{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "religious-agenda",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "internal-minnesota",
   "metadata": {},
   "source": [
    "Pandas is a library for tabular data (dataframe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "premier-growth",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "mathematical-mortality",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.012181</td>\n",
       "      <td>-0.077271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.287084</td>\n",
       "      <td>-0.330829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.162034</td>\n",
       "      <td>0.167187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.319899</td>\n",
       "      <td>-0.548682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.821274</td>\n",
       "      <td>-0.565926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  1.012181 -0.077271\n",
       "1  1.287084 -0.330829\n",
       "2 -1.162034  0.167187\n",
       "3 -0.319899 -0.548682\n",
       "4  0.821274 -0.565926"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(100, 2), columns=['x', 'y'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "informed-blues",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "distinguished-fence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   x       100 non-null    float64\n",
      " 1   y       100 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "confident-tractor",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.190087</td>\n",
       "      <td>-0.028231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.855097</td>\n",
       "      <td>0.879615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.183391</td>\n",
       "      <td>-1.905329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.811593</td>\n",
       "      <td>-0.705061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.230736</td>\n",
       "      <td>-0.134842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.405774</td>\n",
       "      <td>0.646319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.551795</td>\n",
       "      <td>1.802841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x           y\n",
       "count  100.000000  100.000000\n",
       "mean    -0.190087   -0.028231\n",
       "std      0.855097    0.879615\n",
       "min     -2.183391   -1.905329\n",
       "25%     -0.811593   -0.705061\n",
       "50%     -0.230736   -0.134842\n",
       "75%      0.405774    0.646319\n",
       "max      1.551795    1.802841"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "educated-winning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',\n",
       "               '2021-01-05'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"20210101\", periods=100)\n",
    "dates[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "later-grill",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>-1.823644</td>\n",
       "      <td>-1.357490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-02</th>\n",
       "      <td>0.407033</td>\n",
       "      <td>0.140167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-03</th>\n",
       "      <td>-0.219090</td>\n",
       "      <td>1.195670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>1.280075</td>\n",
       "      <td>0.955749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>1.201438</td>\n",
       "      <td>-1.155646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x         y\n",
       "2021-01-01 -1.823644 -1.357490\n",
       "2021-01-02  0.407033  0.140167\n",
       "2021-01-03 -0.219090  1.195670\n",
       "2021-01-04  1.280075  0.955749\n",
       "2021-01-05  1.201438 -1.155646"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(100, 2), columns=['x', 'y'], index=dates)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "metric-mounting",
   "metadata": {},
   "outputs": [
    {
     "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();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "shared-valley",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-11</th>\n",
       "      <td>-0.135543</td>\n",
       "      <td>-1.036254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-12</th>\n",
       "      <td>2.016705</td>\n",
       "      <td>0.791184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-13</th>\n",
       "      <td>0.656834</td>\n",
       "      <td>-0.423262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-14</th>\n",
       "      <td>-0.630841</td>\n",
       "      <td>1.101213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-15</th>\n",
       "      <td>1.684871</td>\n",
       "      <td>1.491705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x         y\n",
       "2021-02-11 -0.135543 -1.036254\n",
       "2021-02-12  2.016705  0.791184\n",
       "2021-02-13  0.656834 -0.423262\n",
       "2021-02-14 -0.630841  1.101213\n",
       "2021-02-15  1.684871  1.491705"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2021-02-11':'2021-02-15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "agreed-productivity",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2021-01-01</th>\n",
       "      <td>-1.077120</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-03</th>\n",
       "      <td>0.071963</td>\n",
       "      <td>-0.305716</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
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       "      <td>0.098572</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>-0.333955</td>\n",
       "      <td>-0.482170</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>-0.384534</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>-0.907123</td>\n",
       "      <td>2.424140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>-0.280448</td>\n",
       "      <td>0.262274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>-0.477009</td>\n",
       "      <td>2.236021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>-0.141062</td>\n",
       "      <td>2.128535</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-10</th>\n",
       "      <td>-0.301844</td>\n",
       "      <td>-1.010541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x         y\n",
       "2021-01-01 -1.077120  0.342451\n",
       "2021-01-03  0.071963 -0.305716\n",
       "2021-01-04  0.469240  0.098572\n",
       "2021-01-06 -0.333955 -0.482170\n",
       "2021-01-08 -0.384534 -0.758705\n",
       "...              ...       ...\n",
       "2021-04-06 -0.907123  2.424140\n",
       "2021-04-07 -0.280448  0.262274\n",
       "2021-04-08 -0.477009  2.236021\n",
       "2021-04-09 -0.141062  2.128535\n",
       "2021-04-10 -0.301844 -1.010541\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.x < 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "damaged-channel",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = [chr(97 + int(num)) for num in abs(df.x.values) * 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "flexible-butter",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0.098572</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>1.226176</td>\n",
       "      <td>1.319617</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x         y label\n",
       "2021-01-01 -1.077120  0.342451     k\n",
       "2021-01-02  2.723008  0.196346     |\n",
       "2021-01-03  0.071963 -0.305716     a\n",
       "2021-01-04  0.469240  0.098572     e\n",
       "2021-01-05  1.226176  1.319617     m"
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bibliographic-winning",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2021-03-12</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>a</td>\n",
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      "text/plain": [
       "                   x         y label\n",
       "2021-01-03  0.071963 -0.305716     a\n",
       "2021-02-25  0.091595  1.604293     a\n",
       "2021-03-12  0.088501 -0.170577     a\n",
       "2021-03-31  0.026616  0.661760     a\n",
       "2021-04-01  0.096728 -2.732678     a"
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     "metadata": {},
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   ],
   "source": [
    "df.loc[df.label == 'a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "designed-watson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XG89fd7b3lh7e7kOSVLSVz1lIkppkWUiSiiwLSVKRZSFJKrIsJElFloUkqciykCQVWRZSCyLiTY2/azAYEZc2/pZBp+8TJp13/lKe1KKI+F1gkJVbv8xm5h9UHElqO8tCalHjLq6PAieBt2bm9yuOJLWdh6Gk1v0gMAxsY2WEIV1wHFlILYqIT7NyJ9crgTdk5u0VR5Larq/qAFIvi4ifA05l5icj4iLgixHx9sz8l6qzSe3kyEKSVOQ5C0lSkWUhSSqyLCRJRZaFJKnIspAkFVkWkqQiy0KSVPR/DqdD1GYrnbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df.label == 'a'].plot('x', 'y', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "crazy-count",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fa986e48dc0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=df[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "celtic-chassis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    .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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.375404</td>\n",
       "      <td>-0.942918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.057332</td>\n",
       "      <td>-0.963949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.211865</td>\n",
       "      <td>-2.870494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.435584</td>\n",
       "      <td>-3.043571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-0.327597</td>\n",
       "      <td>4.056683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.248295</td>\n",
       "      <td>0.227028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.699617</td>\n",
       "      <td>1.612593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.739751</td>\n",
       "      <td>0.201644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.925425</td>\n",
       "      <td>-1.960625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>-1.976830</td>\n",
       "      <td>6.748663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.016653</td>\n",
       "      <td>-0.549432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>1.077869</td>\n",
       "      <td>-3.301742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>-0.067073</td>\n",
       "      <td>1.604039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>-4.061371</td>\n",
       "      <td>-1.622024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>4.295536</td>\n",
       "      <td>0.009890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>-1.506907</td>\n",
       "      <td>-0.548990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>5.002802</td>\n",
       "      <td>0.754617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.005535</td>\n",
       "      <td>2.562163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>-1.984581</td>\n",
       "      <td>1.133631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>2.016705</td>\n",
       "      <td>0.791184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>-0.018609</td>\n",
       "      <td>3.506080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>2.366788</td>\n",
       "      <td>-0.014051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|</th>\n",
       "      <td>2.723008</td>\n",
       "      <td>0.196346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x         y\n",
       "label                    \n",
       "a      0.375404 -0.942918\n",
       "b      0.057332 -0.963949\n",
       "c      0.211865 -2.870494\n",
       "d      0.435584 -3.043571\n",
       "e     -0.327597  4.056683\n",
       "f      1.248295  0.227028\n",
       "g      0.699617  1.612593\n",
       "h      0.739751  0.201644\n",
       "i      0.925425 -1.960625\n",
       "j     -1.976830  6.748663\n",
       "k      0.016653 -0.549432\n",
       "l      1.077869 -3.301742\n",
       "m     -0.067073  1.604039\n",
       "n     -4.061371 -1.622024\n",
       "o      4.295536  0.009890\n",
       "p     -1.506907 -0.548990\n",
       "q      5.002802  0.754617\n",
       "r      0.005535  2.562163\n",
       "t     -1.984581  1.133631\n",
       "u      2.016705  0.791184\n",
       "v     -0.018609  3.506080\n",
       "x      2.366788 -0.014051\n",
       "|      2.723008  0.196346"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=df[\"label\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "incorrect-albania",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(5, 2), columns=['x', 'y'])\n",
    "df2 = pd.DataFrame(np.random.randn(5, 2), columns=['x', 'y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "amber-clinton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.065447</td>\n",
       "      <td>0.508669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.111200</td>\n",
       "      <td>0.029376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.251230</td>\n",
       "      <td>-0.855050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.206806</td>\n",
       "      <td>-1.267250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.195877</td>\n",
       "      <td>-0.586469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  1.065447  0.508669\n",
       "1 -0.111200  0.029376\n",
       "2  0.251230 -0.855050\n",
       "3 -0.206806 -1.267250\n",
       "4 -0.195877 -0.586469"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "recovered-arbor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.879820</td>\n",
       "      <td>-1.054090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.834352</td>\n",
       "      <td>-2.434033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.915162</td>\n",
       "      <td>3.600316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.781719</td>\n",
       "      <td>0.633350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.003787</td>\n",
       "      <td>-0.752671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  1.879820 -1.054090\n",
       "1  0.834352 -2.434033\n",
       "2 -0.915162  3.600316\n",
       "3 -0.781719  0.633350\n",
       "4 -0.003787 -0.752671"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "acute-minister",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.065447</td>\n",
       "      <td>0.508669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.111200</td>\n",
       "      <td>0.029376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.251230</td>\n",
       "      <td>-0.855050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.206806</td>\n",
       "      <td>-1.267250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.195877</td>\n",
       "      <td>-0.586469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.879820</td>\n",
       "      <td>-1.054090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.834352</td>\n",
       "      <td>-2.434033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.915162</td>\n",
       "      <td>3.600316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.781719</td>\n",
       "      <td>0.633350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.003787</td>\n",
       "      <td>-0.752671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  1.065447  0.508669\n",
       "1 -0.111200  0.029376\n",
       "2  0.251230 -0.855050\n",
       "3 -0.206806 -1.267250\n",
       "4 -0.195877 -0.586469\n",
       "5  1.879820 -1.054090\n",
       "6  0.834352 -2.434033\n",
       "7 -0.915162  3.600316\n",
       "8 -0.781719  0.633350\n",
       "9 -0.003787 -0.752671"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "unsigned-prime",
   "metadata": {},
   "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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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')\n",
    "df_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "statewide-spectacular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZIHYzM6uhdpNGRLwbERMi4mvA54HewN1t7LtfRNwTER+rBUTESxFxTkRcXuGuqnS4Cse4ISI+C3wV+GlbMUsam9s9psydO7et3czMbAkVWk9D0g6SLgCmkU437VvwfhtK+r2kP0jausquLcC6JdsDgOfb2jki7gE2kNS3jdvHRcSwiBjWr1+/IqGamVkB7bZpSHoKmE5qeD4+It6usm/PiHivpOinwI9ItYbrgCFt3HUysJGkgcBzpMGDH5nfStKGwBO5IXwLYCXglfbiNzOz2inSED44It4oeLybJV0REVfm7flAMylptDkzbkQskHQkcBvQBFwaEbMkHZZvv4g0HfvBkuaTemSNrnQazMzM6qfayn3fi4gzgFMkVWpfOKrC3XYBDs+9rU4BjgOOAj5BWrypTRExCZhUVnZRyfXTgdOrHcPMzOqrWk3j0fx3StGDRcRC4DxJVwI/BNYGToqIJ5Y+RDMzaxTVlnu9OV+dEREPFjmYpK2A44EPgFNJp5FOkdQC/DQi5i1jvGZm1omKtGmcLWltUkP2+IiYVWXfi4C9gV7AxRGxLTBG0g6khvSdlzVgMzPrPEXGaewIjCCN7h4naWaV9TQWkhq+P02qbbQe4+6IcMIwM+viCo3TiIgXI+LXpIkKp5PaKyrZH9gN2AY4uNIOkioN5DMzsy6gyMp9m0g6Oa/cdx5wP2nwXSXjgKeBCyNi0bQgklaStJOky4FDlj1sMzPrDEXaNH4HXAOMjIg2R2lnu5DmhLomD9R7nTSCvAm4HfhlRExf6mjNzKxTVU0aecryJyLiV0UOlkeDXwBcIKkH0Bd4NyJeX9ZAzcys81VNGhGxUFIfSStFxAfV9q1w3/l4hT8zs+VKoUWYgPskTQQWzTsVEWfXLSozM2tIRZLG8/myArBafcMxM7NG1m7SiIgfd0QgZmbW+IpMjX4nlRdE2qkuEZmZWcMqcnrquJLrPUlTlC+oTzhmZtbIipyemlpWdJ+kisu9mpnZ8q3I6ak1SzZXAIYCa9UtIjMza1hFTk9NJbVpiHRa6ingW/UMyszMGlOR01MDOyIQMzNrfEUmLNxH0mr5+g8kXS9pi/qHZmZmjabI1OgnRcSbkrYjLaJ0OXBhfcMyM7NGVCRpLMx/dydNeX4TsFL9QjIzs0ZVJGk8J+liYF9gkqSVC97PzMyWM0W+/PcFbgN2yVOcrwkcX8+gzMysMRXpPfUOcH3J9gt4ynMzs27Jp5nMzKwwJw0zMyusyDiN04uUmZnZ8q9ITePLFcp2rXUgZmbW+NpsCJd0OHAEsL6kGSU3rQbcV+/AzMys8VSraVwNfAWYmP+2XoZGxIG1DkTSLpJmS5oj6YQKtx8gaUa+3C9pcK1jMDOz6tqsaUTEPGAesB+ApE+SFmHqJalXRPxfrYKQ1AScTzoV1gJMljQxIh4p2e0pYIeIeE3SrsA4YKtaxWBmZu0r0hD+FUmPk7607waeBv5Y4ziGA3Mi4smI+AAYD+xZukNE3B8Rr+XNB4ABNY7BzMzaUaQh/GfAF4B/5GnS/4Xat2n0B54t2W7JZW35FrVPXGZm1o4iSWN+RLwCrCBphYi4ExhS4zhUoSwq7ijtSEoa32/zYNJYSVMkTZk7d26NQjQzsyIr970uqRdwD3CVpJdIK/jVUguwbsn2AOD58p0kDQJ+C+yaE1lFETGO1ObBsGHDKiYfMzNbckVqGnsC7wLfBf4EPEHqRVVLk4GNJA2UtBIwhtRraxFJnybNgXVQRPyjxo9vZmYFFJmw8O2SzcvrEURELJB0JGk23Sbg0oiYJemwfPtFwA+BPsAFkgAWRMSwesRjZmaVtZs0JH0NOB34JKntQUBERO9aBhIRk4BJZWUXlVz/V+Bfa/mYZma2ZIq0aZwBfCUiHq13MGZm1tiKtGn80wnDzMyg+txTX8tXp0i6FrgReL/19oi4vtL9zMxs+VXt9FRpD6l3gJEl20HJan5mZtY9VJt76hsAkraNiI+MAJe0bb0DMzOzxlOkTePcgmVmZracq9amsTWwDdBP0rElN/UmjaUwM7NuplqbxkpAr7zPaiXlbwB71zMoMzNrTNXaNO4G7pZ0WUQ804ExmZlZg2q3TcMJw8zMWhVpCDczMwOcNMzMbAkUWe71DEm9JfWQdIeklyUd2BHBmZlZYylS0xgZEW8Ae5AWS9oYOL6uUZmZWUMqkjR65L+7AddExKt1jMfMzBpYkanRJ0p6jLR63xGS+gHv1TcsMzNrRFVrGpJWAG4GtgaGRcR80uSFe3ZAbGZm1mCqJo2I+BA4KyJei4iFueztiHixQ6IzM7OGUqRN43ZJX1demNvMzLqvIm0axwKrAgslvUud1gg3M7PG127SiIjV2tvHzMy6hyKD+yTpQEkn5e11JQ2vf2hmZtZoirRpXEDqPbV/3n4LOL9uEZmZWcMq0qaxVURsIelBgIh4TdJKdY7LzMwaUJGaxnxJTUAA5MF9H9Y1KjMza0hFksavgRuAT0o6Bfhf4NS6RmVmZg2pSO+pqyRNBf6F1N32qxHxaN0jMzOzhtNu0pC0JvAScE1JWY88pYiZmXUjRU5PTQPmAv8AHs/Xn5I0TdLQWgUiaRdJsyXNkXRChds/K+mvkt6XdFytHtfMzIorkjT+BOwWEX0jog+wKzABOILUHXeZ5Yb28/OxNwX2k7Rp2W6vAkcBZ9biMc3MbMkVSRrDIuK21o2IuB34YkQ8AKxcoziGA3Mi4smI+AAYT9lMuhHxUkRMBnxazMyskxRJGq9K+r6k9fLle8BruXZQq663/YFnS7ZbcpmZmTWQIkljf2AAcCNwE/DpXNYE7FujOCrNoBtLfTBprKQpkqbMnTt3GcIyM7NSRbrcvgx8p42b59QojhZg3ZLtAcDzS3uwiBgHjAMYNmzYUicfMzP7qCJdbjcGjgOaS/ePiJ1qGMdkYCNJA4HngDEsnuvKzMwaRJG5p64DLgJ+CyysRxARsUDSkcBtpNNel0bELEmH5dsvkrQWMAXoDXwo6Rhg04h4ox4xmZnZxxVJGgsi4sJ6BxIRk4BJZWUXlVx/kXTayszMOkmRhvCbJR0haW1Ja7Ze6h6ZmZk1nCI1jUPy3+NLygJYv/bhmJlZIyvSe2pgRwRiZmaNr8hyr5+Q9ANJ4/L2RpL2qH9oZmbWaIq0afwO+ADYJm+3AD+rW0RmZtawiiSNDSLiDPKcTxHxLpVHcJuZ2XKuSNL4QNIqLF7udQPg/bpGZWZmDalI76kfkaZHX1fSVcC2wKH1DMrMzBpTkd5Tf5Y0DfgC6bTU0Xk+KjMz62aK9J7aFngvIm4F1gD+U9J69Q7MzMwaT5E2jQuBdyQNJg3wewa4oq5RmZlZQyqSNBZERJBW0vt1RPwKWK2+YZmZWSMq0hD+pqQTgQOBL+YV+3rUNywzM2tERWoao0ldbL+VZ5rtD/yirlGZmVlDKlTTAH4VEQvzgkyfBa6pb1hmZtaIitQ07gFWltQfuAP4BnBZPYMyM7PGVCRpKCLeAb4GnBsRewGb1TcsMzNrRIWShqStgQOAW3NZU/1CMjOzRlUkaRwNnAjckNftXh+4s75hmZlZIyoyjcg9pHaN1u0ngaPqGZSZmTWmdpOGpH7A90jtGD1byyNipzrGZWZmDajI6amrgMeAgcCPgaeByXWMyczMGlSRpNEnIi4B5kfE3RHxTdKMt2Zm1s0UGdw3P/99QdLuwPPAgPqFZGZmjapI0viZpNWB/wDOBXoD361rVGZm1pDaTBqSegKHARuS5pu6JCJ27KjAzMys8VRr07gcGAbMBHYFzuqQiMzMrGFVOz21aURsDiDpEuDvHROSmZk1qmo1jdYGcCJiQQfEYmZmDa5a0hgs6Y18eRMY1Hpd0hu1DkTSLpJmS5oj6YQKt0vSr/PtMyRtUesYzMysujZPT0VEh01KmFcDPB/4MtACTJY0MSIeKdltV2CjfNmKtHb5Vh0Vo5mZFRvc1xGGA3Mi4smI+AAYT1qTvNSewBWRPACsIWntjg7UzKw7KzJOoyP0B54t2W7h47WISvv0B14oP5ikscDYvPmWpNm1C7Xh9AVe7qgH0+kd9Ujdgt+7rm15f//Wq1TYKElDFcpiKfZJhRHjgHHLGlRXIGlKRAzr7Dhsyfm969q66/vXKKenWoB1S7YHkKYrWdJ9zMysjholaUwGNpI0UNJKwBhgYtk+E4GDcy+qLwDzIuJjp6bMzKx+GuL0VEQskHQkcBtpKdlL8yqBh+XbLwImAbsBc4B3gG90VrwNpluchltO+b3r2rrl+6eIis0CZmZmH9Mop6fMzKwLcNIwM7PCGqJNw6y7kPQJ0nIDALMj4v3OjMdsSbmm0UVI2lDSthXKt5e0QWfEZMVJ6iHpHFLX8d+Rlh54snWeNUmf78TwrApJW0paq2T7YEk35bnw1uzM2DqDk0bXcQ7wZoXyd/Nt1tjOAnoB60XE0Ij4PLAJsL6kC4HrOzU6q+Zi4AMASV8ETgOuAObRDXtQufdUFyHp4Yj4XBu3zWxd+8Qak6Q5wEZR9g+XJ+t8Gdg1z6lmDUbSQxExOF8/H5gbESfn7ekRMaQTw+twrml0HT2r3LZKh0VhS+vD8oQBEBELSV9CThiNq0lSa/vvvwB/Kbmt27ULO2l0HZMl/Vt5oaRvAVM7IR5bMo9IOri8UNKBwKOdEI8Vdw1wt6SbSKeD74XUzkg6RdWt+PRUFyHpU8ANpHOrrUliGLASsFdEvNhZsVn7JPUntVu8S3r/AtiSVEvcKyKe68TwrB156qK1gdsj4u1ctjHQKyKmdWpwHcxJo4uRtCPQ2rYxKyL+Um1/ayySdgI2I83aPCsi7ujkkMyWiJOGmZkV5jYNMzMrzEnDzMwKc9KwLkvSQknTJT0s6bo8RUfR+w6RtNtSPOY1kmZI+m5Z+cmSnsvxtF7WWILj3iVpqVeBkzRC0i1LuP82JduHVerdZVau2/UxtuXKu60DqyRdBRwGnN3enXKf+yGk3meTij5Ynkpim4iouHYy8MuIOLPo8WqlZAzBkhgBvAXcD4vWrDFrl2satry4F9hQ0pqSbsy1gQckDYJFNYFxkm4nTQHxE2B0rhGMLj2QpJ6SfidppqQHc481gNuBT+b7bF8kKEmH5nhulvSUpCMlHZuP+0DZ3EUHSro/15yG5/sPz2UP5r+fKTnudZJuznGVPuaWef/1JX1F0t/y9v9I+pSkZlKC/W7rc8mvz3H5/kNybDMk3SDp/+XyuySdLunvkv5R9DWw5YuThnV5+Zf2rsBM4MfAgxExCPhPUoJoNRTYMyL2B34IXBsRQyLi2rJD/jtAnpplP+ByST2BUcAT+T73Vgil9Ut4uqQ7S8o/B+wPDAdOAd7Jc0/9FSg9JbRqRGwDHAFcmsseA76Y9/8hcGrJ/lsDh0TETiWvxTbARfl5Pgn8L/CFfP/xwPci4um8zy/beC5XAN/Pr+FM4Eclt60YEcOBY8rKrZvw6SnrylaRND1fvxe4BPgb8HWAiPiLpD6SVs/7TIyIdwscdzvg3HyMxyQ9A2wMvNHO/do6PXVnRLwJvClpHnBzLp8JDCrZ75r8mPdI6p3bRFYjJa2NSAMCe5Ts/+eIeLVkexPSBHojI+L5XDYAuFbS2qSBoE9VewL5tVojIu7ORZcD15Xs0jqx4lSgudqxbPnkmoZ1Ze/mX8pDIuI7EfEBadBcudbBSG8XPG6lYyyL0jUzPizZ/pCP/nArHzQVwE9JSedzwFf46Bxk5c/nBeA9oHSa9XOB83Kt6dtUn8OsiNbYF+Ifnd2Sk4Ytb+4BDoDUQwh4OSIq1RDeJP2Kb+8YGwOfBmbXOtAKRufH3A6YFxHzgNWB1ilGDm3n/q8DuwOn5udO2f0PKdm34vPPj/laSXvFQcDd5ftZ9+WkYcubk4FhkmaQ1j04pI397gQ2rdQQDlxAmtl0JnAtcGjBFfZK2zSm5wbnJfGapPtJ7Q3fymVnAD+XdB/Q1N4BIuKfpBrJ+ZK2Ir0e10m6lzQFe6ubgb3aaNQ/BPhFfg2HkDoNmAGeRsTMzJaAaxpmZlaYk4aZmRXmpGFmZoU5aXQgSatIultpXWgkbSxpkqQ5kh6VNEFpsaVlfZzjJD2WRxY/1DqnkKSnJfVdhuMu0fxG9SZpJUnnSHoiv4a3SPp0jY791TwiuvV13LsWx13GmD4yX1QHP/YX8sjy6fmzenINjrlHHqn+kKRHJH17KY/TLGn/ku1DJZ23rPEtYQz/uYz3P1NprZWG56TRsb4JXB8RC/MI41uBCyNiw4jYBLgQ6LcsDyDpMODLwPDct/+L1GDcgZZufqN6H/tUUrfRjSNiQ+APwE2SlulzLWkwcCZpVPVnSb2RTpc0dFmOWwMjgE5JGqRBfmPzXF+fAyYsy8Ek9SANRPxKRAwmjS25aykP10wacd+ZlilpkMbTnFCLQOouInzpoAtpcrjmfP2bwBV1eIz/AzZo47anSdNsTCONRv5sLl+VNG3FZOBB0pclpHEB15G6Z/6F9KV1D2nZ2UdIXUNXyPvul4/5MHB6yWO+VXJ9b+CyfP0y0uSCdwJnARsAD+QYflJ2v+kVnssngFeA3mXl95JGRC/La3gl8M2ysm8BV+frdwHD8vW+wNP5ehPwi/wcZgDfLrn/8SXlP85lzaT1wX8DzCLNIbVKvu2o/BrPIE3/0Qy8SBpzMR3YHlgPuCPvcwdpPEkT8CTph8IapAGEXyx5bTYkdcO9ND+PJ4GjSuKcBKxT4TV5DfhkDT+nawIvtT7fsts+9rxKPjN7l3+28udmXn5dvkv63F4P/Al4HDij5D4XAlPy6/3jsv+NU0lTu0wBtgBuA54ADsv7jKDC55/UtXthfvyr8r7Hkv4XHgaOae/9zrdPBdaq9XdCrS+dHkB3uZCmcHixZPts4OgC91stfxgrXTatsO9rVY71NPCdfP0I4Lf5+qnAgfn6GsA/SInkUKAFWDPfNoI04nh90pfTn0mJYB1SsupHGiX8F+Cr+T7VksYtQFPevgXYL18/rPR+bTyXQaQ5psrLf9n6T1pWfm0br+HBFfadBgwuKxtMTl60nTTGAj/I11cmffkMBEaSflWL9CVzC6kG2AwsAIbk+0woeR+eB1ZufU/y35OB40piupk09xSkHyE35ut/Ii0puwcpUf1XjuepkuPcn8v6kpJvj3Ze7x+SEscN5JHlFfb5TBuv8fTW51C2/29JieMa0mDKFdp5XpdROWmMAG4pKT+UlAxXJ42AfwZYN9/W+lluyu/joJL/jcNLPkMzSP9P/YCXqn3+K3zOh5J+QK0K9CIliM9Xe7/z9m+Ar9fze6gWF08D0HH6kkbsLpFIcxYNKbi7+PhUFOVK5w76Wr4+EhjVOssp6R+ttW2gfH6jv0eaCA9J15DmaZoP3BURc3P5VaQvxRvbieW6iFiYr28NfDVfv5p0eqiatp5rxVNxEVE+gG9Jj13kFN9IYFBJ+8fqwEa5fCSpFgfpi2QjUqJ9KiKm5/LS+ZxmAFdJupG2X8etWfweXkkaCAipRvFFUsL6OfBvpFHdk0vue2ukAYvvS3oJ+BTpB0JFEfGT/L6OJJ0K2o/0JVq6z2yKf1aJiH+VtDnwJeA40mnVQ6s8ryVxR6TR7Uh6hFR7eRbYV9JY0o+btYFNSa81wMT8dybQKxbPF/aeFq+NUunz/99lj70dcENEvJ33u55UM5xI2+83pAS6zlI81w7lpNFx3uWj8/7MAnZo706SViN9CVSyf0Q80roREW9IelvS+q0f7AoqzR0k0i+cj0yVkUcUl89vVGl+pGpfqKX7l897VHQuqErmAOtJWi3/c7fago//EyPpWtIv4XJnR8QVZWWzSGttzCgp24JUc4D0a7G13aT0OYlUk7ut7LF3Bn4eEReXlTfz0XmpFgKr5Ou7k774RwEnSdqsQuzlWl/re0m1tXVINYTjWXxqpVX547b7XRARTwAXSvoNMFdSn4h4peT5fIZUo6tkRES8XuGYM4GZkq4kTaZ4aJXnteh1lyRS7b0tH3t+kgaSktOWEfGapMv46PtXOidY+Xxhra9Ppc9/uWr/D2293+RYikyo2ancEN5BIuI10tQUrR/Sq4FtJO3euo+kXfIvr9L7vRmLJ+UrvzzCx/2cNIVE73zM3vmXVTW3Ad/J/4hI+nyVfYdLGpgbm0eTpt7+G7CDpL65Z9h+LJ6v6J+SNsn771XluA+QZ6cFxpTeIOmx8p3zr7jLgbNLeqMdTDp9cF+F/Ue38RqWJwxItZwTW6cByX+PIbVXQDqV0dooXtqr6jbg8NzI29o7btVc/k1JvXJ5f0mfbOuFyK/VuhFxJ/A90inDXnx8vqj7WfxaHUB6LyC9H9sAH0bEe6TTQ9+m7R8fpY99h6T+Fcp3b/18kGpJCymrOUfE7Cqf1dfLjtdLi+fHglRDeaad5/U0i1/3PVk842+1ecRK9Sb9UJmXeynuWuA+5Sp9/gHmt77vpOT8VUmfyO//XhR47UkzKT+8FDF1KNc0OtbtpKrr/0TEu5L2AM6RdA7pFM8M4OhlfIwLSV8wkyXNz8c9q537/BQ4B5iRvxieJp0Pr+SvpIa/zcmNghHxoaQTSY3aAiZFxE15/xNI5/CfJf1D9GrjuMcAv5f0H6ReZa2nFvrS9i+3E0lf5LMlrQLMBbaOfIJ4aUXEdEnfB26WtDLpFMKOJTWxM4EJkg4itd+0+m3ed1p+HeeS2nZul7QJ8Nf8vfsWcCDpi7eSJtJrsTrpuf8yIl5XWnDpvyXtCXyH1Fh+qaTj82N9I8f/vqRnSYkY0hdWa0eFNuUvwg2BVyvcfBDwS0nvkH7xH1ByanFpCPiepItJv67fZnEto+LzIp3zv0nS30kN5K011RnAAkkPkdo9Xqv0gBHxkKQHSTXJJ6nw46KAj33+c/k40v/PtIg4INdi/p5v+21EPKgqc5HlhLMhi2uzDctzT3Wg/Av+2Ig4qLNjaTRK63u/GxEhaQypUXzPnFjXj4hft3P/tUgNwBdExLgax3YasBWwc6Tp15dLkj5H6jV2bGfH0ohyzei4iGjrB9WyHHsvYIuIOKnWx6411zQ6UP61caekpmX8lbY8Ggqcl3+hv07qNUNEFBpMGBEvsgSNsEsiIrpG//llFBEPk7qKWsdbkfbPCDQE1zTMzKwwN4SbmVlhThpmZlaYk4aZmRXmpGFmZoU5aZiZWWFOGmZmVtj/ByCzTFLHzFt4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic_grouped = df_titanic.groupby('Embarked')\n",
    "\n",
    "(df_titanic_grouped.sum() / df_titanic_grouped.count()).plot.bar(\n",
    "    y='Survived',\n",
    "    ylabel='Passengers that survived per embarkment\\n(%)',\n",
    "    xlabel='Port of Embarkation\\n(C = Cherbourg; Q = Queenstown; S = Southampton)'\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "beneficial-civilization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIO\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mAnyStr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'infer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0musecols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmangle_dupe_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mconverters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtrue_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfalse_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mskipinitialspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mskipfooter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mna_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mkeep_default_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mna_filter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mskip_blank_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minfer_datetime_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mkeep_date_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdate_parser\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdayfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcache_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0miterator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'infer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mthousands\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdecimal\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlineterminator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mquotechar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\"'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mquoting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdoublequote\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mescapechar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcomment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdialect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0merror_bad_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwarn_bad_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdelim_whitespace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlow_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfloat_precision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\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;31mDocstring:\u001b[0m\n",
       "Read a comma-separated values (csv) file into DataFrame.\n",
       "\n",
       "Also supports optionally iterating or breaking of the file\n",
       "into chunks.\n",
       "\n",
       "Additional help can be found in the online docs for\n",
       "`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "filepath_or_buffer : str, path object or file-like object\n",
       "    Any valid string path is acceptable. The string could be a URL. Valid\n",
       "    URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is\n",
       "    expected. A local file could be: file://localhost/path/to/table.csv.\n",
       "\n",
       "    If you want to pass in a path object, pandas accepts any ``os.PathLike``.\n",
       "\n",
       "    By file-like object, we refer to objects with a ``read()`` method, such as\n",
       "    a file handler (e.g. via builtin ``open`` function) or ``StringIO``.\n",
       "sep : str, default ','\n",
       "    Delimiter to use. If sep is None, the C engine cannot automatically detect\n",
       "    the separator, but the Python parsing engine can, meaning the latter will\n",
       "    be used and automatically detect the separator by Python's builtin sniffer\n",
       "    tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n",
       "    different from ``'\\s+'`` will be interpreted as regular expressions and\n",
       "    will also force the use of the Python parsing engine. Note that regex\n",
       "    delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\n",
       "delimiter : str, default ``None``\n",
       "    Alias for sep.\n",
       "header : int, list of int, default 'infer'\n",
       "    Row number(s) to use as the column names, and the start of the\n",
       "    data.  Default behavior is to infer the column names: if no names\n",
       "    are passed the behavior is identical to ``header=0`` and column\n",
       "    names are inferred from the first line of the file, if column\n",
       "    names are passed explicitly then the behavior is identical to\n",
       "    ``header=None``. Explicitly pass ``header=0`` to be able to\n",
       "    replace existing names. The header can be a list of integers that\n",
       "    specify row locations for a multi-index on the columns\n",
       "    e.g. [0,1,3]. Intervening rows that are not specified will be\n",
       "    skipped (e.g. 2 in this example is skipped). Note that this\n",
       "    parameter ignores commented lines and empty lines if\n",
       "    ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n",
       "    data rather than the first line of the file.\n",
       "names : array-like, optional\n",
       "    List of column names to use. If the file contains a header row,\n",
       "    then you should explicitly pass ``header=0`` to override the column names.\n",
       "    Duplicates in this list are not allowed.\n",
       "index_col : int, str, sequence of int / str, or False, default ``None``\n",
       "  Column(s) to use as the row labels of the ``DataFrame``, either given as\n",
       "  string name or column index. If a sequence of int / str is given, a\n",
       "  MultiIndex is used.\n",
       "\n",
       "  Note: ``index_col=False`` can be used to force pandas to *not* use the first\n",
       "  column as the index, e.g. when you have a malformed file with delimiters at\n",
       "  the end of each line.\n",
       "usecols : list-like or callable, optional\n",
       "    Return a subset of the columns. If list-like, all elements must either\n",
       "    be positional (i.e. integer indices into the document columns) or strings\n",
       "    that correspond to column names provided either by the user in `names` or\n",
       "    inferred from the document header row(s). For example, a valid list-like\n",
       "    `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n",
       "    Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n",
       "    To instantiate a DataFrame from ``data`` with element order preserved use\n",
       "    ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n",
       "    in ``['foo', 'bar']`` order or\n",
       "    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n",
       "    for ``['bar', 'foo']`` order.\n",
       "\n",
       "    If callable, the callable function will be evaluated against the column\n",
       "    names, returning names where the callable function evaluates to True. An\n",
       "    example of a valid callable argument would be ``lambda x: x.upper() in\n",
       "    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n",
       "    parsing time and lower memory usage.\n",
       "squeeze : bool, default False\n",
       "    If the parsed data only contains one column then return a Series.\n",
       "prefix : str, optional\n",
       "    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
       "mangle_dupe_cols : bool, default True\n",
       "    Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
       "    'X'...'X'. Passing in False will cause data to be overwritten if there\n",
       "    are duplicate names in the columns.\n",
       "dtype : Type name or dict of column -> type, optional\n",
       "    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n",
       "    'c': 'Int64'}\n",
       "    Use `str` or `object` together with suitable `na_values` settings\n",
       "    to preserve and not interpret dtype.\n",
       "    If converters are specified, they will be applied INSTEAD\n",
       "    of dtype conversion.\n",
       "engine : {'c', 'python'}, optional\n",
       "    Parser engine to use. The C engine is faster while the python engine is\n",
       "    currently more feature-complete.\n",
       "converters : dict, optional\n",
       "    Dict of functions for converting values in certain columns. Keys can either\n",
       "    be integers or column labels.\n",
       "true_values : list, optional\n",
       "    Values to consider as True.\n",
       "false_values : list, optional\n",
       "    Values to consider as False.\n",
       "skipinitialspace : bool, default False\n",
       "    Skip spaces after delimiter.\n",
       "skiprows : list-like, int or callable, optional\n",
       "    Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
       "    at the start of the file.\n",
       "\n",
       "    If callable, the callable function will be evaluated against the row\n",
       "    indices, returning True if the row should be skipped and False otherwise.\n",
       "    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n",
       "skipfooter : int, default 0\n",
       "    Number of lines at bottom of file to skip (Unsupported with engine='c').\n",
       "nrows : int, optional\n",
       "    Number of rows of file to read. Useful for reading pieces of large files.\n",
       "na_values : scalar, str, list-like, or dict, optional\n",
       "    Additional strings to recognize as NA/NaN. If dict passed, specific\n",
       "    per-column NA values.  By default the following values are interpreted as\n",
       "    NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
       "    '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',\n",
       "    'nan', 'null'.\n",
       "keep_default_na : bool, default True\n",
       "    Whether or not to include the default NaN values when parsing the data.\n",
       "    Depending on whether `na_values` is passed in, the behavior is as follows:\n",
       "\n",
       "    * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
       "      is appended to the default NaN values used for parsing.\n",
       "    * If `keep_default_na` is True, and `na_values` are not specified, only\n",
       "      the default NaN values are used for parsing.\n",
       "    * If `keep_default_na` is False, and `na_values` are specified, only\n",
       "      the NaN values specified `na_values` are used for parsing.\n",
       "    * If `keep_default_na` is False, and `na_values` are not specified, no\n",
       "      strings will be parsed as NaN.\n",
       "\n",
       "    Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
       "    `na_values` parameters will be ignored.\n",
       "na_filter : bool, default True\n",
       "    Detect missing value markers (empty strings and the value of na_values). In\n",
       "    data without any NAs, passing na_filter=False can improve the performance\n",
       "    of reading a large file.\n",
       "verbose : bool, default False\n",
       "    Indicate number of NA values placed in non-numeric columns.\n",
       "skip_blank_lines : bool, default True\n",
       "    If True, skip over blank lines rather than interpreting as NaN values.\n",
       "parse_dates : bool or list of int or names or list of lists or dict, default False\n",
       "    The behavior is as follows:\n",
       "\n",
       "    * boolean. If True -> try parsing the index.\n",
       "    * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
       "      each as a separate date column.\n",
       "    * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
       "      a single date column.\n",
       "    * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n",
       "      result 'foo'\n",
       "\n",
       "    If a column or index cannot be represented as an array of datetimes,\n",
       "    say because of an unparseable value or a mixture of timezones, the column\n",
       "    or index will be returned unaltered as an object data type. For\n",
       "    non-standard datetime parsing, use ``pd.to_datetime`` after\n",
       "    ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n",
       "    specify ``date_parser`` to be a partially-applied\n",
       "    :func:`pandas.to_datetime` with ``utc=True``. See\n",
       "    :ref:`io.csv.mixed_timezones` for more.\n",
       "\n",
       "    Note: A fast-path exists for iso8601-formatted dates.\n",
       "infer_datetime_format : bool, default False\n",
       "    If True and `parse_dates` is enabled, pandas will attempt to infer the\n",
       "    format of the datetime strings in the columns, and if it can be inferred,\n",
       "    switch to a faster method of parsing them. In some cases this can increase\n",
       "    the parsing speed by 5-10x.\n",
       "keep_date_col : bool, default False\n",
       "    If True and `parse_dates` specifies combining multiple columns then\n",
       "    keep the original columns.\n",
       "date_parser : function, optional\n",
       "    Function to use for converting a sequence of string columns to an array of\n",
       "    datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
       "    conversion. Pandas will try to call `date_parser` in three different ways,\n",
       "    advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
       "    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
       "    string values from the columns defined by `parse_dates` into a single array\n",
       "    and pass that; and 3) call `date_parser` once for each row using one or\n",
       "    more strings (corresponding to the columns defined by `parse_dates`) as\n",
       "    arguments.\n",
       "dayfirst : bool, default False\n",
       "    DD/MM format dates, international and European format.\n",
       "cache_dates : bool, default True\n",
       "    If True, use a cache of unique, converted dates to apply the datetime\n",
       "    conversion. May produce significant speed-up when parsing duplicate\n",
       "    date strings, especially ones with timezone offsets.\n",
       "\n",
       "    .. versionadded:: 0.25.0\n",
       "iterator : bool, default False\n",
       "    Return TextFileReader object for iteration or getting chunks with\n",
       "    ``get_chunk()``.\n",
       "chunksize : int, optional\n",
       "    Return TextFileReader object for iteration.\n",
       "    See the `IO Tools docs\n",
       "    <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_\n",
       "    for more information on ``iterator`` and ``chunksize``.\n",
       "compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n",
       "    For on-the-fly decompression of on-disk data. If 'infer' and\n",
       "    `filepath_or_buffer` is path-like, then detect compression from the\n",
       "    following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n",
       "    decompression). If using 'zip', the ZIP file must contain only one data\n",
       "    file to be read in. Set to None for no decompression.\n",
       "thousands : str, optional\n",
       "    Thousands separator.\n",
       "decimal : str, default '.'\n",
       "    Character to recognize as decimal point (e.g. use ',' for European data).\n",
       "lineterminator : str (length 1), optional\n",
       "    Character to break file into lines. Only valid with C parser.\n",
       "quotechar : str (length 1), optional\n",
       "    The character used to denote the start and end of a quoted item. Quoted\n",
       "    items can include the delimiter and it will be ignored.\n",
       "quoting : int or csv.QUOTE_* instance, default 0\n",
       "    Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
       "    QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
       "doublequote : bool, default ``True``\n",
       "   When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n",
       "   whether or not to interpret two consecutive quotechar elements INSIDE a\n",
       "   field as a single ``quotechar`` element.\n",
       "escapechar : str (length 1), optional\n",
       "    One-character string used to escape other characters.\n",
       "comment : str, optional\n",
       "    Indicates remainder of line should not be parsed. If found at the beginning\n",
       "    of a line, the line will be ignored altogether. This parameter must be a\n",
       "    single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
       "    fully commented lines are ignored by the parameter `header` but not by\n",
       "    `skiprows`. For example, if ``comment='#'``, parsing\n",
       "    ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n",
       "    treated as the header.\n",
       "encoding : str, optional\n",
       "    Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
       "    standard encodings\n",
       "    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .\n",
       "dialect : str or csv.Dialect, optional\n",
       "    If provided, this parameter will override values (default or not) for the\n",
       "    following parameters: `delimiter`, `doublequote`, `escapechar`,\n",
       "    `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n",
       "    override values, a ParserWarning will be issued. See csv.Dialect\n",
       "    documentation for more details.\n",
       "error_bad_lines : bool, default True\n",
       "    Lines with too many fields (e.g. a csv line with too many commas) will by\n",
       "    default cause an exception to be raised, and no DataFrame will be returned.\n",
       "    If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
       "    returned.\n",
       "warn_bad_lines : bool, default True\n",
       "    If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
       "    \"bad line\" will be output.\n",
       "delim_whitespace : bool, default False\n",
       "    Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be\n",
       "    used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n",
       "    is set to True, nothing should be passed in for the ``delimiter``\n",
       "    parameter.\n",
       "low_memory : bool, default True\n",
       "    Internally process the file in chunks, resulting in lower memory use\n",
       "    while parsing, but possibly mixed type inference.  To ensure no mixed\n",
       "    types either set False, or specify the type with the `dtype` parameter.\n",
       "    Note that the entire file is read into a single DataFrame regardless,\n",
       "    use the `chunksize` or `iterator` parameter to return the data in chunks.\n",
       "    (Only valid with C parser).\n",
       "memory_map : bool, default False\n",
       "    If a filepath is provided for `filepath_or_buffer`, map the file object\n",
       "    directly onto memory and access the data directly from there. Using this\n",
       "    option can improve performance because there is no longer any I/O overhead.\n",
       "float_precision : str, optional\n",
       "    Specifies which converter the C engine should use for floating-point\n",
       "    values. The options are `None` for the ordinary converter,\n",
       "    `high` for the high-precision converter, and `round_trip` for the\n",
       "    round-trip converter.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame or TextParser\n",
       "    A comma-separated values (csv) file is returned as two-dimensional\n",
       "    data structure with labeled axes.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.\n",
       "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
       "read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> pd.read_csv('data.csv')  # doctest: +SKIP\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/pangeo/lib/python3.8/site-packages/pandas/io/parsers.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "pd.read_csv?"
   ]
  },
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   "cell_type": "markdown",
   "id": "powerful-chain",
   "metadata": {},
   "source": [
    "For more information, see the [documentation](https://pandas.pydata.org/)."
   ]
  },
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   "execution_count": null,
   "id": "charitable-philippines",
   "metadata": {},
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