python/Pandas-Dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "Pandas is an opensource library built on top of NumPy. It allows for fast analysis and data cleaning / preparation. Pandas supports Data Frames like in R, where you can have named rows and columns.\n",
    "\n",
    "Pandas is great for performance and productivity, has built-in visualization features, and works with a wide variety of data sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "stats = pd.read_csv('../data/Birth-Internet-Income-Per-Country.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Pandas series is a lot like a Numpy array, but it has an index and a type that you can manage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "labels = [ 'a', 'b', 'c' ]\n",
    "my_data = [ 10, 20, 30 ]\n",
    "arr = np.array( my_data )\n",
    "d = { 'a': 10, 'b': 20, 'c': 30 }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=my_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( data = my_data, index = labels )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( arr, labels )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( d )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( labels )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      <built-in function sum>\n",
       "1    <built-in function print>\n",
       "2      <built-in function len>\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( data=[sum, print, len ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser1 = pd.Series([1,2,3,4], ['USA', 'Germany', 'USSR', 'Japan'])\n",
    "ser2 = pd.Series([1,2,5,4], ['USA', 'Germany', 'Italy', 'Japan'])\n",
    "ser3 = pd.Series( data = labels )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA        1\n",
      "Germany    2\n",
      "USSR       3\n",
      "Japan      4\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print( ser1 )\n",
    "ser1['USA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ser3)\n",
    "ser3[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany    4.0\n",
       "Italy      NaN\n",
       "Japan      8.0\n",
       "USA        2.0\n",
       "USSR       NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 + ser2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A DataFrame is basically just a bunch of Series strung together. In this example, each column (`W`, `X`, `Y`, and `Z`) is a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.random import randn\n",
    "np.random.seed(101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Using Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = randn(5, 4)\n",
    "row_index = ['A', 'B', 'C', 'D', 'E']\n",
    "col_names = ['W', 'X', 'Y', 'Z']\n",
    "\n",
    "df = pd.DataFrame( data=data, index=row_index, columns=col_names )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# The whole think is a DataFrame\n",
    "print( type( df ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# Each row is a Series\n",
    "row_w = df['W']\n",
    "print( type( row_w ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Using a Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  1  5  7\n",
       "1  2  6  8\n",
       "2  3  7  9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = { 'A': [1,2,3], 'B':[5,6,7], 'C':[7,8,9] }\n",
    "dict_df = pd.DataFrame( d )\n",
    "dict_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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",
       "\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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                   Aruba          ABW      10.244            78.9   \n",
       "1             Afghanistan          AFG      35.253             5.9   \n",
       "2                  Angola          AGO      45.985            19.1   \n",
       "3                 Albania          ALB      12.877            57.2   \n",
       "4    United Arab Emirates          ARE      11.044            88.0   \n",
       "..                    ...          ...         ...             ...   \n",
       "190           Yemen, Rep.          YEM      32.947            20.0   \n",
       "191          South Africa          ZAF      20.850            46.5   \n",
       "192      Congo, Dem. Rep.          COD      42.394             2.2   \n",
       "193                Zambia          ZMB      40.471            15.4   \n",
       "194              Zimbabwe          ZWE      35.715            18.5   \n",
       "\n",
       "            Income Group  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "..                   ...  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Give the columns names without spaces\n",
    "stats.columns = [ 'CountryName', 'CountryCode', 'BirthRate', 'InternetUsers', 'IncomeGroup' ]\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len( stats )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `shape` attribute gives a tuple of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 5)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CountryName', 'CountryCode', 'BirthRate', 'InternetUsers',\n",
       "       'IncomeGroup'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len( stats.columns )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Head and Tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>17.716</td>\n",
       "      <td>59.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>13.308</td>\n",
       "      <td>41.9</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>16.447</td>\n",
       "      <td>63.4</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "5             Argentina         ARG     17.716           59.9   \n",
       "6               Armenia         ARM     13.308           41.9   \n",
       "7   Antigua and Barbuda         ATG     16.447           63.4   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  \n",
       "5          High income  \n",
       "6  Lower middle income  \n",
       "7          High income  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head( 8 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>12.500</td>\n",
       "      <td>54.17</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Belize</td>\n",
       "      <td>BLZ</td>\n",
       "      <td>23.092</td>\n",
       "      <td>33.60</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>BMU</td>\n",
       "      <td>10.400</td>\n",
       "      <td>95.30</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>BOL</td>\n",
       "      <td>24.236</td>\n",
       "      <td>36.94</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>14.931</td>\n",
       "      <td>51.04</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>BRB</td>\n",
       "      <td>12.188</td>\n",
       "      <td>73.00</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>BRN</td>\n",
       "      <td>16.405</td>\n",
       "      <td>64.50</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "20            Belarus         BLR     12.500          54.17   \n",
       "21             Belize         BLZ     23.092          33.60   \n",
       "22            Bermuda         BMU     10.400          95.30   \n",
       "23            Bolivia         BOL     24.236          36.94   \n",
       "24             Brazil         BRA     14.931          51.04   \n",
       "25           Barbados         BRB     12.188          73.00   \n",
       "26  Brunei Darussalam         BRN     16.405          64.50   \n",
       "\n",
       "            IncomeGroup  \n",
       "20  Upper middle income  \n",
       "21  Upper middle income  \n",
       "22          High income  \n",
       "23  Lower middle income  \n",
       "24  Upper middle income  \n",
       "25          High income  \n",
       "26          High income  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 20:100 ].head( 7 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "190       Yemen, Rep.         YEM     32.947           20.0   \n",
       "191      South Africa         ZAF     20.850           46.5   \n",
       "192  Congo, Dem. Rep.         COD     42.394            2.2   \n",
       "193            Zambia         ZMB     40.471           15.4   \n",
       "194          Zimbabwe         ZWE     35.715           18.5   \n",
       "\n",
       "             IncomeGroup  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   CountryName    195 non-null    object \n",
      " 1   CountryCode    195 non-null    object \n",
      " 2   BirthRate      195 non-null    float64\n",
      " 3   InternetUsers  195 non-null    float64\n",
      " 4   IncomeGroup    195 non-null    object \n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 7.7+ KB\n"
     ]
    }
   ],
   "source": [
    "stats.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### describe()\n",
    "\n",
    "Looks at all numeric fields and gives statistical analysis across all observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.469928</td>\n",
       "      <td>42.076471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.605467</td>\n",
       "      <td>29.030788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.120500</td>\n",
       "      <td>14.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.680000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.759500</td>\n",
       "      <td>66.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.661000</td>\n",
       "      <td>96.546800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BirthRate  InternetUsers\n",
       "count  195.000000     195.000000\n",
       "mean    21.469928      42.076471\n",
       "std     10.605467      29.030788\n",
       "min      7.900000       0.900000\n",
       "25%     12.120500      14.520000\n",
       "50%     19.680000      41.000000\n",
       "75%     29.759500      66.225000\n",
       "max     49.661000      96.546800"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BirthRate</th>\n",
       "      <td>195.0</td>\n",
       "      <td>21.469928</td>\n",
       "      <td>10.605467</td>\n",
       "      <td>7.9</td>\n",
       "      <td>12.1205</td>\n",
       "      <td>19.68</td>\n",
       "      <td>29.7595</td>\n",
       "      <td>49.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetUsers</th>\n",
       "      <td>195.0</td>\n",
       "      <td>42.076471</td>\n",
       "      <td>29.030788</td>\n",
       "      <td>0.9</td>\n",
       "      <td>14.5200</td>\n",
       "      <td>41.00</td>\n",
       "      <td>66.2250</td>\n",
       "      <td>96.5468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count       mean        std  min      25%    50%      75%  \\\n",
       "BirthRate      195.0  21.469928  10.605467  7.9  12.1205  19.68  29.7595   \n",
       "InternetUsers  195.0  42.076471  29.030788  0.9  14.5200  41.00  66.2250   \n",
       "\n",
       "                   max  \n",
       "BirthRate      49.6610  \n",
       "InternetUsers  96.5468  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unqiue Values in a Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['High income' 'Low income' 'Upper middle income' 'Lower middle income']\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print( stats['IncomeGroup'].unique() )\n",
    "print( stats['IncomeGroup'].nunique() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Value Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High income            67\n",
       "Lower middle income    50\n",
       "Upper middle income    48\n",
       "Low income             30\n",
       "Name: IncomeGroup, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 'IncomeGroup' ].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryName    CountryCode  BirthRate  InternetUsers  IncomeGroup        \n",
       "Zimbabwe       ZWE          35.715     18.5000        Low income             1\n",
       "Denmark        DNK          10.000     94.6297        High income            1\n",
       "Guam           GUM          17.389     65.4000        High income            1\n",
       "Grenada        GRD          19.334     35.0000        Upper middle income    1\n",
       "Greenland      GRL          14.500     65.8000        High income            1\n",
       "                                                                            ..\n",
       "New Zealand    NZL          13.120     82.7800        High income            1\n",
       "New Caledonia  NCL          17.000     66.0000        High income            1\n",
       "Netherlands    NLD          10.200     93.9564        High income            1\n",
       "Nepal          NPL          20.923     13.3000        Low income             1\n",
       "Afghanistan    AFG          35.253     5.9000         Low income             1\n",
       "Length: 195, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing / Filtering Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing a Single Element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using `loc`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.706849839399938"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['A','W'] # row, column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using `iloc`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18869530944922425"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3, 0] # row, column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bracket Notation**\n",
    "\n",
    "Columns are accessed by name, rows by number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18869530944922425"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W']['D'] #[row label][column label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18869530944922425"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W'][3] # [row label][column index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bracket Range Notation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D    0.188695\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[3:4]['W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D    0.188695\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W'][3:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing a Single Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access columns by name (assuming they don't have spaces):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Aruba\n",
       "1               Afghanistan\n",
       "2                    Angola\n",
       "3                   Albania\n",
       "4      United Arab Emirates\n",
       "               ...         \n",
       "190             Yemen, Rep.\n",
       "191            South Africa\n",
       "192        Congo, Dem. Rep.\n",
       "193                  Zambia\n",
       "194                Zimbabwe\n",
       "Name: CountryName, Length: 195, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.CountryName"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, it's best to just access them using the bracket notation with the column name in quotes. This convention prevents confusion when you have columns whose names collide with the methods available on the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Aruba\n",
       "1               Afghanistan\n",
       "2                    Angola\n",
       "3                   Albania\n",
       "4      United Arab Emirates\n",
       "               ...         \n",
       "190             Yemen, Rep.\n",
       "191            South Africa\n",
       "192        Congo, Dem. Rep.\n",
       "193                  Zambia\n",
       "194                Zimbabwe\n",
       "Name: CountryName, Length: 195, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 'CountryName' ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing Rows with `:` (Range Operator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryName CountryCode  BirthRate  InternetUsers IncomeGroup\n",
       "1  Afghanistan         AFG     35.253            5.9  Low income"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the range operator, if you select a single row, you'll get a DataFrame back. This is different than the behavior with `loc`, which we'll see below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type( stats[1:2 ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.900000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZE</td>\n",
       "      <td>18.300</td>\n",
       "      <td>58.700000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>12.500</td>\n",
       "      <td>54.170000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>10.900</td>\n",
       "      <td>85.800000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>CRI</td>\n",
       "      <td>15.022</td>\n",
       "      <td>45.960000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>ECU</td>\n",
       "      <td>21.070</td>\n",
       "      <td>40.353684</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>GAB</td>\n",
       "      <td>30.555</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Greenland</td>\n",
       "      <td>GRL</td>\n",
       "      <td>14.500</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>20.291</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>22.730</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryName CountryCode  BirthRate  InternetUsers          IncomeGroup\n",
       "0        Aruba         ABW     10.244      78.900000          High income\n",
       "10  Azerbaijan         AZE     18.300      58.700000  Upper middle income\n",
       "20     Belarus         BLR     12.500      54.170000  Upper middle income\n",
       "30      Canada         CAN     10.900      85.800000          High income\n",
       "40  Costa Rica         CRI     15.022      45.960000  Upper middle income\n",
       "50     Ecuador         ECU     21.070      40.353684  Upper middle income\n",
       "60       Gabon         GAB     30.555       9.200000  Upper middle income\n",
       "70   Greenland         GRL     14.500      65.800000          High income\n",
       "80       India         IND     20.291      15.100000  Lower middle income\n",
       "90  Kazakhstan         KAZ     22.730      54.000000  Upper middle income"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[0:100:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing Rows with `loc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `loc` to access rows by range, or by label names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "2  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.loc[0:5:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your dataframe has row labels, you can also use those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    0.302665\n",
       "X    1.693723\n",
       "Y   -1.706086\n",
       "Z   -1.159119\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame( randn(5, 4), ['A', 'B', 'C', 'D', 'E'], ['W', 'X', 'Y', 'Z'])\n",
    "\n",
    "df.loc['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you select just a single row, you'll get a Series back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type( df.loc[ 'A' ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, if you select multiple rows, you'll get a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665  1.693723 -1.706086 -1.159119\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[ 'A', 'C', 'E'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type( df.loc[[ 'A', 'C', 'E'] ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing Rows with `iloc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `iloc` to access rows by range or by index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryName        Argentina\n",
       "CountryCode              ARG\n",
       "BirthRate             17.716\n",
       "InternetUsers           59.9\n",
       "IncomeGroup      High income\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.iloc[ 5 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type( stats.iloc[ 5 ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing a Subset of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CountryName CountryCode\n",
       "0                   Aruba         ABW\n",
       "1             Afghanistan         AFG\n",
       "2                  Angola         AGO\n",
       "3                 Albania         ALB\n",
       "4    United Arab Emirates         ARE\n",
       "..                    ...         ...\n",
       "190           Yemen, Rep.         YEM\n",
       "191          South Africa         ZAF\n",
       "192      Congo, Dem. Rep.         COD\n",
       "193                Zambia         ZMB\n",
       "194              Zimbabwe         ZWE\n",
       "\n",
       "[195 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [ 'CountryName', 'CountryCode' ]\n",
    "stats[ columns ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CountryName CountryCode\n",
       "0                   Aruba         ABW\n",
       "1             Afghanistan         AFG\n",
       "2                  Angola         AGO\n",
       "3                 Albania         ALB\n",
       "4    United Arab Emirates         ARE\n",
       "..                    ...         ...\n",
       "190           Yemen, Rep.         YEM\n",
       "191          South Africa         ZAF\n",
       "192      Congo, Dem. Rep.         COD\n",
       "193                Zambia         ZMB\n",
       "194              Zimbabwe         ZWE\n",
       "\n",
       "[195 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ [ 'CountryName', 'CountryCode' ] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing Rows and Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using names and ranges**\n",
    "\n",
    "Since string lists are applied to columns and ranges are applied to rows, the order doesn't matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode\n",
       "0                 Aruba         ABW\n",
       "1           Afghanistan         AFG\n",
       "2                Angola         AGO\n",
       "3               Albania         ALB\n",
       "4  United Arab Emirates         ARE"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ [ 'CountryName', 'CountryCode' ] ][ 0:5 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode\n",
       "0                 Aruba         ABW\n",
       "1           Afghanistan         AFG\n",
       "2                Angola         AGO\n",
       "3               Albania         ALB\n",
       "4  United Arab Emirates         ARE"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 0:5 ][ [ 'CountryName', 'CountryCode' ] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using `loc`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If both the rows and columns are named, you can use `loc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Y         Z\n",
       "B  0.166905  0.184502\n",
       "C  0.638787  0.329646\n",
       "D -0.943406  0.484752"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['B':'D', 'Y':'Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>W</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Z\n",
       "A  0.302665 -1.159119\n",
       "C  0.807706  0.329646"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ [ 'A','C' ], [ 'W', 'Z' ] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using `iloc`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BirthRate  InternetUsers\n",
       "2     45.985           19.1\n",
       "3     12.877           57.2"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [ row:range, col:range ]\n",
    "stats.iloc[ 2:4, 2:4 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>CountryCode</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryCode          IncomeGroup\n",
       "1         AFG           Low income\n",
       "3         ALB  Upper middle income"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [[row,list], [col,list]]\n",
    "stats.iloc[ [ 1, 3 ], [ 1, 4 ] ] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As long as all the columns of a given DataFrame are the same type, you can use a conditional operator and get a DataFrame of conditionals, where each cell in the output DataFrame reflects the output of the given conditional operation applied to the input DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      W      X     Y      Z\n",
       "A  True  False  True   True\n",
       "B  True   True  True   True\n",
       "C  True   True  True   True\n",
       "D  True   True  True   True\n",
       "E  True  False  True  False"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booldf = df < 1\n",
    "booldf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we pass the boolean DataFrame back into the original DataFrame, it will filter the original DataFrame. The true values will result in the number and the false values will result in a `NaN` in this case, since the original values were all of type int. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665       NaN -1.706086 -1.159119\n",
       "B -0.134841  0.390528  0.166905  0.184502\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "D -0.497104 -0.754070 -0.943406  0.484752\n",
       "E -0.116773       NaN  0.238127       NaN"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ booldf ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also do conditional selection on a single column. It will return a Series of type `bool`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: InternetUsers, dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = stats.InternetUsers < 10\n",
    "print( type( filter ))\n",
    "filter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you filter using a Series of type `bool`, the resulting DataFrame will include rows where the Series was `True` and exclude rows where the Series was `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.90</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Benin</td>\n",
       "      <td>BEN</td>\n",
       "      <td>36.440</td>\n",
       "      <td>4.90</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BFA</td>\n",
       "      <td>40.551</td>\n",
       "      <td>9.10</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BGD</td>\n",
       "      <td>20.142</td>\n",
       "      <td>6.63</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CountryName CountryCode  BirthRate  InternetUsers          IncomeGroup\n",
       "1    Afghanistan         AFG     35.253           5.90           Low income\n",
       "11       Burundi         BDI     44.151           1.30           Low income\n",
       "13         Benin         BEN     36.440           4.90           Low income\n",
       "14  Burkina Faso         BFA     40.551           9.10           Low income\n",
       "15    Bangladesh         BGD     20.142           6.63  Lower middle income"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pass in a frame of booleans that will be used to filter out the results\n",
    "filter = stats.InternetUsers < 10\n",
    "stats[ filter ].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.90</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Benin</td>\n",
       "      <td>BEN</td>\n",
       "      <td>36.440</td>\n",
       "      <td>4.90</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BFA</td>\n",
       "      <td>40.551</td>\n",
       "      <td>9.10</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BGD</td>\n",
       "      <td>20.142</td>\n",
       "      <td>6.63</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CountryName CountryCode  BirthRate  InternetUsers          IncomeGroup\n",
       "1    Afghanistan         AFG     35.253           5.90           Low income\n",
       "11       Burundi         BDI     44.151           1.30           Low income\n",
       "13         Benin         BEN     36.440           4.90           Low income\n",
       "14  Burkina Faso         BFA     40.551           9.10           Low income\n",
       "15    Bangladesh         BGD     20.142           6.63  Lower middle income"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pass in a frame of booleans that will be used to filter out the results\n",
    "stats[ stats.InternetUsers < 10 ].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BFA</td>\n",
       "      <td>40.551</td>\n",
       "      <td>9.1</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Mali</td>\n",
       "      <td>MLI</td>\n",
       "      <td>44.138</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Niger</td>\n",
       "      <td>NER</td>\n",
       "      <td>49.661</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>SOM</td>\n",
       "      <td>43.891</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CountryName CountryCode  BirthRate  InternetUsers IncomeGroup\n",
       "11        Burundi         BDI     44.151            1.3  Low income\n",
       "14   Burkina Faso         BFA     40.551            9.1  Low income\n",
       "115          Mali         MLI     44.138            3.5  Low income\n",
       "127         Niger         NER     49.661            1.7  Low income\n",
       "156       Somalia         SOM     43.891            1.5  Low income"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Have to use the elementwise `&` and not `and`\n",
    "stats[ ( stats.InternetUsers < 10 ) & ( stats.BirthRate > 40 ) ].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Mali</td>\n",
       "      <td>MLI</td>\n",
       "      <td>44.138</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CountryName CountryCode  BirthRate  InternetUsers IncomeGroup\n",
       "115        Mali         MLI     44.138            3.5  Low income"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ stats['CountryName'] == 'Mali' ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filtering with a conditional selection, you just get a DataFrame back, so you can continue to operate on the results. Here, we just grab a specific set of column from the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>CountryCode</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>MLI</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CountryCode IncomeGroup\n",
       "115         MLI  Low income"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ stats[ 'CountryName' ] == 'Mali' ][ [ 'CountryCode', 'IncomeGroup' ] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `at()` and `iat()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.90</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZE</td>\n",
       "      <td>18.300</td>\n",
       "      <td>58.70</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>12.500</td>\n",
       "      <td>54.17</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>10.900</td>\n",
       "      <td>85.80</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>CRI</td>\n",
       "      <td>15.022</td>\n",
       "      <td>45.96</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryName CountryCode  BirthRate  InternetUsers          IncomeGroup\n",
       "0        Aruba         ABW     10.244          78.90          High income\n",
       "10  Azerbaijan         AZE     18.300          58.70  Upper middle income\n",
       "20     Belarus         BLR     12.500          54.17  Upper middle income\n",
       "30      Canada         CAN     10.900          85.80          High income\n",
       "40  Costa Rica         CRI     15.022          45.96  Upper middle income"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[::10].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AZE'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[::10].iat[ 1, 1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.9"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[::10].at[ 30, 'BirthRate' ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modifying Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CountryName', 'CountryCode', 'BirthRate', 'InternetUsers',\n",
       "       'IncomeGroup'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.columns = [ 'CountryName', 'CountryCode', 'BirthRate', 'InternetUsers', 'IncomeGroup' ]\n",
    "stats.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add a column using a column (i.e. series) operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyNewColumn</th>\n",
       "      <th>MyNewColumn2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "      <td>808.2516</td>\n",
       "      <td>51.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>207.9927</td>\n",
       "      <td>176.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>878.3135</td>\n",
       "      <td>229.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>736.5644</td>\n",
       "      <td>64.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>971.8720</td>\n",
       "      <td>55.220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  MyNewColumn  MyNewColumn2  \n",
       "0          High income     808.2516        51.220  \n",
       "1           Low income     207.9927       176.265  \n",
       "2  Upper middle income     878.3135       229.925  \n",
       "3  Upper middle income     736.5644        64.385  \n",
       "4          High income     971.8720        55.220  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 'MyNewColumn' ] = stats.BirthRate * stats.InternetUsers\n",
    "stats[ 'MyNewColumn2' ] = stats.BirthRate * 5\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also add a column by creating a new series and assigning it to a column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "      <th>MyNewColumn</th>\n",
       "      <th>MyNewColumn2</th>\n",
       "      <th>ColumnAsSeries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "      <td>808.2516</td>\n",
       "      <td>51.220</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "      <td>207.9927</td>\n",
       "      <td>176.265</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>878.3135</td>\n",
       "      <td>229.925</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>736.5644</td>\n",
       "      <td>64.385</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "      <td>971.8720</td>\n",
       "      <td>55.220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>658.9400</td>\n",
       "      <td>164.735</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>969.5250</td>\n",
       "      <td>104.250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "      <td>93.2668</td>\n",
       "      <td>211.970</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>623.2534</td>\n",
       "      <td>202.355</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "      <td>660.7275</td>\n",
       "      <td>178.575</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                   Aruba         ABW     10.244           78.9   \n",
       "1             Afghanistan         AFG     35.253            5.9   \n",
       "2                  Angola         AGO     45.985           19.1   \n",
       "3                 Albania         ALB     12.877           57.2   \n",
       "4    United Arab Emirates         ARE     11.044           88.0   \n",
       "..                    ...         ...        ...            ...   \n",
       "190           Yemen, Rep.         YEM     32.947           20.0   \n",
       "191          South Africa         ZAF     20.850           46.5   \n",
       "192      Congo, Dem. Rep.         COD     42.394            2.2   \n",
       "193                Zambia         ZMB     40.471           15.4   \n",
       "194              Zimbabwe         ZWE     35.715           18.5   \n",
       "\n",
       "             IncomeGroup  MyNewColumn  MyNewColumn2  ColumnAsSeries  \n",
       "0            High income     808.2516        51.220             1.0  \n",
       "1             Low income     207.9927       176.265             2.0  \n",
       "2    Upper middle income     878.3135       229.925             3.0  \n",
       "3    Upper middle income     736.5644        64.385             NaN  \n",
       "4            High income     971.8720        55.220             NaN  \n",
       "..                   ...          ...           ...             ...  \n",
       "190  Lower middle income     658.9400       164.735             NaN  \n",
       "191  Upper middle income     969.5250       104.250             NaN  \n",
       "192           Low income      93.2668       211.970             NaN  \n",
       "193  Lower middle income     623.2534       202.355             NaN  \n",
       "194           Low income     660.7275       178.575             NaN  \n",
       "\n",
       "[195 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 'ColumnAsSeries' ] = pd.Series( [1, 2, 3 ] )\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a Row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `loc` to reference a row and assign values to it.\n",
    "\n",
    "References:\n",
    "- [Add one row to pandas DataFrame](https://stackoverflow.com/questions/10715965/add-one-row-to-pandas-dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>lib</th>\n",
       "      <th>qty1</th>\n",
       "      <th>qty2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>name0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>name1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>name2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>name3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>name4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lib qty1 qty2\n",
       "0  name0    1    4\n",
       "1  name1    2    9\n",
       "2  name2    0    0\n",
       "3  name3    1    7\n",
       "4  name4    1    6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.random import randint\n",
    "\n",
    "add_row_df = pd.DataFrame(columns=['lib', 'qty1', 'qty2'])\n",
    "for i in range(5):\n",
    "    add_row_df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))\n",
    "    \n",
    "add_row_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing a Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use drop, but it won't happen \"in place\" by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   CountryName     195 non-null    object \n",
      " 1   CountryCode     195 non-null    object \n",
      " 2   BirthRate       195 non-null    float64\n",
      " 3   InternetUsers   195 non-null    float64\n",
      " 4   IncomeGroup     195 non-null    object \n",
      " 5   MyNewColumn     195 non-null    float64\n",
      " 6   MyNewColumn2    195 non-null    float64\n",
      " 7   ColumnAsSeries  3 non-null      float64\n",
      "dtypes: float64(5), object(3)\n",
      "memory usage: 12.3+ KB\n"
     ]
    }
   ],
   "source": [
    "stats[ 'MyNewColumn' ] = stats.BirthRate * stats.InternetUsers\n",
    "stats.drop( 'MyNewColumn', axis=1 )\n",
    "stats.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will effectively create a copy of the DataFrame, so you need to assign it back to the original data frame (or another variable) if you want the change to be persistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   CountryName     195 non-null    object \n",
      " 1   CountryCode     195 non-null    object \n",
      " 2   BirthRate       195 non-null    float64\n",
      " 3   InternetUsers   195 non-null    float64\n",
      " 4   IncomeGroup     195 non-null    object \n",
      " 5   MyNewColumn2    195 non-null    float64\n",
      " 6   ColumnAsSeries  3 non-null      float64\n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 10.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# axis=1 means the columns\n",
    "stats = stats.drop( 'MyNewColumn', axis=1 )\n",
    "stats.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another approach is to provide the `inplace` parameter, which obviates the need to assign it by applying the change directly to the dataframe you're operating on. You can also provide the `errors` argument and set it to `ignore` so that it doesn't throw an error if the column doesn't exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.drop( 'MyNewColumn', axis=1, inplace=True, errors='ignore' )\n",
    "stats.drop( 'MyNewColumn2', axis=1, inplace=True, errors='ignore' )\n",
    "stats.drop( 'ColumnAsSeries', axis=1, inplace=True, errors='ignore' )\n",
    "stats.drop( 'fooBarMadeUp', axis=1, inplace=True, errors='ignore' )\n",
    "df.drop( 'State', axis=1, inplace=True, errors='ignore' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665  1.693723 -1.706086 -1.159119\n",
       "B -0.134841  0.390528  0.166905  0.184502\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "D -0.497104 -0.754070 -0.943406  0.484752\n",
       "E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting an Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "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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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              W         X         Y         Z\n",
       "State                                        \n",
       "CA     0.302665  1.693723 -1.706086 -1.159119\n",
       "NY    -0.134841  0.390528  0.166905  0.184502\n",
       "TX     0.807706  0.072960  0.638787  0.329646\n",
       "FL    -0.497104 -0.754070 -0.943406  0.484752\n",
       "CO    -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column = 'CA NY TX FL CO'.split()\n",
    "df[ 'State' ] = new_column\n",
    "df.set_index( 'State' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping a Row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can drop a row by name as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>17.716</td>\n",
       "      <td>59.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "5             Argentina         ARG     17.716           59.9   \n",
       "\n",
       "           IncomeGroup  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  \n",
       "5          High income  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.drop( 0 ).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop defaults to `axis=0`, which applies the operation to rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>17.716</td>\n",
       "      <td>59.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "5             Argentina         ARG     17.716           59.9   \n",
       "\n",
       "           IncomeGroup  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  \n",
       "5          High income  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.drop(0, axis=0 ).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing and Duplicate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "1  2.0  NaN  2.0\n",
       "2  NaN  NaN  NaN\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = { 'A': [1,2,np.nan,1], 'B':[5,np.nan,np.nan,5], 'C':[1,2,np.nan,1] }\n",
    "missing_df = pd.DataFrame( d )\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df.dropna( how='any' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "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",
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "1  2.0  NaN  2.0\n",
       "2  NaN  NaN  NaN\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df.drop_duplicates( keep='last' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "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>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C\n",
       "0    1.0    5.0    1.0\n",
       "1    2.0  999.0    2.0\n",
       "2  999.0  999.0  999.0\n",
       "3    1.0    5.0    1.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df.fillna( 999 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting a Column Type to Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 559 entries, 0 to 558\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Film            559 non-null    object\n",
      " 1   Genre           559 non-null    object\n",
      " 2   CriticRating    559 non-null    int64 \n",
      " 3   AudienceRating  559 non-null    int64 \n",
      " 4   Budget          559 non-null    int64 \n",
      " 5   Year            559 non-null    int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 26.3+ KB\n"
     ]
    }
   ],
   "source": [
    "movies = pd.read_csv('../data/Movie-Ratings.csv')\n",
    "movies.columns = [ 'Film', 'Genre', 'CriticRating', 'AudienceRating', 'Budget', 'Year' ]\n",
    "\n",
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CriticRating</th>\n",
       "      <th>AudienceRating</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>559.000000</td>\n",
       "      <td>559.000000</td>\n",
       "      <td>559.000000</td>\n",
       "      <td>559.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.309481</td>\n",
       "      <td>58.744186</td>\n",
       "      <td>50.236136</td>\n",
       "      <td>2009.152057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.413091</td>\n",
       "      <td>16.826887</td>\n",
       "      <td>48.731817</td>\n",
       "      <td>1.362632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CriticRating  AudienceRating      Budget         Year\n",
       "count    559.000000      559.000000  559.000000   559.000000\n",
       "mean      47.309481       58.744186   50.236136  2009.152057\n",
       "std       26.413091       16.826887   48.731817     1.362632\n",
       "min        0.000000        0.000000    0.000000  2007.000000\n",
       "25%       25.000000       47.000000   20.000000  2008.000000\n",
       "50%       46.000000       58.000000   35.000000  2009.000000\n",
       "75%       70.000000       72.000000   65.000000  2010.000000\n",
       "max       97.000000       96.000000  300.000000  2011.000000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Year shows up because it is an int64\n",
    "movies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Comedy' 'Adventure' 'Action' 'Horror' 'Drama' 'Romance' 'Thriller']\n",
      "[2009 2008 2010 2007 2011]\n"
     ]
    }
   ],
   "source": [
    "print( movies.Genre.unique() )\n",
    "print( movies.Year.unique() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.Film = movies.Film.astype( 'category' )\n",
    "movies.Genre = movies.Genre.astype( 'category' )\n",
    "movies.Year = movies.Year.astype( 'category' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 559 entries, 0 to 558\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   Film            559 non-null    category\n",
      " 1   Genre           559 non-null    category\n",
      " 2   CriticRating    559 non-null    int64   \n",
      " 3   AudienceRating  559 non-null    int64   \n",
      " 4   Budget          559 non-null    int64   \n",
      " 5   Year            559 non-null    category\n",
      "dtypes: category(3), int64(3)\n",
      "memory usage: 40.3 KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Action', 'Adventure', 'Comedy', 'Drama', 'Horror', 'Romance',\n",
       "       'Thriller'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all the unique category values\n",
    "movies.Genre.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "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>CriticRating</th>\n",
       "      <th>AudienceRating</th>\n",
       "      <th>Budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>559.000000</td>\n",
       "      <td>559.000000</td>\n",
       "      <td>559.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.309481</td>\n",
       "      <td>58.744186</td>\n",
       "      <td>50.236136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.413091</td>\n",
       "      <td>16.826887</td>\n",
       "      <td>48.731817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CriticRating  AudienceRating      Budget\n",
       "count    559.000000      559.000000  559.000000\n",
       "mean      47.309481       58.744186   50.236136\n",
       "std       26.413091       16.826887   48.731817\n",
       "min        0.000000        0.000000    0.000000\n",
       "25%       25.000000       47.000000   20.000000\n",
       "50%       46.000000       58.000000   35.000000\n",
       "75%       70.000000       72.000000   65.000000\n",
       "max       97.000000       96.000000  300.000000"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Year will no longer show up because it is now a category\n",
    "movies.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging, Joining, and Concatenating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                           'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                           'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                           'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                           index=[0, 1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "concat_df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                           'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                           'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                           'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                           index=[4, 5, 6, 7]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                           'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                           'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                           'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                           index=[8, 9, 10, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df4 = pd.DataFrame({'E': ['E0', 'E1', 'E2', 'E3'],\n",
    "                           'F': ['F0', 'F1', 'F2', 'F3'],\n",
    "                           'G': ['G0', 'G1', 'G2', 'G3'],\n",
    "                           'H': ['H0', 'H1', 'H2', 'H3']},\n",
    "                           index=[0, 1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>D0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
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      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>A4</td>\n",
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       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D\n",
       "4  A4  B4  C4  D4\n",
       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6\n",
       "7  A7  B7  C7  D7"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "0    A0   B0   C0   D0\n",
       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "4    A4   B4   C4   D4\n",
       "5    A5   B5   C5   D5\n",
       "6    A6   B6   C6   D6\n",
       "7    A7   B7   C7   D7\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat( [concat_df1, concat_df2, concat_df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "      <td>E2</td>\n",
       "      <td>F2</td>\n",
       "      <td>G2</td>\n",
       "      <td>H2</td>\n",
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       "      <th>3</th>\n",
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       "    A   B   C   D   E   F   G   H\n",
       "0  A0  B0  C0  D0  E0  F0  G0  H0\n",
       "1  A1  B1  C1  D1  E1  F1  G1  H1\n",
       "2  A2  B2  C2  D2  E2  F2  G2  H2\n",
       "3  A3  B3  C3  D3  E3  F3  G3  H3"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat( objs=[concat_df1, concat_df4], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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       "      <td>G3</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\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>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
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       "      <td>D6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A7</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</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": [
       "      A    B    C    D    A    B    C    D    A    B    C    D    E    F    G  \\\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   E0   F0   G0   \n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   E1   F1   G1   \n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   E2   F2   G2   \n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   E3   F3   G3   \n",
       "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8  NaN  NaN  NaN   \n",
       "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9  NaN  NaN  NaN   \n",
       "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10  NaN  NaN  NaN   \n",
       "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11  NaN  NaN  NaN   \n",
       "\n",
       "      H  \n",
       "0    H0  \n",
       "1    H1  \n",
       "2    H2  \n",
       "3    H3  \n",
       "4   NaN  \n",
       "5   NaN  \n",
       "6   NaN  \n",
       "7   NaN  \n",
       "8   NaN  \n",
       "9   NaN  \n",
       "10  NaN  \n",
       "11  NaN  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat( objs=[concat_df1, concat_df2, concat_df3, concat_df4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_left_df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                                'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                                'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "   \n",
    "merge_right_df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                                 'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                                 'D': ['D0', 'D1', 'D2', 'D3']})    \n",
    "\n",
    "merge_left_df2 = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
    "                               'key2': ['K0', 'K1', 'K0', 'K1'],\n",
    "                                  'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                                  'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "    \n",
    "merge_right_df2 = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
    "                                'key2': ['K0', 'K0', 'K0', 'K0'],\n",
    "                                   'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                                   'D': ['D0', 'D1', 'D2', 'D3']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
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       "      <th>3</th>\n",
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       "  key   A   B\n",
       "0  K0  A0  B0\n",
       "1  K1  A1  B1\n",
       "2  K2  A2  B2\n",
       "3  K3  A3  B3"
      ]
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     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "merge_left_df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <th>D</th>\n",
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       "  key   C   D\n",
       "0  K0  C0  D0\n",
       "1  K1  C1  D1\n",
       "2  K2  C2  D2\n",
       "3  K3  C3  D3"
      ]
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     "execution_count": 106,
     "metadata": {},
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   "source": [
    "merge_right_df"
   ]
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   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
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       "0   K0   K0  A0  B0\n",
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       "2   K1   K0  A2  B2\n",
       "3   K2   K1  A3  B3"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "merge_left_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  key1 key2   C   D\n",
       "0   K0   K0  C0  D0\n",
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       "2   K1   K0  C2  D2\n",
       "3   K2   K0  C3  D3"
      ]
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     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "merge_right_df2"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `merge()` function merges two DataFrames together based on common values in one or more columns."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inner join on single key"
   ]
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  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  key   A   B   C   D\n",
       "0  K0  A0  B0  C0  D0\n",
       "1  K1  A1  B1  C1  D1\n",
       "2  K2  A2  B2  C2  D2\n",
       "3  K3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 109,
     "metadata": {},
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   "source": [
    "pd.merge( merge_left_df, merge_right_df, how='inner', on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inner join on multiple keys"
   ]
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  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  key1 key2   A   B   C   D\n",
       "0   K0   K0  A0  B0  C0  D0\n",
       "1   K1   K0  A2  B2  C1  D1\n",
       "2   K1   K0  A2  B2  C2  D2"
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     "metadata": {},
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   "source": [
    "pd.merge( merge_left_df2, merge_right_df2, on=[ 'key1', 'key2' ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outer join on multiple keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>K2</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "  key1 key2    A    B    C    D\n",
       "0   K0   K0   A0   B0   C0   D0\n",
       "1   K0   K1   A1   B1  NaN  NaN\n",
       "2   K1   K0   A2   B2   C1   D1\n",
       "3   K1   K0   A2   B2   C2   D2\n",
       "4   K2   K1   A3   B3  NaN  NaN\n",
       "5   K2   K0  NaN  NaN   C3   D3"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge( merge_left_df2, merge_right_df2, how='outer', on=[ 'key1', 'key2' ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right join on multiple keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  key1 key2    A    B   C   D\n",
       "0   K0   K0   A0   B0  C0  D0\n",
       "1   K1   K0   A2   B2  C1  D1\n",
       "2   K1   K0   A2   B2  C2  D2\n",
       "3   K2   K0  NaN  NaN  C3  D3"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge( merge_left_df2, merge_right_df2, how='right', on=[ 'key1', 'key2' ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joining is like a merge, but the keys that are compared are on the row index or on a key column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_join_df = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
    "                             'B': ['B0', 'B1', 'B2']},\n",
    "                            index=['K0', 'K1', 'K2']) \n",
    "\n",
    "right_join_df = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
    "                              'D': ['D0', 'D2', 'D3']},\n",
    "                             index=['K0', 'K2', 'K3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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       "     A   B\n",
       "K0  A0  B0\n",
       "K1  A1  B1\n",
       "K2  A2  B2"
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     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
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    "left_join_df"
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   "execution_count": 115,
   "metadata": {},
   "outputs": [
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    "right_join_df"
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   "execution_count": 116,
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   "outputs": [
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       "     A   B    C    D\n",
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     "metadata": {},
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   "source": [
    "left_join_df.join( right_join_df )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`left.join( right )` can effectively be accomplished using merge as well, but the join is a lot easier to read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
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       "     A   B    C    D\n",
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       "K2  A2  B2   C2   D2"
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     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "pd.merge( left_join_df, right_join_df, left_index=True, right_index=True, how='left' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      A    B    C    D\n",
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       "K2   A2   B2   C2   D2\n",
       "K3  NaN  NaN   C3   D3"
      ]
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     "execution_count": 118,
     "metadata": {},
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   ],
   "source": [
    "left_join_df.join( right_join_df, how='outer' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     C   D    A    B\n",
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       "K3  C3  D3  NaN  NaN"
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     "execution_count": 119,
     "metadata": {},
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   ],
   "source": [
    "right_join_df.join( left_join_df )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName CountryCode  BirthRate  InternetUsers  \\\n",
       "0                 Aruba         ABW     10.244           78.9   \n",
       "1           Afghanistan         AFG     35.253            5.9   \n",
       "2                Angola         AGO     45.985           19.1   \n",
       "3               Albania         ALB     12.877           57.2   \n",
       "4  United Arab Emirates         ARE     11.044           88.0   \n",
       "\n",
       "           IncomeGroup  \n",
       "0          High income  \n",
       "1           Low income  \n",
       "2  Upper middle income  \n",
       "3  Upper middle income  \n",
       "4          High income  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>InternetUsers</th>\n",
       "      <th>IncomeGroup</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Niger</td>\n",
       "      <td>NER</td>\n",
       "      <td>49.661</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Chad</td>\n",
       "      <td>TCD</td>\n",
       "      <td>45.745</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BDI</td>\n",
       "      <td>44.151</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Mali</td>\n",
       "      <td>MLI</td>\n",
       "      <td>44.138</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CountryName CountryCode  BirthRate  InternetUsers          IncomeGroup\n",
       "127       Niger         NER     49.661            1.7           Low income\n",
       "2        Angola         AGO     45.985           19.1  Upper middle income\n",
       "167        Chad         TCD     45.745            2.3           Low income\n",
       "11      Burundi         BDI     44.151            1.3           Low income\n",
       "115        Mali         MLI     44.138            3.5           Low income"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='BirthRate', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Level Index Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Multi-Level Index\n",
    "\n",
    "You can create DataFrames with an index hierarchy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Monday', 1), ('Monday', 2), ('Monday', 3), ('Tuesday', 1), ('Tuesday', 2), ('Tuesday', 3)]\n",
      "MultiIndex([( 'Monday', 1),\n",
      "            ( 'Monday', 2),\n",
      "            ( 'Monday', 3),\n",
      "            ('Tuesday', 1),\n",
      "            ('Tuesday', 2),\n",
      "            ('Tuesday', 3)],\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "outside = [ 'Monday', 'Monday', 'Monday', 'Tuesday', 'Tuesday', 'Tuesday' ]\n",
    "inside = [ 1, 2, 3, 1, 2, 3 ]\n",
    "hier_index = list( zip( outside, inside ) )\n",
    "\n",
    "print(hier_index)\n",
    "\n",
    "hier_index = pd.MultiIndex.from_tuples( hier_index )\n",
    "\n",
    "print(hier_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"3\" valign=\"top\">Monday</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.993263</td>\n",
       "      <td>0.196800</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.136645</td>\n",
       "      <td>0.000366</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Tuesday</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.031579</td>\n",
       "      <td>0.649826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.154846</td>\n",
       "      <td>-0.610259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.755325</td>\n",
       "      <td>-0.346419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  A         B\n",
       "Monday  1 -0.993263  0.196800\n",
       "        2 -1.136645  0.000366\n",
       "        3  1.025984 -0.156598\n",
       "Tuesday 1 -0.031579  0.649826\n",
       "        2  2.154846 -0.610259\n",
       "        3 -0.755325 -0.346419"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df = pd.DataFrame( data=randn( 6, 2 ), index=hier_index, columns=['A', 'B'])\n",
    "multi_level_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_level_df.index.names = [ 'DOW', 'Num' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOW</th>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Monday</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.993263</td>\n",
       "      <td>0.196800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.136645</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Tuesday</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.031579</td>\n",
       "      <td>0.649826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.154846</td>\n",
       "      <td>-0.610259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.755325</td>\n",
       "      <td>-0.346419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A         B\n",
       "DOW     Num                    \n",
       "Monday  1   -0.993263  0.196800\n",
       "        2   -1.136645  0.000366\n",
       "        3    1.025984 -0.156598\n",
       "Tuesday 1   -0.031579  0.649826\n",
       "        2    2.154846 -0.610259\n",
       "        3   -0.755325 -0.346419"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting All Elements of the Outside Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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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       "</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",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.993263</td>\n",
       "      <td>0.196800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.136645</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B\n",
       "Num                    \n",
       "1   -0.993263  0.196800\n",
       "2   -1.136645  0.000366\n",
       "3    1.025984 -0.156598"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df.loc[ 'Monday' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.993263\n",
       "B    0.196800\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df.loc[ 'Monday' ].loc[ 1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19679950499134005"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df.loc[ 'Monday' ].loc[ 1 ]['B']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Section\n",
    "\n",
    "The `xs` method let's you filter on an index at any level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOW</th>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Monday</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.993263</td>\n",
       "      <td>0.196800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.136645</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Tuesday</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.031579</td>\n",
       "      <td>0.649826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.154846</td>\n",
       "      <td>-0.610259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.755325</td>\n",
       "      <td>-0.346419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A         B\n",
       "DOW     Num                    \n",
       "Monday  1   -0.993263  0.196800\n",
       "        2   -1.136645  0.000366\n",
       "        3    1.025984 -0.156598\n",
       "Tuesday 1   -0.031579  0.649826\n",
       "        2    2.154846 -0.610259\n",
       "        3   -0.755325 -0.346419"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "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",
       "    .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",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.993263</td>\n",
       "      <td>0.196800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.136645</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B\n",
       "Num                    \n",
       "1   -0.993263  0.196800\n",
       "2   -1.136645  0.000366\n",
       "3    1.025984 -0.156598"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_level_df.xs( 'Monday' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOW</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>-0.993263</td>\n",
       "      <td>0.196800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>-0.031579</td>\n",
       "      <td>0.649826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A         B\n",
       "DOW                        \n",
       "Monday  -0.993263  0.196800\n",
       "Tuesday -0.031579  0.649826"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all the Num=1 across the different DOWs\n",
    "multi_level_df.xs( 1, level='Num' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `apply()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665  1.693723 -1.706086 -1.159119\n",
       "B -0.134841  0.390528  0.166905  0.184502\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "D -0.497104 -0.754070 -0.943406  0.484752\n",
       "E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>W2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>0.605331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "      <td>-0.269681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "      <td>1.615412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "      <td>-0.994208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "      <td>-0.233547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z        W2\n",
       "A  0.302665  1.693723 -1.706086 -1.159119  0.605331\n",
       "B -0.134841  0.390528  0.166905  0.184502 -0.269681\n",
       "C  0.807706  0.072960  0.638787  0.329646  1.615412\n",
       "D -0.497104 -0.754070 -0.943406  0.484752 -0.994208\n",
       "E -0.116773  1.901755  0.238127  1.996652 -0.233547"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def times2(x):\n",
    "    return x*2\n",
    "\n",
    "df[ 'W2' ] = df['W'].apply(times2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just cleaning up\n",
    "df.drop( 'W2', axis=1, inplace=True, errors='ignore' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    11\n",
       "2     6\n",
       "3     7\n",
       "4    20\n",
       "Name: CountryName, dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['CountryName'].apply( len ).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    315.6\n",
       "1     23.6\n",
       "2     76.4\n",
       "3    228.8\n",
       "4    352.0\n",
       "Name: InternetUsers, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['InternetUsers'].apply( lambda x: x*4 ).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `drop()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z State\n",
       "A  0.302665  1.693723 -1.706086 -1.159119    CA\n",
       "B -0.134841  0.390528  0.166905  0.184502    NY\n",
       "C  0.807706  0.072960  0.638787  0.329646    TX\n",
       "D -0.497104 -0.754070 -0.943406  0.484752    FL\n",
       "E -0.116773  1.901755  0.238127  1.996652    CO"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column = 'CA NY TX FL CO'.split()\n",
    "df[ 'State' ] = new_column\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665  1.693723 -1.706086 -1.159119\n",
       "B -0.134841  0.390528  0.166905  0.184502\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "D -0.497104 -0.754070 -0.943406  0.484752\n",
       "E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop( 'State', axis=1, inplace=True, errors='ignore' )\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `drop_duplicates()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "1  2.0  NaN  2.0\n",
       "2  NaN  NaN  NaN\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = { 'A': [1,2,np.nan,1], 'B':[5,np.nan,np.nan,5], 'C':[1,2,np.nan,1] }\n",
    "drop_duplicates_df = pd.DataFrame( d )\n",
    "drop_duplicates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "1  2.0  NaN  2.0\n",
       "2  NaN  NaN  NaN"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_duplicates_df.drop_duplicates( keep='first' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "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",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "1  2.0  NaN  2.0\n",
       "2  NaN  NaN  NaN\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_duplicates_df.drop_duplicates( keep='last' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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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       "        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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  2.0  NaN  2.0\n",
       "1  NaN  NaN  NaN\n",
       "2  1.0  5.0  1.0"
      ]
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     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "drop_duplicates_df.drop_duplicates( keep='last', ignore_index=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `dropna()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B     C\n",
       "0  1.0  5.0   1.0\n",
       "1  2.0  NaN   2.0\n",
       "2  NaN  NaN   NaN\n",
       "3  1.0  5.0   1.0\n",
       "4  NaN  7.0  22.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = { 'A': [1,2,np.nan,1,np.nan], 'B':[5,np.nan,np.nan,5,7], 'C':[1,2,np.nan,1,22] }\n",
    "dropna_df = pd.DataFrame( d )\n",
    "dropna_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropna_df.dropna( how='any' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B     C\n",
       "0  1.0  5.0   1.0\n",
       "1  2.0  NaN   2.0\n",
       "3  1.0  5.0   1.0\n",
       "4  NaN  7.0  22.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropna_df.dropna( how='all' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B     C\n",
       "0  1.0  5.0   1.0\n",
       "3  1.0  5.0   1.0\n",
       "4  NaN  7.0  22.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropna_df.dropna( subset=[ 'B' ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `thresh` argument defines the number of non-NA values a row or columns needs to not be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      C\n",
       "0   1.0\n",
       "1   2.0\n",
       "2   NaN\n",
       "3   1.0\n",
       "4  22.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropna_df.dropna( axis=1, thresh=4 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `fillna()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <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>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "1  2.0  NaN  2.0\n",
       "2  NaN  NaN  NaN\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = { 'A': [1,2,np.nan,1], 'B':[5,np.nan,np.nan,5], 'C':[1,2,np.nan,1] }\n",
    "fillna_df = pd.DataFrame( d )\n",
    "fillna_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C\n",
       "0    1.0    5.0    1.0\n",
       "1    2.0  999.0    2.0\n",
       "2  999.0  999.0  999.0\n",
       "3    1.0    5.0    1.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillna_df.fillna( 999 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "     A    B    C\n",
       "0  1.0  5.0  1.0\n",
       "1  2.0  5.0  2.0\n",
       "2  2.0  5.0  2.0\n",
       "3  1.0  5.0  1.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillna_df.fillna( method='ffill' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A    B    C\n",
       "0  1.000000  5.0  1.0\n",
       "1  2.000000  NaN  2.0\n",
       "2  1.333333  NaN  NaN\n",
       "3  1.000000  5.0  1.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillna_df[ 'A' ] = fillna_df[ 'A' ].fillna( value=fillna_df['A'].mean() )\n",
    "fillna_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `groupby()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
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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>Company</th>\n",
       "      <th>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Javid</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Austin</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Ramiar</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Arianna</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FB</td>\n",
       "      <td>Sina</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FB</td>\n",
       "      <td>Kian</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   Person  Sales\n",
       "0    GOOG    Javid    200\n",
       "1    GOOG   Austin    120\n",
       "2    MSFT   Ramiar    340\n",
       "3    MSFT  Arianna    124\n",
       "4      FB     Sina    243\n",
       "5      FB     Kian    350"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    'Company': [ 'GOOG', 'GOOG', 'MSFT', 'MSFT', 'FB', 'FB' ],\n",
    "    'Person': [ 'Javid', 'Austin', 'Ramiar', 'Arianna', 'Sina', 'Kian' ],\n",
    "    'Sales': [ 200, 120, 340, 124, 243, 350 ]\n",
    "}\n",
    "\n",
    "groupby_df = pd.DataFrame( data )\n",
    "groupby_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print( type( df ) )\n",
    "\n",
    "byCompany = groupby_df.groupby( 'Company' )\n",
    "print( type( byCompany ) )\n",
    "\n",
    "print( type( byCompany.mean() ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>296.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "FB       296.5\n",
       "GOOG     160.0\n",
       "MSFT     232.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byCompany.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "FB         593\n",
       "GOOG       320\n",
       "MSFT       464"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byCompany.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales    593\n",
      "Name: FB, dtype: int64\n",
      "Sales    593\n",
      "Name: FB, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print( byCompany.sum().loc[ 'FB' ] )\n",
    "print( groupby_df.groupby('Company').sum().loc[ 'FB' ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>75.660426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>56.568542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>152.735065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales\n",
       "Company            \n",
       "FB        75.660426\n",
       "GOOG      56.568542\n",
       "MSFT     152.735065"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byCompany.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Person  Sales\n",
       "Company               \n",
       "FB            2      2\n",
       "GOOG          2      2\n",
       "MSFT          2      2"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_df.groupby('Company').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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",
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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>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>Sina</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>Javid</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>Ramiar</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Person  Sales\n",
       "Company               \n",
       "FB         Sina    350\n",
       "GOOG      Javid    200\n",
       "MSFT     Ramiar    340"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_df.groupby('Company').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "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",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Sales</th>\n",
       "    </tr>\n",
       "    <tr>\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>Company</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>2.0</td>\n",
       "      <td>296.5</td>\n",
       "      <td>75.660426</td>\n",
       "      <td>243.0</td>\n",
       "      <td>269.75</td>\n",
       "      <td>296.5</td>\n",
       "      <td>323.25</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>56.568542</td>\n",
       "      <td>120.0</td>\n",
       "      <td>140.00</td>\n",
       "      <td>160.0</td>\n",
       "      <td>180.00</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>152.735065</td>\n",
       "      <td>124.0</td>\n",
       "      <td>178.00</td>\n",
       "      <td>232.0</td>\n",
       "      <td>286.00</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sales                                                        \n",
       "        count   mean         std    min     25%    50%     75%    max\n",
       "Company                                                              \n",
       "FB        2.0  296.5   75.660426  243.0  269.75  296.5  323.25  350.0\n",
       "GOOG      2.0  160.0   56.568542  120.0  140.00  160.0  180.00  200.0\n",
       "MSFT      2.0  232.0  152.735065  124.0  178.00  232.0  286.00  340.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_df.groupby('Company').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Company</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Sales</th>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>296.500000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>75.660426</td>\n",
       "      <td>56.568542</td>\n",
       "      <td>152.735065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>269.750000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>296.500000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>323.250000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>286.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Company              FB        GOOG        MSFT\n",
       "Sales count    2.000000    2.000000    2.000000\n",
       "      mean   296.500000  160.000000  232.000000\n",
       "      std     75.660426   56.568542  152.735065\n",
       "      min    243.000000  120.000000  124.000000\n",
       "      25%    269.750000  140.000000  178.000000\n",
       "      50%    296.500000  160.000000  232.000000\n",
       "      75%    323.250000  180.000000  286.000000\n",
       "      max    350.000000  200.000000  340.000000"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_df.groupby('Company').describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales  count      2.000000\n",
       "       mean     296.500000\n",
       "       std       75.660426\n",
       "       min      243.000000\n",
       "       25%      269.750000\n",
       "       50%      296.500000\n",
       "       75%      323.250000\n",
       "       max      350.000000\n",
       "Name: FB, dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_df.groupby('Company').describe().transpose()['FB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `isnull()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns booleans representing whether the value of each cell of the DataFrame was null or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>W2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       W      X      Y      Z     W2\n",
       "A  False  False  False  False  False\n",
       "B  False  False  False  False  False\n",
       "C  False  False  False  False  False\n",
       "D  False  False  False  False  False\n",
       "E  False  False  False  False  False"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `nunique()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns the number of unique values in a given Series or DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['IncomeGroup'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryName      195\n",
       "CountryCode      195\n",
       "BirthRate        177\n",
       "InternetUsers    181\n",
       "IncomeGroup        4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `pivot_table()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C  D\n",
       "0  foo  one  x  1\n",
       "1  foo  one  y  3\n",
       "2  foo  two  x  2\n",
       "3  bar  two  y  5\n",
       "4  bar  one  x  4\n",
       "5  bar  one  y  1"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
    "        'B':['one','one','two','two','one','one'],\n",
    "        'C':['x','y','x','y','x','y'],\n",
    "        'D':[1,3,2,5,4,1]}\n",
    "\n",
    "pivot_table_df = pd.DataFrame(data)\n",
    "pivot_table_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>C</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C          x    y\n",
       "A   B            \n",
       "bar one  4.0  1.0\n",
       "    two  NaN  5.0\n",
       "foo one  1.0  3.0\n",
       "    two  2.0  NaN"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table_df.pivot_table(values='D', index=['A', 'B' ], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `read_csv()`\n",
    "\n",
    "Reads a csv file into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Internet users</th>\n",
       "      <th>Income Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>10.244</td>\n",
       "      <td>78.9</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>35.253</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>45.985</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>12.877</td>\n",
       "      <td>57.2</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>11.044</td>\n",
       "      <td>88.0</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>32.947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>20.850</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>COD</td>\n",
       "      <td>42.394</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>40.471</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>35.715</td>\n",
       "      <td>18.5</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country Name Country Code  Birth rate  Internet users  \\\n",
       "0                   Aruba          ABW      10.244            78.9   \n",
       "1             Afghanistan          AFG      35.253             5.9   \n",
       "2                  Angola          AGO      45.985            19.1   \n",
       "3                 Albania          ALB      12.877            57.2   \n",
       "4    United Arab Emirates          ARE      11.044            88.0   \n",
       "..                    ...          ...         ...             ...   \n",
       "190           Yemen, Rep.          YEM      32.947            20.0   \n",
       "191          South Africa          ZAF      20.850            46.5   \n",
       "192      Congo, Dem. Rep.          COD      42.394             2.2   \n",
       "193                Zambia          ZMB      40.471            15.4   \n",
       "194              Zimbabwe          ZWE      35.715            18.5   \n",
       "\n",
       "            Income Group  \n",
       "0            High income  \n",
       "1             Low income  \n",
       "2    Upper middle income  \n",
       "3    Upper middle income  \n",
       "4            High income  \n",
       "..                   ...  \n",
       "190  Lower middle income  \n",
       "191  Upper middle income  \n",
       "192           Low income  \n",
       "193  Lower middle income  \n",
       "194           Low income  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_df = pd.read_csv('../data/Birth-Internet-Income-Per-Country.csv')\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `reset_index()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns a new DataFrame where the named index is set to a column called `index` and the index becomes numerical. To do it in place, use `inplace=true`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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",
       "\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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665  1.693723 -1.706086 -1.159119\n",
       "B -0.134841  0.390528  0.166905  0.184502\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "D -0.497104 -0.754070 -0.943406  0.484752\n",
       "E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index         W         X         Y         Z\n",
       "0     A  0.302665  1.693723 -1.706086 -1.159119\n",
       "1     B -0.134841  0.390528  0.166905  0.184502\n",
       "2     C  0.807706  0.072960  0.638787  0.329646\n",
       "3     D -0.497104 -0.754070 -0.943406  0.484752\n",
       "4     E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `set_index()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              W         X         Y         Z\n",
       "State                                        \n",
       "CA     0.302665  1.693723 -1.706086 -1.159119\n",
       "NY    -0.134841  0.390528  0.166905  0.184502\n",
       "TX     0.807706  0.072960  0.638787  0.329646\n",
       "FL    -0.497104 -0.754070 -0.943406  0.484752\n",
       "CO    -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column = 'CA NY TX FL CO'.split()\n",
    "df[ 'State' ] = new_column\n",
    "df.set_index( 'State' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.166905</td>\n",
       "      <td>0.184502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.807706</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.638787</td>\n",
       "      <td>0.329646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.497104</td>\n",
       "      <td>-0.754070</td>\n",
       "      <td>-0.943406</td>\n",
       "      <td>0.484752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.116773</td>\n",
       "      <td>1.901755</td>\n",
       "      <td>0.238127</td>\n",
       "      <td>1.996652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.302665  1.693723 -1.706086 -1.159119\n",
       "B -0.134841  0.390528  0.166905  0.184502\n",
       "C  0.807706  0.072960  0.638787  0.329646\n",
       "D -0.497104 -0.754070 -0.943406  0.484752\n",
       "E -0.116773  1.901755  0.238127  1.996652"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop( 'State', axis=1, inplace=True, errors='ignore' )\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `unique()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns the unique values in a given Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['High income', 'Low income', 'Upper middle income',\n",
       "       'Lower middle income'], dtype=object)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['IncomeGroup'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `value_counts()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of times each unique value occurs in a given Series or DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High income            67\n",
       "Lower middle income    50\n",
       "Upper middle income    48\n",
       "Low income             30\n",
       "Name: IncomeGroup, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[ 'IncomeGroup' ].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryName    CountryCode  BirthRate  InternetUsers  IncomeGroup        \n",
       "Zimbabwe       ZWE          35.715     18.5000        Low income             1\n",
       "Denmark        DNK          10.000     94.6297        High income            1\n",
       "Guam           GUM          17.389     65.4000        High income            1\n",
       "Grenada        GRD          19.334     35.0000        Upper middle income    1\n",
       "Greenland      GRL          14.500     65.8000        High income            1\n",
       "                                                                            ..\n",
       "New Zealand    NZL          13.120     82.7800        High income            1\n",
       "New Caledonia  NCL          17.000     66.0000        High income            1\n",
       "Netherlands    NLD          10.200     93.9564        High income            1\n",
       "Nepal          NPL          20.923     13.3000        Low income             1\n",
       "Afghanistan    AFG          35.253     5.9000         Low income             1\n",
       "Length: 195, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.value_counts()"
   ]
  }
 ],
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