUIFi4GubeACvvhku/Bh/6GXz8KVh2h9pP78oIAYfKGmnv8pEc62JahjUGJxakxfGptVMA+N7Lh+j2jmM0gKb1/PdcMjKhaWtnN//8++3sPFtPcqyLx+9aGXBZHg9zc5OYmZVAZ7ePV/eVD/8Cvdyy7+lxn1sYmOgIPszSZqszaamyeW8q65m066eyqZ3q5g7stp4vdCuge32crmmhtXNkU3sHRDcXy5wTXZNsh0K/4xvhwEFhlJTvVdlPT3JPmWu06F0YR1+FluqQLEsvuSwtTBl/h1gE+bdLZpAa5+J4ZTNPbi8e+4FKd6lr3hnTM7Z+CNq7vNz9xw/YdqqWRI+Tx+9aGTIfJJvNxo1L8gD4++4RmKnpwcfpjdBUEZI1CL2JjuCj0QTupsG44yDXrxbvY7Wul1ymZsQT67aOs2dGgoeMBA+aBkcrxjEFMzDPZQJkPXT04KMqggO8JhJ6yaVw9djdcrPnqkycrztkd7tW0nsEkxzr4ouXzwLg4TeO0tg+xplOetaj6LphdRMd3V7+9U87eO94DfFuB7//55UszB/6NaPlhsXq5nTzyRrKG9qH3jltqtKkaT448FxI1yEooiT4MInYNJhBhszpYtO5FnA27Yuu+xjXhNuyveoxd/H4F2QVstT4csl8hIkz/uBjyvnjO46e/dgVmtLLTj3zYbHgA+C2lYVMy4yntqWTR98+MfoDdLX3BHHDlFy6vD7ufWIX7xypItbl4Hd3rghLwJafGsfKKWloGjy/ZwTZD91uXWa9hIXoCD7MMNelL/kr1OMgmQ8rdbrozPbXXsel+yj3Bx85IfQRMDtZReqx6qiM6w41Pl/PFNIpg+g9Rsr8m5U4uGJfT5A8Rkrr2yhtaMdht7EoxHfwkcDlsPO1a1TG7nebTlFc2zq6Axx+EdobVAfR1IsG3a3b6+O+p3bzxsEK3E47/3fHclZNSx/P0ofkhiXqBvW5XaXD7zzvJqVNK9k+eIeUMGaiI/gIZD5MFHzomY/yfcrR008g82EhsalOkX/Nh8aa+Wiu8vux2CB7XugWZnZSpoAzVk30rD1l9Gqii4r9ahaHOxFyFo3vWHFpMNvf6bL7iXEdaqffXGxObiLxHms2FV5alMV509Pp9Pr4wWtHRvdiveSy+LZBS2Fen8aXn97DS/vKcDvs/OoTy1g7I2Ocqx6aaxfk4nLYOFTWOHznXmJOTzZt/9/Cuq6JSHQEH4HMR56x6wgmOV+VgTRvQOfQ0tEdsAieY8XgI2i6rTZAF8+wlO9Rj+kzJo7YFJS3h579kNJLaNFLLoWrwBGCL3m99LLvr+OabBrQexRar+SiY7PZ+Nq1c7DZ4IU9pYGAalgaSuDE22p78ccH3MXn0/jK3/by992lOO02Hrl9KZfMDq3B20CkxLm52H+ekQlP/aWXfRJ8hJroCD7MMtclGJstqOVWlV4OlzehaZCV6CEz0WPg4sbGjKwEHHYb9a1dVDR2jP4Aeip7rB0JViag+5B225Cim4sN1mI7WqZfCgk50FrTY441Bqys9whm3qRkblmaD8B3Xzw4spuOPU8Cmvp/kjat39OapvH1f+zn6R0lOOw2fnbbEi6fmx3ilQ/OTf6ul+d3lw4/IXfuh8DuUrOoqkaZ/RGGxPrBR3enmhsA5hKcQm+/D4LFptbLeoAaQDXNP4vm0Fgm3JbvU4+5E0jvoZMpmY+Q00vvcUFojulwwiL/yPcxll7aOr0BLx+rdboMxJevnE2sy8HOs/W8tK9s6J01ree/2wBD5DRN49svHuTPW89is8GPP7qolyNpJLi0KItEj5Nz9W1sP1079M6xqcoxF3qybEJIsH7w0VwOaCo6jQufUGlMBHe8aJqlxaY6uu7jcNkYRKcTUWyqo2c+pN02dFQdVjOUXPEwaXHojrvIXyo4+ppyPx0le0vq6fZp5CTFkJcSG7p1GUR2Ugz/cpHKYHz/1cO0dw0hmj67GWpPgjtBmbmhAo5T1S08s6OEe57YyWPvnVbHunkhNyyOfKk8xuXg6gXKYv3vu0cgPNWHhZbIfKZQYv3gI1hsara5GTkLweFRH5A1J4LEptZrs9XRdR+HR5v56GiGGn/LXu44hYFWRPf6qDkO3WMoWQn90UsuBStDOo+FrCJl/a95Ye9fR/3yHWd7/D3GPRvFJHzmwmlkJ3korm3jD++fHnxHf5tyZeHVPLq5nE//4QOWffdNLvnRO3z56T287HcX/d5N8/no8oIIrHxgbvQHPS/tLaWje5gONL1zsWR7mFc1sTDZt/UYMGObrY7TDXlLAfCe3RLwx7Bq2QWCgo/RZj4q9gOaKo3Fh1fRbkqSJoEnSZlY1Rw3ejXRwRl/8DHeFtuB0EsGu/884IiEoYgWvUcwcW4nX75iNgA/f+s4Nc09AXRpfRsv7Cnlv5/bTttu5Ynxbwfm8INXj/DmoQpqWzpxO+0sn5zKv1w4jb98ZjW3rzJ2xtGqaenkJMXQ2N7NO0eqht5ZDz6qj0LbCEW3wrBYswcsmIDY1GR6D538FXB2M83H3qOj+zri3A4mp8UZvaoxo5ddTlQ109ntw+0cYfw6kcWmoATIWXOU+Ljy0MRqNQ4HmhZ6vUcw8z8Mrz6gNDplu0c8h0jTNMs6mw7HzUvzeey90xwsa+S+v+wmKcbFzrN1lPndQm9xbCDW1c5JXw7FCQu5ZkoaSwtTWTY5lXmTkkf+WREBHHYbH1o8iV+/e5K/7xpm0m18uhLO1p5UQzFnrovcQqMY81wNY8Vsc1364td92EqU6HRObpKl5jz0ZVJyDIkxTrp9GieqRmGzrus9JqLYVCcw40U6XsZN9VElNHfGqllKoSY2FYquVdujEJ6erG6hrrULj9NuSS+fobDbbfzXteoa3nismpf2lVHmN1JbkJfMvamqqy997Z1s+eo6Hr19GZ++YBpLClNNFXjo6KWX9YcraWgbxkJeSi8hx3xXxGgxY5ttMP6Ol8SmEyTRYvkPJJvNxpzAhNtR6D4msthUR9ptQ8fpjeqxYIUqb4YD3RZ839Mj1unoWY9F+Smm/MIdL+fNyOD+y2dx+dxs/v3K2Tx592r2ffMKXvh4LlOad4PNTvLqT1pC6zInN5FZ2f5Jt/uH6eKR4CPkWP/doQ+VM5O7aTAJWZA6FRsai+3HLd3polOUO0rdh7er5wt3Imc+pN02dOjD5CaPc57LUEy7RH2utNXBkVdG9JJo1Hv05fOXzeQ3n1zOPZfMYM30dOLczp7s0LRLINlEZo9D0GvS7XB264Hg4wPV4i2MG+sHHwHBqUnLLoBWoC7cZfZjlhab6szMUu6kJ6pahtnTT9Vh8HaqkecpxgrNDEXPfNSdhs5RzsoQetC0oGFyYRCb6tgdsOhjanuEpZdo1XsMic/rNxZj2CFyZuNDi9T3xpZTNZQ1tA2+Y/Y8VeLraICaYxFaXXRj7eBD08w516UPjRnLAFhmP8os/3A2K1OYrozGztaOMPgIFptaIB0bNhIyIS4D0KBa3BLHTM0JaK5Qbex5y8N7Lr3r5fib0FQ+5K4NrV0cq1Q6qKWFKeFdl5k4+Q40noOYFJh9rdGrGRX5qXGsnOqfdDuU54fD1SM6ltJLSLB28NFWp4Z1gamDjyMulW5fYj9BzMAzlizFlHTVrXOmpnV4e2KY2M6mfRHR6fjRW2zzV4ArJrznypipTKY0L+z9y5C77ixWWY+pGfGkJ1hvfMKY0YfILfhI+P9/hAFdePrcrmFmvRTok8q3hXlFEwNrBx+63iMu3dQX/fbWHJq1GOJpi4ovnbyUWJx2Gx3dPiqa2od/gYhNewgEH6L7GDOnw+jvMRD6cLTdTwzp+RHQe1h4mNyoaauDQy+qbYuVXHSuXZCL22HncHnT0CL6YN2HMG6sHXw06SUX8+o9AA6Ut7DbN139wz9kzso4HXbyUpVt9JmaYbQLPp9kPoIJBB9isz4mNC1IbBqh4GP+h8EZo7RLpTsH3W1C6j32/01ln7PmQe5io1czJpLjXFw8OxMYRniqBx+VB6FjDOMlhF5YO/hoNLG7aRAHSxvZoc1S/4iSlN1kv+7jTM0wuo/609DRqOrzGbPCvzCzI+2246PulBKZ2109XwbhJiYZ5lyvtv324X3p9vrYXVwPTLDgQ//vseR2S+u5eibdnhu8lJyYA8mFgAbnBg9ChZERHcGHifUeTe1dnK5pZafP/8VbEh3Bh677OD1c5kMXm2bNCe38Dauit9s2lkB7g7FrsSJ61iN/Obgj6BSsl172PwNd/UuNh8ubaO30kuhxBrrBop7KQyoTZHfCwluNXs24uKQoi8QYJ6UN7WwbatJtvl/gHCWf40Zi7eDDAm22h8tVeq40Yb76Re1JaB5mloAFKPRbxJ8dLviQkktvYlN6yoRV0vEyas5EuOSiM/UiSMpXAeORl/s9vdM/TG7J5FRLOxiPil1/Uo+zrrL8vKYYl4Nr5qub2L8PJTwV3UfIsHbwYYE224OlSsBUOCm35643CqLmKf6yy+nhyi4iNu2PiE7HTqTFpjq9PD/6l14Ceo+JIjb1dvV0/yy2ptC0LzcsUTcFL+0ro71rkEm3wU6noxw4KPTG2sGHLjhNMq+jnh58zJuUFLBajwbR6ZSMnnZbbag3oV52yV0UgVVZBGm3HRt1Z6ChWKX5/TOTIopeejnxVk/J18+EE5see13N1onPgpmXG72akLB6ajq5yTE0tXfzzpHKgXfKXQgON7TWqCy2MGasHXxYQHB6sEwFH3MnJfV8YBZb36QmPzUOmw2aO7qpbekceKfmSmguB2wyxTUYyXyMDb3kMmkJuOMjf/706VC4BjQf7Hkq8OuKxnZK6tqw22BRQXLk12UEutB00a1Ro+Wy220Bx9NBu16cnp4bKSm9jAvrBh9d7dDmFwaZtOzS5fVxxK/5mJub3BN8lO6E7kG+sC1CjMtBbpLyVhlUdKpnPdJnGPNlYVak3XZs6GLTKWGc5zIcA3h+6P4es3OSSIyJji/iIWmugmOvqe3F/2TsWkKMPuvlrcOVNLQOMulWhsyFBOsGH7rY1Bmjxl+bkBNVzXR6fSR6nOSnxqov4dhU6G7vEWJamGHbbcv3qEcRm/ZG1/60VEJLtbFrsRL6JNtwDpMbjrk3qhkfNccCd749JZcU49YVSfb+BXzdkLcMsoqMXk1ImZObxOzsRDq9Pl4ZbNKtBB8hwbrBR7DY1KT95breY05uklLA22zKqhmiQvcxOchmfUD0AEvEpr1xx0PqFLUtuo+R0VAC9WfA5oBCA/QeOjFJMPcGtb1bdXvsODuB9B6a1iO4jRKhaV/07Megdut68FGxXwZEjgPrBh8Bsal522z14KPXJFtddBoFHS/DZj4CYlMJPvqRKaLTUaGXXHIXgcfg4YwBz49naW9tZv855deyrDDNwEVFiJLtSqvkjIX5Nxu9mrDwocXqO2XrqVrO1Q8w6TY5HxJyVPanbHdkFxdFWDf4aDS/x0cvsalOQHRq/eBjSKOxjiaoPaG2JfPRH133USXBx4g4Y1CL7UBMuUA5XXY0UvXBs3R5NZJjXRSkxRq9svDzwWPqcf6HlWdNFJKXEsuqqSqQHHDSrc3WM2ROSi9jxrrBR5O5PT40TeOAnvnIDQo+8paq1HHjOZVKtjCF/uDjbO0AwUf5fvWYOMnyBkRhQWzWR0dAbHqBsesAsNth8W0AePY9CcDMrARsJi3/hoy2OjjwrNpe9ilj1xJm9NLLP3YPU3qJgptIo7Bu8GHyzEdpQzsNbV047TZmZgfZLbvjIcfvdmpx3Ydedqlt6aShrY8yvFxKLkOiC/UqD4pZ0XA0lqksms0OhauNXo1ikQo+Mqs2k0sN0zMngKX6nqeUWD57fo/NeJRyzfyeSbeHygaYdCtmY+PGusGHyTMfut5jRlYCHqej95NR4veR4HGSkeABBrBZF2fToUmfqTJg7Q3QVG70asyN7u+Rs0ANeTMDaVNh8vnY0LjJsZEZ0T7PRdN6Si7LP2VakX+oSI5zcUmRf9LtQNmP3MXK7K65wvIZbKOwbvBh8szHgGJTnWjseKntIzoVsenQuGKUaRWI2dhw6JbqRrbYDoRfePoRxwZmZEa5j82Z96H6CLjiYcFHjV5NROiZdFvaf9KtO05lgCAqmgeMwJrBh89n/sxHmVLAz5s0wJ2a3vFSvtfyrVoDttt2d/ZoGSTzMThisz4yzpjAXGwAuouup0XzMNVewVxvlP8/3OHPeiy4WbUbTwAunq0m3ZY1tLP11ACTbmXI3LiwZvDRWq3anLBBYo7RqxmQQKdL7gBv1JTCnlat0l0RXlloCQyYqw7KfFQdBl+XSpGnFBq0Mgsg7bbD01wJ1UcBG0xeY/RqelHc4uBlryqhZp34m8GrCSMtNXDwH2p7+T8bu5YIEuNycO2CISbditnYuLBm8KGXXBKyTDlXoKGti+Ja1R8+YPBhs0WN30dP2SUo8xGs94jy2vC4kHbb4dFLLtnzTedkfLyymWe8FwFgO/Cc5bOYg7L7z+DtVDqHSUuMXk1EuWGxKr28vH+ASbd6u23ZHujuiPDKrI81gw+Tl1x0dXReSizJcYMER4EJt1YPPgYwGhNn05ERaLc9rEqJQn8CJRcT+Hv04XhlM9u02VS7cqGzCQ6/aPSSQo+mwY7fq+3l0d1eOxCrpqaRk6Qm3fYrvaROhbh0FZjpGjdhxFgz+LCy2FQn0PGy1dKtWrrRWEVjB22d/jsDEZuOjLRpajx3Vws0nDV6NeZE9/eYbL7g40RVMxp2judcp36x+wljFxQOTr2r2pzdiTD/FqNXE3HsdhurpynDsV1+G/0ANpuUXsaBtYMPk2Y+dL3HvKGCj9xF6ountQZqT0ZoZaEnJc5NUowT8JuN+XyS+RgpDidkzFLbovvoT0t1T0nKhMHH8cpmAFrn+rs/Tr4DDYOYUlmVD36nHhd+FDxR3k48CEsKVblv59n6/k9K8DFmrBl8BOa6mDT4GMjZtC9OT0/91OKllykZftFpTQvUnVIpaIcHMmYavDILIB0vg6OXXLLmQny6sWvpg6ZpnPAHH3lT50DheYAGe58ydmGhpLmyp5Q0AUsuOkv9wcfus3X9W24l+Bgz1gw+ApkP85VdOrt9HKtsAoYpu0CQRa+1/T566T50sWn2XFOKgU2HBB+DY+KSS2VTB00d3dhtMCUjrmfY3O4nLV1G7cWuP6mOvLzlyuBtglKUm0iMy05jezcnq/v4GeUtBWzQUNwzaV0YEdYMPkw80fZYZRNdXo2kGCd5KcMMmoqSIXOT04K8PqTkMjqk3XZwTC42BRV4e5wOmHuDmvRacwzO7TB4dSHA5wsSmk6c9tqBcDnsLMxLAWBnX92HJ7FHOH5O/D5GgzWDj0bzBh/BYtNhB03pHS+VB5XNtkXpZTQmYtPRoWc+qo+Ct9vYtZiJ1lqoOKC2TZj50IOPwEyXmCSYc73ajgbh6cm3oP4MeJJh3k1Gr8ZwlhSmALBrIN2H3nJr8ZvISGO94KOzBTr8X9QmFJz2mIuNYAZFYg6kTAY0S98t9dJ8BDw+Fhm4IguRMhlcceDtUHoZQXF2M6BBxmzl52My9OCj10wXvfSy/xnoajdgVSFEn+Oy6GPKSnyC0xN81PV/UpxOx4T1gg896+FOMKXNr575GLLTJRg9+3HWuroPvezSWV+mBi1hU5oPYXjsdsgMmnArKHRzMROWXGCQ4GPqhZCUp7KYR18xaGUhoLEMjvjXP4GFpsHoHS9HKppo7uiTodSDj9Jd4O0z3VsYFAsGH/5WNhNmPTRN68l8jDT4KPRbRuv1bQuSmegh1uVgru20+kXGTHBH+aCtUCKi0/4EhsmZM/g4UaWXXYKuc7sDFt6qtnc/acCqQsSux0Hzqs8m/dqc4GQnxZCXEoumwd7i+t5Pps9UoyS623pKhcKwWC/4MHGbbUldG03t3bgd9p5a8HDoH64l29VANgtis9mYnB7XE3yI2HR0SPDRm7b6HuGyyYbJATS2d1HZpOy0p2f1eZ/rpZfjb0JTRYRXFgJ8XtjxB7W9TLIeweill36iU7tddQSBtNyOAusFHwF30zxj1zEAB/wll5nZCbidI/xPmzlbWfR2t1t6yNzk9Djm2s+of4jYdHRI8NGbs1sADdKmm3JwpF5yyU7ykBTTp508Y6ZKw2te2PdXA1Y3To6/CY0lao7O3BuMXo2p0EsvA4pOxe9j1Fgv+DDxXJchJ9kOhs0Gk89T2xYuvUxJj2eeZD7Ght6qV3NcBlQBnN6oHk2Y9YBB9B7BLLpNPVrR80N3NF18O7hijF2LyQiITovr0fr+fy2Q4GO0WC/4MPFcl1GLTXX00ouFg4/pSRpT7f40swQfoyMxV7U0al6oPmb0aown4O9hzuBDdzadMVhpdf6HlcNv5YGe7i8r0FACx15X28vuNHQpZmTepCTcDju1LZ3KViCYvGXqsfakGgsgDIv1gg8TZz4OBcSmI2izDUbPfJzdalmvhzn+kkulLd10Vtimx2brKb1UHTZ2LUbT3qhGlINpxabDZj5iU2H21WrbSp4fO/8Img+mXCCjEQbA43QwL0/dWO4q7qP7iE3tmdMkLbcjwnrBRyDzYa7go66lk3P1bYCy4x0V2fPBk6RmolTsC8Pqwk9Bh7pj3+edjLfv/ANheAK6jwneblu8VX0Bpk6BZPPpugCO650ugwUfoMoWAPuetoaQ3Nutgg+Q9toh0Oe87DxT3/9J0X2MCmsFH95uv48Eppvromc9CtPi+ovQhsPugMLVavvM+yFeWWRIalB37Pt9Uyj1B2HCKBDRqSLQYmvOkkt7l5fiWpVyH7TsAjD9UkjIVlOrj78RodWNg6OvqqxyXAYUXW/0akxLj+5jKLMxCT5GgrWCj5ZKdVdkc5jO9XBMYtNgAroPawYfdn9t+4Bvcv96qDA8EnwoAuZi5gw+Tte04NMgMcZJZqJn8B0dTljwEbVthdLLDr+j6ZLbwek2di0mRu94OVTWRGvnIGZj53aolmVhSKwVfOjupok5KltgIsYsNtUJFp36fCFaVYTo7oRKlfk4qE3hTG3LMC8Q+qF3vNSdViMEJiIdzT3t5hZwNh12dpPu+XH0NWipCfPKxkHdaTi+Xm0vvcPQpZidSckxZCd58Po09pX0mceVNUc5b3c2i3ZrBFgr+Gjy6z1MKDYdtbNpX3IXqRkfbXXWu3CrDoGvizZHIiVahmQ+xkJ8BsRnAhpUHTF6NcZQtkd1/CTlQUqh0asZkOPDdboEkz1Pva99XWrei1nZ8QdAg2mXQPp0o1djamw2G0sK/H4ffZ1O7Q7IW6q2pfQyLKMOPt59912uv/56Jk2ahM1m4+9//3uv5zVN48EHHyQ3N5fY2FjWrVvHsWMhah9sNKe7aXuXl2P+D6UxBx9Od0/azmott/5JtvVJRYCN09UT9M59vARmvEzQ0osuts2eb+w6hmDYTpe+LPJnP8xaeunuhF1/UtsiNB0RSyenALDzzBC6j2IJPoZj1MFHS0sLixYt4pFHHhnw+R/84Af87Gc/45e//CVbt24lPj6eK6+8kvb2EEx5DGQ+zCU2PVbRjNenkRrnIidpHMY8VtV9+K2wu7IWAHC2VjIfY0IvvVRN9ODDvEMJRx18LLgF7E4o2w0VJuxkOvKS0tIlZMPsa4xejSUIOJ0OZDYmotMRM+rg4+qrr+a73/0uN910U7/nNE3jJz/5Cf/1X//FDTfcwMKFC/njH/9IaWlpvwzJmDBpm+3BMlX7mzspafg68FBMCQo+rOSM6BebxhQuAZQor9+bcgJzqKyRlr6TMAdiootO9S/nrHnGrmMQvD6Nk/6s3oiDj/gMmHWV2t5jwuzHB7rQ9BPgGGWX3gRlQV4yTruNqqaOgL1CAD34qD6iZhQJgxJSzcepU6coLy9n3bp1gd8lJyezatUqNm/ePOBrOjo6aGxs7PUzKI3mzHzoM13G3Omik7cMHG5oLldOeVbA5wtkPlKnLsVug/YuX2Dw1kTn/ePVXP3TjVzx8LvsGChNG4ye+ZiIwYem9fzdJs18nKtro7Pbh9tpJz81buQv1O3W9/7VXCaCNSfg1AbABstEaDpSYlyOQHl9Z985L/EZkDpVbZ8Ts7GhCGnwUV5eDkB2dnav32dnZwee68tDDz1EcnJy4KegoGDwEwQm2por+NC/VBYVpIzvQK7YHpteq+g+6k4pdbczBld2EXmpsQAiOvXzwl51zZ6rb+PWX23mVxtO4BvMhC1ztnpsPAftDQPvE600lEBHgypRpJvTXfN4VRMA0zLicdhHkeGceQXEpimPopNvh2l1Y0Bvr52xzrQCX7OyxP9Zv6vvhFsIKr1I8DEUhne7PPDAAzQ0NAR+iouLB95R04IEp+YJPpo7ugMGY8snp43/gIEhcxbRfehW2FlzweFkSno8oEovEx1N03j7cCUAi/KT6fZpPPTKYe76w3ZqWwZwvYxN6ZnWXGmxjqfxous9MmaZ1mdC13sM6Ww6EE43LPyo2jaL8LS7o2ctIjQdNUsn+51OZcLtmAlp8JGTo8ZfV1RU9Pp9RUVF4Lm+eDwekpKSev0MSEcjdPm/0EzUarvrbB0+DfJTY8lJDsEUSKsNmfOXXMhVw+QK01Q6+owEHxwsa6S8sZ1Yl4O//Msa/vumBXicdt4+UsU1P93ItlO1/V80UW3WKw6oxyxzllxglG22fdFLL4dfUu30RnPoBeW+mjgJZl5p9Gosh95ue7C0gfauPoZiBUGZD6t5NkWQkAYfU6dOJScnh/Xr1wd+19jYyNatW1mzZs34Dq5nPWKSwT2KemuY+eC0+iBZ7o+Ex03BSuXgWn8W6gfJApkJfWqnf5KtnvmQsgu8dUhlPdbOyCDG5eDjqwr5+z1rmZYZT3ljOx/79WZ+/tax3mWYidpuG42dLsHkLlKBlbcDDjwX4pWNEk2D7f+ntpd+UrmxCqOiIC2W9Hg3XV4toPkLkD0fnDHQXg81xw1ZnxUYdfDR3NzM7t272b17N6BEprt37+bs2bPYbDbuu+8+vvvd7/L888+zb98+PvnJTzJp0iRuvPHG8a208Zx6NJnYVNd7LJ8SgpILgCdRfVCBNUovZb2Dj8npeuZDgo+3jqjg47I5PaMA5uQm8cK95/PhJXn4NPjR60e547FtVOkC3YnabhvodDFn8KFp2viCD5utJ/ux+8kQrmwMbP0VnN2s9DVLP2HsWiyKzWbrabntq/twuGCS6vyT0svgjDr4+OCDD1iyZAlLlqj/uPfffz9LlizhwQcfBOA//uM/+NznPsdnPvMZVqxYQXNzM6+++ioxMeMsSTSZz2Cs2+tj51k9+AhR5gOCdB8mL700lSuPAJtduTkCUzJ6NB8Tud22prmD3X4HxEtm955DFO9x8r8fXcQPbllIjMvOxmPVXPOzjbx/vHpittt6u6D6qNo2afBR1dxBY3s3dhtM9V/jo2bhR1VWs2QbVBt0R3z6PXj9a2r78u9Acr4x64gCAkPmRPcxJkYdfFx88cVomtbv5/e//z2gIsJvf/vblJeX097ezptvvsmsWbPGv9LAXBfzZD4OlzfR2uklMcbJrKzE0B3YKmZjetYjfWagFKZrPprau6lv7TJqZYbzzpEqNE3N+hlIC2Sz2fjo8gKev/d8ZmYlUNXUwe2/3cr/2+dAwwYtVdBcZcDKDaDmuLIgdyeatutCz3oUpMUR4xrjXKnEHJhxmdo2wvOjsRSevgN83Wro3erPRn4NUcTSQl10KhNux4Lh3S4jRnc3NVGnywenlWBwaWEq9tG03g3H5DWADWqOQXNl6I4basr9nS5+sSmoHnjd5XUid7y85e9yuaxo6OnLs7ITef7e8/no8nw0Df73nRIqHX5x9kQpvQTEpnNUecKEnKhS1/L0sYhNg9FLL3v+ElkxYncH/PWTKqjNng/X/9S0/62twsL8ZOw2KGtop6xhELOxyoPQ0RT5xVkA6wQfJpzrst2v91gRypILQGxqoIxh6uyH3umSs7DXrye67qPL6+PdoyprcckwwQdArNvBD25ZxMO3LiLO7WBvpwqwj++fIHdNFhCbnhiP3iOY2dco0XxjCZx+NwQrGyGv/Ke6C49JhlsfB/cYS0dCgHiPk6Ic1Z25u2/pJSkXkgtA8/VMahZ6YZ3gw2RzXTRNY4e/02VZKPw9+mIF3UdAbLqg168nevCx/XQtTR3dpMe7WZSfMuLX3bQkn+fvPZ/quGkAbN32Hj987TDd3ihv1zO5rTqMs802GFcMzL9ZbUdKeLrzj35DMRvc/FtImxaZ804AdN3HwKWX5epRSi8DYp3gw2SZj3P1bZQ3tuO021g8XmfTgTC72Vh7g3I3hZ7uHD+TA+22E7PsorfYXjw7a9TluBlZCdx89RUAzLIV88jbJ3jw+QMhX6OpqPT/fSbOfIzZYGwg9Em3h54Pf0r+3A546Utq+5KvwczLw3u+CUZPx0t9/ydlwu2QWCP46O5UXRVgmsyH7u8xLy+ZWPcYBWhDoYtOKw5A6wBmVEZTvl89JuVDXO/Mz0R3OQ3oPeYMX3IZCHeO+hJe5CkHNJ7fXUpXtGY/OpqUpw2YttOlqb2L8kY1lXvcZRdQd8TpM6CrFQ7+Y/zHG4zmKvjLJ8DbCbOvhQu+FL5zTVCW+jMfe8810Nnd5z0aLDqdwJ1/g2GN4KPZPxfG7oK4dGPX4ueDMyogCJm5WF8SsvwzLjQo3hqec4wH3Vwsd2G/p/Syy9naiVd2OVXdwsnqFpx2GxfMzBjbQTJmgs2Bu6uRWbFNNHd0s8fftht16C3FCTn9glizoItNMxM9JMeGYPKrzQaL/dmPcJVevN3wzKeUP1L6DLjpF2C3xse9lZiaEU9KnIvObl9gzEaA3EVqUGhrdU+WWAhgjasxuORikjdQyJ1NB8LMuo9BxKYAhf7go7q5k6b2idVuq2c9Vk5NIzFmjF9UTo/6wgA+NEkNmNt4rDok6zMdFdYpuYxb7xHMwo8BNjizCepOh+64Om9+A05vBHcC3PpnJTQVQo7NZht8yJzT0/P5KEPm+mGOb/IB6JVmNpnYtKGtiyMVqla7LNSdLsHopZfTJgw+BhGbAiTFuEiPV8PBJproVB8kd+kIulyGJEvZrK9NVF0zm45HafBRaW5nU4ATVSHqdAkmOQ+mXaS29zwVuuMC7HsGNv9cbd/4aOBaEsLDkoDfR33/J8XvY1BMG3xsDR66ZTKx6a6zdWiaKi9kJYZgmNxg6JmPsj3m6hXv7ujxoBig7AI92Y+JVHppau9i66kaIBTBh/oynmkvAWB3cX10ZpH0sku2+TtdpmeGuD1VF55+8Ds4+HxofD/K98Pzn1Pb538R5t4w/mMKQxJwOi0eoOOlQIKPwTBt8NGrb9pkmQ99nsuycJZcAFIKlOOj5oXibeE912ioPKRcEmNSVC/7AExE0emmY9V0eTWmZsQzbbwper/NekL9Uaakx+H1aWw5aULh8XjQNEtMs+3x+AihizHAnOvV+7u5Av76CXh0tcqCeLvHdry2OvjLPykh67RL4NKvh3a9woAsKkjBZoPi2raeGU06euajfB90tfV/8QTGtMFHL4Fdo+5uao7Mx3a/s+mKUA2TGwozWq0Hi00HcUkMeH1UT5zMx1uhKrlA0IC5w1wwQ11nm45Fmd16cwW01arZQJmzjV7NgHR2+zjjz96FtOwCaiTB3e/Ahf8OnmSoPgLP/Qv8v6UqG9LdMewhAvh88OxnlLAxuRBu+R3Yw9CFJ/QjKcbFTP+10U/3kVwACdnqZq10d+QXZ2LMG3yU1PWYKwXmuhgffHR5fYGBYWEVm+qY0e/jxNvqMW/ZoLsEgo/aiZH58Pk03j4SwuAjdapSyne1si63E4hC3Yee9UibDq5YY9cyCKdrWvD6NBI8TrKTPKE/QXw6XPpf8MV9cNmDEJcB9WfgxS/CTxfB+z+HzhG8hzb8Dxx7XY1yv/Vx03YORSv6nJddfbvSbLYg3YeJstcmwLTBR2unLyDq7JnrkmfcgvwcLG2kvctHcqxr/HMeRoKe+Tj3gTnSdl3t6kMOoOi6QXfrMRqbGJmPfecaqG7uJMHjDE1GzOGEDJUNWB5fjt2mWj77zZCwMhawVQ82F7OFcxZKTLLy4bhvH1z1P6rE3FSmJtA+PB82/BDa6gd+7eGXYcP31fZ1P4FJi8O3TmFAAk6nZ2TI3EgxbfAB/v+RmmYqwaleclk+OcTD5AYjbZpK23k7lVuh0ZzaAJ3N6sNx0tJBd9M1H2UN7bR3eSO1OsNY7y+5XDAzA7czRG8rv+4jvv4oC/027VHVcjuRbNVHijtOTZv9wm64/mcqA9ZWC29/VwUhb36z97Tj6uOqVAOw8jOw+LbIrFPohd7xsrekof84hIKV6rFYzMaCMXXwseNMnRJRef21TxOUXQJi03C22AZjs5lL93HoefU457ohPVdS41wkepwAFE+Ajpe3DlcAISq56PiDDyoPBQzLNkVT8KHbqut/pwk5HqqBcqPF6YFld8C9H8CH/w8y50BnE2x6GH6yQA2KqzoCf7kdOhqhYDVc8b3IrlEIMCMzgUSPk7YuL4fL+3Qm5i4Gm0OZZTaUGLI+M2Lq4OODM3U9YtO4dPWGNBBN09SagOXhGCY3GGYxG/N2qxQvKKX+ENhsNiZnKN3H6SgvvVQ0trP/XCM2m5rnEjJ00WnFQdbOUMHHe8er8fmi4O7J51VfnmDqNlvd4yPkbbYjxeGEhR+Bz76vzMImLYHuNtj6S3hkJVQdVu6wH/0DON3GrFHAbrexONByW9/7SXcc5MxX21J6CWDa4MNug5K6NuoqzqhfmKDN9mxtK1VNHbgddhbmR9AxUM98FG8Dr4FeD2ffVyng2DQoPG/Y3SfKgDndWGxhfgqZiSEMkPUPrOojLJ0UR5zbQU1LZ/87KytSexK628EZC6lTjF7NgPh8WngMxsaC3a6yjXe/Df/0bM9ngt0FH/0jJOYYuz4haMjcQLoPf+lFnE4DmDb4mJmteupLzhxXvzCB3kO3VJ+fl0SMK4JtbJlFEJuq+veNbNc69IJ6LLpG3ZENw+Q0f8dLlGc+dL3HZaEsuYASWMekgK8bd91RVk31t9wej4KW24CzaZFpW0LP1bfR3uXD7bBT6L+WDcdmgxmXwadehs+8A//yLhSuMnpVAkFmY0M6nUrHi45pg4/FBSqzUFemZz5MEHzoJZdI+HsEY7cH6T4MKr34fHDoRbU950MjeslEMBpr7/Lynr8FNqR6D1BfNLp9ffk+zp+ZCcCm4zWhPY8RWEFs6s96TMmIw+kw4UflpCWm7hSaaOgzXk5Vt1DX0tn7yfzl6rFsz+j8W6IYE76jFIv9/yPb6/wCHRO02X5wOsyTbIfCaL+P0p2q5dmdAFMvGtFLAl4fUZz52HqqltZOL9lJHuZNSgr9CfTBVOX7Od+v+9h2qsb6HUSV5h8od8IosalgSVLi3Ezza4P6Wa2nTVO6RW9nz1DOCY6Jgw/1Be9uVV0ERpdd6ls7Oeb/MAq7rfpA6MHH2S1KrBdp9JLLzCvANbJ5Nrrm41x9W+9BgVFE8CC5sPhA6LqP8n3Myk4gK9FDe5dvYD8BKxHIfJg3+Ih4m61geZYU6LqP+t5PBJuNmWlUhoGYNvjIT40lM9FDNv55FgYLTnf6RUTTMuJJTzCg6yZ7AbgToaOhxxkyUmhaUIvt0F0uwWQleohx2fH6NM7VRZE5lh9N01jvb7G9JJRdLsEElV1sEMh+bLSy22lnqxKcgqk7XYINxgRhJCydnAIMpvvwl16k4wUwcfBhs9lYVphKts0ffBic+dh+Wtd7GJD1ACXw1IVlkdZ9VB5SXxYOD8y8fMQvs9ttTE6LXt3H8cpmimvbcDvtgVbYkJMxW3U0dDRAQ3GvllvLUnUY0FQaOj7T6NUMimk6XQTLoGc+dhfX4+3bEh/oeJHgA0wcfACszI8lzaY+AIwWnO44bYC/R1+MEp3qJZfpl4JndJM9C6NY96EPklszLZ14z/DdP2PC6VbdTuAXnargY9+5hv6iNqtQGVRyCadl+Tioae6grrULmw2mZUjwIYyMWdkJxLkdNHd0BzJnAfKWqiGKDcU9rt0TGHMHH5nqw7UdN1pMimHr6Oj2sqekHoigs+lABDudRtKmVw8+RlFy0ZkSxcHH+lBOsR2KoNJLdlIMs7IT0DR4/4RFu150vYcFSi55KbHEus3ZCiyYD6fDziL/KISdff0+PIk9Gqdz4vdh6uBjVqwyUyrzpXK61jjNwP5zjXR0+0iLdzMtwyCnQ1Ctdc4YaK2B6qOROWftSajYp+yBZ1896pdHq9FYQ2tXwGo/ksEHwPkz9JZbi5ZeArbqJhabSslFGCM9fh8DmY35dR8iOjV38KF3ulSQFmhzNYIdZ9S5l01ODe9ky+FwunsU05EqvejeHlPWjmlMt95uG22ajw3HqvD6NGZmJVAQbgOqoI4XgPNnpgMWNhurPKQeLZD5kE4XYbQsLRyk4wWCzMYk82Hq4EOf61KupfZPYUWQDwJ6DwNLLjpTzlePpyMUfBwenbFYX3SjseLatv4CLAvz1iH/ILk5Yc56AGT7g4/6M9DewKqp6bgcNopr26yXUWqpgWZ/+7yuZTEhhg2UEyyPPuPlWGUzDW19xmHootPSXcaOyjAB5g4+mpQop1xLC6S4I42maYFzG9bpEkyw2Vi4dR9N5VC8VW0XXTumQ+Qmx+By2Oj0+ihvbA/h4ozD69N456jKOlxWlB3+E8alQVK+2q44QLzHGZgjsdFqU271kkvqFPCY94tdDMaEsZKR4AnY8e/pO2QufQbEJKvhgBX7I784E2Hu4MOf+ajQUjlaMUAUGQFOVbdQ09KJ22lnfl4Eh8kNRt5y1XrZVAp1p8N7Lj3rkb8Cksbms+J02MlP9YtOqy12lz4Iu87WUd/aRXKsi6X+u5yw00/3YdGWWwvYqrd0dFPaoALl6VJ2EcaA/rnQL2Nvt6vPcJjwpRdLBB++BNVma0TpRZ/nsig/GY/TBKp3d5xq2YLwW62Po8slmIDNem10dLzoXS4XzcqM3MyPQPCxFyDQcvv+iRprlbMsYKt+skoFyenxblLjZUy9MHqWDKX7KBC/DzB78OEvu6TlTgEwxFJaF7ouM9Lfoy+RmPPSWgunNqrtouvGdahoGzD3dqRabIMJiE5VqnZhXjKJMU4a2rrYd64hcusYL4HMxxxj1zEEx6tUl504mwpjRRed7i6ux9fPbEw6XsDMwYfPFwg+Jk+ZCWCI7kPPfKwwg95DZ7JfdHpmU/jOcfRV0LxK7Jg+fVyH0uufZ6qtn/koqWvlcHkTdpvKfEQMPfNReQi8XTgdds6brrpeLFN68fl6Ol1MXHYRsakwXopyE4lx2Wlo6+Jk33Jz3jL1WHcKWizy3g0D5g0+WmvA1w3YmDNzBqCiyO4IDiirae4IpGANGSY3GAUrlVNe3WloOBeec+gttuPMeoAaSQ7RUXbRsx7LJqdGNiWfMkXN9vF2QPUxIGjOyzGLtNzWn4GuFnC4xx3QhhNpsxXGi8thZ2FeCjCA30dsqhqbABNa92He4MOf9SAhi5m5qSR6nLR2ejlc3hSxJeiZlhlZCaTEmaj2G5PUM2r97ObQH7+jGU6sV9vj1HtAb6MxLZLOrGFAt1S/JJIlF1BCNd0Xw6+SP3+myrzsOFNHa2d3ZNczFnRb9YzZ4HAZu5YhkMyHEAoCZmN9O14gyO9j4pZezBt86F4AibnY7TaW+DMPkSy97DBjyUUnnHNejr8J3e2QOjUkRlD5qbHYbNDa6aWquSMECzSGtk5vwNI8Ii22fekjOp2SHkdeSixdXo2tp4wz4RsxevBhYrFpl9cXGAUgwYcwHvTgY0Ctoky4NXHw4e900Vs8lxsQfOh6D1OJTXXCKToN7nIJgaOrx+lgUnIsAGctPOPl/RPVdHT7yEuJZVa2AV9MfdptbTZbT8utFfw+AmJT8wYfZ2pa6PZpxLsd5CbHGL0cwcLoHS9HK5po7uiTmdQ7Xs7tBJ83wiszB+YNPprK1aN/mu2yCAcf7V1e9pWoLgJTOJv2RQ8+qg6HVrTU3QFHX1PbY3Q1HQhd93HawsFH8CA5Q2z2gzte/OUrveXWEnNeApkPM4tNlcZrelaCsaMUBMuTnRRDXkosPg329i29ZBYpDVdnc48Ie4Jh3uBDL7v4Mx+LClKw2+BcfRvlDeF3ytx3roFOr4+MBE/Ap8JUxKX13EGGMvtxcgN0NqmgT1dlh4DCNGsPmNM0rafFNhKW6gORNVcJjVurA8H52hkZ2GxwuLyJyiYTO8h29whlzZz5OOEfKCfmYkIoWDyY7sPu6PFrmqClF/MGH7rg1B98JHiczMlNAiKT/Qie52LaOyBd97HpYegK0RfPoefVY9F1SuQYIqboRmMWzXwcKmuirKGdGJedNdPSjVmEKxYyZqltf+klLd7NvEnqffH+8Rpj1jUSqo+q1m1P8pjdciOBiE2FULKkIAVQnZr9CIhOJfgwF009glOdSJZedHMxU8xzGYzz7oWYFCjdCS/dP/5ZLz4vHHlZbc8Zf4ttMMEdL1bkrcPqejx/RgYxLgOdbvUhcxX7Ar9aG2i5NXHppSJIbGrWYJ6e4EMyH0IoWOwPPnadre/f6SfBh0nRNR9Bd0mB4CPMNus+nxY4x/IpJhSb6qROgY88plLxu/8MW381vuOd3az8VWJTe7IqIUIvXVlV82FYi21f+ohOAS6YoVpuNx2vMm8rs26rbuKSi8+nBcoukvkQQsH8vGScdhvVzR2cq2/r/aQefFQfhTbjprYbhXmDj85G9RiU+dAtaw+ca6CtM3wK4ZPVzdS3dhHjsgdS2qZl+qVw+bfV9mtfhVPvjv1YepfL7GtC7sOgBx8NbV3Ut3aG9Njhpqa5I1Czjail+kAMEHwsn5KKx2mnorEjcOduOirM32Zb1thOa6cXp91mTp2XYDliXI6AXKDfnJf4dEibprZLdkR2YSbAvMEHgDtBGWr5yU+NJTvJQ7dPY29JfdhOu/20PkwuBVekBoeNhzX3wsJbVU39r3dA3ZnRH0PTQjZIbiDi3E6yEj2A9XQf7x6rQtNgTm4Suf6WYcPQg4+aE9CpSlgxLgcrp6oMnWm7XvROFwvYqk/JiLfG+16wBLrfh+g+emPud1gfYZrNZotI6UUXm64wc8klGJsNrv8p5C6Gtlp46vbAF9OIKd0JjefAFQ/TLgnLMntKL9bSfWz2G4td6G9rNZSELEjIBrSebAI9uo9NZtR9tNWrawtMPVDuhNiqC2FgsYhOB8TcwUdQyUVHL72Ec8LtjjP+SbZmFpv2xRULH/szxGcqMeI/7h2dAFXPesy6AlzhMVfSRadWMxrbclJdD6uN6nLpSx+nU+iZ87LlZA1dEZx/NCJ0H4OkfIhNMXQpQ3Fcb7PNijd4JUI0oQcf+8410Nnd570ZCD4+UIMXJxDmDj4GaMkL7ngJh7iuqqmD0zWt2Gw9gY5lSM6Hj/4R7E448KxqwR0JYS656EyxoOj0XH0bZ2tbcdht5ul8CnS87A/8am5uEmnxblo6vQPfYRmJLjY1sd4DpM1WCA9TM+JJjnXR2e3jcHlj7yez54EzFjoaoOaYMQs0CHMHHwNkPuZNSsbjtFPXOsCo4hCgZz1mZyeSHGve4VeDMvk8uPr7anv9t+HYG8O/puoI1BxX00ZnXhG2pRVasN12i7/kMj8vmcQYk1wPA4hO7XYb501XmRnTtdwGbNXNW3KB4LJLosErEaIJm802eOnF4eoxGyueWEPmzB18DJD5cDvtLMpPAcLj96HrPZaZ0VJ9pCy/C5beAWjwzF1QfXzo/fWsx7RLwBO+D96A0VitdTIfW06q4GP1NBPpf/SJxhUHe82FuEC3Wj9WZcSqBscCYtO6lk5qWlQXlpRdhFAT7PfRjwk6ZM7cwccAmQ+ApZPDp/vYfkb397Bw8GGzwTU/goJVKp331MehvXHw/XVX0zCWXAAm+y3Wq5o6aOk7aMmkbPYHH4a5mg5E+nSVqu1qgdpTgV+fP1P5fewpaaCxvcuo1fVG0yzRZqvrPfJSYolzOw1ejRBtjKzj5YOIrccMmDv4SBo4+NAHvX0Q4uCjrdPLgXP6MDkT3emOBacbPvo4JE6C6iPw7GcGFjTVnVbCRZtd+XuEkeQ4FylxqnRhhXbb4tpWSura/HoPE10PdkfPF3mQ6DQvJZZpGfF4fVqgXGQ4jedUAGxz9FjDm5CAs6noPYQwoGc+TlW3UNfSx+dIDz4qDw59kxhlmDz4yBvw13rm43hlc0gNq/aU1NPt08hO8pCfarCfQyhIzIaP/QkcHjj6Cmz4n/77HHpRPU5eq0xvwkyg46XW/LqPraeU/mdhfjIJHpPdDQ8gOoWglluz+H3oWY+MmeD0GLuWITgubbZCGEmJczM1Q3327e7rUZWYA8mFgKYsDyYIJg4+HKptdADS4t1M8/+PHLCGNkYC81wmp5l3mNxoyVumPEAANnwfDj7f+/lAl8uHIrIcK3W86P4epmmxDWYA0SnA+TNN5vdhAVt1CJpmK3oPIUwEhswN9J1VMPH8PswbfCRmqfTyICwNw5C5D6JB7zEQi2+DVZ9V28/9a8/daFMFFG9V20XXRmQpk9NU8HGqyvyZjx6xqRmDD7/otE/wsWZ6OnYbnKxu6T9Lwgh0jw8T6z1AMh9C+Fk8Et1HsQQfxpOQM+TTPbqP2pCczufTAoGM5fUeA3HFd2HqhUqk+NRt0FoLR14CNMhbDskDl7hCTZF/zsE+v7bGrBTXtnKuvg2n3Ra41kyF/mXeVAYtPVmOpBgXi/x3WO+ZIftRYf5Ol7ZObyBQE48PIVwsKVCfI7uLh5lwa9bhkCHGvMFH4tDBh94Ku6e4ISSOjkcrm2hq7ybO7WBObhT2+TuccMvvIaVQiUyf+Wc48Hf13JzrIrYMXXh1pKIprMMBx4ve5bIwP5l4s+k9QLVE60Op+mQ/LvDrPjYarfvwdimxM5g683G8shlNU+Xc9ATz6lIEa1OUm4jHaaehrYtTfT2qchYqbV5bLdSeNGaBEcaywcf0zASSYpy0dXk5XNY07tPp/h5LClNwRutQqfh0+NiT4IqDk2/DqQ3q90XhbbENJjc5huwkD16fZursh15yWTPdhCUXnUF1H0or9f7xanw+A++iak6At1PNC0ouNG4dw3DI7zo5OzsKbzoE0+By2JmflwwMoFV0uiF3kdqeILoP837LDuLxoWO324J0H+MrvTR3dPOnLWoS7LJoLLkEkzMfbny0599ZcyFjRsROH+z2tyuMwwHHg6b1tKqaUu+hk+0PPvp0vCwpTCHe7aCmpTPwxWoIAbHpHLCb96PmSLm6eSmKxoynYCqWyJC5AOb9RBgm8wGh8fvo9vr43BM7OVzeREaCm9tWFoz5WJZh3k1w0X+q7SWfiPjpFwfVPs1IcW0bpQ3tuBw2czvdDpL5cDnsrPIHTYZ2vVjAXAyCgo8cCT6E8DKk6FTveJkgNuvmDT6mXjzsLuN1OtU0jW+/eJC3j1Thcdr5vztWkJscBf4eI+GSr8KXjsLqz0b81EOOmDYBm0+qL+xF+SnmdrvUg4+qI9DV3uup883g92EBW3UgMOyrKCfJ4JUI0Y7+2XeorJH2rj6aNz3zUXEAOs3fDThezBt8xA9f/liUn4LDbqO0oZ3SMbQVPvbeaf64WZVbfnLr4sCFMWFIzFZW7BFmYX4ydhuUNbRT0dg+/AsizJaTqoxn6pILqNlHsamgeaHqcK+nzpuh1r7jTB3dIRBkj4kK80+zrWrqoLq5E5sNZonmQwgzeSmxZCZ66PZp7O+reUvKU3IDzQuluw1ZXyQxb/AxAuI9zkBnys5R6gfeOFjBd15Sd2YPXF3E1QuG1pgIoSPe4wx80IfSJC4UaJpmDbEpqMBxkNLLrCw1lbm108vBMgN0Hx1NUK8CezNnPvSSy5T0eGLdg/sKCUIoGHLCrc0WpPuI/tKLpYMP6PHk0LtVRsK+kgY+/+QuNA1uW1nIZy6cFq7lCYOgD1raVWwu0emZmlbK/HqPpYUm1nvoDGI2Zg/yJ9l2KjReOKOiyt9im5AdEdv+sXJYOl2ECDP0hNuJM2TO8sFHQPcxwsxHaX0bd/1hO21dXi6YmcG3b5gXPVbqFmLxUFbDBqJnPRYXpFjjTniQGS9AYBjeaALzkFFhDVv1w/7Mx2wRmwoRYsgJtwUr1eMEMBuzfPChdyMcKG2ktXPoMe3NHd388++3U9nUwazsBB65fSmuaPX0MDl6x8u+cw14jfSi6INuLrbG7HoPneCyS58Pq5VT1X/j7adr+zsqhpuA2NTcwYdedolKY0HBlCzMT8Fmg3P1bVT21bzlLgK7E5oroP6sMQuMEJb/5s1LiSU3OQavT2NvyeCmVd1eH/cGWmo9/O7OFSTFuCK4UiGYGVkJxLsdtHZ6OVoxfpO4UBCs9zC92FQnYxY43NDR2KOx8DM/Lxm3005NSycn+zoqhhsLiE29Pi1w7c2WThchQiR4nMzK8mve+mY/XLE9NxRR7vdh+eADhh8yp2ka33rhIO8cqSLGZef/7lhOfmpcJJco9MFht7EwPwUwT8vt6ZpWKho7cDvsgWvK9DjdkDlbbZf3Lr14nI5AeUuf2BwRNM0SmY/TNS10dPuIdTkoTJPPAyFyDFl6yddLL9Gt+4iK4GNZ4dDBx+/eO83jW85gs8FPbl0y8VpqTUrgDWgS3cdmv6vp4sIUYlwW0HvoDCI6BVgxRRedRlD30VwJrTWADTKLInfeUaKXXGZlJ+Cwi+5LiBxDat4mSMdLdAQfQaLTvrMsXj9QzneDWmqvmj+8c6oQGQKqb5N0vGyxmt5DZ5B2W4AVuug0RNOfR4Ruq542DdzmzSgcLhNzMcEYlvhvmPeW1PfXvOUvV49le/uZB0YTURF8zJ2URIzLTn1rV6/a9r6SBr7w1G40DT6+qpC7L5CWWjOhWw0fq2ymqb3L0LVomhYQm1pG76ET6HjpH3wsnZyKzaZaiPuJ28KFRWzVpdNFMApd89bS6eVYZR/NW+oUiM8EXxeU7TFkfZEgKoIPl8POIr9+QB8y17el9lsfkpZas5GVGENeSiyapgJFIzlZ3UJVUwdupz1QDrIMOf7go/4stNX3eiopxsUc/5399ki13FrGVl1mugjGEKx56+f30ctsLHpFpyEPPr75zW9is9l6/RQVhb/uuyxIdBrcUjs7O1Faak1MT+ml3tB16CWXpVbTe4CyWNdH1g/g96HrPrZHSnRaaf7MR0tHN2drWwHJfAjGMKTmTS+9SPAxOubNm0dZWVngZ9OmTeE4TS/04GP76bpeLbW/vXO5tNSamCFV3xFEF5taruSio2c/ygcIPqYq3UdEgg+fFyr9c2ZMnPnQW2wzEz2kJ3gMXo0wERlywGZ+kNlYlBKWkZ1Op5OcnJEJOzs6Oujo6Aj8u7FxbHModCvsU9UtnKpuIcZl57fSUmt6gq2GNU0zpDSm/D3UF7PlxKY6OQvgyMtDik4PlTXS1N5FYjiD8dpT0N0GzhhImxq+84wTKbkIRqNr3o5WNvV/X05aAjY7NJ6DhnOQnGfMIkfLnr+MeNewZD6OHTvGpEmTmDZtGrfffjtnzw7u1PbQQw+RnJwc+CkoKBjTOVPj3UzPjAcItNQukpZa0zM/Lxmn3UZ1cwfnxjCZOBScqGqhurkDj9Nu3Wsm0PGyt99T2UkxFKbF4dNgZ7jbmk++rR4nLQG7ectXRyT4EAxmSM2bJwGy/ZnDM+9HfnFj5cDfRrxryIOPVatW8fvf/55XX32VX/ziF5w6dYoLLriApqaBXSwfeOABGhoaAj/FxcVjPvcV81S25WvXzJGWWosQ43JQ5Le2Nqr0sjmg90i1nt5DR+94qToM3v6dQ3r2Y3u4h8wdeVk9zr46vOcZJ4f8bbbibCoYyeLAgM36/k9Ov0w9Hn01YusZF11tUDxyY7SQBx9XX301H/nIR1i4cCFXXnklL7/8MvX19fz1r38dcH+Px0NSUlKvn7Hy71fMZtvXLuPT0lJrKYweMhfw95hu0ZILQMpk8CSBtxOqj/Z7OmA2Fk7dR3sjnNqotmeZN/jQNI0jFZL5EIxnyVATbvUA/vgb4B16bpkpKN4Kvs4R7x72FpCUlBRmzZrF8ePHw30q7HYbWYkxYT+PEFqW+IfMGZH50DSNrVb19wjGbu9J0w6k+/CLTvcU19PR7Q3PGk68pbwJ0qZDxszwnCMEVDR2UN/ahcNuY0ZWgtHLESYwwYL7fsMf81dAbBq0N0DxlsgvbrSc3DCq3cMefDQ3N3PixAlyc3PDfSrBouipx33nGujy+iJ67uOVzVQ3dxLjsrOoIDmi5w45QzidTsuIJz3eTUe3j/3nwuSpcuQV9Tj7aiW8MimHy1XJZWpGvHXLbEJUMG/SEJo3uwNmXqG2rVB6OfXuqHYPefDx5S9/mQ0bNnD69Gnef/99brrpJhwOB7fddluoTyVECVPT40mKcdLR7eNwWWQn3Ooll2WTU/E4Lf5FNETwYbPZWD6lpx095Hi74djratvkeo8j4mwqmIQYl4M5uUpqMGDpZdaV6vGIyYOP9gYo3Tmql4Q8+CgpKeG2225j9uzZfPSjHyU9PZ0tW7aQmZkZ6lMJUYLdbgt0meyO8JyXgKX6VAuXXHR00Wn5PjVZtg9hFZ2WbIO2WohJgYLVoT9+CNHbbOdI8CGYgCG9jmZcBnYn1ByDmhMRXdeoOP0eaD5IHXl7fciDj6eeeorS0lI6OjooKSnhqaeeYvr06aE+jRBlLDHA6bSXv4eVxaY6WXPA5lBBQFNZv6d7hsz1H8A4bvQul1lXgiMs9kEho2emi3S6CMYzpNlYTDJMXqu2zVx6OeXXe0xZO+KXiOe4YAoWG+B0erSimdqWTmJdjsCcBUvjioWMWWp7gNLL3ElJxLocNLR1cayyObTn1tPCJi+5dHl9HK+UThfBPOgTbveda6CzewDN26yr1KOuqTIjuth0sgQfgsVY7O94OVnVQkNrZCbc6nqP5VNScTuj5K0QsFnvbzbmcthZOjkFCLHVevUxlRa2u3q8CUzKqeoWurwaCR4neSmxRi9HEJiSHkdKnIvObl9ADN2L2f7g4+zmfoMjTUFTBVQdUtuFEnwIFiMt3s3kdGWFv7ukPiLn3BINLbZ9CYhO+894gSDdRyiDD/2ObMr5EGPuUoZecpmVnYDdbt6OHGHiYLPZAlPZBxSdpk2DjNng64YT6yO6thFx2u/tk7MA4tNG/DIJPgTTEEmzMZ9PCwo+Rv6GMT1DdLxAkO4jlB0vei169jWhO2aYOOx3Ni3KNXeQJEwshh2waeaul5PvqMepF43qZRJ8CKZhcQQ7Xo5WNlHX2hU9eg+dbH/wUXsSOvrrOpYUpuCw2zhX3xaaWTqttSodDD3pYRMjM10EMzKk6BTM7Xaqi02nXTyql0nwIZiG4DdgP7e/ELP5RI/ew+WIordBQiYk5AAaVB7s93Sc28n8SequPyQtt8deVy122fMhpXD8xwszPdNsJfMhmAf9s+9UdQt1LQNYlOevVG3sbXWqrd0s1J6C+rOqHbhwzaheGkWfuoLVmTspCbfDTl1rF2drW8N6rqiY5zIYQ0y4hRDrPoJdTU1OY3tXINszO1syH4J5SIlzMy1DTWUfUPPmcJrT7VTPeuQtV5N4R4EEH4Jp8DgdzPXflYez5dbn09jqv+uPKrGpTk6Q2dgALA9V8NHdAcf9AjgTD5LT0UsuuckxJMe5DF6NIPRmWM2bXtY0k+5Db7GdNjq9B0jwIZiMxUNNeQwRh8ubqG/tIs7tYEGexee5DMSwHS+qrfloRTP1rSOfQtmP05ugswkSsmHSkrEfJ0IcFr2HYGJ0r6NBjRan+91Oq48oTZfRaFrPPJdRik1Bgg/BZCwZ7g0YAvSSy4opadGl99DJWageKw6Ar/8E2/QED9MyVYp3XF0vevp31lVqqq7JOeL3UBBnU8GM6NO99wymeYtN6dFVHH0tcgsbjMqD0FoNrjg1gXeUmP8TQ5hQ6JmPQ6WNYRv9vjka/T2CSZsGzljobht0HsRKvfRyZoylF02zlN4DCAwtnJMrmQ/BfBTlJuJx2mlo6+JUdcvAO+nvNTO4neoll8I14HSP+uUSfAimojAtjrR4N51eHwdLB3D7Gyc+n8a2U1E0z2Ug7A7Inqe2K4bRfYy146ViPzQUqyBnDCnXSKNpmkyzFUyNy2Fnvr8MPGjZWbdaP/OemiRrJKfGrvcACT4Ek2Gz2YbveR8Hh8obaWjrIt7tCLScRiXDmI3pmY995xpo7xpDhkkXvU2/BNxxY1lhRDlX30ZTRzcuh41pGaNT5QtCpFgy3Gdf+nRIn+l3O30rYuvqh7dbTbIFmHrhmA4hwYdgOsIpOtX9PVZMTcMZjXoPHb3jpXT3gE8XpMWSneShy6uNLcgLTLE1v7EY9HS6TM9MiJ45PkLU0SM6HUKLZQa309KdSmwek9KjMRsl8i4UTEc4Mx9bTvpLLtGq99ApPE89nnxHCU/7YLPZxl56aSxTHz5gmeDjsJRcBAugT7g9XNY0eEZS130ce31AQXlE0EsuUy9QZd4xIMGHYDoW+YOPs7Wt1DR3hOy4Xp/G1lNRLjbVyZ4Lc28ENHjjGwPu0iM6HWXHyzG/0j5vOSRmj32NEUScTQUrMCk5hsxED90+jf3nBtF0FKyGmGRoq4WS7ZFdoI4uNh2H3kuCD8F0JMe6Aq2ge0I44fZQWSNN7d0kepzMi2a9h85lD6ox98ff6Bn+FMRyv9/HzjN1eH2jsLO3WJcL9LTZiseHYGaCNW+Dlp0dTphxudo2ouulqw2K/Rbvo5znEowEH4Ip0XveQznhNuDvEe16D5306bDiLrX9+tfB5+v1dFFOEokeJ80d3RwqG2FnUWdrTyBjkeCjo9vLiSrVulgkbbaCyRl2wi30vPeM8Ps4uwW8HZA4CdJnjPkwE+ATWLAiw7r9jQFdbBr1eo9gLvwP8CSpOS/7nu71lMNuY+lkFeSN2Gr95DvQ3a6GyGXNDfFiw8OJyha8Po2kGCc5STFGL0cQhmREmrcZl4HNAVWHoO50JJbVQ0DvcSHYbGM+jAQfgikJbjnzjaYkMAjeIH+PqNd7BBOfDud/UW2/9R3oau/19MqpSvcxYqdTvctl9jXj+uCJJIcDJZckbBZZszBxWZifgs2m2sMrG9sH3ik21Ti303HMcwlGgg/BlMzOUW5/Te3dnBzM7W8UHChtoKmjm8QYZ2B43YRh9WchKU+Zgm37Va+nlvszH9tO1w5s6RyMz9fzQWeRLhfoabOVkotgBRI8zsDU5SEzv4FBcxHUfbTVQ9lutT1Oc0EJPgRT4nLYA0Pfxtty6/Np/PC1I4AquTjsE+zu1xULl3xNbb/7v9DaU2JZVJCC22GnqqmDs7WtQx+ndCe0VKoyzuS1YVxwaDkkbbaCxRhR6UW/ATi9CdpD7wY9IGfeA82ntB7JeeM6lAQfgmnpEV6NY/gZ8Og7x9l4rJoYl50vXzk7BCuzIIs+BtnzoaMB3v1R4NcxLgcL8lWQt204vw+95DJj3ZhmORjFkaCyiyBYgZ6OlyE++zJmQtp08HXBybcjs7AQtNjqSPAhmJbFesfLODIfW0/W8OM3jgLw7RvmMyt7gt792h1w+bfU9rZf9xKprZgyQt2HBVts61o6qWhUXjGS+RCsgm42tq+kYeg2+MCguQi5nY5znkswEnwIpkXveDlU1kRb5+id/GqaO/j8U7vwafDhJXl8ZFl+iFdoMWasg2mXqDul9d8O/Hrl1BF0vNSdViO0bQ51HIugm4sVpMWS4HEavBpBGBkzshKIdzto6fRytKJp8B11q/VIuJ02lUPVYcAGUy4Y9+Ek+BBMi+725/Vp7C8d3QRHn0/j/r/uoaKxg+mZ8XznxvnS6QBw+bcBG+z/G5zbAcCywjRsNjhZ3UJV0yCOsvqd1eTzIC4tMmsNAXrJZXa2lFwE6+Cw2wLZD92faEAK14AnGVqrA+/nsHHqXfWYsyAknwESfAimpdeE21Gajf3y3RNsOFqFx2nnkduXEi93vYrchUr/AfD6g6BpJMe5Aur6HWcGyX5YbJCcTo+tupRcBGtx4awMADYcrRp8J4cLZvozkeHueglhyQUk+BBMzojc/vqw/XQt//u60nl860PzRGjYl0u+Bg4PnNkUaJ3Vrda3nRpA99HeoFTuYCm9BwQFH9JmK1iMi2ZlAcoccdAhc9BzQxBOvw9Ng5P+zMfUi0NySAk+BFMz2gm3dS2dfP7JXXh9GjcsnsStKwrCtzirklKgvD8A3ngQvN09otOBMh/H3wRfN2TMVpbtFsHn0wL1csl8CFZjVnYCOUkxdHT72DpUJ9qMdWCzQ+UBqD8bnsXUnYKGs2pW1OQ1ITmkBB+CqRmR258fn0/jS0/voayhnWkZ8XzvpgWi8xiM878IsWlQfQR2/ykQfBwobaSlo7v3vhbscgEormultdOL22lnSnq80csRhFFhs9m4eHYmABuODFF6iUtTk24hfNkPvcU2fwW4Q/NekuBDMDUJHiezskbg9gf836aTvHW4ErfTzs8/vlS6G4YiNgUu+g+1/fZ/MynWS15KLF6f1nuaprdLKenBcsGHXnKZmZUwMQYJClHHRbP8wcfRyqF3DLfbafA8lxAh70jB9Iyk9LLjTB3ff1W5mH7j+rkTz0J9LCy/C1KnQHMFbH6EFVN6rNYDnN2iNB9x6equx0IcLtNLLnItCNbkvBkZOOw2TlS1UDyUA/Es/43B6Y3QMURr7ljw+Xo6XUIkNgUJPgQLoPt9DNbxUt/ao/O4bmEuH19ZGLnFWRmnGy77htp+76dcMMkHwAfBwYd+JzXrKmVUZiGOVOjOpqL3EKxJcqyLpf7Pv3ePDVF6yZgJqVPB26kmT4eSyoPQWgOuOMhbHrLDSvAhmB6942VvSX0/tz9N0/jy03s5V9/GlPQ4Hvqw6DxGxbybIG8ZdLVwWfljAOw6W0+X16cU7hZtsYWezIc4mwpWJlB6GUr3YbOFz+1UL7lMPi+kYxUk+BBMz8ysxIDb3/HK5l7P/XbTKd48VIHboXQeiTEug1ZpUWw2uPw7ACQfeoLFsZW0dXk5UNoI1UeVyt3hhumXGrzQ0dHe5eV0jZqGLG22gpXRW27fO15NZ7dv8B31G4Rjr6lSSagI4TyXYCT4EEyPw24LDD8LHrS0u7ie7796GICvXzeH+f4puMIombIWZl+DTfPyjdi/ArD9VG1P1mPqReBJMHCBo+dYRTM+DdLi3WQmeIxejiCMmXmTkshIcNPS6WXHmSHmLxWuUROnW6rUBOpQ4O3q8fgJod4DJPgQLELfIXMNrV3c8+eddHk1rlmQwz+tnmzg6qKAdd8Em4Mlre+zwnZYiU4DLbbWK7kcKu/Re0gZTrAydruNC2fqXS9DlF6cbphxmdoOVdfLuZ3Q2QyxqZC9IDTH9CPBh2AJgjteNE3j35/Zw7n6NgrT4vifmxfKF8x4yZwNSz8JwFddT3Di1Cm04m3quVnWarEFOFIueg8herho9giCDwi926mu95hyAdhDGy5I8CFYAl10erSiiUffOcHrB5XO45GPLyVJdB6h4eIH0FzxLLEf56vdj2BDg9xFkJxn9MpGzWF/5mOOtNkKUcD5MzKw2eBQWSMVQ5ktzrxCuZ1W7IP64vGfOAwttjoSfAiWIDsphknJMfg0+OFrys/jq9cUBbQgQghIzMa29vMArHPsUr+zYNYDJPMhRBfpCR4W+jVt7w6V/YhLg4JVavvYOLMfna1QvFVtT714fMcaAAk+BMug+30AXDUvhzvOm2LYWqKWNffS7Erv+bfFXE0Bqpo6qG7uxGaDWdkSfAjRQY/b6XCllyvV43hbbou3KN+QpLwRz3R6aW/piA8vwYdgGZZNVvNH8lNj+f4tovMIC54EypZ8EYByMvBlLzR4QaNHz3pMSY8n1m0tYzRBGAxd97HxWHU/v6Ne6NnKU+9CR/Pg+w1HcIvtCD9rn915bsSHl+BDsAwfX1nIV64u4olPryY5VnQe4aLw8n/lW9pn+JeOz7NjhNOEzYSu95gtWQ8hiliUn0JSjJOGtq6hp3xnzlZjE7wd8OuLYPcT4O0efP/BGOU8l4bWLj4YqhW4DxJ8CJYh1u3gXy+aTmF6nNFLiWo8LhcN825njzaDF/eMPI1qFvSBcmIuJkQTToedC0ZSerHZ4OofqPbYmuPw98/C/1sKO34P3Z0jO1lbHZTuVtsjFJu+c7Ry6IxMHyT4EAShH9cvnATAy/vLR/WBYgb0sovMdBGijVHpPu7bD5d/G+Izof4MvPAF+NkS2PYb6BqiYwbg9HuABukzIWnSiNa2/tAwk3f7IMGHIAj9WDsjg+RYF1VNHWw7VTv8C0yC16dxtELvdJE2WyG60IOPvSX11LYMk8XwJMDaL8AX9sKVD0FCDjSWwMtfhp8ths2Pqo6WgdBLLiPMenR5fbx9RIIPQRDGidtp58p52QC8tM86pZfTNS10dPuIdTkoTJPynBBdZCfFUJSTiKbBxqGm3AbjjoM1/wZf2APX/AiS8qGpDF57AH66EN77aX9h6ijnuWw/VUtTezdpcSPX4knwIQjCgFzrL728sq+cbm8IB1WFEX2S7azsBBx26YYSoo8Ru532xRUDK++Gz++C638KKZPVHJg3HoSfLIB3fwjtDdBYBtVHABtMOX9Eh37TX3K50J+ZGQkSfAiCMCDnTU8nNc5FTUsnWy1SejkSmOkiJRchOtFLL+8ercY3Fj2W0w3L7oTP7YAbfwFp06GtFt76rgpCXviC2i93oTItGwZN03jjULla2+ysES9Dgg9BEAbE5bBz1fwcAF4chXmQkRwWZ1Mhylk+OY04t4Pq5g4OljWO/UAOFyz+ONy7HW7+LWQWqcyH7ow6wpLLscpmimvbcDvtnDc9ffgX+JHgQxCEQbnOX3p5dX85XRYovUibrRDtqC/5DGAMpZeBsDtgwS3w2c3wkT+o6bXOGFh464he/uahCgDWTk8n3uMc+WnHtFhBECYEq6amkR7vpq61i/dP1Bi9nCFp6ejmbK1S70vZRYhmLtZ1H0dCEHzo2O0w70b47Cb4WjnkzB/Ry948qIKPy+Zkj+50o12fIAgTB6fDztUL/KUXkxuOHfG32GYmekiLdxu8GkEIH7ruY8fZOhrbu0J/ghHaqVc3d7DL77Z62ZyR6z1Agg9BEIZBL728dqCczm7zll7EXEyYKBSkxTEtMx6vT+P949WGreOtw5VoGizISyY3OXZUr5XgQxCEIVkxJY3MRA+N7d1sOh7CNG+IOVymd7pI8CFEPyN2Ow0jPSWX0WU9QIIPQRCGwWG3ce2CXABe3Ftm8GoGJyA2Fb2HMAEIBB9HqtC0yI9AaO/ysvGYyrqsG6XeAyT4EARhBFy7UAUfbxyooL3La/Bq+qNpWkDzIW22wkRg9bR0PE47pQ3tHKtsHv4FIWbziRraurzkJscwb9LoA34JPgRBGJZlhankJMXQ1NEduNsxEyV1bdS3duGw25iRlWD0cgQh7MS4HKyapnw1Qtr1MkLe8LfYrpuTjW2EAtVgJPgQBGFY7HYb1wRKL+brenlmRwkAyyanEuNyGLwaQYgMFxuk+9A0jfWHxq73AAk+BEEYIdctUsHHmwfNVXrp8vp4cttZAD6xerLBqxGEyKHPedl2qpbWzu6InXf/uUYqGjuIdztYMwpX02Ak+BAEYUQsKUghLyWWlk4v74xyfHY4eeNgBZVNHWQkeLhyXo7RyxGEiDEtI5781Fg6vT62nIycCaBecrlwViYe59gyjRJ8CIIwImw2W0B4+oKJul7+tOUMAB9bUYDbKR9pwsTBZrP16nqJFGN1NQ1G3qmCIIwYveX2rUOVEU3zDsbxyibeP1GD3Qa3rSo0ejmCEHEi7fdRWt/GwbJG7Da4xF/2GQsSfAiCMGIW5idTkBZLW5eXtw4bX3r50xal9bi0KJu8lNE5LApCNHDejAycdhuna1o5Xd0S9vPpQtNlk1NJT/CM+TgSfAiCMGJsNhvXLlB26y8ZXHpp7ezmbztVl8sn1ojQVJiYJHicLJ+SCkQm+/HGIXXTMZ6SC0jwIQjCKLnOr/t463AlzR3GlV6e311KU3s3k9PjuGBGhmHrEASjuXi2ancNd/DR3NHNFv9067G4mgYjwYcgCKNi3qQkpqTH0dHtC6RgI42maTzuF5revqoQu330JkeCEC3ouo/NJ2rC2ga/8WgVnV4fUzPimZ4ZP65jSfAhCMKosNlsgUm3Rs162V1cz4HSRtxOOx9ZVmDIGgTBLBTlJJKV6KGty8sHp+vCdh69xfayoqwxuZoGI8GHIAijRjcc23Ckisb2roifX896XL9wEqnx7oifXxDMRK+W26PhEYJ7fRpv+0Xm6+aOr+QCEnwIgjAGZmcnMj0znk6vL9DzHynqWjoDGZd/Wi3ttYIAPW6n4dJ97DxbR11rF8mxLpZPTh338cIWfDzyyCNMmTKFmJgYVq1axbZt28J1KkEQIoyRpZendxTT2e1jfl4SiwtSInpuQTAr58/IwG6DoxXNlNa3hfz4+k3GJbMzcTrGHzqEJfj4y1/+wv333883vvENdu7cyaJFi7jyyiuprDTeF0AQhNCgd71sPFZFQ2tkSi8+nxbw9vjE6snjrjsLQrSQEucOBOPhyH68qU+xDUHJBcIUfPz4xz/m7rvv5lOf+hRz587ll7/8JXFxcfzud78Lx+kEQTCAmdmJzM5OpMur8drB8oic891jVZytbSUxxsn1iyZF5JyCYBUumuVvuQ2x1frJqmZOVLXgcti4cNbYXU2DCXnw0dnZyY4dO1i3bl3PSex21q1bx+bNm/vt39HRQWNjY68fQRCsgT7rJVKGY3rW45Zl+cS5nRE5pyBYhYv9uo/3jlfT5fWF7Ljr/cZiq6amkxTjCskxQx58VFdX4/V6yc7unZrJzs6mvLz/3dFDDz1EcnJy4KegQNrmBMEq6MHHe8erqWvpDOu5SupaeeuwSv3+02pxNBWEvizISyYt3k1TRze7ztaH7LiBksucrJAd0/BulwceeICGhobAT3FxsdFLEgRhhEzPTGBObhLdPo3XDoS39PLktrP4NDhvejrTMxPCei5BsCJ2u40LZiq331C13Na1dPLBGeUdMl5L9WBCHnxkZGTgcDioqOjdfldRUUFOTk6//T0eD0lJSb1+BEGwDrrwNJxdL53dPv6yXd2YfEKyHoIwKKGecvvO0Uq8Po2inEQK0uJCckwIQ/DhdrtZtmwZ69evD/zO5/Oxfv161qxZE+rTCYJgMHrw8f6JaqqbO8JyjlcPlFPd3El2kidkantBiEYumKmCj/3nGqlsah/38d706z3GO8ulL2Epu9x///385je/4Q9/+AOHDh3is5/9LC0tLXzqU58Kx+kEQTCQyenxLMhLxqfBq/vDU3r502blaPqxFYW4QuAxIAjRSmaih4X5yQDc9fsPKG8YewDS2e0LdM6EOugPy7v41ltv5Uc/+hEPPvggixcvZvfu3bz66qv9RKiCIEQH1wZKL6UhP/aR8ia2na7FYbdx20pxNBWE4fj2DfNJi3ez71wDH/r5JvaW1I/pOFtP1dDc0a0CmrzkkK4xbLcQ9957L2fOnKGjo4OtW7eyatWqcJ1KEASDuXaBCj62nqoNSao3mD/557hcPiebnOSYkB5bEKKRxQUp/OOetczKTqCyqYOP/HLzmG4M9Bbby4qyQj45WvKXgiCMm4K0OBYVpKBp8Mq+0JVemju6eXZnCQCfWCNCU0EYKQVpcfzts+dxaVEWHd0+7n1iFw+/cRRN00b0ek3TeOOg3mIb+qqFBB+CIISE68NgOPb3Xedo6fQyLTOe86anh+y4gjARSIxx8ZtPLufuC6YC8NP1x7j3yV20d3mHfe3h8ibO1bfhcdpZOyMj5GuT4EMQhJBwjb/0sv1M7bhEbjqapgVKLrevkjkugjAWHHYbX7t2Lj+4eSEuh42X9pbx0V9tpqJx6Pfoer+x2AUzM4h1O0K+Lgk+BEEICZNSYlk2ORVNg5f2jT/78cGZOg6XNxHjsnPL0vwQrFAQJi4fXVHAn+5aRWqci70lSoi6r6Rh0P3fCFOLrY4EH4IghAzd8+P/Np7kzYMVI64vD4Se9bhhUR7JcaGZJyEIE5lV09L5xz3nMzMrgYrGDj7yq/cHLJNWNrazp7gegEuLQmepHowEH4IghIwbFueRmxxDWUM7n/7jB9z2my1D3l0NRnVzBy/7sycyx0UQQkdhehzP/tt5XDw7k/YuH/c8sZOfrT/W60bhrcMq67GoIIWspPB0mEnwIQhCyEiLd/PaFy/kXy+ajttpZ8vJWq7/+Sbue2oXJXWtIz7OX7YX0+XVWFSQwoL80PoLCMJEJzHGxW/vWMFd5ysh6o/fOMrnn9odEKLqg+QuD+Egub5I8CEIQkhJinHxlauLeOtLF3HTkjwA/r67lEv/dwMPvXKIhrauIV/v9Wk8sfUsIHNcBCFcOOw2vn7dXP7nwwtw2m28sKeUW3+1mTM1LWw8Vg2E3tU0GAk+BEEIC/mpcTx862JeuPd81kxLp7Pbx682nOTiH77NY++dorPbN+Dr3jlSybn6NpJjXQENiSAI4eFjKwt5/K5VpMS52FPSwFU/2UhHt4+8lFhmZyeG7bwSfAiCEFYW5CfzxN2r+N2dy5mRlUBdaxffeuEgVzy8gZf3lfUTpT7uF5p+dHk+Ma7Qt/gJgtCbNdPT+cc9a5meGU+bv/Ry+dzssLa3S/AhCELYsdlsXFqUzatfuIDv3TSfjAQPp2ta+bc/7+TmX7zPjjN1AJytaQ2MAr99lZRcBCFSTE6P57l71nJZURZuh52bw9zebtPG0wsXBhobG0lOTqahoYGkpCSjlyMIQhho7ujm1xtO8JuNpwJ3WtcsyCHG5eDZnee4YGYGj98l86AEwQg6ur14nKPPOo7m+9s51sUJgiCMlQSPk/uvmM3tqyfz49eP8tcdxbwcNBNGhKaCYBxjCTxGi5RdBEEwjOykGL5/y0Je+cIFXDQrE4CpGfFhMzYSBMEcSOZDEATDKcpJ4g//vJLD5Y2kx3twOuS+SBCiGQk+BEEwDUU5ovMShImA3F4IgiAIghBRJPgQBEEQBCGiSPAhCIIgCEJEkeBDEARBEISIIsGHIAiCIAgRRYIPQRAEQRAiigQfgiAIgiBEFAk+BEEQBEGIKBJ8CIIgCIIQUST4EARBEAQhokjwIQiCIAhCRJHgQxAEQRCEiCLBhyAIgiAIEcV0U201TQOgsbHR4JUIgiAIgjBS9O9t/Xt8KEwXfDQ1NQFQUFBg8EoEQRAEQRgtTU1NJCcnD7mPTRtJiBJBfD4fs2bNYseOHdhstlG/fsWKFWzfvn1M555orzXy3Ea8trGxkYKCAoqLi0lKSorYeeW18tpwvFauZ3mt2V6raRrLli3j6NGj2O1DqzpMl/mw2+243e5ho6bBcDgcY3ojTsTXGnluI//mpKSkMb3ein+vvDa6XwtyPctrzfVat9s9bOABJhWc3nPPPfLaCLzWyHMb+TcbcV55rbw2HK8dD1b8e+W10fNa05VdBCFcNDY2kpycTENDw7juNAXBDMj1LFgZU2Y+BCEceDwevvGNb+DxeIxeiiCMG7meBSsjmQ9BEARBECKKZD4EQRAEQYgoEnyYFJvNxt///nejlyEIIUOuaSGakOt5fEjwESHuvPNObrzxRqOXIQghQ65pIZqQ6zmySPAhCIIgCEJEkeDDAKZMmcJPfvKTXr9bvHgx3/zmNw1ZTzQgdy3GItd06JFr2jjkeg4/EnwIgiAIghBRJPgQoo5XX32V888/n5SUFNLT07nuuus4ceJE4PnTp09js9l49tlnueSSS4iLi2PRokVs3rzZwFULwuDINS1EGxJ8CFFHS0sL999/Px988AHr16/Hbrdz00034fP5eu33ta99jS9/+cvs3r2bWbNmcdttt9Hd3W3QqgVhcOSaFqIN0w2WmwjY7Xb6ert1dXUZtJro4+abb+7179/97ndkZmZy8OBB5s+fH/j9l7/8Za699loAvvWtbzFv3jyOHz9OUVFRRNcbDcg1HV7kmo4scj2HH8l8GEBmZiZlZWWBfzc2NnLq1CkDVxRdHDt2jNtuu41p06aRlJTElClTADh79myv/RYuXBjYzs3NBaCysjJi64wm5JoOL3JNRxa5nsOPBB8GcOmll/L444+zceNG9u3bxx133IHD4TB6WVHD9ddfT21tLb/5zW/YunUrW7duBaCzs7PXfi6XK7Bts9kA+qWxhZEh13R4kWs6ssj1HH6k7BIhfD4fTqf6z/3AAw9w6tQprrvuOpKTk/nOd74jUXWIqKmp4ciRI/zmN7/hggsuAGDTpk0Gryo6kWs6Msg1HRnkeo4sEnxEiMrKSmbMmAFAUlISTz31VK/n77jjjl7/lnl/YyM1NZX09HR+/etfk5uby9mzZ/nKV75i9LKiErmmI4Nc05FBrufIImWXMFNXV8eLL77IO++8w7p164xeTtSi37XY7XaeeuopduzYwfz58/niF7/ID3/4Q6OXF1XINR0Z5JqODHI9G4NkPsLMP//zP7N9+3a+9KUvccMNNxi9nKgl+K5l3bp1HDx4sNfzwXcpU6ZM6XfXkpKSIncyI0Su6cgg13RkkOvZGGyaXJ2Chamrq+O9997jlltu4amnnhI7asHyyDUtTAQk8yFYGrlrEaINuaaFiYBkPgRBEARBiCgiOBUEQRAEIaJI8CEIgiAIQkSR4EMQBEEQhIgiwYdgGR566CFWrFhBYmIiWVlZ3HjjjRw5cqTXPu3t7dxzzz2kp6eTkJDAzTffTEVFRa99Pv/5z7Ns2TI8Hg+LFy8e8FyvvfYaq1evJjExkczMTG6++WZOnz4dpr9MmIhE8nr+61//yuLFi4mLi2Py5MniEyIYjgQfgmXYsGED99xzD1u2bOGNN96gq6uLK664gpaWlsA+X/ziF3nhhRd4+umn2bBhA6WlpXz4wx/ud6x//ud/5tZbbx3wPKdOneKGG27g0ksvZffu3bz22mtUV1cPeBxBGCuRup5feeUVbr/9dv71X/+V/fv38+ijj/Lwww/z85//PGx/myAMiyYIFqWyslIDtA0bNmiapmn19fWay+XSnn766cA+hw4d0gBt8+bN/V7/jW98Q1u0aFG/3z/99NOa0+nUvF5v4HfPP/+8ZrPZtM7OztD/IYKghe96vu2227Rbbrml1+9+9rOfafn5+ZrP5wvtHyEII0QyH4JlaWhoACAtLQ2AHTt20NXV1csiuaioiMLCQjZv3jzi4y5btgy73c5jjz2G1+uloaGBxx9/nHXr1vWaGioIoSRc13NHRwcxMTG9fhcbG0tJSQlnzpwJwcoFYfRI8CFYEp/Px3333cfatWuZP38+AOXl5bjdblJSUnrtm52dTXl5+YiPPXXqVF5//XW++tWv4vF4SElJoaSkhL/+9a+h/BMEIUA4r+crr7ySZ599lvXr1+Pz+Th69Cj/+7//C0BZWVnI/gZBGA0SfAiW5J577mH//v39Jk+GgvLycu6++27uuOMOtm/fzoYNG3C73dxyyy0yK0MIC+G8nu+++27uvfderrvuOtxuN6tXr+ZjH/sYAHa7fAUIxiBXnmA57r33Xl588UXefvtt8vPzA7/Pycmhs7OT+vr6XvtXVFSQk5Mz4uM/8sgjJCcn84Mf/IAlS5Zw4YUX8qc//Yn169ezdevWUP0ZggCE/3q22Wx8//vfp7m5mTNnzlBeXs7KlSsBmDZtWkj+BkEYLRJ8CJZB0zTuvfdennvuOd566y2mTp3a6/lly5bhcrlYv3594HdHjhzh7NmzrFmzZsTnaW1t7XdH6HA4AJUeF4RQEKnrWcfhcJCXl4fb7ebJJ59kzZo1ZGZmjvvvEISxIIPlBMtwzz338MQTT/CPf/yDxMTEQN07OTmZ2NhYkpOTueuuu7j//vtJS0sjKSmJz33uc6xZs4bVq1cHjnP8+HGam5spLy+nra2N3bt3AzB37lzcbjfXXnstDz/8MN/+9re57bbbaGpq4qtf/SqTJ09myZIlRvzpQhQSqeu5urqaZ555hosvvpj29nYee+yxQOuuIBiGwd02gjBigAF/HnvsscA+bW1t2r/9279pqampWlxcnHbTTTdpZWVlvY5z0UUXDXicU6dOBfZ58skntSVLlmjx8fFaZmam9qEPfUg7dOhQhP5SYSIQqeu5qqpKW716tRYfH6/FxcVpl112mbZly5YI/qWC0B+ZaisIgiAIQkQRzYcgCIIgCBFFgg9BEARBECKKBB+CIAiCIEQUCT4EQRAEQYgoEnwIgiAIghBRJPgQBEEQBCGiSPAhCIIgCEJEkeBDEARBEISIIsGHIAiCIAgRRYIPQRAEQRAiigQfgiAIgiBElP8PXoqwQfAyEIcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-D_RBBxFb6Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "ce4450fb-57d2-4522-e9c1-23b506bfdca9"
      },
      "source": [
        "df.plot()\n",
        "# ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8bfd5610>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rT2pmMfHFb6Y"
      },
      "source": [
        "df = pd.DataFrame({'value':[2, 4, 5, 6], 'date':['20201201', '2019-9-1', '1 jul 2018', '2017q1']})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnmRFNCgFb6Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "2b6a501d-3be9-4e88-cae5-ec7dcea99731"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>20201201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-9-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>1 jul 2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2017q1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   value        date\n",
              "0      2    20201201\n",
              "1      4    2019-9-1\n",
              "2      5  1 jul 2018\n",
              "3      6      2017q1"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxYNZfJ6Fb6Y"
      },
      "source": [
        "df.date = pd.to_datetime(df.date)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iK7z3YBFb6Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "410f1279-a75a-4593-9453-3de957edecf7"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-07-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2017-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   value       date\n",
              "0      2 2020-12-01\n",
              "1      4 2019-09-01\n",
              "2      5 2018-07-01\n",
              "3      6 2017-01-01"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOIIlVeOFb6Y"
      },
      "source": [
        "df = df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62qIa9ggFb6Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "33a7746a-1e9d-49e7-ce81-bda8dddc0c51"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-01</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-01</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-01</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            value\n",
              "date             \n",
              "2020-12-01      2\n",
              "2019-09-01      4\n",
              "2018-07-01      5\n",
              "2017-01-01      6"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrtGEUTLFb6Z"
      },
      "source": [
        "- 전기 대비 증가율, 차분 등 함수를 원래 의도대로 적용하기 위해서는 데이터프레임의 데이트타임인덱스가 빠짐없이 순서대로 정렬되어야 함\n",
        "- date_range()로 전체 샘플기간에 대한 데이터타임인덱스를 생성한 후 데이터프레임의 reindex(index)를 이용하여 적용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3Xi9XvJFb6Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "873f051b-ed91-4035-eebd-c68111262be4"
      },
      "source": [
        "df.pct_change()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-01</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-01</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-01</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            value\n",
              "date             \n",
              "2020-12-01    NaN\n",
              "2019-09-01   1.00\n",
              "2018-07-01   0.25\n",
              "2017-01-01   0.20"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzKeBEk-Q1Gx"
      },
      "source": [
        "## offsets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwUPCpwcFb6Z"
      },
      "source": [
        "from pandas.tseries.offsets import YearEnd, YearBegin, QuarterEnd, QuarterBegin"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zZx49MNFb6Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cbd740d2-b0d9-439f-98fc-2c2dd65c818d"
      },
      "source": [
        "df.index = df.index + YearEnd()\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            value\n",
              "date             \n",
              "2020-12-31      2\n",
              "2019-12-31      4\n",
              "2018-12-31      5\n",
              "2017-12-31      6"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1DjpmHDFb6Z"
      },
      "source": [
        "df = pd.DataFrame({'value':[2, 4, 5, 6], 'date':['20201201', '2019-9-1', '1 jul 2018', '2017q1']})\n",
        "df.date = pd.to_datetime(df.date)\n",
        "df = df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93DSNi5hFb6Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ef00006a-49fd-4d36-b0fa-0811d6221fa5"
      },
      "source": [
        "df.index = df.index + QuarterEnd()\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-30</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-03-31</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            value\n",
              "date             \n",
              "2020-12-31      2\n",
              "2019-09-30      4\n",
              "2018-09-30      5\n",
              "2017-03-31      6"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8fdQyUDFb6Z"
      },
      "source": [
        "데이트타임에서 연도, 분기, 월 추출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebz68UQ4Fb6a"
      },
      "source": [
        "df = pd.DataFrame({'value':[2, 4, 5, 6], 'date':['20201201', '2019-9-1', '1 jul 2018', '2017q1']})\n",
        "df.date = pd.to_datetime(df.date)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVdNsNtxFb6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "e32c4ab3-1014-49d7-c92a-c62e5343169f"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-07-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2017-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   value       date\n",
              "0      2 2020-12-01\n",
              "1      4 2019-09-01\n",
              "2      5 2018-07-01\n",
              "3      6 2017-01-01"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZlR35FlFb6a"
      },
      "source": [
        "df['month'] = df.date.dt.month"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtAsguFiFb6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "cb84724c-684b-40c0-9ec2-35dd4b0f858c"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>date</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-07-01</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   value       date  month\n",
              "0      2 2020-12-01     12\n",
              "1      4 2019-09-01      9\n",
              "2      5 2018-07-01      7\n",
              "3      6 2017-01-01      1"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3bjvDnPFb6a"
      },
      "source": [
        "df['day'] = df.date.dt.day"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3uG6hLOFb6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "d752308e-10af-4533-f2e7-3c4665873940"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>date</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-07-01</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   value       date  month  day\n",
              "0      2 2020-12-01     12    1\n",
              "1      4 2019-09-01      9    1\n",
              "2      5 2018-07-01      7    1\n",
              "3      6 2017-01-01      1    1"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzapvEsUFb6a"
      },
      "source": [
        "# save/read"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGy5IMEMFb6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bf1aef38-8c7c-46fc-9777-77b52d1b159f"
      },
      "source": [
        "pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPHBnj9fPhxd"
      },
      "source": [
        "import os\n",
        "os.mkdir('pickles')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHBvtS69Fb6b"
      },
      "source": [
        "## to_pickle()\n",
        "\n",
        "- python에서 매우 효율적임"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sudf6dF8Fb6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "991ab0a1-9bfe-4748-be3a-df70e80a3de3"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mpickles\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pV312C01Fb6b"
      },
      "source": [
        "df.to_pickle('pickles/df.pkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlqEli4fFb6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "556f68e2-878a-41d4-d4f7-7f140d8b7bac"
      },
      "source": [
        "ls pickles/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "df.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0iFuyMiFb6c"
      },
      "source": [
        "df1 = pd.read_pickle('pickles/df.pkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ecr9LGWXFb6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "bca8d7bd-9fcd-4aef-af49-7648d1aac746"
      },
      "source": [
        "df1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>date</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-12-01</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-07-01</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   value       date  month  day\n",
              "0      2 2020-12-01     12    1\n",
              "1      4 2019-09-01      9    1\n",
              "2      5 2018-07-01      7    1\n",
              "3      6 2017-01-01      1    1"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa4YU84eFb6c"
      },
      "source": [
        "## to_csv()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDVUIJ7-Fb6c"
      },
      "source": [
        "df.to_csv('df.csv')\n",
        "df_csv = pd.read_csv('df.csv', index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M42uPNdAFb6c"
      },
      "source": [
        "## to_stata()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14SU7FHpFb6c"
      },
      "source": [
        "df1.to_stata('df.dta')\n",
        "df1_dta = pd.read_stata('df.dta')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLdjst57Fb6c"
      },
      "source": [
        "## to_excel()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mZShRYTFb6c"
      },
      "source": [
        "writer = pd.ExcelWriter('dfs.xlsx')\n",
        "df.to_excel(writer,'df')\n",
        "df1.to_excel(writer,'df1')\n",
        "df2.to_excel(writer,'df2')\n",
        "writer.save()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2KhYeW6Fb6d"
      },
      "source": [
        "## save multiple dataframes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NATICZFtFb6d"
      },
      "source": [
        "dfs = {'df': df, 'df1': df1, 'df2': df2}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBSZFPzcFb6d"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzP9frzwFb6d"
      },
      "source": [
        "with open('dfs.pkl', 'wb') as handle:\n",
        "    pickle.dump(dfs, handle, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNupKad5Fb6d"
      },
      "source": [
        "with open('dfs.pkl', 'rb') as handle:\n",
        "    dfs_pkl = pickle.load(handle)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cMzkWzHvFb6e"
      },
      "source": [
        "# matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-jEj8aKFb6e"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXasMp6QFb6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "b72b81e9-804e-4eb1-f8d0-cb7810e21420"
      },
      "source": [
        "df = pd.DataFrame([0, 1, 2, 3, 4, 4], columns = ['y'])\n",
        "df.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8a9e5b10>"
            ]
          },
          "metadata": {},
          "execution_count": 164
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ucjHgaWCRu/9lP8e0Bta7u5vZAMr/otgU1aQiUebuPPHJcu59ezFdWzdmwoj+Gq4lcS2SJZchwAhgnpnlhh/7HZAG4O6PA+cD15hZCbAbGObuWlKRamv3nlJ+89JcZsxZwxm92vDn83tRv7Y+liHxLZKrXD4DfvDfn+7+CPBItEKJxNLqrbsZPTGLBWu28eufHcWYE47QEoskBJ2SSFL5esVmrpmUTWFxGU+NDHHy0RquJYlDhS5JY/Ks77ljxgJSm9VnWkZ/Oh+m4VqSWFTokvD2lJRx5+sLmDxrJccf2Yq/XtSXJvU0XEsSjwpdEtrGHUWMmZTDVys2M/r4Ttzys67UrKH1cklMKnRJWPNXF5CRmcWmnXt4eFgfzurTLuhIIjGlQpeENGPOGm6ZPodm9Wsz/erB9ExtEnQkkZhToUtCKS1z7n93CY999C3HpDfj0Uv606pRnaBjiVQJFbokjILdxVw/bTYfLcnn4oFp3PHz7tSupc0oJHmo0CUhLNuwg4zMLFZu3sUfzu7B8EEdgo4kUuVU6BL3Pli8nuun5lK7Vg0mXzWQgZ1aBB1JJBAqdIlb7s6jH33L/e8uoVubxkwYGaJd03pBxxIJjApd4tKuPSXcMn0ub8xdy897t+W+83pRr7Y2o5DkpkKXuJO3ZRcZmdksWreN35zalauP76ThWiKo0CXOzFy+iTGTcyguLeOZS4/hxK6HBR1JpNpQoUtccHcmzVrJnTMWkNaiPk+ODHFEq4ZBxxKpVlToUu3tKSnj9hkLmPrVSk48qhUPX9SXxnU1XEukogN+6sLM2pvZh2a20MwWmNn1+zjGzOyvZrbMzOaaWb/YxJVkk7+9iIufnMnUr1Yy5oQjeOrSY1TmIvsRyRl6CXCzu+eYWSMg28zec/eFex1zGtAl/DUQeCz8X5FKm5u3ldETs9myaw9/u6gvP+/dNuhIItXaAc/Q3X2tu+eEb28HFgEVx9adBWR6uZlAUzNrE/W0kjRey13NBY9/SQ0zpl89WGUuEoGDWkM3s3SgLzCrwlPtgFV73c8LP7a2wq/PADIA0tLSDi6pJIXSMufetxcz4ZPlDEhvzqPD+9GyoYZriUQi4kI3s4bAS8AN7r6tMi/m7hOACQChUMgr8z0kcRXsKuaX02bzydJ8hg9KY9xQDdcSORgRFbqZpVBe5pPd/eV9HLIaaL/X/dTwYyIRWbZhO1c9l8Xqrbv54zk9uXig/gUncrAiucrFgKeBRe7+l/0cNgMYGb7aZRBQ4O5r93OsyH/458L1nD3+C3YUlTBl1CCVuUglRXKGPgQYAcwzs9zwY78D0gDc/XHgTeB0YBmwC7g8+lEl0bg74z9cxgPvLaV728ZMGBGirYZriVTaAQvd3T8DfnBQhrs7cG20Qkni21lUwq+nz+HNees4q09b7j2vF3VTNFxL5FDok6JS5VZt3sWozCyWrt/O707vyqgfabiWSDSo0KVKffHtRq6dnENJmfPMZcdwwlEariUSLSp0qRLuznNfrODufywiPTxcq5OGa4lElQpdYq6opJT/eXU+L2Tl8ZOjD+PBC/vQSPNYRKJOhS4xtWFbIVdPyiZn5VZ+eVJnbvzJkdSoofVykVhQoUvM5K7ayuiJWWzbXcL4i/txRi+N9xGJJRW6xMRL2Xnc+so8WjWsw0vXDKZb28ZBRxJJeCp0iaqS0jL+963FPPXZdwzq1JxHL+lP8wa1g44lkhRU6BI1W3ft4ZdTZ/PpNxu59NgO3Da0Gyk1NVxLpKqo0CUqlq4vH661tmA3957XkwuP0TwWkaqmQpdD9s6Cddz0fC71atdiWsYg+ndoHnQkkaSkQpdKKytz/vbBMh7851J6pTbhiRH9adNEw7VEgqJCl0rZWVTCzS/M4e0F6zi3bzv+eG5PDdcSCZgKXQ7ayk3lw7W+2bCd2844miuP66jhWiLVgApdDsrnyzZy7ZQc3OG5Kwbwoy6tgo4kImEqdImIu/P3z1dwz5uL6NSyAU+ODJHeskHQsURkL5FsQfeMmW0ws/n7ef4EMysws9zw17jox5QgFRaX8uvpc7nrjYWc1PUwXrl2iMpcpBqK5Az9WeARIPMHjvnU3YdGJZFUK+u3FTJ6Yja5q7Zy3clduOHkLhquJVJNRbIF3Sdmlh77KFLd5KzcwtUTs9lRVMLjw/txag8N1xKpzqL1uexjzWyOmb1lZt33d5CZZZhZlpll5efnR+mlJRZeyFrFsCdmUielBi+PGawyF4kD0fihaA7Qwd13mNnpwKtAl30d6O4TgAkAoVDIo/DaEmUlpWXc8+Yi/v75CgYf0YLxF/ejmYZricSFQz5Dd/dt7r4jfPtNIMXMWh5yMqlyW3buYeQzX/H3z1dwxZCOZF4xQGUuEkcO+QzdzFoD693dzWwA5X9JbDrkZFKlFq/bxqjMLNYXFPHn83txQah90JFE5CAdsNDNbCpwAtDSzPKA24EUAHd/HDgfuMbMSoDdwDB313JKHHlr3lpufnEODevUYtroQfRLaxZ0JBGphEiucrnoAM8/QvlljRJnysqch97/hr++/w192jfliRH9Obxx3aBjiUgl6ZOiSWpHUQk3Pp/LewvXc16/VO45p4eGa4nEORV6ElqxcSejMrNYvnEn44Z24/Ih6RquJZIAVOhJ5pOl+YydkkONGkbmFQMY0lkXJIkkChV6knB3nv7sO/745iK6HNaIJ0eGSGtRP+hYIhJFKvQkUFhcyu9ensfLs1dzavfWPPCL3jSoo//1IolGf6oT3LqCQkZPzGJOXgE3nXIkY0/srOFaIglKhZ7Asr/fzNWTcthVVMITI/rzs+6tg44kIjGkQk9Qz3+9kttenU/bpvWYfNVAjjy8UdCRRCTGVOgJpri0jD+8sZDnvvyeH3Vpyd8u6kvT+prHIpIMVOgJZPPOPYyZnM3M5Zu56riO/Pa0rtSqGa0JySJS3anQE8TCNeXDtfJ3FPHABb05r39q0JFEpIqp0BPAP+au5VcvzqFxvVq8OPpYerdvGnQkEQmACj2OlZU5f3lvKY98uIx+aU15fHh/DtNwLZGkpUKPU9sLi7nx+Vz+uWgDF4bac9fZ3alTS8O1RJKZCj0OLc/fQcbEbL7buJM7z+zOyGM7aLiWiBx4Czoze8bMNpjZ/P08b2b2VzNbZmZzzaxf9GPKv3y0ZANnjf+cTTuKmHjlAC4drEmJIlIukmvangVO/YHnT6N8U+guQAbw2KHHkorcnSc+/pYrnv2adk3rMWPscQw+QpMSReT/RbJj0Sdmlv4Dh5wFZIa3nZtpZk3NrI27r41SxqRXWFzKb16ay2u5azi9Z2vuv6A39WtrtUxE/lM0WqEdsGqv+3nhx/6r0M0sg/KzeNLS0qLw0olvzdbdZEzMYsGabfzqp0dy7YmdtcQiIvtUpad57j4BmAAQCoW0kfQBfL1iM9dMyqawuIwJI0Kc0u3woCOJSDUWjUJfDbTf635q+DE5BFNmreT2GfNp17Qe0zJCdD5Mw7VE5IdFo9BnAGPNbBowECjQ+nnl7Skp4643FjBp5kp+fGQr/jasL03qpwQdS0TiwAEL3cymAicALc0sD7gdSAFw98eBN4HTgWXALuDyWIVNdBt3FDFmcg5ffbeZ0T/uxC2ndqWmNqMQkQhFcpXLRQd43oFro5YoSc1fXcDoidls3FHEQxf24ey+7YKOJCJxRte+VQOvz1nDr6fPoVn92ky/ejA9U5sEHUlE4pAKPUClZc797y7hsY++JdShGY8N70+rRnWCjiUicUqFHpBthcVcP3U2Hy7J56IB7bnzzB7UrqXNKESk8lToAfg2fwejMrNYuWkXd5/dg+ED0/RhIRE5ZCr0Kvbh4g1cN3U2KbVqMPmqgQzs1CLoSCKSIFToVcTdefzj5dz3zmKObt2YCSP7k9qsftCxRCSBqNCrwO49pdzy0lxen7OGob3a8Ofze1OvtjajEJHoUqHH2Oqtu8nIzGLh2m3ccupRXHP8EVovF5GYUKHH0KzlmxgzOYc9JWU8fWmIk7pquJaIxI4KPUYmzfyeO2YsIK15fSaMDNH5sIZBRxKRBKdCj7I9JWXc8foCpsxayYlHteKhYX1pUk/DtUQk9lToUZS/vYgxk7P5esUWrjnhCH7106M0XEtEqowKPUrmry4gIzOLzbv28PCwPpzVR8O1RKRqqdCj4LXc1dwyfS4tGpQP1+rRTsO1RKTqqdAPQWmZc987i3ni4+UMSG/Oo8P70bKhhmuJSDBU6JVUsLuY66bO5uOl+QwflMa4od01XEtEAhVRA5nZqWa2xMyWmdlv9/H8ZWaWb2a54a+roh+1+li2YTtnj/+cz5dt5J5zevCHs3uqzEUkcJFsQVcTGA+cAuQBX5vZDHdfWOHQ5919bAwyVivvL1rP9dNyqVOrBlNGDWJAx+ZBRxIRASJbchkALHP35QDhzaDPAioWekJzdx796Fvuf3cJ3ds25okRIdo1rRd0LBGRf4tknaAdsGqv+3nhxyo6z8zmmtl0M2u/r29kZhlmlmVmWfn5+ZWIG4xde0oYO3U2f35nCWf2bsuLowerzEWk2onWwu/rQLq79wLeA57b10HuPsHdQ+4eatWqVZReOrZWbd7FeY99yZvz1nLraV156MI+mpQoItVSJEsuq4G9z7hTw4/9m7tv2uvuU8B9hx4teDPDw7WKS8t45rJjOPGow4KOJCKyX5GcoX8NdDGzjmZWGxgGzNj7ADNrs9fdM4FF0YtY9dydiV+uYPhTs2hWP4XXrh2iMheRau+AZ+juXmJmY4F3gJrAM+6+wMzuArLcfQZwnZmdCZQAm4HLYpg5popKSrn9tQVM+3oVJ3U9jIeG9aFxXQ3XEpHqz9w9kBcOhUKelZUVyGvvz4bthVwzKYfs77dw7YlHcNMpGq4lItWLmWW7e2hfz+mTomFz87aSkZlNwe5iHrm4L0N7tQ06kojIQVGhA6/MzuO3L82jZcM6TL/mWLq31XAtEYk/SV3opWXOvW8vZsInyxnYsTmPXtKPFhquJSJxKmkLvWBXMWOn5vDpNxu59NgO3Da0Gyk1NY9FROJXUhb6N+u3Myozi9Vbd/O/5/Zk2IC0oCOJiByypCv09xau54Zps6lXuxZTRw0ilK7hWiKSGJKm0N2dRz5YxgPvLaVXahOeGNGfNk00j0VEEkdSFPrOohJ+PX0Ob85bxzl92/Gnc3tSN0XzWEQksSR8oa/avItRmVksXb+d359+NFf9qCNm+rCQiCSehC70L5Zt5NopOZSWOX+/fADHHxkfEx5FRCojIQvd3XnuixXc/Y9FdGzZgKdGhkhv2SDoWCIiMZVwhV5UUsr/vDqfF7Ly+MnRh/Pghb1ppOFaIpIEEqrQN2wrZPSkbGav3Mp1J3Xmhp8cSQ0N1xKRJJEwhZ67aiujJ2axbXcJj17Sj9N7tjnwLxIRSSAJUegvZedx6yvzOKxRHV4eM5ij2zQOOpKISJWL60IvKS3jT28t5unPvuPYTi0Yf0k/mjeoHXQsEZFARDSNysxONbMlZrbMzH67j+frmNnz4ednmVl6tINWtHXXHi77+9c8/dl3XDY4ncwrB6jMRSSpHfAM3cxqAuOBU4A84Gszm+HuC/c67Epgi7t3NrNhwL3AhbEIDLBkXflwrXUFhdx3Xi9+cUz7A/8iEZEEF8kZ+gBgmbsvd/c9wDTgrArHnAU8F749HTjZYvRxzE+W5nPuo5+zu7iUqRmDVOYiImGRFHo7YNVe9/PCj+3zGHcvAQqAFhW/kZllmFmWmWXl5+dXKnD75vXpn96c18ceR/8OzSr1PUREElGV7ujg7hPcPeTuoVatKvcx/I4tG5B5xQBaN6kb5XQiIvEtkkJfDey9rpEafmyfx5hZLaAJsCkaAUVEJDKRFPrXQBcz62hmtYFhwIwKx8wALg3fPh/4wN09ejFFRORADniVi7uXmNlY4B2gJvCMuy8ws7uALHefATwNTDSzZcBmyktfRESqUEQfLHL3N4E3Kzw2bq/bhcAF0Y0mIiIHQ9vci4gkCBW6iEiCUKGLiCQIFbqISIKwoK4uNLN84PtK/vKWwMYoxokHes/JQe85ORzKe+7g7vv8ZGZghX4ozCzL3UNB56hKemkHMzAAAAKTSURBVM/JQe85OcTqPWvJRUQkQajQRUQSRLwW+oSgAwRA7zk56D0nh5i857hcQxcRkf8Wr2foIiJSgQpdRCRBxF2hH2jD6kRjZs+Y2QYzmx90lqpiZu3N7EMzW2hmC8zs+qAzxZqZ1TWzr8xsTvg93xl0pqpgZjXNbLaZvRF0lqpgZivMbJ6Z5ZpZVtS/fzytoYc3rF7KXhtWAxdV2LA6oZjZj4EdQKa79wg6T1UwszZAG3fPMbNGQDZwdoL/fzaggbvvMLMU4DPgenefGXC0mDKzm4AQ0NjdhwadJ9bMbAUQcveYfJAq3s7QI9mwOqG4+yeUz5hPGu6+1t1zwre3A4v4731sE4qX2xG+mxL+ip+zrUows1TgDOCpoLMkingr9Eg2rJYEYmbpQF9gVrBJYi+8/JALbADec/dEf88PAbcAZUEHqUIOvGtm2WaWEe1vHm+FLknEzBoCLwE3uPu2oPPEmruXunsfyvftHWBmCbvEZmZDgQ3unh10lip2nLv3A04Drg0vqUZNvBV6JBtWSwIIryO/BEx295eDzlOV3H0r8CFwatBZYmgIcGZ4TXkacJKZTQo2Uuy5++rwfzcAr1C+jBw18VbokWxYLXEu/APCp4FF7v6XoPNUBTNrZWZNw7frUf6D/8XBpoodd7/V3VPdPZ3yP8cfuPvwgGPFlJk1CP+QHzNrAPwUiOrVa3FV6O5eAvxrw+pFwAvuviDYVLFlZlOBL4GjzCzPzK4MOlMVGAKMoPysLTf8dXrQoWKsDfChmc2l/MTlPXdPikv5ksjhwGdmNgf4CviHu78dzReIq8sWRURk/+LqDF1ERPZPhS4ikiBU6CIiCUKFLiKSIFToIiIJQoUuIpIgVOgiIgni/wCD309eBPvbVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yQahyLPFb6e"
      },
      "source": [
        "# %matplotlib notebook"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xFyoa4uFb6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "56d71ad0-4a28-4b1b-becc-036eaf9b78bc"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "df.plot(ax=ax[0])\n",
        "df.loc[2:].plot(ax=ax[1])\n",
        "ax[1].legend(loc=4, fontsize=18)\n",
        "ax[0].set_title('ax1')\n",
        "fig.suptitle('graph', fontsize=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4GQbJDoFb6f"
      },
      "source": [
        "## x, y-axis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yEmNk-rFb6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "e318d407-3036-4c59-f955-075202103308"
      },
      "source": [
        "from matplotlib.ticker import FuncFormatter as FF\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "df.plot(ax=ax[0])\n",
        "df.loc[2:].plot(ax=ax[1])\n",
        "ax[1].legend(loc=4, fontsize=18)\n",
        "ax[0].set_title('ax1')\n",
        "fig.suptitle('graph', fontsize=18);\n",
        "\n",
        "dp2 = lambda x, p: '{:,.2f}'.format(x)\n",
        "dp0 = lambda x, p: '{:,.0f}'.format(x)\n",
        "ax[1].yaxis.set_major_formatter(FF(dp2))\n",
        "ax[1].xaxis.set_major_formatter(FF(dp0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEajV4QoFb6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b511d56b-e1a8-489a-85a6-34297281e60a"
      },
      "source": [
        "df['x'] = df['y'] * 2\n",
        "\n",
        "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
        "\n",
        "df.plot.bar(ax=ax[0])\n",
        "df.plot.bar(stacked=True, ax=ax[1])\n",
        "df.plot.scatter(x='x', y='y', ax=ax[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8a6cd4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 168
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pu+1Iciad38l7epdSarfJzq5QwBeTFPDH3a9OXiDJRmAjwNq1a2cuoSRJc89ZwJuA+5Js7277TWAtQFX9EXAB8G+THAL2ARdWlcMRpBky2SL3X1TVo0n+D+D2JN+oqq+MbOCYIUmSOqrqq0AmaPNR4KO9SSTNP5MartB9ihJV9QTwp8CZsxlKkiRJmo4Ji9wkx3QnsibJMcD/Cdw/28EkSZKkqZrMcIUTgD/tjo1fBNxYVV+Y1VSSJEnSNExY5FbVw8BLe5BFkiRJmhFOISZJkqTWsciVJElS61jkSpIkqXUsciVJktQ6FrmSJElqHYtcSZIktY5FriRJklrHIleSJEmtY5ErSZKk1rHIlSRJUutY5EqSJKl1LHIlSZLUOha5kiTNsCQnJflSkgeTPJDk0jHaJMlHkjyUZEeSlzWRVUduz94DfH3XU+zZe6DpKDqMRU0HkCSphQ4Bv1FV9yRZDtyd5PaqenBEm18CTu2+fhb4w+6f6mOf2f4ol926g4EFCzg4PMxV569jw+mrm46lMXglV5Kkw0jyziQrjuQ9VfVYVd3TXX4G2AmMroTOA66vjjuB45KcOCOhNSv27D3AZbfuYP/BYZ45cIj9B4d57607vKLbpyxyJUk6vBOAu5J8Msm5SXIkb04yCJwBbB21azWwa8T6ED9eCJNkY5JtSbbt3r37iIJrZg09uY+BBS8snQYWLGDoyX0NJdLhWORKknQYVfVbdIYUXAO8BfhWkv+S5J9O9N4ky4BbgXdX1femeP7NVbW+qtavWrVqKofQDFmzYikHh4dfsO3g8DBrVixtKJEOZ9JFbpKFSe5N8tnZDCRpepIcl+SWJN9IsjPJK5rOJM11VVXAd7qvQ8AK4JYkV433niQDdArcG6rq02M0eRQ4acT6mu429amVyxZz1fnrWDKwgOWLF7FkYAFXnb+OlcsWNx1NYziSG88upTOm6CdmKYukmfFh4AtVdUGSo4Cjmw4kzWXdmREuBv4BuBr4D1V1MMkC4FvAe8d4T+hc+d1ZVR8c59BbgEuS3EznhrOnq+qx2fg7aOZsOH01Z51yPENP7mPNiqUWuH1sUkVukjXA64D/DPz7WU0kacqSHAu8is5XqlTVD4EfNplJaoF/BPybqvq7kRurajjJ68d5z1nAm4D7kmzvbvtNYG33vX8E3Aa8FngI+AHw1lnIrlmwctlii9s5YLJXcj9E55Pq8vEaJNkIbARYu3bt9JMJ3n/sBPuf7k2OWTR4+ecOu/+RJfMjwww6GdgN/I8kLwXuBi6tqu+PbGR/nYKJ+uOE75/7/XW+qqorD7Nv5zjbvwoc9ga17hCIfze9dJLGM+GY3O6n1Ceq6u7DtXNgvNQXFgEvA/6wqs4Avg9cPrqR/VWS1HaTufHsLGBDkkeAm4Gzk3x8VlNJmqohYKiqnpuq6BY6Ra8kSfPKhEVuVV1RVWuqahC4ELijqi6a9WSSjlhVfQfYleRF3U3nAA8e5i2SJLWSj/WV2uedwA3dmRUexptZJEnz0BEVuVX1ZeDLs5JE0oyoqu3A+qZzSJLUJJ94JkmSpNaxyJUkSVLrWORKkiSpdSxyJUmS1DoWuZIkSWodi1xJkiS1jkWuJEmSWsciV5IkSa1jkStJkqTWsciVJElS61jkSpIkqXUsciVJmmFJPpbkiST3j7P/1UmeTrK9+9rU64xS2y1qOoAkSS10LfBR4PrDtPnLqnp9b+JI849XciVJmmFV9RXgu03nkOYzi1xJkprxiiRfT/L5JC8Zr1GSjUm2Jdm2e/fuXuaT5jSLXEmSeu8e4Keq6qXAfwf+bLyGVbW5qtZX1fpVq1b1LKA011nkSpLUY1X1vara212+DRhIcnzDsaRWmbDITbIkyV91v1J5IMlv9yKYJEltleQfJ0l3+Uw6v4/3NJtKapfJzK5wADi7qvYmGQC+muTzVXXnLGeTJGlOSnIT8Grg+CRDwJXAAEBV/RFwAfBvkxwC9gEXVlU1FFdqpQmL3G6n29tdHei+7IiSJI2jqt4wwf6P0pliTNIsmdQ8uUkWAncDpwB/UFVbx2izEdgIsHbt2pnMqFkyePnnDrv/kSU9CiJJkjTDJnXjWVU9W1WnA2uAM5OcNkYb7/6UJElSXzii2RWq6ingS8C5sxNHkiRJmr7JzK6wKslx3eWlwGuAb8x2MEmSJGmqJjMm90Tguu643AXAJ6vqs7MbS5IkSZq6ycyusAM4owdZJEmSpBnhE8+kFkqyMMm9SfzWRZI0L1nkSu10KbCz6RCSJDXFIldqmSRrgNcBVzedRZKkpkzqYRCS5pQPAe8Flo/XwIe3aCoG99847WM80oIMkuYGr+RKLZLk9cATVXX34dr58BZJUttZ5ErtchawIckjwM3A2Uk+3mwkSZJ6zyJXapGquqKq1lTVIHAhcEdVXdRwLEmSes4iV5IkSa3jjWdSS1XVl4EvNxxDkqRGeCVXkqQZluRjSZ5Icv84+5PkI0keSrIjyct6nbFf7Nl7gK/veoo9ew80HUUt45VcSZJm3rXAR4Hrx9n/S8Cp3dfPAn/Y/XNe+cz2R7ns1h0MLFjAweFhrjp/HRtOX910LLWEV3IlSZphVfUV4LuHaXIecH113Akcl+TE3qTrD3v2HuCyW3ew/+Awzxw4xP6Dw7z31h1e0dWMsciVJKn3VgO7RqwPdbf9mCQbk2xLsm337t09CdcLQ0/uY2DBC8uQgQULGHpyX0OJ1DYWuZIk9bG2PrxlzYqlHBwefsG2g8PDrFmxtKFEahuLXEmSeu9R4KQR62u62+aNlcsWc9X561gysIDlixexZGABV52/jpXLFjcdTS3hjWeSJPXeFuCSJDfTueHs6ap6rOFMPbfh9NWcdcrxDD25jzUrllrgakZZ5EqSNMOS3AS8Gjg+yRBwJTAAUFV/BNwGvBZ4CPgB8NZmkjZv5bLFFreaFRMWuUlOojMFyglAAZur6sOzHUySpLmqqt4wwf4C/l2P4kjz0mSu5B4CfqOq7kmyHLg7ye1V9eAsZ5MkSZKmZMIbz6rqsaq6p7v8DLCTcaY5kSRJkvrBEY3JTTIInAFsHWPfRmAjwNq1a2cgWsPef+wE+5+e1uEHL//chG0eWTKtU0jtMVF/nPD90+uv/WJw/43Tev8jMxNDkuaESU8hlmQZcCvw7qr63uj9bZ3HT5IkSXPPpIrcJAN0CtwbqurTsxtJkiRJmp4Ji9wkAa4BdlbVB2c/kiRJkjQ9k7mSexbwJuDsJNu7r9fOci5JkiRpyia88ayqvgqkB1kkSZKkGTHpG88kSZKkucIiV5IkSa1jkStJkqTWsciVJElS61jkSpIkqXUsciVJktQ6FrmSJElqHYtcSZIktY5FrtQiSU5K8qUkDyZ5IMmlTWeS5qsk5yb5ZpKHklw+xv63JNk94mmi72gip9RWEz7xTNKccgj4jaq6J8ly4O4kt1fVg00Hk+aTJAuBPwBeAwwBdyXZMkZf/ERVXdLzgNI84JVcqUWq6rGquqe7/AywE1jdbCppXjoTeKiqHq6qHwI3A+c1nEmaV7ySK7VUkkHgDGDrGPs2AhsB1q5d29Ncc9Xg/hun9f5HZiaG5o7VwK4R60PAz47R7vwkrwL+GnhPVe0a3cD+Kk2NV3KlFkqyDLgVeHdVfW/0/qraXFXrq2r9qlWreh9QEsCfA4NVtQ64HbhurEb2V2lqLHKllkkyQKfAvaGqPt10HmmeehQ4acT6mu6251XVnqo60F29GviZHmWT5gWLXKlFkgS4BthZVR9sOo80j90FnJrk5CRHARcCW0Y2SHLiiNUNdMbQS5ohjsmV2uUs4E3AfUm2d7f9ZlXd1mAmad6pqkNJLgH+J7AQ+FhVPZDkA8C2qtoCvCvJBjqzonwXeEtjgaUWssiVWqSqvgqk6RySoPvh8rZR2zaNWL4CuKLXuaT5YsLhCkk+luSJJPf3IpAkSZI0XZMZk3stcO4s55AkSZJmzIRFblV9hc5YIUmSJGlOmLExuXNtsurByz932P2PLOlREEmSJM24GZtCzMmqJUmS1C+cJ1eSJEmtY5ErSZKk1pnMFGI3AV8DXpRkKMnbZz+WJEmSNHUT3nhWVW/oRRBJkiRppjhcQZIkSa1jkStJkqTWsciVJElS68zYwyAkabYM7r9xWu9/ZGZiSJLmEK/kSpIkqXUsciVJktQ6FrmSJElqHYtcSZJmQZJzk3wzyUNJLh9j/+Ikn+ju35pkcLrn3LP3AF/f9RR79h6Y7qGkOc8bzyRJmmFJFgJ/ALwGGALuSrKlqh4c0eztwJNVdUqSC4HfBX51quf8zPZHuezWHQwsWMDB4WGuOn8dG05fPZ2/hjSneSVXkqSZdybwUFU9XFU/BG4GzhvV5jzguu7yLcA5STKVk+3Ze4DLbt3B/oPDPHPgEPsPDvPeW3d4RVfzmkWuJEkzbzWwa8T6UHfbmG2q6hDwNLBy9IGSbEyyLcm23bt3j3myoSf3MbDghb/SBxYsYOjJfVP+C0hznUWuJEl9rKo2V9X6qlq/atWqMdusWbGUg8PDL9h2cHiYNSuW9iKi1JcsciVJmnmPAieNWF/T3TZmmySLgGOBPVM52cpli7nq/HUsGVjA8sWLWDKwgKvOX8fKZYuncjipFbzxTJKkmXcXcGqSk+kUsxcCbxzVZgvwZuBrwAXAHVVVUz3hhtNXc9YpxzP05D7WrFhqgat5zyJXkqQZVlWHklwC/E9gIfCxqnogyQeAbVW1BbgG+JMkDwHfpVMIT8vKZYstbqUui1xJkmZBVd0G3DZq26YRy/uBX+l1Lmm+cEyuJEmSWmdSRe5ET22R1D/sr5IkTaLIHfHUll8CXgy8IcmLZzuYpCNnf5UkqWMyV3In89QWSf3B/ipJEpCJZitJcgFwblW9o7v+JuBnq+qSUe02Ahu7qy8CvjmNXMcD/zCN988EMzR//jZl+KmqGnsW9xlkfzWDGeZOf52KJLuBv5ugWdP/DabCzLNvruWFyWUet7/O2OwKVbUZ2DwTx0qyrarWz8SxzDB3z2+G2WN/NUNbMzR9/tk2meJ7Lv4bmHn2zbW8MP3MkxmuMJmntkjqD/ZXSZKYXJH7/FNbkhxFZ7LqLbMbS9IU2V8lSWISwxXGe2rLLOeaka9Rp8kMzZ8fzHBE7K+NMkNH0xmaPn8/mIv/BmaefXMtL0wz84Q3nkmSJElzjU88kyRJUutY5EqSJKl1LHIlSWqJufZY7yQnJflSkgeTPJDk0qYzTUaShUnuTfLZprNMRpLjktyS5BtJdiZ5RdOZJpLkPd2fifuT3JRkyREfox/G5Cb553SeyrS6u+lRYEtV7WwuVW91/w1WA1urau+I7edW1Rd6lOFMoKrqru6jYM8FvlFVt/Xi/ONkur6qLm7w/P+CzlPE7q+qLzaVo5/YXzvss2Pmsb82qPtY778GXgMM0Zlt5Q1V9WCjwQ4jyYnAiVV1T5LlwN3Av+7nzABJ/j2wHviJqnp903kmkuQ64C+r6uruzDtHV9VTTecaT5LVwFeBF1fVviSfBG6rqmuP5DiNX8lNchmdR48G+KvuK8BN/fApNMlbe3COdwGfAd4J3J9k5GNY/8tsn7+b4UrgI8AfJvmvwEeBY4DLk7yvRxm2jHr9OfBvnlvvUYa/GrH8f9H5d1gOXNkPP49Ns78+f55532ftr31pzj3Wu6oeq6p7usvPADv50QfovpRkDfA64Oqms0xGkmOBVwHXAFTVD/u5wB1hEbA0ySLgaODvj/gIVdXoi86nzoExth8FfKsP8n27B+e4D1jWXR4EtgGXdtfv7dHf8z46U04dDXyPzqdTgKXAjh5luAf4OPBq4Oe7fz7WXf75HmW4d8TyXcCq7vIxwH29yNDPL/vr8+eZ933W/tp/L+AC4OoR628CPtp0riPIPwh8+7mf5X59AbcAP9P9mf9s03kmkfd0OhckrgXupVOcH9N0rknkvhTYC+wGbpjKMWbssb7TMAz8JD/+LO4Tu/tmXZId4+0CTuhBhAXV/bqzqh5J8mrgliQ/1c3QC4eq6lngB0n+pqq+182zL0lP/jvQ+ernUuB9wH+oqu1J9lXV/9ej8wMsSLKCzrccqardAFX1/SSHepijX9lfO+yz9uGwr4IAAALxSURBVFfNoCTLgFuBdz/3s9yPkrweeKKq7u72+7lgEfAy4J1VtTXJh4HLgf/YbKzxdfv1ecDJwFPAp5JcVFUfP5Lj9EOR+27gL5J8C9jV3bYWOAW4pEcZTgD+JfDkqO0B/ncPzv94ktOrajtAVe3tdqSPAT/dg/MD/DDJ0VX1AzqfUIHnv+boSfFSVcPA/5vkU90/H6f3P6PH0hkTFqCSnFhVj3X/B9yr4qWf2V875n2ftb/2pTn5WO8kA3QK3Buq6tNN55nAWcCGJK8FlgA/keTjVXVRw7kOZwgYqqqt3fVb6BS5/ewXgb997oNrkk8Dr6Tz7dGkNV7kVtUXkvwzOmOJRt7Iclf3KkUvfJbOV4/bR+9I8uUenP9i4AVXHarqEHBxkj/uwfkBXlVVB7rnHvkLcgB4c48y0D3/EPArSV5H52vYXp57cJxdw8Av9zBKX7K/Ps8+22V/7SvPP9abTr+8EHhjs5EOL0nojBXdWVUfbDrPRKrqCuAKgO6V3P+nzwtcquo7SXYleVFVfRM4B+jrG/voDFv5uSRHA/voZN52pAfpi9kVJEnS9HWvMH6IHz3W+z83HOmwujNi/CWdMebPfVj7zWpwVp/JGlHkzoXZFU6nMxb3KOBh4K1VNfrbsL6S5LeBX6VzQeFe4B3PfbCf9DEsciVJktQ2jU8hJkmSJM00i1xJkiS1jkWuJEmSWsciV5IkSa1jkStJkqTWsciVJElS61jkSpIkqXUsciVJkiYhycuT7EiyJMkxSR5IclrTuTQ2HwYhSZI0SUn+E7AEWAoMVdV/bTiSxmGRK0mSNElJjgLuAvYDr6yqZxuOpHE4XEGSJGnyVgLLgOV0ruiqT3klV5IkaZKSbAFuBk4GTqyqSxqOpHEsajqAJEnSXJDkYuBgVd2YZCHwv5OcXVV3NJ1NP84ruZIkSWodx+RKkiSpdSxyJUmS1DoWuZIkSWodi1xJkiS1jkWuJEmSWsciV5IkSa1jkStJkqTW+f8BK4Vo3MgnhWIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPqcvGzsFb6f"
      },
      "source": [
        "## bar chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgBAIR5sFb6g"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randint(1, 20, 24).reshape(12, 2), columns=['con', 'gdp'],\n",
        "                  index = pd.date_range('2017-3-31', '2019-12-31', freq='Q'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jl3unOYeFb6g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "affef664-72a4-4d1e-ea5c-2882e5da24c6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "\n",
        "df.plot.bar(ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8a966ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 170
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPq9dz58Fb6g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "48d94540-e792-4d02-e554-48f741cd2d60"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "\n",
        "df.plot.bar(ax=ax)\n",
        "\n",
        "ticklabels = [pd.to_datetime(t.get_text()).strftime('%b\\n%y') for t in ax.get_xticklabels()]\n",
        "ax.set_xticklabels(ticklabels, rotation=0);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NLy3GDwFb6g"
      },
      "source": [
        "def xaxis_date_format(ax, fmt = '%y'):\n",
        "    '''fmt: '%b\\n%y', '%y-%m-%d', etc '''\n",
        "    ticklabels = [pd.to_datetime(t.get_text()).strftime(fmt) for t in ax.get_xticklabels()]\n",
        "    ax.set_xticklabels(ticklabels, rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKGTAx5tmzAF"
      },
      "source": [
        "## 한글"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rtNh9-Em6vo",
        "outputId": "5c8234de-3d24-412d-a7a9-64dc18b382f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randn(100).cumsum(), columns=['환율'])\n",
        "df.plot()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 54872 (\\N{HANGUL SYLLABLE HWAN}) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 50984 (\\N{HANGUL SYLLABLE YUL}) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 54872 (\\N{HANGUL SYLLABLE HWAN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50984 (\\N{HANGUL SYLLABLE YUL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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4bkeR8j2DChER+YJBJUg9eOlULMtNwBWz0rx+TmpMBLITzCht7FRmMmRrZw9/n0lJUdBpBLR1O1Bj7UZarGnY5wzm2R1FsHY7MCkpEmfqO9DQbkdrVw9iTXqf70lEROMPg0qQios04Mo56SN6jiAIeO/e83C6rh16jQZajQCdVoBJr0VG3PChw6DTICcxEqfr2lFY0+ZzUKlo6sRLX5UBAH525Qxs+McR1FptKG3owNxMi0/3JCKi8Yk1KmEmOkKPBVlxmJ0RixnpMZiSEo3MeLPXyzhT/FCn8putp2B3urBiUgJWTUlCTmIkAKC4ge35iYhoZBhUqI+pozzz53hVq7LDZ+MV0yEIAnISpdqZknrWqRAR0cioFlRKS0tx2223IScnByaTCZMmTcIjjzwCu51dUdU0ZZS9VJ74sACiCFw1Nx2zM6Rt1bnKjAqDChERjYxqNSoFBQVwuVx44YUXkJeXh2PHjuH2229HR0cHnnrqKbWGNe7JW5RP17XB6RKh1Xi/8+fzU/X44nQD9FoBP75sqvJzeemHO3+IiGikVAsql19+OS6//HLl+9zcXBQWFuL5559nUFFRVrwZRp0G3T0uVDR1ItsdMobjdIn49QcnAQDfXZ6NrITeYwByknqDymi3PRMR0fgSVDUqra2tiI8fuimZzWaD1Wrt80X+o9UISj+WkZyk/NaBChTUtCHWpMe9F+f1eSwzzgytRkCn3Ym6Nptfx0tEROEtaIJKUVERnnnmGdxxxx1DXrdp0ybExsYqX5mZmWM0wvFDqVPxspV+h82Bpz45BQC49+I8WMyGPo8bdBpkurdHF7OgloiIRsDvQWXDhg0QBGHIr4KCgj7PqaysxOWXX47rr78et99++5D337hxI1pbW5WviooKf/8rjHvKzh8vZ1T++Hkx6ttsyIo345blEwe8hnUqRETkC7/XqDz44INYv379kNfk5uYq/1xVVYWLLroIK1aswB//+Mdh7280GmE0Gkc7TBrCSHqp1Fq78cfPiwEAG66YBqNOO+B1OYlR2FFYjxL2UiEiohHwe1BJSkpCUlKSV9dWVlbioosuwsKFC/Hiiy9CowmalahxbZo7qBTXd8DucMGgG/zP5X8+KURXjxMLJ8bhilmpg17nWVBLFCgulwjNCHaqEVHwUy0ZVFZWYtWqVcjKysJTTz2F+vp61NTUoKamRq0hkVtqTASiI3RwuMQhu8meqLLizQNnAQAPrZ0+5G4e9lKhQHvusyJM/fmHyK9oUXsoRORHqm1P3rp1K4qKilBUVISMjIw+j4miqNKoCJDODJqWGo19pc04crYV01JjBrzuhc/PQBSBtXPSsCArbsh7yjUq5Y2dcDhd0Gk5e0b+9cHRavQ4RbxzqBLzeKYUUdhQ7dNi/fr1EEVxwC9S39KcBADAV0UNAz7ucon44rT02Lrl2cPeLzUmAhF6DRwuEWebu/w2TiJA6uNzulaa/dtT0qTyaIjIn/jXWhrQyrxEAMCuosYBw+OJaiuaOuyIMuowP8sy7P00GgHZCaxTocAobeyAzeECABTUWNHa2aPyiIjIXxhUaEALJlpg0mvR0G5DwQD9VOTZlGW58dB7uYwzKUlqJMc6FfK3Qo/fUVEE9pdxVoUoXDCo0ICMOi2W5Ehdgr8cYPnni9P1AIDzJ3u3wwvw7KXCLcrkX+eGaS7/EIUPBhUa1PmTpeUfefZE1mV3Yn9pMwDgPPc13pCDCrvTkr8V1khHacyeIJ3YzaBCFD4YVGhQcgjZU9IIm8Op/HxPSSPsThcmWEzKtmNvsJcKBYq89CN3Rj5W2YoOm0PNIRGRnzCo0KCmpkQjMcqI7h4XDpa1KD/f5Z5hOS8vcUQnIcuhprq1G512foiQf3TaHShr6gQAXDwtGRMsJjhdIg6UNas8MiLyBwYVGpQgCDgvT9qmvKuoXvn5LnfNykiWfQDAYjYgzqwHAJQ2dPpplDTena5thygCCZEGJEYZsTRXqq3ay+UforDAoEJDOs9dLCvPotRZu1FQ0wZB6N3CPBI8nJD8TV72meo++mFpDoMKUThhUKEhnecOI0cqW9Ha2aPMpsxKj0V8pGHE98tJlLYoc+cP+UvBOUFlibtZYX5FC7p7nIM+j4hCA4MKDSk1NgJ5yVEQReCrMw3KDqDzR7jsI8tN4pk/5F+FtdKOH/kwzewEM5KjjbA7XTz3hygMMKjQsORZlc9PN/hcnyLj0g/5W2GNNDs31X0mlSAISg8gLv8QhT4GFRqWHFT+lV+J+jYbTHotFk4c+hDCwTCokC/eza/E3X872K81fmO7DQ3tNggCMCUlSvm5XKeyp6RxTMdJRP7HoELDWjYpATqNgA67tN6/NDceRp3Wp3tlJ0RCEICWzh6cbebOH/LO85+dwftHq/HKnrI+P5cLabPizTAbeg+DX5or1akcKGuG3X0GEBGFJgYVGta5Bw+e58NuH5nJoFX+tvvqnvLRDo3Gibo2GwDgjX0VcLl6D8lUCmlTovtcn5cUhTizHt09Lhyrah27gRKR3zGokFc8tyKP5HyfgaxfkQMAeG1v+aC7Mn655QS++dyXqGrpGtVrUejrcbrQ1GEHAJQ3deLr4t7lHHlGRS6klWk0AhZnu5d/ilmnQhTKGFTIKxdNTQYATLCY+tQC+GL1dKl7aHNnD/51uKrf43uKG/F/u0pwqLwF39u8D9bungHuQuOFHFJkr++rUP65oFbemhzT73ny8s9e1qkQhTQGFfLK3EwL/rJ+EV68dfGI2uYPRKfVKGeybP6yFKLYO5Xvcon49Qcnle8LatrwH68cRI+TdQbjVb172UenkX7vPjpWg+YOO1wuEaeVoNI/PMtLjPtLm+H0WC4iotDCoEJeu3haCqacUwvgq+8szkSEXoMT1VbsK+09k2XL0WocPtuKSIMWm29dDLNBi11FDdj49tE+gYbGj/p2KahMTonGjLQY2J0uvJNfiYrmTnTanTDoNMhO6H845rTUaEQatGizOXDKHWiIKPQwqJAqLGYDvjl/AgBg81clAIDuHif++8MCAMCdF07CqqnJePbGBdBqBLx14Cye3nZatfGSeuQZleRoI76zJBMA8PreCqWQNi8pCjpt//+U6bQazM+SttHvL2WdClGoYlAh1axbkQ0A+Ph4LapauvDX3aWobOlCSowR3z8/FwBw0bRk/PKaWQCA//30NN7Nr1RruKSSBveMSmKUEdfMnQCjToPC2jb83V2rcm4hradF2e6gwpOUiUIWgwqpZlpqDJblxsPpEvHM9iI8s70IAPCfl02FydDbp+XGpVn4wQVScPnLrhJVxkrqkWdUkqKNiDXr8Y3ZaQCAbQV1AHrP+BnIoom9dSpEFJoYVEhVnluV27odmJ4Wg39bkNHvuuvcPyttZJO48UYOKolR0iGY316c2efxoYLKvCwLNAJQ2dKF6lZudScKRQwqpCp5q7Lsp9+YBq2m/66irHgzAKC1qwctnfZ+j1P4kpd+kqKNAKTdPNkJZuXxaQNsTZZFGXWYniY9zlkVotDEoEKq0mk1uHVlNgBg1dSkQZvJmQxapMRIH1ScVRlflKWfKOnPXxAEfMs9qxJr0iu/F4ORG78dYJ0KUUjSDX8JUWB9b2UOcpMisTQnYcjrJiZEotZqQ1ljB+ZlWsZmcOQzURTx83ePobvHhSf/fY7P/Xca2qUZNHlGBQBuWJyFnYX1uGBK0rD3XTgxDpu/KsX+Mu78IQpFDCqkOo1GwMXTUoa9LjvBjL0lTSjjjEpIONvchVe+ls5zuvfiPEwcoNfJcGwOJ1q7pM7EnkElLtKAN+5Y7tU95J0/J6qsaLc5EGXkf/aIQgmXfihkyB90pY0dKo+EvOG51FLS4NufWaN7NkWvFRBr0vt0j7RYEyZYTHCJQH55i0/3ICL1MKhQyJjoLqDkjEpo8FxqKfUxqPTu+DGO6uiG3n4qXP4hCjUMKhQy5DbpZZxRCQmeu2x8LYD2bPY2GosmSkGFBbVEoYdBhUJGlntGpaHdjnabQ+XR0FCs3T0o9Dhfx9elH89mb6Ox0N347WBZMxw84JIopDCoUMiIidAjIVJq+sVZleB2qLwFnmdI+vrndW6zN19NTY1GtFGHDrtTOSOIiEIDgwqFlCzWqYQEeYllxSRpy3lFcxd6fJjJOLfZm6+0GgHzufxDFJIYVCikZHPnT0g44C5avWJ2Gkx6LZwuEWebR97Cvt5PNSpAb50KDygkCi0MKhRS5J0/5ZxRCVoOpwuH3NuAF2fHKX9mvuz8aWjr3+zNV0pBbSl3/hCFkqAIKjabDfPmzYMgCMjPz1d7OBTEOKMS/Apq2tBpdyI6QocpydHISZT+zHwpqJVnVJL8MKMyL8sCrUZAVWs3Klt4QCFRqAiKoPKTn/wE6enpag+DQgBrVILffveMxYKsOGg0wqga9TXIxbR+mFExG3SYmS4fUMhZFaJQoXpQ+fDDD/HJJ5/gqaeeUnsoFALkGZXq1m509zhVHg0NRK4BkZdachKlcDnSGZXuHifa3NvQ/bH0I41J2qb8dXGjX+5HRIGnalCpra3F7bffjpdffhlms3n4J0BaJrJarX2+aPyIM+sRHSGd1VLRxFkVfxBFER1+7Esj76pZ6O4G6+tynbw12aDTINpP5/OcPzkRAPD5qQaInvuniShoqRZURFHE+vXrceedd2LRokVeP2/Tpk2IjY1VvjIzMwM4Sgo2giB4fPAxqPjDxrePYsEvt2LridpR36uypQvVrd3QagTlhGu5RqWyuQt2h/dblD3rU0bTPt/T0tx4GLQaVLZ0odjHJnRENLb8HlQ2bNgAQRCG/CooKMAzzzyDtrY2bNy4cUT337hxI1pbW5WviooKf/8rUJDrrVPhB40/7CpqgM3hwr2vHcTB8tFt3ZVrP2amx8BskGZBkqKNiDRo4RKB8hHMgtX7sT5FZjbosDhHmun5/FS93+5LRIHj96Dy4IMP4uTJk0N+5ebmYvv27di9ezeMRiN0Oh3y8vIAAIsWLcK6desGvb/RaERMTEyfLxpfsuXtrgwqo+ZwulDd2g0A6O5x4fsv7fe53T3gsezjrk8BpFkwpaB2BPdu8OOOH0/nT04CAHxxusGv9yWiwPDPwq+HpKQkJCUlDXvd7373O/zqV79Svq+qqsKaNWvwxhtvYOnSpf4eFoWRicrhhFz6Ga3q1m44XSIMWg2mpkbjaGUr1r+4F/+4a4VPTdbkgwjlolVZTmIkTlRbRxQu/XXOz7kumJyEJz4swO4zjbA5nDDqtH69PxH5l2o1KllZWZg1a5byNWXKFADApEmTkJGRodawKARkM6j4TUWz9B5mxJnwl/WLkRlvQlljJ27bvA+d9pEV2LbbHCiokYrbF2XH9Xks24edP70zKqM75+dc09OikRRtRFePEwdK2aWWKNipvj2ZaKTkTqdnmztHVJxJ/clt7SfEmZAUbcRLty5BnFmPw2db8bN/HhvRvfLLW+ASpdCTEhPR5zFfdv4EakZFEARl98/O06xTIQp2QRNUsrOzIYoi5s2bp/ZQKMglRxsRodfAJSLoOoy2dffA6Qqdba9n3cWtmfFS+MtNisLvbpgPAPjkRO2ItvDucxfSetanyOSdP6UN3s+CNbRL7fP9cc7PuS6cIi1Pf36KdSpEwS5oggqRtzy3KAfTzp9dpxuw8Jef4oG/56s9FK/JMyoZcSblZ8tyE6DXCmi3OUZ0kOAX7tmJZbkJ/R7LdgeVqtYurxv1BWpGBQBW5kkzKierrahr6/b7/YnIfxhUKCRNDLJW+i2ddjz4Zj7sThfeza/C9oLR9yQZC3KNSmZcb8NFvVaDSUlRAKRze7zR0mlHfkULAGDV1P7F9AmRBkQbdRAH2KJc1tgxYEv7Bj+enHyuxCgjZk2Qdgzu4u4foqDGoEIhaTTnxwTCw+8eR63VBp1Gakz2yL+Oh0SL/4FmVABgamo0AKCwxrvOz5+fboBLBKamRCMt1tTvcUEQlFkVz4LaTrsD1z2/G9e/sFspxAWADpsDnXbp/QvEjAog7f4B2E+FKNgxqFBICqYZlfcOV+Ffh6ug1Qj4621LkBoTgYqmLjy3o0jtoQ3J5nCixiote8g1KrJpqdJsg7czKp8V1AEAVk0bvDVBdmL/Xip/3V2GhnYbRBF4/0i18nN52cek1yLST+3zz3XBlN5+Ki6PuqL9pU244+X9OFDGgwuJggGDCoWkYKlRqbV242fvSLtj7rkoDysmJeKRq2YAAP6ws3hUzdMCrbqlG6IIROg1SIjsuwV4mjKjMnxQcblE7HTPSqyakjzodTnnNOrrsDnwx8+Llcc/PFaj/LOyNTlAsymAdLpzpEGLxg47TlRbIYoi/vxFMb7zx6/x8fFa/H57cAdNovGCQYVCkjyjUtHUpdouG1EU8ZO3jqC1qwezJ8Tinoul7sqXz0rFBVOSYHe68PC7x4L28LveHirmfmfpyEs/xQ0dsDmGXsI6VtWKxg47ooy6fv1TPPV2p5Ve96+7y9DUYUdmvAl6rYCiunYU1UnBKJCFtDKDToPlk6TC3w+PVePuVw/iV++fhMP9+7SvtBkOJ7e/E6mNQYVCUlqs9OFmd7pQ3arOFuVX95Zj56l6GHQa/Pbbc6HXSv93EgQBv7h6Jgw6Db443YAPjtYMcyd1yPUpmXH9a0rSYiMQHaGD0yXiTN3Qs0KfFUqzKSvzEpT3YCDK0k9jB9ptDvzx8zMAgPsvmaLswvnQ/V71FtL6t9nbueTln2d3nMEHR2ug1wp45KoZiDXp0W5z4FgVT2cnUhuDCoUkrUZQ6ipG0pvDX0RRxNOfngYA/GTNVOQlR/d5PDsxEnddOAkA8Istx7GvtCnoZlYqmnpnVM4lCELv8k/t0B/WnxW661OmDr7sA/T2Uqlu7cYfd55Bc2cPchIjcc28dFwxKxUA8NFxKaiMxYwK0FtQC0jh7I07luPWlTlYkiMdAbD7TGNAX5+IhsegQiFrurvgc7Qn/vribHMX6tqkXT43L5s44DV3rZqEiQlm1FptuP4Pu7Hmfz/H5i9L0NrVM8ajHZgyoxLff0YF6F3+GaqgtrnDjkNDbEv2FGfWIyZCKox9fqc0m/LDS/Kg02pw6YxUaATgeJUV5Y2dqA9gszdP2YmRWL8iG1fPTceWe8/Dgixp6Wq5uxfM18UMKkRqY1ChkCXXF3x1Zuz7YMg9Q6anxSBCP/ChdhF6LV65bSm+vSgTEXoNTtW249H3TmDprz/Fq3vKx3C0A/OsURnIVHcQHKqg9vPT9RBFqfh2oG3JngRBUGZVepwichMjcdWcdABAfKQBS3OkP8+PjleP2YwKADx69Uz87ob5SPAIRfLv1r7SJvSwToVIVQwqFLLkuoaDZS3oso9tzxI5qMzLtAx5XWa8Gf/973Ow56er8djVMzElJQrdPS48/v4J1T8Ae2tUBg4q0+UZlerBg8pOd33KhcPMpsjkOhUA+OElk6HzqGm5YrZ7+edYDeoD2OzNG1NTohFn1qPT7sSRs62qjIGIJAwqFLKyE8xIi42A3enCgbKxXf455F5ump9l8er6WJMe61Zk46P7LkCcWY8OuxNHzrYEboDD6O5xKrMW5zZ7k01xB5UaazdaO/svV3m7LdlTbqLU8TY3KRJXzU3v89iamVJQOVjeguK6dgBjM6MyEI1GUGZ4uPxDpC4GFQpZgiCosvxjd7iU3SDDzaicS6PpHfOXRYH/AHzxyxL811tH+s3eyLMpUUYdLGb9gM+NidBjgkUKMQUDdKj1dluypxuWZOLaeen47bfmQavpuyU6JSZCOdCwzeYAACSpNKMCAMtypYJaBhUidTGoUEhbMUla/vlqDHdnFNRYYXe4EGvSKzUXIyGP+cuiwIars82d+OWWE3hjf0W/NvG99Smmfj1UPCmt9Gv7L//sKJDueV5e4pDbkj0lx0Tgf78zH3MHCXiXu2dVZGot/QDAcvef0/7SZtgdrFMhUguDCoW0Fe7ZiSNnW2DtHpvdNJ71KUN9yA9GHvOh8sDW1rz0VSnkXnjnBpXeM34Grk+RDbXz57NT8rZk7+pTvHH5rN6gEm3UwWQYuFB5LExJiUJ8pAFdPeou0xGNdwwqFNLSLSbkJEbCJQL7SsbmbJZD5S0ARr7sI8tJjFRqa/YH6DyZdpsDr++tUL7/4pwTgs829c6oDGWwVvo1rd047A5s3hbSeiMz3qycapyoUn2KTBAEZfmH/VSI1MOgQiFvLGs+AI8ZFS8Lac8lCILH8k9gxvzm/gq02RzIjDdBqxFQ3NChNHgDPHuoDD2jIh9OeKqmrU/Duuc/K4JLBJZkxw+7LXmkrpiVBkC9QlpPcj+V3axTIVINgwqFvBUBKKgtqmvHiQHap7d02pWDBudlWHy+/8q8wBUBO10iXvyyFADwgwsmYYE7UHnOqnjWqAwlNykSeq2ANpsDlS1SuKlu7cJr7tma+1dP9vPogZuXTcR1CzJwr/vsJDUtcweVA2XNw555RESBwaBCIU/+MCmoaUOju//GaByrbMXa332Ba5/9EuWNfdvzy7MpOYmRiIv0/RwaeUblaGXrgFt/R+PTk7Uob+pErEmP6xZMwPnuNvGedSrD9VCR6bUaTEqSthTLyz/P7TgDu9OFJTnxymyWP8Wa9Pifb81Vxq2mvOQoJEYZYXO4kO9e8iOiscWgQiEvMcqo1FJ8XTy6mo/GdhvuePkAbA4X7E4X/m9XcZ/HR1ufIkuNjUBuUiREEfi6xL/LCv+3qwQAcOPSLJgNOuXgvS/PNMDhdKHD5kBTh9SiPmOQ9vmePAtqK1u68Po+qavuj1ZP8amYOJT0qVPh8g+RKhhUKCz4o5+Kw+nCPa8eQmVLF+LcvUX+vv8smt0f6oD3HWm9sVLeWu3HbcrHKluxt6QJOo2AdcuzAQCzJ8TCYtajrduBw2dblNmUWJMeMRED91Dx5BlUnt1RhB6niGW5gZlNCUbLeO4PkaoYVCgsyB/6o9md8esPCrC7uBGRBi3euGM5ZqbHoKvHiZe/LgMgnZh82L1N1duOtEOR61S+9OOOkr+4Z1PWzklDamwEAOmkafm4gc9PNXicmuxdEaw8W7WnuBFv7pdqU360eorfxhzs5EB2sLwF3T2sUyEaawwqFBaW5MZDIwDFDR2obu0a8fPfPngWf/lS+pD/n2/NxZSUaPzgglwAUj+S7h4nShs70dLZA4NOo+yGGY1luQkQBKlwt9baPer71Vm78d6RKgDAbefl9HnsgsnuoHK6HmfdhbTD1afI5MMJ69ps6HGKWJmXgKW542M2BQBy5e3kDhfeO1yl9nCIxh0GFQoLMRF6zHbvwvlqhFt+T1RZsfHtowCAey/Ow+Xu7bFrZ6dhgsWExg473jpwVjnfZ1Z6DAy60f9fx2I2YGa6FAL8sftn81el6HGKWJwdhznn7EiSC1MPV7TguHs3k7czKumxEYiO0Cnfj6fZFECqU7l1ZTYA4LnPzsDpEod+AhH5FYMKhY3ebcojCyovfVUKm8OFC6ck9fkQ1mk1+P750szEn78oxkHlIELvzrXxRm+dyuiWf9ptDmWJ6vvn5/Z7PN1iQl5yFFwi8P7RagDeBxVBEJTln/MnJ2JRdvyoxhqKblo6ERazHiUNHcr7R0Rjg0GFwoYcVL44XQ/XCP7We8AdQG5eNhGacw7K+9aiTMSa9Cht7MSb+88C8E8hrWxFXu9ZRZ4N1Ubq9b3laOt2IDcpEpdOTxnwmgvcsyqd7rb9wzV783TL8mzMTI/Bz9bO8HmMoSzSqMNtK6XQ+uz2ohH9fhHR6DCoUNhYkhOP6Agd6tps2Ffq3Tbl1s4eFNW1A4DSGM1TpFGHW5ZNBADY3AfT+TOoLM6Og14roLKlC2Xn9Gzxlt3hUrYk33FBbr+wJTt/SmKf74c758fT1XPT8f4Pz1d2AI1H312RjWijDoW1bdh6srbf4y6XiNausTlvimg8YVChsGHUabHGffruliPeTc8fqpBmU7ITzEgY5KTedSuylZqUxCiD10sm3jAbdJifKS0l7Sis8+ke7x2uQnVrN5Kijbh2/oRBr1uWkwCDxynH/vz3GA9iTXqsW5ENAPj99qI+M2BVLV24+tldWPSrrTg9wEnTROQ7BhUKK1fOkQphPzxWDYfTNez1B90N3BYMUXeSFG3EdQsyAEj1Kf5ucvaN2VK4en1vxYiXf0RRxAufnwEAfG9lDoy6wU8bNhm0WJwj/XvGRxoQadQNei0N7Hvn5cCk1+JoZSt2ujv95le04Jpnv8SxSit6nCL+xZ1BRH7FoEJhZWVeIuLMejS027HHi9OU5Z088ycOXSD7X5dPxQ8uyMVP1kz1yzg9fXNBBiL0GhTWtuFAWfOInvtZYT1O1bYjyqjDjUuzhr1erlPJ5GyKT+IjDbh5mfQ+P7O9CO8drsK3X9iN+jabsjNq64n+y0JE5DsGFQoreq0Gl8+Sl3+G/put0yUqLfEXDrOTx2I24KffmI7JKf6v0Yg16XH13HQAwN/2lI/ouX/YKc2m3Lg0C7Gm4bvMfntxJi6fmYq7Vql/4F+ouv38XBh0Ghwoa8a9rx2CzeHCxdOS8cEPz4dGkDr4ep5UTUSjw6BCYefKOdKH/ofHatAzxPLP6bo2tNsciDRoVS8SvWmpVLD7/tFq5Rye4Rwqb8aekibotb19PoZjMRvwh1sWKmGORi45JgI3LM5Uvv/+eTn403cXITPerGzd3jZAsS0R+YZBhcLO0px4JEYZ0NLZgy+HOEfnYFkLAGBupgXaQXbKjJU5GbGYNSEGdocL/zhwdtjrmzvs+M3WUwCAa+ZNQFosl3LG0v2rp+BbizLwv9+eh59dOUP5/ZG3hn960rfCaCLqj0GFwo5Oq8EV7u6yQ+3+kRu4DVVIO1YEQVBmVV7dWz5oUe3Rs6348ZuHsWzTNnxxugGCAKXVP42duEgD/t+/z+23y2r1DCmofF3cCGs3tyoT+YPqQeX999/H0qVLYTKZEBcXh2uvvVbtIVEYkHf/fHy8BjbHwAfJHXQXri4cppB2rFw9Nx1RRh1KGjr6Ha54qLwZ33zuS1z1+11488BZ2BwuzEyPwQs3L8SUANTNkG9yEiMxKSkSDpeInYX1ag+HKCyoGlT+8Y9/4JZbbsGtt96Kw4cP48svv8SNN96o5pAoTCzOjkdKjBFt3Q58car/8k9zhx3FDR0A/HMSsj9EGnX4pvtv6HJRrSiKeHl3Kb71wm4cKm+BXivgmnnp+MddK7Dl3vNw2UzWmgQbeVaFdSpE/qFaIwWHw4H77rsPTz75JG677Tbl5zNmjM8W3eRfGo2Ab8xOw4tflmLLkSrlw0MmN3rLTYqExWxQY4gDunFpFl7+ugwfH69BeWMnfvvpKfzzUCUA4IpZqXjsmplIjo5QeZQ0lEunp+CFncXYXlCHHqcLeq3qE9dEIU21/wcdPHgQlZWV0Gg0mD9/PtLS0nDFFVfg2LFjQz7PZrPBarX2+SIaiLz7Z+uJWnT39F3+kQtpg6E+xdP0tBgsyLLA4RJxxdOf45+HKqHVCHjoG9Px3E0LGFJCwPysOMRHGmDtdmB/6cj64hBRf6oFleLiYgDAo48+ip/97GfYsmUL4uLisGrVKjQ1Dd6oa9OmTYiNjVW+MjMzB72WxrcFWRZMsJjQYXfiX/l9e6ocCLL6FE9yUW2H3YnEKANeuW0pbr8g1+8dcSkwtBoBF01NBgB8yuUfolHze1DZsGEDBEEY8qugoAAul9Tf4qGHHsJ1112HhQsX4sUXX4QgCHjzzTcHvf/GjRvR2tqqfFVUVPj7X4HChCAIuGGJFGQffe84CmukM1gcThcOn20BEHwzKgCwdk4aLpiShEumJWPLvedjuftUaAodl87oDSqjORWbiAJQo/Lggw9i/fr1Q16Tm5uL6mpp26hnTYrRaERubi7Kywfvzmk0GmE0Dnx4HNG57rxwEnYXN+LLokbc8fJ+vHvPeTjb3IlOuxPRRh0mJ0epPcR+IvRa/PV7S9QeBo3C+ZOTYNBqUNbYiaK69oB0NCYaL/weVJKSkpCUlDTsdQsXLoTRaERhYSHOO+88AEBPTw9KS0sxceJEfw+LximdVoNnbliAq57ZhdLGTtz/+iFcNE362+68LAs0Kjd6o/AUadRhRV4CPiusx9aTtQwqRKOgWo1KTEwM7rzzTjzyyCP45JNPUFhYiLvuugsAcP3116s1LApD8ZEGvHDLQhh1GuworMdTHxcCCM5lHwofq91dancUsEst0Wioes77k08+CZ1Oh1tuuQVdXV1YunQptm/fjrg4foCQf82aEItff3M2HnzzMKzdDgDAgiAspKXwMS/TAgAoaeABhUSjoeoGf71ej6eeegq1tbWwWq3YunUrZs6cqeaQKIxdtzAD65ZLy4qC0PtBQhQIqbHSVvLGDtuQh2MS0dBUnVEhGms/u3IGBEFAWmwEYk16tYdDYSzebIBeK6DHKaK+zYZ0Cw+OJPIFgwqNK3qtBo9ezVk7CjyNRkBydAQqW7pQa+1mUCHyEXs7ExEFSHKM1Eqh1tqt8kiIQheDChFRgKTGSHUqtVabyiOhcLKnuBFbjlQNf2GY4NIPEVGApLiDSg1nVMiP/uNvB9HYYcf0tBhMSgq+ppX+xhkVIqIA4dIP+Vun3YHGDjsAYG/J4OfihRMGFSKiAJGXfuq49EN+Ut/W+7u0r5RBhYiIRoFLP+RvnkFFPgU+3DGoEBEFSAqXfsjPPINKWWMn6sbB7xaDChFRgMgzKm3dDnTaHSqPhsJBXVvfZcT942BWhUGFiChAoow6mA1aANyiTP5Rf25QKWVQISIiHwmC4NFLJfyn6Cnw5KAyOVnalry/LPwLahlUiIgCiFuUyZ/q26WgcsXsNADA8SorOmzhvazIoEJEFEApnFEhP5JnVOZmxGKCxQSnS8ThihZ1BxVgDCpERAEkL/3UtLJGhUavrk0KvMnREVg4MQ4AsC/M61QYVIiIAihZnlFp44wKjY7LJaKhXepKmxRtxOJsKaj4Wqeyo6AOd71yAK1dPX4bYyAwqBARBVBvd1oGFRqd5k47nC4RAJAQZcCi7HgAwMGyZjicrhHf76lPCvHhsRq8m1/p13H6G4MKEVEAyU3f2J2WRksupI2PNECv1WBKSjSijTp02J0oqGkb0b26e5wodD/nVO3InjvWGFSIiAKot5jWBlEUVR4NhTK5kDYpSgq/Wo2ABe46lf0jPPfnZLUVDvfszKnadj+O0v8YVIiIAkjenmx3uNDSGdy1ABTc5MMt5d8pAB51KiMrqD1a2ar886natqAO0QwqREQBZNRpEWfWA2BBLY2OvPQjz6gAwMKJUp3KvtKmEYWNwxW9QaWls0e5dzBiUCEiCjDP5R8iXylLP9G9QWVepgU6jYBaqw1nm7u8vtfRypY+358O4uUfBhUiogBTgkorZ1TIdwMFFZNBi1kTYgEAB7xc/umwOVBUJwWT+VkWAMFdUMugQkQUYClso09+IDd78wwqQG+dyj4vC2qPV1nhEqWt8ysnJQJgUCEiGteU7rQMKjQKA82oAMDMdGlGpbi+w6v7HDnbAgCYkxGLySnS4YbBvPOHQYWIKMCSWaNCfiAHleRzgor8fZ2XxdpHzkqFtHMyYjE1NRpAcO/8YVAhIgqwVB5MSKPU3eOEtVs6JTkpKqLPY/J2ZTnIDEfemjw7w4KcxEhoNQLauh1BG6QZVIiIAownKNNoNbi3Dxt0GsSYdH0eS4qWfr+s3Q509ziHvE9rVw9KGqQlojkTYmHUaZGdYAYAFAZpnQqDChFRgMnFtA3tNp/OZCGq8+hKKwhCn8diInQw6KSP8+FmVY65Z1My402IizQAgLL8c5pBhYhofEqIMkKrEeASoZx+SzQSgxXSAoAgCF7XqRxWCmktys8mJ/fWqQQjBhUiogDTagSlmyiXf8gXQwUVoLegdrgZlaNyIa279woATEmRg0pw7vxhUCEiGgMpsdyiTL4bPqhIv191wwSV3h0/FuVnU9xblE8H6c4fBhUiojGQIk/NM6iQD+ra+p/z4ylJ+f0aPKg0tttQ2SK12Z81IUb5eXZiJPRaAR12p/J4MGFQISIaAzzvh0ZD6aES4/vSzxF3IW1uUiSiI/TKz/VaDXIT5VmV4Fv+UTWonDp1Ctdccw0SExMRExOD8847Dzt27FBzSEREAZHKpR8ahYFOTvYkB5ihimmPuE9Mnuux7CPr7VAbfAW1qgaVK6+8Eg6HA9u3b8eBAwcwd+5cXHnllaipqVFzWEREfif/jZfFtOSLhmFqVJSlnyFmVOQTk2d7FNLK5ILaYOylolpQaWhowOnTp7FhwwbMmTMHkydPxhNPPIHOzk4cO3ZMrWEREQWEPKPCoEIjJYqi18W0gy39iKKIw+5C2rmZgwcVLv14SEhIwNSpU/HXv/4VHR0dcDgceOGFF5CcnIyFCxcO+jybzQar1drni4go2LFGhXzV2tUDu7tRYOJgSz/RvU0Fna7+O3dqrTbUt9mg1QiYkTZQUJGWforq2uEa4PlqUi2oCIKATz/9FIcOHUJ0dDQiIiLwm9/8Bh999BHi4uIGfd6mTZsQGxurfGVmZo7hqImIfJPi/htva1fPsG3OiTzJsySxJj0i9NoBr4mPNEAQAJcINHb0D8MnqqXZlLykKJgM/e8xMSESBp0GXT1OnG0Orp0/fg8qGzZsgCAIQ34VFBRAFEXcfffdSE5OxhdffIG9e/fi2muvxVVXXYXq6upB779x40a0trYqXxUVFf7+VyAi8rsYkw4Reuk/uVz+oZEYbtkHAHRaDRIiB9/5U9LQCQCYlBw54PO1GgGTkqRZlWCrU9ENf8nIPPjgg1i/fv2Q1+Tm5mL79u3YsmULmpubERMj7ed+7rnnsHXrVrz00kvYsGHDgM81Go0wGgf/wyIiCkaCICAlJgJljZ2otdowMWHgDwyicw2340eWHG1EQ7sNdW02zDznsfJG6SDCrPjBf++mpkThZLUVp2rbcOmMlFGN2Z/8HlSSkpKQlJQ07HWdnVK602j6TupoNBq4XDy0i4jCT0q0FFSqW4Nrap2Cm9zEbagZFeXxaqB+gDqo0kbpM1c+KXkgk1OC83BC1WpUli9fjri4OKxbtw6HDx/GqVOn8OMf/xglJSVYu3atWsMiIgqYGenS7PGHR9mCgbynzKgME1SUpm/t/YNKeZMUVLKGCCq9W5SDa+ePakElMTERH330Edrb23HxxRdj0aJF2LVrF959913MnTtXrWEREQXMjUuzAABbT9ZyVoW8pnSlHS6oxAx8TIPD6UJFkzyjMvjSz/S03hmVTrvD5/H6m6oN3xYtWoSPP/4YjY2NsFqt2L17N6644go1h0REFDBTUqKxNCceTpeI1/aUqz0cChHeFNMCgx9MWN3aDYdLhEGnQap7m/xAMuLMmGAxweESsb+0eZSj9h+e9UNENIa+uzwbAPDq3grYHf3r8apbu/DsjiJUBeHhcKQOb4PKYN1pS5VCWjM0GmHIeyzLTQAAfF3c6NNYA4FBhYhoDF02M0XZnfHx8b61KjaHE7e+uA9PflyIq3+/C/tLm1QaJQUT+fwer2tUzgkqZe5C2onxg9enyJZPYlAhIhrX9FoNblgi1aq8vLusz2P/88kpFNRIOy4a2u244U9f4+/72StqPLM7XGju7AHQu7QzmN6ln26IYm932TL3jIo3W+KX5sQDAI6cbUWHLTjqVBhUiIjG2I1Ls6DVCNhb2oSCGukYkN1nGvGnL4oBAE9/Zx4un5mKHqeIn7x1BL/acmLAtugU/uQuszqNAItJP+S18oxLd48LbR4hQ55RyU4cfkYlM96MjDh3nUpZcNSpMKgQEY2xlJgIrJkpNdR6eXcZWrt68ODf8yGKwHcWZ+KaeRPw3E0L8MNLJgMA/ryrBHe8vD/ozmChwJOXcRKjjMPWl5gMWkQbdX2eB/QGlSwvln4AYHmQ1akwqBARqeCWZdkAgH8eqsRP3jqMqtZuTEww4+dXzgAAaDQCHrh0Cp69cQGMOg0+PVmH1/Zxp9B4420hrSxJ2aIsPU8URZQ1SUs/Q21N9iQX1O4+w6BCRDRuLcuNx+TkKHTanfj4eC20GgG//fY8RBr7NgxfOycNG66YBgD47w8LBjzHhcJX3UiDSpS886dbeX53jwtajYAJcSav7rE0V6pTOVrZivYgqFNhUCEiUoEgCLhl+UTl+3suysOCrIFPjr9l2UTMmhADa7cDmz44OVZDpCCgzKgMc86PLNndJ0V+nrzsM8Figl7r3Ud+RpwZmfEmOF1iUOw8Y1AhIlLJvy3IwIy0GFw0NQn3XJw36HU6rQaPXzsbggC8fagSX51pGMNRkppq3F1mU2KH3vEjO3eLcqmy48e7+hRZb50KgwoR0bgVZdThg/vOx4u3Lhn2b7tzMy24eak0A/Ozd47B5nCOxRBJZTWtUlAZqqOsp3ObvpX5GFSUOpUgKKhlUCEiChH/uWYqEqOMKK7vwJ8+L1Z7ODQG5KCS5uOMSm+zN+8KaWVyUDlW2Yq27p4RPdffGFSIiEJErEmPn185HQDwzPYilLs/hCh81cpLP17OqHg2fQM8gsoIZ1TSLSZMTDBLdSoq91NhUCEiCiFXz03HyrwE2BwuPLP9tNrDoQCyOZxo7LADAFK9nFHxXPoRRdGjRmVkMyoAsCzHXaei8jZlBhUiohAiCALuuUhqBLf1ZC0czv4HG1J4kHuhGHQaxJmH7kork5d+Wjp7UNdmQ1u3tL3Y22ZvnpZNkrYpq934jUGFiCjELM6OQ5xZj5bOHuwtUX9XBgVGtUchrSAM3ZVWZjHrYXAXZh9wL9mkxkTAZNCO+PXlOpWjKtepMKgQEYUYnVaDS2dILfg/OucEZgof8tZkb5d9AGnGTV7+2efugZI1wvoUWVqsCdkJZrjE3nupgUGFiCgErZmZCgD45HgtzwAKU7Uj3JosSzwnqGT7GFSA3lkVNfup6Ia/hIiIgs3KvEREGrSosXbj8NkWzB+kqy2FLmXpZwQzKkBvncqJKulkbl8KaWXXL8rAgqw4rMhL8Pkeo8UZFSKiEBSh12LVtGQAwMfHa1UeDQWCvDV5pDMqclCRJ9pGujXZ08KJ8fjW4kxkxPl+j9FiUCEiClGXu5d/Pj5eA1Hk8k+48aVGBeh/gOFIm70FGwYVIqIQtWpqEgxaDUoaOnC6rl3t4ZCfyV1pvW32JpObvsl8LaYNFgwqREQhKjpCj/MmJwIAPj7G3T/hxOUSlaUfb9vny5I9ZlTizHrEmrzrwRKsGFSIiELYmpncphyOGjpscLhECEL/pZzheF4/mkLaYMGgQkQUwlZPT4FGAI5XWVHRxLN/wkVtq9SVNinKOOzJ2udKjvEMKqG97AMwqBARhbSEKCMWZ0utzj/mrErY8LWQFgASozijQkREQeTyWb3N3yg81LR2ARh5IS0A6LUaxEcaAIyu2VuwYFAhIgpxl7m3Ke8ra0JDu03l0ZA/1PhYSCubmhINAJg9IdZvY1ILgwoRUYibYDFhRloMRBH4sqhB7eGQH1T7uDVZ9txNC7Dl3vMw2R1YQhmDChFRGFjpbnHOoBIefO1KK4uLNGBWGMymAAwqRERhYUWe1E/ly6JGdqkNA3KzN1+XfsIJgwoRURhYkh0PvVZAZUsXyrlNOeQpXWkZVBhUiIjCQaRRh/mZ0gnKXxY1qjwaGo227h502J0AfF/6CScMKkREYWIF61TCglyfEh2hQ6RRp/Jo1BewoPL4449jxYoVMJvNsFgsA15TXl6OtWvXwmw2Izk5GT/+8Y/hcDgCNSQiorC20l2n8tWZBrhcrFMJVfKOH86mSAIWVOx2O66//nrcddddAz7udDqxdu1a2O12fPXVV3jppZewefNmPPzww4EaEhFRWJubYYHZoEVzZw9O1ljVHg75SK5P8aUrbTgKWFB57LHH8KMf/QizZ88e8PFPPvkEJ06cwCuvvIJ58+bhiiuuwC9/+Us8++yzsNvtgRoWEVHYMug0WJojtdP/inUqIauGMyp9qFajsnv3bsyePRspKSnKz9asWQOr1Yrjx4+rNSwiopAmL//sGuM6lXcOVeK9w1Vj+prhajTn/IQj1ap0ampq+oQUAMr3NTWDH6xls9lgs/W2iLZaOb1JRCRbMUkKKntLmmB3uGDQjf7voy6XCEEABEEY8PGXvirFI/86Dq1GwKqpSYiO0I/6NcezWgaVPkb0G7xhwwYIgjDkV0FBQaDGCgDYtGkTYmNjla/MzMyAvh4RUSiZlhqN+EgDunqcyK9oGfX9Wrt6sOqpz3Dts1+iYoD+LJ8cr8Gj70mz4E6XqBSCku9YTNvXiILKgw8+iJMnTw75lZub69W9UlNTUVvb96RP+fvU1NRBn7dx40a0trYqXxUVFSP5VyAiCmsajYAVk/y3Tfnd/EqUN3Xi8NlWXPX7XX3ueai8GT98/RA8G+EyqIweZ1T6GtHST1JSEpKSkvzywsuXL8fjjz+Ouro6JCcnAwC2bt2KmJgYzJgxY9DnGY1GGI1Gv4yBiCgcrcxLxJYj1fiyqAE/unTKqO71+l7pL4OxJj1aOntwy//twU+/MR2XzkjB91/aj+4eFy6amoQep4hdRQ2oZVAZFZvDiYZ2aUMJZ1QkASumLS8vR35+PsrLy+F0OpGfn4/8/Hy0t7cDAC677DLMmDEDt9xyCw4fPoyPP/4YP/vZz3D33XcziBARjcJKd51KfkULOmy+96Y6VtmKE9VWGHQafPKjC3Ddggy4ROBX75/EN57+Ao0ddsyaEIPf37gAGXEmAJxRGa06q1SDadBqEB9pUHk0wSFgQeXhhx/G/Pnz8cgjj6C9vR3z58/H/PnzsX//fgCAVqvFli1boNVqsXz5ctx888347ne/i1/84heBGhIR0biQlWBGRpwJDpeIvSVNPt/n9X3lAIA1M1OREhOBp66fg0evmgGtRkCH3YkJFhP+sm4xIo06ZZmixtrll3+H8Upe9kmJNQ5avDzeBGzXz+bNm7F58+Yhr5k4cSI++OCDQA2BiGjcOi8vEa/vq8CXRQ24aFryiJ/fZXfi3UPSduPvLJY2LQiCgPUrczAjPRb/PFSJ28/PQbJ7eUI+5ZczKqPDQtr+eIgAEVEYWpITj9f3VeBIZatPz//wWDXabA5kxpuwPDeh372XuBvLyVJjpaWfGgaVUVFmVBhUFDyUkIgoDGXFmwEAVS2+LcW8vk8qov3WwkxoNMMvQcgzAHKzMvKNHPTSuONHwaBCRBSG0i3SDEettRvOER5QWFzfjr0lTdAIwL8vyvDqOXKNSktnD7rszpENlhTVnFHph0GFiCgMJUcbodUI6HGKaGi3Df8ED3/ffxYAsGpqMtLcSzrDiYnQwWzQAuCsymjUKjMq3r3v4wGDChFRGNJpNUiJllo9VI5g+afH6cJbB6Sg8q1F3nf+FgRBmVWpbuXOH18pxbSxbNMhY1AhIgpT8vLPSOpUthfUoaHdhsQoAy6ZPrLdQkqdCgtqfeJyiahr49LPuRhUiIjClBxUqlu8Dw7/ype2JF+3IAN67cg+Inp7qTCo+OJAeTN6nCJMei2DigcGFSKiMCUHlZEs/Zyolk6kv2DKyI9LkXeqcEZFKkh+++BZOJwur5/z6h6pwd5Vc9NGHBLDGfuoEBGFqXSLFBy8Xfrp7nGitLEDADAlJXrEryf3UhnPTd96nC68sPMMfretCHanCwadBlfOSR/2ec0ddrx/tBoAcMOSrEAPM6QwqBARhal0d3Co8rK4taiuHaIIxEcakBg18nNm0sZ5jcqxylb85K0jyqwUAJyosnoVVN4+VAm7w4XpaTGYl2kJ4ChDD4MKEVGYGmmNSmFNGwBgSkqUT+fMjNcaFbvDhae3ncIfdhbD6RJhMeuxMCsO2wrqcKa+fdjni6KIV/eUAQBuXJrFM37OwaBCRBSmJriDSmOHHd09TkTotUNef6pWCipTfVj2AXqDSkO7DXaHtOwR7iqaOnHPa4dwuKIFALB2Thoeu3omTlRZsa2gDkV1wweVvSVNOFPfAbNBi2vnDT/7Mt4wqBARhakYk9SErdPuRFVLF3KTooa8vtAdVKak+hZU4s0GGLQa2J0u1LV1IyPO7NN9QsXHx2vw4zcPw9rtQKxJj/++bjYun5UGAMhLlt7rssZO9DhdQxbHvrpXKqK9em46oiP0gR94iAn/uEtENE4JguDRS2X45ZhTNaObUdFoBKS4G5WFc52K3eHCY+8dxx0vH4C124F5mRa8/8PzlJACSD1lzAYtHC4R5U2dg96rqcOOD4/WAJCWfag/BhUiojCmBJVhCmqt3T2ocoeLyT4GFQBIiwn/nT//+eZhvPhlKQDg9vNz8Pc7lvebPdJoBOQmRQIAzgyx/POPA2dhd7owMz0GsyfEBmzMoYxBhYgojE3wcovyafeyT1psBGJNvi8/pLjrVGrDtKC2pbN3G/Efbl6Ah9bOGLQWZ5J7qa1okIJaURTxmnvZh0W0g2NQISIKY8oW5WGCSmGN9GHqS/8UT2nKeT/hGVQ+PVkHp0vEtNToPks9A8lzB5UzdR0DPv51cROKGzoQadDimnkT/D7WcMGgQkQUxtK8rFFRdvz4WEgrC/fzfj4+LtWTXDYzddhrJ7kLagfbovzOoUoAwNXz0hFl5N6WwTCoEBGFMaU77TA1Kr09VPw1oxJ+Jyh32h34/FQ9AGDNzJRhr5eXfs7Ut0MUxX6P7ytrAgCsnj78vcYzBhUiojA2weME5YE+LGWj7aEiSw3j834+P1UPm8OFzHgTZqTFDHt9dqIZGgFo63agvs3W57GmDjuK66UloQVZcQEZb7hgUCEiCmNycOjucaG5s2fAaxrabWjssEMQevt/jPb16tpscLoGD0ah6KNj0rLPmhmpXhW+GnVaZMVLu4HOLag9WNYMAJiUFIm4yJEfVzCeMKgQEYUxo06LxCipt8lgBbVy/5SJ8WaYDEN3rx1OUpQRGgFwuEQ0ttuGf0KIsDtc2FZQBwBYM2v4+hRZ7/JP34LaA+VSUFk4kbMpw2FQISIKc8NtUVY60o5y2QcAdFoNkqPDb+fP18WNaOt2IDHKOKKlGqWg9pxeKgfcMyqLJsb7b5BhikGFiCjMpVuG3qLsrx0/stQw3KIs7/a5dEYKtBrv+51Mkpu+eSz92B0u5WygBZxRGRaDChFRmOvtTjtwcPDXjh9ZmlJQGx47f1wuEZ+cqAXg3W4fT3kDzKicqLbC5nDBYtYrQYYGx6BCRBTm5OBQOcCMiiiKOFUrfYj6a0YlRe6lYg2PGpVDFc2ob7Mh2qjDikmJI3pubqIUVKpau9FhcwAA9pdK25IXZsWxG60XGFSIiMKcvEW5eoCgUtXajXabA3qtgOwE//ztPtxmVD4+Ls2mXDw9edB2+YOJizQgwb2rp6RBKqg96C6k5bKPdxhUiIjC3FAnKMs7fnITo0b8ITyYcKpREUVRqU9Z40U32oEoZ/7USY3fegtpGVS8waBCRBTm5KBS29aNHqerz2PKjh8/LfsAQJr7fKGaMDiYsKCmDWWNnTDoNLhwSpJP9/BspX+2uQu1Vht0GgFzMix+HGn4YlAhIgpzCZEGGHQaiGL/jrHyjMrUlNE1evOU5tGddqhuuKHgy6IGAMB5eYmI9PE8Hs+dP/Kyz8wJsaPuWTNeMKgQEYU5jUYY9FRjf/ZQkSXHSA3mbA4XWgbphhsqDpW3AAAWZfu+TNPbS6VDWfZZyLb5XmNQISIaB9Jj+/dScbpEnK7z744fQOqGKxeQhnqdyiH3DMj8TN+DRZ67RqWkoQN7S6QdP6MJPuMNgwoR0Tgg16l4blEuaWiH3eFChF6DzDizX19POZzQGro7f2qt3ahq7YZGAOZkxPp8nwkWE4w6DexOFwrcS21sne89BhUionHg3Db6oiji8fdPAgDmZligGUG3VW8MttQUSuRlnykp0T7XpwDS0ltuUm8NUEacSek1Q8MLWFB5/PHHsWLFCpjNZlgsln6PHz58GDfccAMyMzNhMpkwffp0PP3004EaDhHRuJYm91JxB4f/21WCHYX1MOg0ePTqmX5/PfmDuDaEg0q+u839/CzLqO/l2YGWsykjE7CgYrfbcf311+Ouu+4a8PEDBw4gOTkZr7zyCo4fP46HHnoIGzduxO9///tADYmIaNzyPO/n6NlW/PdHBQCAn6+djulpMX5/Pbl53EfHa/ptiQ4V/qhPkU3ymFFh/5SR8X0uaxiPPfYYAGDz5s0DPv69732vz/e5ubnYvXs33n77bdxzzz2BGhYR0bgkL/1UNHXi3tcOoscpYs3MFNy8bGJAXu/6RRl4fucZnKptx4tfluAHF0wKyOsEisPpwtHKVgDAPH/MqCT3BhV2pB2ZoKpRaW1tRXw8j7wmIvI3uQlbh92J0sZOpMdG4L+vmxOws2YsZgM2XjENAPC/n55GdYi10z9V245OuxPRRp2ya2c0prt3VcVE6DAt1f8zWOEsaILKV199hTfeeAM/+MEPhrzOZrPBarX2+SIioqFFGnWwmPUAAI0APH3DfFjMhoC+5nULMrBoYhw67U78csuJgL6Wv8n1KXMyY/1SaDw5JRr/79/n4PmbF0Lr58LlcDeioLJhwwYIgjDkV0FBwYgHcezYMVxzzTV45JFHcNlllw157aZNmxAbG6t8ZWZmjvj1iIjGI7lO4v7VU7A4O/Cz1xqNgF9eOwtajYAPjtZg56n6gL+mv/izPkX2rUWZWJk3stOXCRDEEfQ3rq+vR2Nj45DX5ObmwmDoTembN2/G/fffj5aWlgGvP3HiBC666CJ8//vfx+OPPz7sGGw2G2y23qPDrVYrMjMz0draipgYTqcREQ2mqK4dx6taceWc9DH9W/2vtpzAn3eVIDvBjI/uvwAR+uBvHX/pb3bidF07/vzdRVg9I0Xt4YQlq9WK2NjYYT+/R1RMm5SUhKQk3w5lGsjx48dx8cUXY926dV6FFAAwGo0wGo1+GwMR0XiRlxyFvGT/nenjrfsvnYL3jlShtLETL+wsxn2rJ4/5GEbC2t2DonqpY68/CmlpdAJWo1JeXo78/HyUl5fD6XQiPz8f+fn5aG+X/vCPHTuGiy66CJdddhkeeOAB1NTUoKamBvX1oTM1SEREw4sy6vDzK2cAAJ79rAhNHXaVRzS0IxWtEEUgM96ExCj+xVhtAQsqDz/8MObPn49HHnkE7e3tmD9/PubPn4/9+/cDAN566y3U19fjlVdeQVpamvK1ePHiQA2JiIhUsnZ2GnITI2F3uHDkbIvawxlSIOpTyHcBCyqbN2+GKIr9vlatWgUAePTRRwd8vLS0NFBDIiIilQiCgBnpUh2CfN5NsJJ3/MzLtKg6DpIEzfZkIiIKb9PcvUQKgzioiKKIQ35snU+jx6BCRERjQm50FswzKhVNXWjqsMOg1SgzQKQuBhUiIhoTU90zKkV1bUF7/s+hCqk+ZUZ6DIy64N9GPR4wqBAR0ZjIiDMhyqhDj1NESUOH2sMZ0KHyFgBc9gkmATuUkIiIyJMgCJiaGo0DZc04WW3FlJRoVcfT3GHHn3cVo7Hdju4eJ7p6nNhfKs2osJA2eDCoEBHRmJGDSjAU1L78dRme3XGm3891GgFLcnhAbrBgUCEiojEjnyIcDAW1p+ukBqSXTEvG8kkJMOq1iNBpMDU1WjltmtTHoEJERGNmqnvnT6BnVM42d+KVr8tx54W5g54SXdYo1clcvygTl89KDeh4yHcspiUiojEj7/ypbOlCa1dPwF7n4XeP4w87z+DFL0sHfFwUewt6cxIjAzYOGj0GFSIiGjOxJj3SYyMAAKdqAzOrUmftxs5T0rlxp+sGfo2mDjvauh0AgIkJ5oCMg/yDQYWIiMbUtLTANn57J78STpcIAChy16Gcq7SxEwCQFhuBCD37pQQzBhUiIhpT8vJPQbXV7/cWRRFvHTirfF/a0AnHAM3lSt3LPtkJXPYJdgwqREQ0pgJ55s/Rylacqm2HUaeBUaeB3elCRXNXv+tK3YW02axPCXoMKkRENKameez8EUXRr/eWZ1Mun5WKSUlRAIAzAyz/yEs/OYmsTwl2DCpERDSmcpMiodcKaLM5UNnSf7bDVzaHE+/mVwEArluQgUnJ7qBSP0BQcS/9TOTST9BjUCEiojGl12qU2Y6C6qGXfxxOFzZ9cBIfHK0e9r7bTtahtasHqTERWJmXiElJUgg5t6BWFEUlqHBrcvBjUCEiojGn1KkMs0X5i9MNeOHzYvzH3w7i6U9PD7lUJC/7/NuCCdBqBOQNMqPS1GFHm80BQQCy4rn0E+wYVIiIaMzJW5RPDrPzxzPI/PbTU/jpP48OuIunrq23d8p1CzMAQJm1Kapr7xNw5ELatBhuTQ4FDCpERDTmpnq58+d0rTQbMicjFhoBeG1vBX7w8gF02h19rnv3UBWcLhELsixKQMlJjIQgANZuBxra7cq1pQ1SIS13/IQGnvVDRERjTl76KW7ogM3hhFE38MxGkXvZ5s4LJ0GnEXDva4ewvaAO//bcV1g4MQ5GnRYReg3eOyIV0f77wkzluRF6LTLjzChv6sSZ+nYkRRsBcGtyqGFQISKiMZcaE4FYkx6tXT0oqmvHzPTYfteIoqhsLZ6cHIXJKdF49fZl+P5L+1BQ09avs61Rp8HaOWl9fjYpKRLlTZ0oqmvHstwEAFDO+Mlm6/yQwKBCRERjThAETE2Nxt6SJhTWtA0YVGqs3Wi3OaDVCMo24oUT47Dlh+fjgyPV6LA70N3jgs3hhM3hwqopSYg16fvcIy85CjsK6/sU1Ja5e6iwK21oYFAhIiJVTHcHlcHO/JHrU7ITzDDoeksqJ1hMuP2CXK9ew7OgFuDW5FDEYloiIlKFvPPnRNXAO3/kcCFvM/aF/NzieimcNHpsTc7k1uSQwKBCRESqmJMhLfccPtsCl6t/f5TTSn1KtM+vIc+oVLZ0odPuQJm7kDY91sStySGCQYWIiFQxNSUaJr0Wbd0OFDf0b3N/xg8zKnGRBsRHGgBIsyolytZkzqaECgYVIiJShU6rwWz3rMrB8pZ+j5+uk2pXRhNUACAvqbdDbamy44f1KaGCQYWIiFQzP8sCAMivaOnz88Z2G5o7eyAIvcs3vpqU3Hvmj9JDhUElZHDXDxERqWZ+pgUAcOicGRW5PiUjzgSTYXS1JJM8ZlTKm9iVNtRwRoWIiFQzPysOAFBYY0WHrbctvrLjZ5SzKQAwKbl3i3KZu0YlhzUqIYNBhYiIVJMSE4H02Ai4RODI2Vbl53JQmZzi+44fWZ5HLxV5a3JGHINKqGBQISIiVc0boE7FnzMqEywmGHUayDuguTU5tDCoEBGRquZnSss/h8qblZ8pO35SRh9UNBoBuR6Bhx1pQwuDChERqUre+XOoogWiKMLa3YNaqw3A6LcmyyYl9YaTiTyMMKQELKg8/vjjWLFiBcxmMywWy5DXNjY2IiMjA4IgoKWlJVBDIiKiIDRrQix0GgH1bTZUtXYryz7J0UbEROiHebZ3PAMPZ1RCS8CCit1ux/XXX4+77rpr2Gtvu+02zJkzJ1BDISKiIBah12K6+9yfQ+XNHoW0/plNAfr2YmEPldASsKDy2GOP4Uc/+hFmz5495HXPP/88Wlpa8J//+Z+BGgoREQU5ZfmnvMWvhbQyzxkVts8PLao2fDtx4gR+8YtfYM+ePSguLvbqOTabDTabTfneah341E0iIgod8zIt+OvuMuRXtCDWJC335Plha7IsJzESsSY9T00OQaoFFZvNhhtuuAFPPvkksrKyvA4qmzZtwmOPPRbg0RER0ViSG78drWxFgvsQQX/OqETotdhy73kAAKOOW5NDyYiWfjZs2ABBEIb8Kigo8OpeGzduxPTp03HzzTePaMAbN25Ea2ur8lVRUTGi5xMRUfDJTjDDYtbD7nChurUbgH9rVABpJoWzKaFnRDMqDz74INavXz/kNbm5uV7da/v27Th69CjeeustAIAoSp14EhMT8dBDDw06a2I0GmE0Gr0fNBERBT1BEDA/04IdhfUAAItZr8ys0Pg2oqCSlJSEpKQkv7zwP/7xD3R1dSnf79u3D9/73vfwxRdfYNKkSX55DSIiCh3zMuOUoDI5OQqCIKg8IgoGAatRKS8vR1NTE8rLy+F0OpGfnw8AyMvLQ1RUVL8w0tDQAACYPn36sH1XiIgo/Mg7fwD/NXqj0BewoPLwww/jpZdeUr6fP38+AGDHjh1YtWpVoF6WiIhC1NxMi/LPecn+2/FDoS1gfVQ2b94MURT7fQ0WUlatWgVRFDmbQkQ0TsWa9Erjt1npMSqPhoKFqn1UiIiIPP3uO/NwotqKpbkJag+FggSDChERBY3JKdGY7MdGbxT6eHoyERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGgxqBAREVHQYlAhIiKioMWgQkREREGLQYWIiIiCFoMKERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGgxqBAREVHQCvnTk0VRBABYrVaVR0JERETekj+35c/xwYR8UGlrawMAZGZmqjwSIiIiGqm2tjbExsYO+rggDhdlgpzL5UJVVRWio6MhCIJf7221WpGZmYmKigrExMT49d7UF9/rscP3euzwvR47fK/Hjr/ea1EU0dbWhvT0dGg0g1eihPyMikajQUZGRkBfIyYmhr/4Y4Tv9djhez12+F6PHb7XY8cf7/VQMykyFtMSERFR0GJQISIioqDFoDIEo9GIRx55BEajUe2hhD2+12OH7/XY4Xs9dvhej52xfq9DvpiWiIiIwhdnVIiIiChoMagQERFR0GJQISIioqDFoEJERERBi0FlEM8++yyys7MRERGBpUuXYu/evWoPKeRt2rQJixcvRnR0NJKTk3HttdeisLCwzzXd3d24++67kZCQgKioKFx33XWora1VacTh44knnoAgCLj//vuVn/G99p/KykrcfPPNSEhIgMlkwuzZs7F//37lcVEU8fDDDyMtLQ0mkwmrV6/G6dOnVRxxaHI6nfj5z3+OnJwcmEwmTJo0Cb/85S/7nBXD99o3n3/+Oa666iqkp6dDEAS88847fR735n1tamrCTTfdhJiYGFgsFtx2221ob28f/eBE6uf1118XDQaD+Je//EU8fvy4ePvtt4sWi0Wsra1Ve2ghbc2aNeKLL74oHjt2TMzPzxe/8Y1viFlZWWJ7e7tyzZ133ilmZmaK27ZtE/fv3y8uW7ZMXLFihYqjDn179+4Vs7OzxTlz5oj33Xef8nO+1/7R1NQkTpw4UVy/fr24Z88esbi4WPz444/FoqIi5ZonnnhCjI2NFd955x3x8OHD4tVXXy3m5OSIXV1dKo489Dz++ONiQkKCuGXLFrGkpER88803xaioKPHpp59WruF77ZsPPvhAfOihh8S3335bBCD+85//7PO4N+/r5ZdfLs6dO1f8+uuvxS+++ELMy8sTb7jhhlGPjUFlAEuWLBHvvvtu5Xun0ymmp6eLmzZtUnFU4aeurk4EIO7cuVMURVFsaWkR9Xq9+OabbyrXnDx5UgQg7t69W61hhrS2tjZx8uTJ4tatW8ULL7xQCSp8r/3nv/7rv8Tzzjtv0MddLpeYmpoqPvnkk8rPWlpaRKPRKL722mtjMcSwsXbtWvF73/ten5/927/9m3jTTTeJosj32l/ODSrevK8nTpwQAYj79u1Trvnwww9FQRDEysrKUY2HSz/nsNvtOHDgAFavXq38TKPRYPXq1di9e7eKIws/ra2tAID4+HgAwIEDB9DT09PnvZ82bRqysrL43vvo7rvvxtq1a/u8pwDfa3/617/+hUWLFuH6669HcnIy5s+fjz/96U/K4yUlJaipqenzXsfGxmLp0qV8r0doxYoV2LZtG06dOgUAOHz4MHbt2oUrrrgCAN/rQPHmfd29ezcsFgsWLVqkXLN69WpoNBrs2bNnVK8f8ocS+ltDQwOcTidSUlL6/DwlJQUFBQUqjSr8uFwu3H///Vi5ciVmzZoFAKipqYHBYIDFYulzbUpKCmpqalQYZWh7/fXXcfDgQezbt6/fY3yv/ae4uBjPP/88HnjgAfz0pz/Fvn378MMf/hAGgwHr1q1T3s+B/pvC93pkNmzYAKvVimnTpkGr1cLpdOLxxx/HTTfdBAB8rwPEm/e1pqYGycnJfR7X6XSIj48f9XvPoEKquPvuu3Hs2DHs2rVL7aGEpYqKCtx3333YunUrIiIi1B5OWHO5XFi0aBF+/etfAwDmz5+PY8eO4Q9/+APWrVun8ujCy9///nf87W9/w6uvvoqZM2ciPz8f999/P9LT0/lehzEu/ZwjMTERWq223+6H2tpapKamqjSq8HLPPfdgy5Yt2LFjBzIyMpSfp6amwm63o6Wlpc/1fO9H7sCBA6irq8OCBQug0+mg0+mwc+dO/O53v4NOp0NKSgrfaz9JS0vDjBkz+vxs+vTpKC8vBwDl/eR/U0bvxz/+MTZs2IDvfOc7mD17Nm655Rb86Ec/wqZNmwDwvQ4Ub97X1NRU1NXV9Xnc4XCgqalp1O89g8o5DAYDFi5ciG3btik/c7lc2LZtG5YvX67iyEKfKIq455578M9//hPbt29HTk5On8cXLlwIvV7f570vLCxEeXk53/sRuuSSS3D06FHk5+crX4sWLcJNN92k/DPfa/9YuXJlv232p06dwsSJEwEAOTk5SE1N7fNeW61W7Nmzh+/1CHV2dkKj6fuxpdVq4XK5APC9DhRv3tfly5ejpaUFBw4cUK7Zvn07XC4Xli5dOroBjKoUN0y9/vrrotFoFDdv3iyeOHFC/MEPfiBaLBaxpqZG7aGFtLvuukuMjY0VP/vsM7G6ulr56uzsVK658847xaysLHH79u3i/v37xeXLl4vLly9XcdThw3PXjyjyvfaXvXv3ijqdTnz88cfF06dPi3/7299Es9ksvvLKK8o1TzzxhGixWMR3331XPHLkiHjNNddwy6wP1q1bJ06YMEHZnvz222+LiYmJ4k9+8hPlGr7XvmlraxMPHTokHjp0SAQg/uY3vxEPHToklpWViaLo3ft6+eWXi/Pnzxf37Nkj7tq1S5w8eTK3JwfSM888I2ZlZYkGg0FcsmSJ+PXXX6s9pJAHYMCvF198Ubmmq6tL/I//+A8xLi5ONJvN4je/+U2xurpavUGHkXODCt9r/3nvvffEWbNmiUajUZw2bZr4xz/+sc/jLpdL/PnPfy6mpKSIRqNRvOSSS8TCwkKVRhu6rFareN9994lZWVliRESEmJubKz700EOizWZTruF77ZsdO3YM+N/ndevWiaLo3fva2Ngo3nDDDWJUVJQYExMj3nrrrWJbW9uoxyaIokdLPyIiIqIgwhoVIiIiCloMKkRERBS0GFSIiIgoaDGoEBERUdBiUCEiIqKgxaBCREREQYtBhYiIiIIWgwoREREFLQYVIiIiCloMKkRERBS0GFSIiIgoaDGoEBERUdD6/6unkm3JPjANAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzbG6CMfeT2J"
      },
      "source": [
        "현재 matplotlib가 사용할 수 있는 한글폰트 확인"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHPZKLv0eSev",
        "outputId": "518ff707-a18f-4129-8077-8d15326f538e"
      },
      "source": [
        "import matplotlib.font_manager as fm\n",
        "[f for f in fm.fontManager.ttflist if 'Nanum' in f.name]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Ajqpmaye2NY"
      },
      "source": [
        "나눔글꼴 설치(리눅스)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDR1vHAwe7cW",
        "outputId": "5e475c4b-77ee-43c0-fec2-6893782786e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!apt-get -y install fonts-nanum"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "fonts-nanum is already the newest version (20200506-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcJrhvOHfUsx"
      },
      "source": [
        "matplotlib가 사용할 수 있는 폰트 리스트를 재구성하고, 코랩 재시작"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_hc8QKPfEWi",
        "outputId": "16176b98-90d7-4230-9f9b-aca25960992f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "import os\n",
        "\n",
        "fm._rebuild()\n",
        "os.kill(os.getpid(), 9)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "module 'matplotlib.font_manager' has no attribute '_rebuild'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-e353339fb9c6>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rebuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.font_manager' has no attribute '_rebuild'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lwu5Qm_tfPeL"
      },
      "source": [
        "현재 matplotlib가 사용할 수 있는 한글폰트 확인"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xC8_paBMP2vB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4smCyJZfPeM",
        "outputId": "f2095c0c-873e-44ae-fdf1-fd0379bfb541"
      },
      "source": [
        "import matplotlib.font_manager as fm\n",
        "[f for f in fm.fontManager.ttflist if 'Nanum' in f.name]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rj2yFknNfKru"
      },
      "source": [
        "matplotlib에서 사용하려는 한글 폰트 지정하고, 그래프의 마이너스 라벨이 제대로 표시되도록 설정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1REWbPVPrAvC"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', family='NanumGothic')\n",
        "plt.rcParams['axes.unicode_minus'] = False"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymiKTxcGf-jw",
        "outputId": "532e1af0-8f20-4b95-ae1e-c0f9b15442f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randn(100).cumsum(), columns=['환율'])\n",
        "df.plot()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 54872 (\\N{HANGUL SYLLABLE HWAN}) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 50984 (\\N{HANGUL SYLLABLE YUL}) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 54872 (\\N{HANGUL SYLLABLE HWAN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50984 (\\N{HANGUL SYLLABLE YUL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNN1r1O2Fb6g"
      },
      "source": [
        "# 연습문제\n",
        "\n",
        "1. 성인 500명에 대한 키(cm)와 몸무게(kg) 데이터를 아래 관계에 따라 데이터프레임으로 생성하시오\n",
        "\n",
        "$height \\sim norm(170, 15)$\n",
        "\n",
        "$weight = (height - 100)^{1.2} + u$, $u \\sim norm(0, 5)$\n",
        "\n",
        "2. 키와 몸무게 각각에 대한 히스토그램(5 kg/cm 구간)을 subplots을 이용하여 그리시오\n",
        "\n",
        "3. 키 180cm 이상 및 이하로 그룹을 나누어 키와 몸무게의 산포도를 그리시오"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install countryinfo"
      ],
      "metadata": {
        "id": "qyru7z-KEa0f",
        "outputId": "62077184-b05f-49ef-f048-d3d969b5f8a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: countryinfo in /usr/local/lib/python3.10/dist-packages (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pycountry"
      ],
      "metadata": {
        "id": "hPbw6JI2F9G4",
        "outputId": "fb28b565-c3de-48ae-aca0-cf16c702a49d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pycountry\n",
            "  Downloading pycountry-24.6.1-py3-none-any.whl (6.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pycountry\n",
            "Successfully installed pycountry-24.6.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pycountry\n",
        "from countryinfo import CountryInfo"
      ],
      "metadata": {
        "id": "OAxOYj25EV0n"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country_names = [country.name for country in pycountry.countries]\n",
        "\n",
        "[i for i in country_names if 'Korea' in i]"
      ],
      "metadata": {
        "id": "PLMomjsPGE2X",
        "outputId": "c9f5ba6b-4536-4e5f-ebfe-8196327b2325",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Korea, Republic of', \"Korea, Democratic People's Republic of\"]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_dict = {}\n",
        "\n",
        "for country in pycountry.countries:\n",
        "  try:\n",
        "    if country.name == 'Korea, Republic of':\n",
        "      country.name = 'South Korea'\n",
        "    elif country.name == \"Korea, Democratic People's Republic of\":\n",
        "      country.name = 'North Korea'\n",
        "\n",
        "    country_info = CountryInfo(country.name)\n",
        "    info = {\n",
        "        'name': country.name,\n",
        "        'continent': country_info.region(),\n",
        "        'capital': country_info.capital(),\n",
        "        'currency': country_info.currencies(),\n",
        "        'language': country_info.languages(),\n",
        "        'borders': country_info.borders()\n",
        "    }\n",
        "    country_dict[country.alpha_3] = info\n",
        "\n",
        "  except KeyError:\n",
        "    print('country name error: ', country.name)\n",
        "    continue\n"
      ],
      "metadata": {
        "id": "c2yAaRR1GcZ2",
        "outputId": "7eb837af-c149-48ac-cbc9-a0034cd5a17b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "country name error:  Åland Islands\n",
            "country name error:  Andorra\n",
            "country name error:  Antarctica\n",
            "country name error:  French Southern Territories\n",
            "country name error:  Bonaire, Sint Eustatius and Saba\n",
            "country name error:  Bahamas\n",
            "country name error:  Saint Barthélemy\n",
            "country name error:  Bolivia, Plurinational State of\n",
            "country name error:  Brunei Darussalam\n",
            "country name error:  Bouvet Island\n",
            "country name error:  Côte d'Ivoire\n",
            "country name error:  Congo, The Democratic Republic of the\n",
            "country name error:  Congo\n",
            "country name error:  Cabo Verde\n",
            "country name error:  Curaçao\n",
            "country name error:  Czechia\n",
            "country name error:  Falkland Islands (Malvinas)\n",
            "country name error:  Micronesia, Federated States of\n",
            "country name error:  Gambia\n",
            "country name error:  Iran, Islamic Republic of\n",
            "country name error:  Macao\n",
            "country name error:  Saint Martin (French part)\n",
            "country name error:  Moldova, Republic of\n",
            "country name error:  North Macedonia\n",
            "country name error:  Myanmar\n",
            "country name error:  Montenegro\n",
            "country name error:  Pitcairn\n",
            "country name error:  Palestine, State of\n",
            "country name error:  Saint Helena, Ascension and Tristan da Cunha\n",
            "country name error:  Sao Tome and Principe\n",
            "country name error:  Eswatini\n",
            "country name error:  Sint Maarten (Dutch part)\n",
            "country name error:  Turks and Caicos Islands\n",
            "country name error:  Timor-Leste\n",
            "country name error:  Türkiye\n",
            "country name error:  Taiwan, Province of China\n",
            "country name error:  Tanzania, United Republic of\n",
            "country name error:  United States Minor Outlying Islands\n",
            "country name error:  Holy See (Vatican City State)\n",
            "country name error:  Venezuela, Bolivarian Republic of\n",
            "country name error:  Virgin Islands, British\n",
            "country name error:  Virgin Islands, U.S.\n",
            "country name error:  Viet Nam\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(country_dict['KOR'])\n",
        "print(country_dict['PRK'])"
      ],
      "metadata": {
        "id": "hJAonWH1HlJB",
        "outputId": "25aa6635-dc14-4742-a508-95080e143f30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'name': 'South Korea', 'continent': 'Asia', 'capital': 'Seoul', 'currency': ['KRW'], 'language': ['ko'], 'borders': ['PRK']}\n",
            "{'name': 'North Korea', 'continent': 'Asia', 'capital': 'Pyongyang', 'currency': ['KPW'], 'language': ['ko'], 'borders': ['CHN', 'KOR', 'RUS']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get update -qq\n",
        "!apt-get install -qq fonts-nanum"
      ],
      "metadata": {
        "id": "Sws0dGycVaKO"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install -y fonts-nanum"
      ],
      "metadata": {
        "id": "_umN1k_CV5FC",
        "outputId": "4bd7619e-098b-4069-8e10-0e28f3a6ad21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "fonts-nanum is already the newest version (20200506-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 52 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ],
      "metadata": {
        "id": "5ERv6jPpWbXK",
        "outputId": "1233a050-e9ae-461f-f9d1-1fad818b7be7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 12 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/usr/share/fonts/truetype: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/liberation: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/nanum: skipping, looped directory detected\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', family = 'NanumBarunGothic')"
      ],
      "metadata": {
        "id": "jn0rnIeGWfn4"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randn(100).cumsum(), columns=['환율'])\n",
        "df.plot()"
      ],
      "metadata": {
        "id": "ixrYimTVWwPs",
        "outputId": "283acf04-4837-4763-cf9a-7137b89a7987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}