part_2_data_science/part_2_2_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "<img src=\"../logos/pandas.png\" width=\"256px\" />\n",
    "\n",
    "In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.\n",
    "\n",
    "* Web: https://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from numpy.random import randn\n",
    "\n",
    "np.random.seed(110)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part I: Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst, labels = [10, 20, 30], ['a', 'b', 'c']\n",
    "\n",
    "arr = np.array(lst)\n",
    "dic = {'a': 10, 'b': 20, 'c': 30}"
   ]
  },
  {
   "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=lst)"
   ]
  },
  {
   "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=lst, 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(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "series_1 = pd.Series([1, 2, 3, 4], ['USA', 'Germany', 'USSR', 'Japan'])\n",
    "series_2 = pd.Series([1, 2, 5, 4], ['USA', 'Germany', 'Italy', 'Japan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1['USA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1 + series_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part II: Data Frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.328597 -0.796199  1.403124 -1.547793\n",
       "B  1.166730  1.147723  0.130109  0.431422\n",
       "C -0.868320  0.027159 -1.518739 -0.815561\n",
       "D -0.223637 -2.631096  0.046867 -0.376922\n",
       "E -0.162840 -0.744918  1.069300 -0.734714"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(randn(5, 4), ['A', 'B', 'C', 'D', 'E'], ['W', 'X', 'Y', 'Z'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing a Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.328597\n",
       "B    1.166730\n",
       "C   -0.868320\n",
       "D   -0.223637\n",
       "E   -0.162840\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8683197629930438"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W']['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.328597\n",
       "B    1.166730\n",
       "C   -0.868320\n",
       "D   -0.223637\n",
       "E   -0.162840\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.W  # similar to sql, but it is not recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>1.403124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>0.130109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>-1.518739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>0.046867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>1.069300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Y\n",
       "A  0.328597  1.403124\n",
       "B  1.166730  0.130109\n",
       "C -0.868320 -1.518739\n",
       "D -0.223637  0.046867\n",
       "E -0.162840  1.069300"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['W', 'Y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  0.328597 -0.796199  1.403124 -1.547793\n",
       "B  1.166730  1.147723  0.130109  0.431422"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>C</th>\n",
       "      <td>-0.86832</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         W         X         Y         Z\n",
       "C -0.86832  0.027159 -1.518739 -0.815561"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['C']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W   -0.868320\n",
       "X    0.027159\n",
       "Y   -1.518739\n",
       "Z   -0.815561\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W   -0.868320\n",
       "X    0.027159\n",
       "Y   -1.518739\n",
       "Z   -0.815561\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <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>0.328597</td>\n",
       "      <td>-1.547793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>0.431422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Z\n",
       "A  0.328597 -1.547793\n",
       "B  1.166730  0.431422"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['A', 'B'], ['W', 'Z']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>XI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>1.731721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>1.296840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>-2.387059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>-0.176771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>0.906460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z        XI\n",
       "A  0.328597 -0.796199  1.403124 -1.547793  1.731721\n",
       "B  1.166730  1.147723  0.130109  0.431422  1.296840\n",
       "C -0.868320  0.027159 -1.518739 -0.815561 -2.387059\n",
       "D -0.223637 -2.631096  0.046867 -0.376922 -0.176771\n",
       "E -0.162840 -0.744918  1.069300 -0.734714  0.906460"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['XI'] = df['W'] + df['Y']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>XI</th>\n",
       "      <th>XII</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>1.731721</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>1.296840</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>-2.387059</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>-0.176771</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>0.906460</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z        XI XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793  1.731721   a\n",
       "B  1.166730  1.147723  0.130109  0.431422  1.296840   b\n",
       "C -0.868320  0.027159 -1.518739 -0.815561 -2.387059   c\n",
       "D -0.223637 -2.631096  0.046867 -0.376922 -0.176771   d\n",
       "E -0.162840 -0.744918  1.069300 -0.734714  0.906460   e"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['XII'] = ['a', 'b', 'c', 'd', 'e']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>XII</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793   a\n",
       "B  1.166730  1.147723  0.130109  0.431422   b\n",
       "C -0.868320  0.027159 -1.518739 -0.815561   c\n",
       "D -0.223637 -2.631096  0.046867 -0.376922   d\n",
       "E -0.162840 -0.744918  1.069300 -0.734714   e"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('XI', axis=1)  # axis = 0 is for row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "        text-align: left;\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>XI</th>\n",
       "      <th>XII</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>1.731721</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>1.296840</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>-2.387059</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>-0.176771</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>0.906460</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z        XI XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793  1.731721   a\n",
       "B  1.166730  1.147723  0.130109  0.431422  1.296840   b\n",
       "C -0.868320  0.027159 -1.518739 -0.815561 -2.387059   c\n",
       "D -0.223637 -2.631096  0.046867 -0.376922 -0.176771   d\n",
       "E -0.162840 -0.744918  1.069300 -0.734714  0.906460   e"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>XII</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793   a\n",
       "B  1.166730  1.147723  0.130109  0.431422   b\n",
       "C -0.868320  0.027159 -1.518739 -0.815561   c\n",
       "D -0.223637 -2.631096  0.046867 -0.376922   d\n",
       "E -0.162840 -0.744918  1.069300 -0.734714   e"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('XI', axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>XII</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793   a\n",
       "B  1.166730  1.147723  0.130109  0.431422   b\n",
       "C -0.868320  0.027159 -1.518739 -0.815561   c\n",
       "D -0.223637 -2.631096  0.046867 -0.376922   d"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('E', axis=0)  # default value for axis is 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr 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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>XII</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>False</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",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       W      X      Y      Z   XII\n",
       "A   True  False   True  False  True\n",
       "B   True   True   True   True  True\n",
       "C  False   True  False  False  True\n",
       "D  False  False   True  False  True\n",
       "E  False  False   True  False  True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "      <th>Z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>True</td>\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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       W      X      Y      Z   XII\n",
       "A   True  False   True  False  True\n",
       "B   True   True   True   True  True\n",
       "C  False   True  False  False  True\n",
       "D  False  False   True  False  True\n",
       "E  False  False   True  False  True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_df = df > 0\n",
    "bool_df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z XII\n",
       "A  0.328597       NaN  1.403124       NaN   a\n",
       "B  1.166730  1.147723  0.130109  0.431422   b\n",
       "C       NaN  0.027159       NaN       NaN   c\n",
       "D       NaN       NaN  0.046867       NaN   d\n",
       "E       NaN       NaN  1.069300       NaN   e"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[bool_df]"
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   "execution_count": 30,
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       "          W         X         Y         Z XII\n",
       "A  0.328597       NaN  1.403124       NaN   a\n",
       "B  1.166730  1.147723  0.130109  0.431422   b\n",
       "C       NaN  0.027159       NaN       NaN   c\n",
       "D       NaN       NaN  0.046867       NaN   d\n",
       "E       NaN       NaN  1.069300       NaN   e"
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     "execution_count": 30,
     "metadata": {},
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   "source": [
    "df[df > 0]"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B     True\n",
       "C    False\n",
       "D    False\n",
       "E    False\n",
       "Name: W, dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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   "source": [
    "df['W'] > 0"
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  {
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   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z XII\n",
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     "metadata": {},
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   "source": [
    "df[df['W'] > 0]"
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   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793   a\n",
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       "E -0.162840 -0.744918  1.069300 -0.734714   e"
      ]
     },
     "execution_count": 33,
     "metadata": {},
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   ],
   "source": [
    "df[df['Z'] < 0]"
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  {
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   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z XII\n",
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     "execution_count": 34,
     "metadata": {},
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   "source": [
    "res_df = df[df['W'] > 0]\n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.796199\n",
       "B    1.147723\n",
       "Name: X, dtype: float64"
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     "execution_count": 35,
     "metadata": {},
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    "res_df['X']"
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  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df[(df['W'] > 0) and (df['Y'] > 1)]"
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  {
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   "execution_count": 36,
   "metadata": {},
   "outputs": [
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    "df[(df['W'] > 0) & (df['Y'] > 1)]  # Use '&' for AND"
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   "execution_count": 37,
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       "          W         X         Y         Z XII\n",
       "A  0.328597 -0.796199  1.403124 -1.547793   a\n",
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   "source": [
    "df[(df['W'] > 0) | (df['Y'] > 1)]  # Use '|' for OR"
   ]
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>XII</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index         W         X         Y         Z XII\n",
       "0     A  0.328597 -0.796199  1.403124 -1.547793   a\n",
       "1     B  1.166730  1.147723  0.130109  0.431422   b\n",
       "2     C -0.868320  0.027159 -1.518739 -0.815561   c\n",
       "3     D -0.223637 -2.631096  0.046867 -0.376922   d\n",
       "4     E -0.162840 -0.744918  1.069300 -0.734714   e"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()  # you may set the 'inplace' argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>XII</th>\n",
       "      <th>States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>a</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>b</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>c</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>d</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>e</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z XII States\n",
       "A  0.328597 -0.796199  1.403124 -1.547793   a     CA\n",
       "B  1.166730  1.147723  0.130109  0.431422   b     NY\n",
       "C -0.868320  0.027159 -1.518739 -0.815561   c     WY\n",
       "D -0.223637 -2.631096  0.046867 -0.376922   d     OR\n",
       "E -0.162840 -0.744918  1.069300 -0.734714   e     CO"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newind = 'CA NY WY OR CO'.split()\n",
    "df['States'] = newind\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>XII</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>States</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>CA</th>\n",
       "      <td>0.328597</td>\n",
       "      <td>-0.796199</td>\n",
       "      <td>1.403124</td>\n",
       "      <td>-1.547793</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>1.166730</td>\n",
       "      <td>1.147723</td>\n",
       "      <td>0.130109</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>-0.868320</td>\n",
       "      <td>0.027159</td>\n",
       "      <td>-1.518739</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>-0.223637</td>\n",
       "      <td>-2.631096</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>-0.376922</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>-0.162840</td>\n",
       "      <td>-0.744918</td>\n",
       "      <td>1.069300</td>\n",
       "      <td>-0.734714</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               W         X         Y         Z XII\n",
       "States                                            \n",
       "CA      0.328597 -0.796199  1.403124 -1.547793   a\n",
       "NY      1.166730  1.147723  0.130109  0.431422   b\n",
       "WY     -0.868320  0.027159 -1.518739 -0.815561   c\n",
       "OR     -0.223637 -2.631096  0.046867 -0.376922   d\n",
       "CO     -0.162840 -0.744918  1.069300 -0.734714   e"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('States')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-level Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "outside = ['G1', 'G1', 'G1', 'G2', 'G2', 'G2']\n",
    "inside = [1, 2, 3, 1, 2, 3]\n",
    "hier_index = list(zip(outside, inside))\n",
    "hier_index = pd.MultiIndex.from_tuples(hier_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],\n",
       "           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
       "      <th>1</th>\n",
       "      <td>-1.467635</td>\n",
       "      <td>1.794009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.661406</td>\n",
       "      <td>-0.058978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.243002</td>\n",
       "      <td>1.578052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
       "      <th>1</th>\n",
       "      <td>1.566281</td>\n",
       "      <td>-1.243515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.537586</td>\n",
       "      <td>-0.843777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.104712</td>\n",
       "      <td>-0.481808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A         B\n",
       "G1 1 -1.467635  1.794009\n",
       "   2 -1.661406 -0.058978\n",
       "   3  0.243002  1.578052\n",
       "G2 1  1.566281 -1.243515\n",
       "   2  0.537586 -0.843777\n",
       "   3  0.104712 -0.481808"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(randn(6, 2), hier_index, ['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.467635\n",
       "B    1.794009\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['G1'].loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr 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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Groups</th>\n",
       "      <th>Numbers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
       "      <th>1</th>\n",
       "      <td>-1.467635</td>\n",
       "      <td>1.794009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.661406</td>\n",
       "      <td>-0.058978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.243002</td>\n",
       "      <td>1.578052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
       "      <th>1</th>\n",
       "      <td>1.566281</td>\n",
       "      <td>-1.243515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.537586</td>\n",
       "      <td>-0.843777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.104712</td>\n",
       "      <td>-0.481808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       A         B\n",
       "Groups Numbers                    \n",
       "G1     1       -1.467635  1.794009\n",
       "       2       -1.661406 -0.058978\n",
       "       3        0.243002  1.578052\n",
       "G2     1        1.566281 -1.243515\n",
       "       2        0.537586 -0.843777\n",
       "       3        0.104712 -0.481808"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.names = ['Groups', 'Numbers']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numbers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.467635</td>\n",
       "      <td>1.794009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.661406</td>\n",
       "      <td>-0.058978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.243002</td>\n",
       "      <td>1.578052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A         B\n",
       "Numbers                    \n",
       "1       -1.467635  1.794009\n",
       "2       -1.661406 -0.058978\n",
       "3        0.243002  1.578052"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs('G1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>-1.467635</td>\n",
       "      <td>1.794009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>1.566281</td>\n",
       "      <td>-1.243515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A         B\n",
       "Groups                    \n",
       "G1     -1.467635  1.794009\n",
       "G2      1.566281 -1.243515"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs(1, level='Numbers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>C</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1\n",
       "1  2.0  NaN  2\n",
       "2  NaN  NaN  3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'A':[1, 2, np.nan], 'B':[5, np.nan, np.nan], 'C':[1, 2, 3]}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropna drops any rows holding a NaN value.\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\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>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   C\n",
       "0  1\n",
       "1  2\n",
       "2  3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1\n",
       "1  2.0  NaN  2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=2)  # at least have two NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    1.0\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'].fillna(value=df['A'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    1.0\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'A':[1, 2, np.nan], 'B':[5, np.nan, np.nan], 'C':[1, 2, 3]}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df\n",
    "\n",
    "# dropna drops any rows holding a NaN value.\n",
    "df.dropna()\n",
    "\n",
    "df.dropna(axis=1)\n",
    "\n",
    "df.dropna(thresh=2)  # at least have two NaN values.\n",
    "\n",
    "df['A'].fillna(value=df['A'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part III: Group By"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Groupby allows you to group together rows based off of a column and perform an aggregate function on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Company': ['GOOG', 'GOOG', 'MSFT', 'MSFT', 'FB', 'FB'], \n",
    "        'Person': ['Sam', 'Charlie', 'Amy', 'Vanessa', 'Carl', 'Sarah'], \n",
    "        'Sales': [200, 120, 340, 124, 243, 350]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\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",
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       "      <th>Person</th>\n",
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       "      <th>0</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Sam</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Amy</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FB</td>\n",
       "      <td>Carl</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FB</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   Person  Sales\n",
       "0    GOOG      Sam    200\n",
       "1    GOOG  Charlie    120\n",
       "2    MSFT      Amy    340\n",
       "3    MSFT  Vanessa    124\n",
       "4      FB     Carl    243\n",
       "5      FB    Sarah    350"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7fe2fb310780>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_company = df.groupby('Company')\n",
    "by_company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas automatically ignores non-numeric columns\n",
    "by_company.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_company.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales    593\n",
       "Name: FB, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_company.sum().loc['FB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>Carl</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>Amy</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Person  Sales\n",
       "Company                \n",
       "FB          Carl    243\n",
       "GOOG     Charlie    120\n",
       "MSFT         Amy    124"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr 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>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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "<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",
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       "      <th>std</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
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       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part IV: Joining and Concatenating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                     'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                    index=[0, 1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'], 'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                     'C': ['C4', 'C5', 'C6', 'C7'], 'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                    index=[4, 5, 6, 7]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'], 'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                     'C': ['C8', 'C9', 'C10', 'C11'], 'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                    index=[8, 9, 10, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
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       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
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       "      <td>D6</td>\n",
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       "      <td>A7</td>\n",
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       "      <td>D7</td>\n",
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       "    <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",
       "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df_1, df_2, df_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\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",
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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>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
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       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D    A    B    C    D    A    B    C    D\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
       "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
       "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
       "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
       "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
       "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
       "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
       "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df_1, df_2, df_3], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NaN values are because there are no values for the indices in original data frames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It merges two data frames with respect to given shared columns' names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'], \n",
    "                     'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                     'B': ['B0', 'B1', 'B2', 'B3']}, index=[0, 1, 2, 3])\n",
    "   \n",
    "right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                      'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                      'D': ['D0', 'D1', 'D2', 'D3']}, index=[0, 1, 2, 3]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<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>key</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>K1</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>K2</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>K3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key   C   D\n",
       "0  A0  B0  K0  C0  D0\n",
       "1  A1  B1  K1  C1  D1\n",
       "2  A2  B2  K2  C2  D2\n",
       "3  A3  B3  K3  C3  D3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, how='inner', on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = 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",
    "right = 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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>B</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</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>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key1 key2   C   D\n",
       "0  A0  B0   K0   K0  C0  D0\n",
       "1  A2  B2   K1   K0  C1  D1\n",
       "2  A2  B2   K1   K0  C2  D2"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, how='inner', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "      <th>key1</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
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       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K2</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K2</td>\n",
       "      <td>K0</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B key1 key2    C    D\n",
       "0   A0   B0   K0   K0   C0   D0\n",
       "1   A1   B1   K0   K1  NaN  NaN\n",
       "2   A2   B2   K1   K0   C1   D1\n",
       "3   A2   B2   K1   K0   C2   D2\n",
       "4   A3   B3   K2   K1  NaN  NaN\n",
       "5  NaN  NaN   K2   K0   C3   D3"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, how='outer', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "      <th>0</th>\n",
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       "      <td>C0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
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       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K2</td>\n",
       "      <td>K0</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
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      "text/plain": [
       "     A    B key1 key2   C   D\n",
       "0   A0   B0   K0   K0  C0  D0\n",
       "1   A2   B2   K1   K0  C1  D1\n",
       "2   A2   B2   K1   K0  C2  D2\n",
       "3  NaN  NaN   K2   K0  C3  D3"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, how='right', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>B1</td>\n",
       "      <td>K0</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K2</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key1 key2    C    D\n",
       "0  A0  B0   K0   K0   C0   D0\n",
       "1  A1  B1   K0   K1  NaN  NaN\n",
       "2  A2  B2   K1   K0   C1   D1\n",
       "3  A2  B2   K1   K0   C2   D2\n",
       "4  A3  B3   K2   K1  NaN  NaN"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, how='left', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It joins two data frames based on the \"**indices**\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
    "                     'B': ['B0', 'B1', 'B2']},\n",
    "                      index=['K0', 'K1', 'K2']) \n",
    "\n",
    "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
    "                      'D': ['D0', 'D2', 'D3']},\n",
    "                      index=['K0', 'K2', 'K3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "  <tbody>\n",
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       "      <th>K0</th>\n",
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       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B    C    D\n",
       "K0  A0  B0   C0   D0\n",
       "K1  A1  B1  NaN  NaN\n",
       "K2  A2  B2   C2   D2"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.join(right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "      <th>B</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>C0</td>\n",
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       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>C2</td>\n",
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       "      <td>B2</td>\n",
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       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     C   D    A    B\n",
       "K0  C0  D0   A0   B0\n",
       "K2  C2  D2   A2   B2\n",
       "K3  C3  D3  NaN  NaN"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.join(left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>D</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      C    D    A    B\n",
       "K0   C0   D0   A0   B0\n",
       "K1  NaN  NaN   A1   B1\n",
       "K2   C2   D2   A2   B2\n",
       "K3   C3   D3  NaN  NaN"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.join(left, how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part V: Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1  col_2 col_3\n",
       "0      1    444   abc\n",
       "1      2    555   def\n",
       "2      3    666   ghi\n",
       "3      4    444   xyz"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col_1': [1, 2, 3, 4], \n",
    "                   'col_2': [444, 555, 666, 444], \n",
    "                   'col_3': ['abc', 'def', 'ghi', 'xyz']})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding unique values in a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([444, 555, 666])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_2'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the number of unique values in a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_2'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "444    2\n",
       "555    1\n",
       "666    1\n",
       "Name: col_2, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_2'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### conditional selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "Name: col_1, dtype: bool"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_1'] > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1  col_2 col_3\n",
       "2      3    666   ghi\n",
       "3      4    444   xyz"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['col_1'] > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1  col_2 col_3\n",
       "3      4    444   xyz"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['col_1'] > 2) & (df['col_2'] == 444)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def times_2(x):\n",
    "    return x * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "3    8\n",
       "Name: col_1, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_1'].apply(times_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "3    3\n",
       "Name: col_3, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_3'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     888\n",
       "1    1110\n",
       "2    1332\n",
       "3     888\n",
       "Name: col_2, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_2'].apply(lambda x: x * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_2 col_3\n",
       "0    444   abc\n",
       "1    555   def\n",
       "2    666   ghi\n",
       "3    444   xyz"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('col_1', axis=1)  # you may use the 'inplace' aurgument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1  col_2 col_3\n",
       "0      1    444   abc\n",
       "1      2    555   def\n",
       "2      3    666   ghi\n",
       "3      4    444   xyz"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['col_1', 'col_2', 'col_3'], dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting and Ordering a Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1  col_2 col_3\n",
       "0      1    444   abc\n",
       "3      4    444   xyz\n",
       "1      2    555   def\n",
       "2      3    666   ghi"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='col_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>col_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_1  col_2  col_3\n",
       "0  False  False  False\n",
       "1  False  False  False\n",
       "2  False  False  False\n",
       "3  False  False  False"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 97,
     "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",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part VI: Data Input and Output\n",
    "\n",
    "* CSV\n",
    "* Excel\n",
    "* HTML\n",
    "* SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ali/projects/quick-tutorial-on-data-science/part_2_data_science'"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('../datasets/example.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../datasets/example.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('../datasets/example_new.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excel File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('../datasets/Excel_Sample.xlsx', sheetname='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be careful you have to use the 'sheet_name', but not 'sheetname'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('../datasets/Excel_Sample_new.xlsx', sheet_name='NewSheet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_data = pd.read_html('https://www.fdic.gov/bank/individual/failed/banklist.html')\n",
    "type(html_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas tries to find all tables in the html file, and put them in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bank Name</th>\n",
       "      <th>City</th>\n",
       "      <th>ST</th>\n",
       "      <th>CERT</th>\n",
       "      <th>Acquiring Institution</th>\n",
       "      <th>Closing Date</th>\n",
       "      <th>Updated Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington Federal Bank for Savings</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>30570</td>\n",
       "      <td>Royal Savings Bank</td>\n",
       "      <td>December 15, 2017</td>\n",
       "      <td>February 21, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Farmers and Merchants State Bank of Argonia</td>\n",
       "      <td>Argonia</td>\n",
       "      <td>KS</td>\n",
       "      <td>17719</td>\n",
       "      <td>Conway Bank</td>\n",
       "      <td>October 13, 2017</td>\n",
       "      <td>February 21, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fayette County Bank</td>\n",
       "      <td>Saint Elmo</td>\n",
       "      <td>IL</td>\n",
       "      <td>1802</td>\n",
       "      <td>United Fidelity Bank, fsb</td>\n",
       "      <td>May 26, 2017</td>\n",
       "      <td>July 26, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guaranty Bank, (d/b/a BestBank in Georgia &amp; Mi...</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>WI</td>\n",
       "      <td>30003</td>\n",
       "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
       "      <td>May 5, 2017</td>\n",
       "      <td>March 22, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>First NBC Bank</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>LA</td>\n",
       "      <td>58302</td>\n",
       "      <td>Whitney Bank</td>\n",
       "      <td>April 28, 2017</td>\n",
       "      <td>December 5, 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Bank Name         City  ST   CERT  \\\n",
       "0                Washington Federal Bank for Savings      Chicago  IL  30570   \n",
       "1    The Farmers and Merchants State Bank of Argonia      Argonia  KS  17719   \n",
       "2                                Fayette County Bank   Saint Elmo  IL   1802   \n",
       "3  Guaranty Bank, (d/b/a BestBank in Georgia & Mi...    Milwaukee  WI  30003   \n",
       "4                                     First NBC Bank  New Orleans  LA  58302   \n",
       "\n",
       "                 Acquiring Institution       Closing Date       Updated Date  \n",
       "0                   Royal Savings Bank  December 15, 2017  February 21, 2018  \n",
       "1                          Conway Bank   October 13, 2017  February 21, 2018  \n",
       "2            United Fidelity Bank, fsb       May 26, 2017      July 26, 2017  \n",
       "3  First-Citizens Bank & Trust Company        May 5, 2017     March 22, 2018  \n",
       "4                         Whitney Bank     April 28, 2017   December 5, 2017  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_data[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "connector = create_engine('sqlite:///:memory:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_sql('my_table', connector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_df = pd.read_sql('my_table', connector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   index   a   b   c   d\n",
       "0      0   0   1   2   3\n",
       "1      1   4   5   6   7\n",
       "2      2   8   9  10  11\n",
       "3      3  12  13  14  15"
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     "execution_count": 112,
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    "sql_df"
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   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part VII: Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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