pandas-cheat-sheet.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/boostcourse-ds-510/blob/master/pandas-cheat-sheet.ipynb)\n",
    "\n",
    "## Pandas basic\n",
    "* https://pandas.pydata.org/\n",
    "* https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf\n",
    "* [10 minutes to pandas](https://pandas.pydata.org/pandas-docs/version/1.0.0/getting_started/10min.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>4</td>\n",
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       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "   a  b   c\n",
       "1  4  7  10\n",
       "2  5  8  11\n",
       "3  6  9  12\n",
       "4  4  9  10"
      ]
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     "execution_count": 2,
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   ],
   "source": [
    "# 공식문서(도움말) 활용하기\n",
    "# pd.DataFrame?\n",
    "# pd.DataFrame(shift+tab+tab)\n",
    "\n",
    "df = pd.DataFrame(\n",
    "{\"a\" : [4 ,5, 6, 4],\n",
    "\"b\" : [7, 8, 9, 9],\n",
    "\"c\" : [10, 11, 12, 10]},\n",
    "index = [1, 2, 3, 4])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4\n",
       "2    5\n",
       "3    6\n",
       "4    4\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "   a\n",
       "1  4\n",
       "2  5\n",
       "3  6\n",
       "4  4"
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "# dataframe\n",
    "df[[\"a\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <th>b</th>\n",
       "      <th>c</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   a  b   c\n",
       "2  5  8  11\n",
       "3  6  9  12"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df[\"a\"] > 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4\n",
       "2    5\n",
       "3    6\n",
       "4    4\n",
       "Name: a, dtype: int64"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[\"a\"]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "      <th>a</th>\n",
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       "   a  b\n",
       "1  4  7\n",
       "2  5  8\n",
       "3  6  9\n",
       "4  4  9"
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     "execution_count": 7,
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   "source": [
    "df[[\"a\", \"b\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    2\n",
       "6    1\n",
       "5    1\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[\"a\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping\n",
    "\n",
    "## sort_values, drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "   a  b   c\n",
       "3  6  9  12\n",
       "2  5  8  11\n",
       "1  4  7  10\n",
       "4  4  9  10"
      ]
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df.sort_values(\"a\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
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      "text/plain": [
       "   a  b\n",
       "1  4  7\n",
       "2  5  8\n",
       "3  6  9\n",
       "4  4  9"
      ]
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     "metadata": {},
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   "source": [
    "df = df.drop([\"c\"], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group Data\n",
    "## Groupby, pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
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       "  <tbody>\n",
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       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   mean  sum  count\n",
       "a                  \n",
       "4     8   16      2\n",
       "5     8    8      1\n",
       "6     9    9      1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"a\"])[\"b\"].agg([\"mean\", \"sum\", \"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  mean       std  min  25%  50%  75%  max\n",
       "a                                                \n",
       "4    2.0   8.0  1.414214  7.0  7.5  8.0  8.5  9.0\n",
       "5    1.0   8.0       NaN  8.0  8.0  8.0  8.0  8.0\n",
       "6    1.0   9.0       NaN  9.0  9.0  9.0  9.0  9.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"a\"])[\"b\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "1  4  7\n",
       "2  5  8\n",
       "3  6  9\n",
       "4  4  9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    b\n",
       "a    \n",
       "4  16\n",
       "5   8\n",
       "6   9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index=\"a\", values=\"b\", aggfunc=\"sum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a3ac978>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
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