pandas.ipynb

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      "source": [
        "# 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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        "id": "82inE3YJFb5_"
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      "source": [
        "import pandas as pd"
      ]
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        "id": "KPKC8iAUFb6A"
      },
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      "source": [
        "df = pd.DataFrame([[1, 2], [3, 4]])"
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        "df.columns = ['KOR', 'USA']"
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        "df.index = [17, 18, 19]"
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              "    KOR  USA\n",
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      "source": [
        "- 데이터프레임 통계량"
      ]
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              "KOR    3.0\n",
              "USA    4.0\n",
              "dtype: float64"
            ]
          },
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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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        "df.apply(['mean', 'median', 'sum', 'std'])  # prod"
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      "source": [
        "## 슬라이스"
      ]
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              "    KOR  USA\n",
              "17    1    2\n",
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              "USA    2\n",
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              "    <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>"
            ],
            "text/plain": [
              "    KOR  USA\n",
              "17    1    2"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc[[17]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_Jni6I5OFb6G"
      },
      "outputs": [],
      "source": [
        "df = df.loc[18:, :].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A41vwQa8Fb6H"
      },
      "source": [
        "- 값 지정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "YMxrIhwkFb6H",
        "outputId": "f137bc46-4caa-466d-8ca8-c092a4caaa47"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\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",
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            "text/plain": [
              "    KOR  USA\n",
              "18    3    4\n",
              "19    5    6"
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          "execution_count": 21,
          "metadata": {},
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        "df"
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      "cell_type": "code",
      "execution_count": 22,
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      "outputs": [],
      "source": [
        "df.loc[18, 'KOR'] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "LQENYkKOFb6H",
        "outputId": "90654740-c22e-4087-ff4d-aeb10c2b4ed0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "      <th>KOR</th>\n",
              "      <th>USA</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>18</th>\n",
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              "      <td>5</td>\n",
              "      <td>6</td>\n",
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              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "    KOR  USA\n",
              "18    0    4\n",
              "19    5    6"
            ]
          },
          "execution_count": 23,
          "metadata": {},
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        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "WqspKdNaFb6H"
      },
      "outputs": [],
      "source": [
        "df.loc[:, 'USA'] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "XdrGp4lLFb6H",
        "outputId": "d1e54ce4-e56a-4fc6-d1ee-7b6b51352eba"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>USA</th>\n",
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              "      <td>5</td>\n",
              "      <td>1</td>\n",
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              "</div>"
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              "    KOR  USA\n",
              "18    0    1\n",
              "19    5    1"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df"
      ]
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    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "6L_-qYKYFb6I"
      },
      "outputs": [],
      "source": [
        "df.loc[:, :] = [[1, 2], [3, 4]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "YZiRi6yLFb6I",
        "outputId": "a248dd27-2114-45dc-d12c-1f5817deb74e"
      },
      "outputs": [
        {
          "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>KOR</th>\n",
              "      <th>USA</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "</div>"
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            "text/plain": [
              "    KOR  USA\n",
              "18    1    2\n",
              "19    3    4"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "WEL4erYjFb6I"
      },
      "outputs": [],
      "source": [
        "df = [[1, 2], [3, 4]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8i_pNlvmFb6I",
        "outputId": "eddd3c76-d3ac-49ca-d1da-e2f49f3ae26c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[1, 2], [3, 4]]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvfXMKPVFb6I"
      },
      "source": [
        "## 멀티인덱스"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "llFendVRFb6I"
      },
      "outputs": [],
      "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])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "zRMikIq-Fb6J",
        "outputId": "45ec767d-68f1-439a-c99b-ffcb285dc4a4"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "tkeihnctFb6J"
      },
      "outputs": [],
      "source": [
        "df = df.set_index('country', append=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3rHSjfjfFb6J",
        "outputId": "21c36ef8-dda7-4a6a-8ebd-a83b5e8ac91c"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "sf0QR9hHFb6J"
      },
      "outputs": [],
      "source": [
        "from pandas import MultiIndex as MI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "siMrWviQFb6J"
      },
      "outputs": [],
      "source": [
        "df.index = MI.from_product([['16', '17'], ['미국', '한국']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ZMrhzfKWFb6J",
        "outputId": "7e13f01e-82c9-46fe-e6a7-2a79c4c1ddca"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "5VHxRRa2Fb6K"
      },
      "outputs": [],
      "source": [
        "df.index = [':'.join(c).strip() for c in df.index.values]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "CqhnEpXCFb6K",
        "outputId": "38a9ffd8-f39a-4cc2-fcc7-4b95ce43fc6a"
      },
      "outputs": [
        {
          "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: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"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPjt_0TpFb6K",
        "outputId": "a7c6ae3e-5a5a-4af4-9bbc-ba5d396bc1e7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[['16', '미국'], ['16', '한국'], ['17', '미국'], ['17', '한국']]"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index.str.split(':').tolist()"
      ]
    },
    {
      "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",
      "execution_count": 45,
      "metadata": {
        "id": "Q8splM2pFb6K"
      },
      "outputs": [],
      "source": [
        "df.index = MI.from_tuples(df.index.str.split(':').tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "CME_jUWeFb6K",
        "outputId": "f037a06b-7f15-4097-91eb-086d98092e41"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <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"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTSiA2zcFb6K"
      },
      "source": [
        "IndexSlice: 다양한 층위에서 데이터 선택"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "ZUd5WGaFFb6K"
      },
      "outputs": [],
      "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])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "0Qnx37FrFb6L"
      },
      "outputs": [],
      "source": [
        "df = df.set_index('country', append=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "9AFbUmSnFb6L"
      },
      "outputs": [],
      "source": [
        "idx = pd.IndexSlice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "vKt3N6P7Fb6L",
        "outputId": "8f55ff78-eb36-417e-e526-32663c7836bd"
      },
      "outputs": [
        {
          "data": {
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              "      <th>gdp</th>\n",
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              "    <tr>\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <th>USA</th>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <th>USA</th>\n",
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              "      <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"
            ]
          },
          "execution_count": 50,
          "metadata": {},
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      ],
      "source": [
        "df.loc[idx[:, 'USA'], :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6HeKk_DFb6L",
        "outputId": "45c6b5a1-c911-47fb-e7e7-69eefd67f031"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pop    1\n",
              "gdp    3\n",
              "Name: (16, USA), dtype: int64"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc[idx[16, 'USA'], :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "gVwZilgIFb6L"
      },
      "outputs": [],
      "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])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 175
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            "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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          "metadata": {},
          "output_type": "execute_result"
        }
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        "df"
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    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "lbO_yKewFb6L",
        "outputId": "29dc70f1-b7a9-45a4-a245-ec762638c8db"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
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              "      <th>pop</th>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>KOR</td>\n",
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            "text/plain": [
              "   country  pop  gdp\n",
              "17     USA    3    5\n",
              "17     KOR    4    6"
            ]
          },
          "execution_count": 54,
          "metadata": {},
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      "source": [
        "df.loc[df['pop'].ge(3)]"
      ]
    },
    {
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              "      <td>6</td>\n",
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              "</table>\n",
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            "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"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
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    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
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          "height": 112
        },
        "id": "wkB6zT6TFb6M",
        "outputId": "acb91f9c-7139-405b-c47a-00ac35673340"
      },
      "outputs": [
        {
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              "      <th>16</th>\n",
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              "      <td>USA</td>\n",
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            ],
            "text/plain": [
              "   country  pop  gdp\n",
              "16     USA    1    3\n",
              "17     USA    3    5"
            ]
          },
          "execution_count": 56,
          "metadata": {},
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      ],
      "source": [
        "df.loc[df.country.isin(['USA', 'JPN'])]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjyhnkPTFb6M"
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      "source": [
        "- droplevels\n",
        "- get_level_values\n",
        "- reorder_levels"
      ]
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    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "2ZdVvSvAFb6M",
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        {
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            "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"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
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    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "nP0xVFvJFb6M"
      },
      "outputs": [],
      "source": [
        "df = df.set_index('country', append=True)"
      ]
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    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "57HNR0HLFb6M"
      },
      "outputs": [],
      "source": [
        "df.index.names = ['yr', 'country']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hty55evqFb6N",
        "outputId": "89da230b-a74c-43a0-8d9f-80b987569b98"
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              "    </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"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyTNXzmvFb6N",
        "outputId": "e52f6260-d17c-41f5-b158-f4c860f9cef8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Int64Index([16, 17], dtype='int64', name='yr')"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index.get_level_values(0).unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKl-hA3xFb6N",
        "outputId": "75e35f70-b256-47af-da7e-b28eb1090fbb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['USA', 'KOR'], dtype='object', name='country')"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index.get_level_values(1).unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "JphVi29kFb6N"
      },
      "outputs": [],
      "source": [
        "df = df.loc[idx[:, 'USA'], :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "QlhDiRfiFb6N",
        "outputId": "f78e2280-a0a2-41a7-cf66-ad324f65007d"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "l2QWkTNXFb6N"
      },
      "outputs": [],
      "source": [
        "df.index = df.index.droplevel(level = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "CwW-khqYFb6N",
        "outputId": "b01dff8e-9687-42c3-d3ae-6d9bddc28837"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "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",
      "execution_count": 81,
      "metadata": {
        "id": "x1Riglx2Fb6O"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "cgS0MsnGFb6O"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({'KOR':[1, 3], 'USA':[2, 4]})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "6ui4W9sPFb6P"
      },
      "outputs": [],
      "source": [
        "df1 = df + 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "A0SMErWnFb6P"
      },
      "outputs": [],
      "source": [
        "df1.columns = ['USA', 'CHN']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "i_EoQTidFb6P",
        "outputId": "ad99f3d5-ec50-42a9-d44b-cf4764e5a538"
      },
      "outputs": [
        {
          "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": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display_by_side(df1, df + df1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "9y3TvVHBFb6P",
        "outputId": "58902dd4-6291-4ef0-8494-edc1e5d9479e"
      },
      "outputs": [
        {
          "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": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(df, df.T)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "gy-VVs5zFb6P",
        "outputId": "701b5f79-7437-46d4-c33d-7e869e66dd48"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dot(df.T)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jiUXEJ29Fb6P",
        "outputId": "8cd2e9f2-18c6-491e-bf53-e1c6d8fa6f98"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.T.dot(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "H1XtL82SFb6Q",
        "outputId": "930a90cf-205f-429e-8e46-8b12f8365468"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "ignored",
          "output_type": "error",
          "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"
          ]
        }
      ],
      "source": [
        "df.dot(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "KsbUXKftFb6Q"
      },
      "outputs": [],
      "source": [
        "df1 = pd.DataFrame({'USA':[1, 3], 'CHN':[2, 4]})\n",
        "df2 = pd.DataFrame({'JPN':[2, 4], 'KOR':[6, 8]})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "yT7WQIaeFb6Q",
        "outputId": "156dca58-0b6e-4d07-c88f-74b4d37abae4"
      },
      "outputs": [
        {
          "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",
              "    }\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>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": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(df1, df2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-uOYUIFgFb6Q",
        "outputId": "fa00a57f-e77c-4a00-ed23-a569a347f85a"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([df1, df2], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "zoE2i4Q7Fb6Q",
        "outputId": "b0a29c03-4813-47d5-afe8-fbfb002261cb"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([df1, df2], axis = 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1QanSsPDFb6Q"
      },
      "source": [
        "## merge, concat\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "CguyYdjRFb6Q"
      },
      "outputs": [],
      "source": [
        "df2.columns = ['CHN', 'KOR']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "UP0uAmN3Fb6R",
        "outputId": "d1f3be48-1455-4723-eb3f-cb0e97e1df75"
      },
      "outputs": [
        {
          "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>KOR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   USA  CHN  KOR\n",
              "0    1    2    6\n",
              "1    3    4    8"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.merge(df2, on = 'CHN', how = 'left')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z83FoLYFFb6R"
      },
      "source": [
        "## add, sub, mul, div"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "nuXeYsyaFb6R",
        "outputId": "e920a7b9-d5e4-476d-93ca-9f580b229375"
      },
      "outputs": [
        {
          "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",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   KOR  USA  Total\n",
              "0    1    2      3\n",
              "1    3    4      7"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Total'] = df.sum(axis = 1)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "6h2nFaPpFb6R",
        "outputId": "aa3bea56-d4b6-46b7-818f-ceb6076d3e05"
      },
      "outputs": [
        {
          "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",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        KOR       USA  Total\n",
              "0  0.333333  0.666667    1.0\n",
              "1  0.428571  0.571429    1.0"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.div(df.Total, axis = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "UMO_Zgs1Fb6R",
        "outputId": "af155acf-ce8a-4d82-9ed4-54c388abfcb3"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.div(df.Total, axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O46OqJEnFb6R"
      },
      "source": [
        "## cut, qcut\n",
        "\n",
        "- 숫자 데이터를 범주형 데이터로 변환"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "vacW6ZpHFb6R",
        "outputId": "824e17d0-45d1-452b-9025-ddda7309af29"
      },
      "outputs": [
        {
          "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>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"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from numpy.random import randint\n",
        "df = pd.DataFrame({'a':range(4), 'b':randint(1,10,4)})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eI2fE8V5Fb6S",
        "outputId": "d892a714-2ac2-449f-8026-32ea43f4e11f"
      },
      "outputs": [
        {
          "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]]"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.cut(df.b, [0, 5, 10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "vCRKd05rFb6S"
      },
      "outputs": [],
      "source": [
        "df.loc[:, 'c'] = pd.cut(df.b, [0, 5, 10], labels=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "xn7zvZKSFb6S",
        "outputId": "eab18f54-b196-4199-9d32-772ebc905f02"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>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"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_08fMH40Fb6S",
        "outputId": "62432f12-73c4-46a5-8fdf-00a18baf03b1"
      },
      "outputs": [
        {
          "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]]"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.qcut(df.b, 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ob2Q1l0Fb6S"
      },
      "source": [
        "## groupby"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "I8C-x8ScFb6S"
      },
      "outputs": [],
      "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])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "goXsR01iFb6S",
        "outputId": "30774a03-bf42-4abe-be24-fbec0e52b27a"
      },
      "outputs": [
        {
          "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>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KOR</th>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         pop  gdp\n",
              "country          \n",
              "KOR        6   10\n",
              "USA        4    8"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.groupby('country').sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "lqFZ7Hh8Fb6S"
      },
      "outputs": [],
      "source": [
        "df = df[['pop', 'gdp']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9xWkOwvFb6T",
        "outputId": "40cda880-7d6c-4793-9db5-c8e3995275cb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pop\n",
              "(0, 2]    3.5\n",
              "(2, 4]    5.5\n",
              "Name: gdp, dtype: float64"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.groupby(pd.cut(df['pop'], [0, 2, 4]))['gdp'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMHEb2UbFb6T"
      },
      "source": [
        "## lambda 함수"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "7x6M5I2sFb6U"
      },
      "outputs": [],
      "source": [
        "square = lambda x: x * x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdS8A-5NFb6U",
        "outputId": "db2ec017-aef5-40d9-c2c9-7741ce4b1624"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "square(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Texb31pkFb6U"
      },
      "source": [
        "## apply/applymap/map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "RABzpkabFb6U"
      },
      "outputs": [],
      "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])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "qy7ZDNGVFb6U",
        "outputId": "aa107cbf-ff8a-4c90-d4b4-ebd8260a1146"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[['pop', 'gdp']].apply(lambda x: x.div(x.sum()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "j-fk7mzNFb6V",
        "outputId": "600ab638-de7a-49b3-968a-0c7a47f6e45a"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = df[['pop', 'gdp']].apply(lambda x: x.div(x.sum()))\n",
        "df.applymap(lambda x: '{:,.2f}'.format(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvgmlRAkFb6V"
      },
      "source": [
        "## stack/unstack"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "WsCMBSD5Fb6V"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Nme-3ZupFb6V",
        "outputId": "d4373fd9-5f07-446e-b613-44a28787a0bc"
      },
      "outputs": [
        {
          "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <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"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.unstack(level = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "q0UGrRKaFb6V"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "HMs9Lko9Fb6V"
      },
      "outputs": [],
      "source": []
    },
    {
      "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",
      "execution_count": 120,
      "metadata": {
        "id": "e421xR4yFb6W"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randint(1, 20, 24).reshape(12, 2), columns=['con', 'gdp'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "XqDVcDx2Fb6W",
        "outputId": "baba9733-01dd-4d17-efde-07ee4e156441"
      },
      "outputs": [
        {
          "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>con</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    con  gdp\n",
              "0     9   12\n",
              "1     9   17\n",
              "2     6    7\n",
              "3     3   13\n",
              "4    10    4\n",
              "5     7   13\n",
              "6    10    3\n",
              "7     1    8\n",
              "8     9    3\n",
              "9    17   10\n",
              "10    9    4\n",
              "11    3   11"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "gwgF96pWFb6W"
      },
      "outputs": [],
      "source": [
        "df.index = pd.date_range('2017-3-31', '2019-12-31', freq='Q')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "lG9j_OCvFb6W",
        "outputId": "a916231e-ccd1-4325-9d35-f472322dc3d2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>con</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-03-31</th>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-30</th>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09-30</th>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-31</th>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-06-30</th>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-30</th>\n",
              "      <td>10</td>\n",
              "      <td>3</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>2019-03-31</th>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-30</th>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>9</td>\n",
              "      <td>4</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",
              "2017-03-31    9   12\n",
              "2017-06-30    9   17\n",
              "2017-09-30    6    7\n",
              "2017-12-31    3   13\n",
              "2018-03-31   10    4\n",
              "2018-06-30    7   13\n",
              "2018-09-30   10    3\n",
              "2018-12-31    1    8\n",
              "2019-03-31    9    3\n",
              "2019-06-30   17   10\n",
              "2019-09-30    9    4\n",
              "2019-12-31    3   11"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wywtDpnFb6W",
        "outputId": "8dd54182-6758-42ef-80b2-d65426dd998f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pandas.core.indexes.datetimes.DatetimeIndex"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(df.index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0ah28fmRHMb"
      },
      "source": [
        "## resample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "KeQtJkTeFb6X",
        "outputId": "152a52bf-9a41-49e6-c64e-d1cd7e0c0123"
      },
      "outputs": [
        {
          "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",
              "        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>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>38</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            con  gdp\n",
              "2017-12-31   27   49\n",
              "2018-12-31   28   28\n",
              "2019-12-31   38   28"
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.resample('A').sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "K0m0d_84Fb6X",
        "outputId": "99b1e4f1-8eaa-4868-da08-94143ebc58ae"
      },
      "outputs": [
        {
          "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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              "        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"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.resample('A').last()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "shkBcxDFFb6X",
        "outputId": "4e62dd27-df92-4a23-cedd-d8c87657ff34"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.resample('A').mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQvJHYdUPHbd"
      },
      "source": [
        "## simple graph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "fUTUDC33Fb6X",
        "outputId": "f2e10993-bba7-4c90-8bf6-3c503bc48bd5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b989b0a10>"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "Mc217b1RFb6X",
        "outputId": "fcd6638b-4ec0-4200-c551-a52cd72c9301"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b988c91d0>"
            ]
          },
          "execution_count": 129,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.plot.bar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "h5ilVq7XFb6X",
        "outputId": "f189eba9-718c-4332-ac52-dd543517e3c3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8e087e50>"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.resample('M').interpolate(method='cubic').plot()\n",
        "# ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "0-D_RBBxFb6Y",
        "outputId": "ce4450fb-57d2-4522-e9c1-23b506bfdca9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8bfd5610>"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.plot()\n",
        "# ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "rT2pmMfHFb6Y"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({'value':[2, 4, 5, 6], 'date':['20201201', '2019-9-1', '1 jul 2018', '2017q1']})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "lnmRFNCgFb6Y",
        "outputId": "2b6a501d-3be9-4e88-cae5-ec7dcea99731"
      },
      "outputs": [
        {
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <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"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "id": "OxYNZfJ6Fb6Y"
      },
      "outputs": [],
      "source": [
        "df.date = pd.to_datetime(df.date, format='mixed')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "-iK7z3YBFb6Y",
        "outputId": "410f1279-a75a-4593-9453-3de957edecf7"
      },
      "outputs": [
        {
          "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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              "\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>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"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "qOIIlVeOFb6Y"
      },
      "outputs": [],
      "source": [
        "df = df.set_index('date')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "62qIa9ggFb6Z",
        "outputId": "33a7746a-1e9d-49e7-ce81-bda8dddc0c51"
      },
      "outputs": [
        {
          "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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              "\n",
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              "        text-align: right;\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",
              "    </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"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "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",
      "execution_count": 138,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "l3Xi9XvJFb6Z",
        "outputId": "873f051b-ed91-4035-eebd-c68111262be4"
      },
      "outputs": [
        {
          "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>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"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.pct_change()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzKeBEk-Q1Gx"
      },
      "source": [
        "## offsets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "QwUPCpwcFb6Z"
      },
      "outputs": [],
      "source": [
        "from pandas.tseries.offsets import YearEnd, YearBegin, QuarterEnd, QuarterBegin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3zZx49MNFb6Z",
        "outputId": "cbd740d2-b0d9-439f-98fc-2c2dd65c818d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\n",
              "\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",
              "    </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"
            ]
          },
          "execution_count": 140,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index = df.index + YearEnd()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "id": "i1DjpmHDFb6Z"
      },
      "outputs": [],
      "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, format='mixed')\n",
        "df = df.set_index('date')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "93DSNi5hFb6Z",
        "outputId": "ef00006a-49fd-4d36-b0fa-0811d6221fa5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <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"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index = df.index + QuarterEnd()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8fdQyUDFb6Z"
      },
      "source": [
        "데이트타임에서 연도, 분기, 월 추출"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "id": "ebz68UQ4Fb6a"
      },
      "outputs": [],
      "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, format='mixed')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "DVdNsNtxFb6a",
        "outputId": "e32c4ab3-1014-49d7-c92a-c62e5343169f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "id": "1ZlR35FlFb6a"
      },
      "outputs": [],
      "source": [
        "df['month'] = df.date.dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "QtAsguFiFb6a",
        "outputId": "cb84724c-684b-40c0-9ec2-35dd4b0f858c"
      },
      "outputs": [
        {
          "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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              "    }\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"
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "N3bjvDnPFb6a"
      },
      "outputs": [],
      "source": [
        "df['day'] = df.date.dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "X3uG6hLOFb6a",
        "outputId": "d752308e-10af-4533-f2e7-3c4665873940"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzapvEsUFb6a"
      },
      "source": [
        "# save/read"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XGy5IMEMFb6a",
        "outputId": "bf1aef38-8c7c-46fc-9777-77b52d1b159f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "id": "nPHBnj9fPhxd"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.mkdir('pickles')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHBvtS69Fb6b"
      },
      "source": [
        "## to_pickle()\n",
        "\n",
        "- python에서 매우 효율적임"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sudf6dF8Fb6b",
        "outputId": "991ab0a1-9bfe-4748-be3a-df70e80a3de3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mpickles\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ],
      "source": [
        "ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "pV312C01Fb6b"
      },
      "outputs": [],
      "source": [
        "df.to_pickle('pickles/df.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlqEli4fFb6b",
        "outputId": "556f68e2-878a-41d4-d4f7-7f140d8b7bac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df.pkl\n"
          ]
        }
      ],
      "source": [
        "ls pickles/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "id": "o0iFuyMiFb6c"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_pickle('pickles/df.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Ecr9LGWXFb6c",
        "outputId": "bca8d7bd-9fcd-4aef-af49-7648d1aac746"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 155,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa4YU84eFb6c"
      },
      "source": [
        "## to_csv()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "uDVUIJ7-Fb6c"
      },
      "outputs": [],
      "source": [
        "df.to_csv('df.csv')\n",
        "df_csv = pd.read_csv('df.csv', index_col=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M42uPNdAFb6c"
      },
      "source": [
        "## to_stata()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "id": "14SU7FHpFb6c"
      },
      "outputs": [],
      "source": [
        "df1.to_stata('df.dta')\n",
        "df1_dta = pd.read_stata('df.dta')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLdjst57Fb6c"
      },
      "source": [
        "## to_excel()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "-mZShRYTFb6c"
      },
      "outputs": [],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2KhYeW6Fb6d"
      },
      "source": [
        "## save multiple dataframes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "NATICZFtFb6d"
      },
      "outputs": [],
      "source": [
        "dfs = {'df': df, 'df1': df1, 'df2': df2}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "id": "yBSZFPzcFb6d"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "id": "tzP9frzwFb6d"
      },
      "outputs": [],
      "source": [
        "with open('dfs.pkl', 'wb') as handle:\n",
        "    pickle.dump(dfs, handle, protocol=pickle.HIGHEST_PROTOCOL)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "uNupKad5Fb6d"
      },
      "outputs": [],
      "source": [
        "with open('dfs.pkl', 'rb') as handle:\n",
        "    dfs_pkl = pickle.load(handle)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cMzkWzHvFb6e"
      },
      "source": [
        "# matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "S-jEj8aKFb6e"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "NXasMp6QFb6e",
        "outputId": "b72b81e9-804e-4eb1-f8d0-cb7810e21420"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8a9e5b10>"
            ]
          },
          "execution_count": 164,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = pd.DataFrame([0, 1, 2, 3, 4, 4], columns = ['y'])\n",
        "df.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "2yQahyLPFb6e"
      },
      "outputs": [],
      "source": [
        "# %matplotlib notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "6xFyoa4uFb6e",
        "outputId": "56d71ad0-4a28-4b1b-becc-036eaf9b78bc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4GQbJDoFb6f"
      },
      "source": [
        "## x, y-axis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "4yEmNk-rFb6f",
        "outputId": "e318d407-3036-4c59-f955-075202103308"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "eEajV4QoFb6f",
        "outputId": "b511d56b-e1a8-489a-85a6-34297281e60a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8a6cd4d0>"
            ]
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPqcvGzsFb6f"
      },
      "source": [
        "## bar chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "id": "IgBAIR5sFb6g"
      },
      "outputs": [],
      "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'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "jl3unOYeFb6g",
        "outputId": "affef664-72a4-4d1e-ea5c-2882e5da24c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b8a966ed0>"
            ]
          },
          "execution_count": 170,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "\n",
        "df.plot.bar(ax=ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "CPq9dz58Fb6g",
        "outputId": "48d94540-e792-4d02-e554-48f741cd2d60"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "6NLy3GDwFb6g"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKGTAx5tmzAF"
      },
      "source": [
        "## 한글"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "id": "8rtNh9-Em6vo"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randn(100).cumsum(), columns=['환율'])\n",
        "df.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzbG6CMfeT2J"
      },
      "source": [
        "현재 matplotlib가 사용할 수 있는 한글폰트 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHPZKLv0eSev",
        "outputId": "b54fbd57-b2af-43f7-b831-0625f30dfe7e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 176,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import matplotlib.font_manager as fm\n",
        "[f for f in fm.fontManager.ttflist if 'Nanum' in f.name]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Ajqpmaye2NY"
      },
      "source": [
        "나눔글꼴 설치(리눅스)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDR1vHAwe7cW",
        "outputId": "2cf09247-9da7-4c1c-df59-a4df6af282da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  fonts-nanum\n",
            "0 upgraded, 1 newly installed, 0 to remove and 37 not upgraded.\n",
            "Need to get 9,604 kB of archives.\n",
            "After this operation, 29.5 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-nanum all 20170925-1 [9,604 kB]\n",
            "Fetched 9,604 kB in 1s (9,713 kB/s)\n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 155222 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20170925-1_all.deb ...\n",
            "Unpacking fonts-nanum (20170925-1) ...\n",
            "Setting up fonts-nanum (20170925-1) ...\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcJrhvOHfUsx"
      },
      "source": [
        "matplotlib가 사용할 수 있는 폰트 리스트를 재구성하고, 코랩 재시작"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lwu5Qm_tfPeL"
      },
      "source": [
        "현재 matplotlib가 사용할 수 있는 한글폰트 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4smCyJZfPeM",
        "outputId": "c68211c0-fb9c-42a0-ee2b-67a390e66758"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<Font 'NanumGothic' (NanumGothic.ttf) normal normal 400 normal>,\n",
              " <Font 'NanumSquareRound' (NanumSquareRoundB.ttf) normal normal bold normal>,\n",
              " <Font 'NanumMyeongjo' (NanumMyeongjoBold.ttf) normal normal bold normal>,\n",
              " <Font 'NanumBarunGothic' (NanumBarunGothicBold.ttf) normal normal 700 normal>,\n",
              " <Font 'NanumBarunGothic' (NanumBarunGothic.ttf) normal normal 400 normal>,\n",
              " <Font 'NanumSquare' (NanumSquareB.ttf) normal normal bold normal>,\n",
              " <Font 'NanumGothic' (NanumGothicBold.ttf) normal normal 700 normal>,\n",
              " <Font 'NanumSquareRound' (NanumSquareRoundR.ttf) normal normal regular normal>,\n",
              " <Font 'NanumSquare' (NanumSquareR.ttf) normal normal regular normal>,\n",
              " <Font 'NanumMyeongjo' (NanumMyeongjo.ttf) normal normal 400 normal>]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import matplotlib.font_manager as fm\n",
        "[f for f in fm.fontManager.ttflist if 'Nanum' in f.name]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rj2yFknNfKru"
      },
      "source": [
        "matplotlib에서 사용하려는 한글 폰트 지정하고, 그래프의 마이너스 라벨이 제대로 표시되도록 설정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1REWbPVPrAvC"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', family='NanumGothic')\n",
        "plt.rcParams['axes.unicode_minus'] = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "ymiKTxcGf-jw",
        "outputId": "66ce4e1c-a6f3-4ff8-b8d1-7e753ff13f56"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f35dd3b2ad0>"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.randn(100).cumsum(), columns=['환율'])\n",
        "df.plot()"
      ]
    },
    {
      "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"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "EvgmlRAkFb6V",
        "WVXOvyFEFb6V",
        "HHBvtS69Fb6b",
        "Aa4YU84eFb6c",
        "M42uPNdAFb6c",
        "FLdjst57Fb6c",
        "K4GQbJDoFb6f",
        "oPqcvGzsFb6f",
        "aNN1r1O2Fb6g"
      ],
      "include_colab_link": true,
      "name": "pandas.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}