11_15_to_01_15.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.1 Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     aardvark\n",
       "1    artichoke\n",
       "2          NaN\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data = pd.Series(['aardvark', 'artichoke', np.nan, 'avocado'])\n",
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     aardvark\n",
      "1    artichoke\n",
      "2          NaN\n",
      "3      avocado\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2    NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(string_data)\n",
    "string_data[string_data.isnull()]\n",
    "string_data[string_data.isna()]\n",
    "string_data[(string_data.isnull()) | (string_data.isna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     aardvark\n",
      "1    artichoke\n",
      "2          NaN\n",
      "3      avocado\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     aardvark\n",
       "1    artichoke\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(string_data)\n",
    "string_data[~string_data.isnull()]\n",
    "string_data[~string_data.isna()]\n",
    "string_data[(~string_data.isnull()) | (~string_data.isna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     aardvark\n",
      "1    artichoke\n",
      "2          NaN\n",
      "3      avocado\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     aardvark\n",
       "1    artichoke\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(string_data)\n",
    "string_data[string_data.notnull()]\n",
    "string_data[string_data.notna()]\n",
    "string_data[(string_data.notnull()) | (string_data.notna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         None\n",
       "1    artichoke\n",
       "2          NaN\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data[0] = None\n",
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 7-1. NA handling methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         None\n",
       "1    artichoke\n",
       "2          NaN\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         None\n",
      "1    artichoke\n",
      "2          NaN\n",
      "3      avocado\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    artichoke\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(string_data)\n",
    "string_data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering Out Missing Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.0\n",
      "1    NaN\n",
      "2    3.5\n",
      "3    NaN\n",
      "4    7.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import nan as NA\n",
    "data = pd.Series([1, NA, 3.5, NA, 7])\n",
    "print(data)\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
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      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame([[1., 6.5, 3.], [1., NA, NA],\n",
    "                     [NA, NA, NA], [NA, 6.5, 3.]])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "     0    1    2\n",
       "0  1.0  6.5  3.0"
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     "execution_count": 22,
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   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
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       "     0    1    2\n",
       "0  1.0  6.5  3.0"
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     "execution_count": 23,
     "metadata": {},
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   ],
   "source": [
    "cleaned = data.dropna()\n",
    "cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0    1    2\n",
      "0  1.0  6.5  3.0\n",
      "1  1.0  NaN  NaN\n",
      "2  NaN  NaN  NaN\n",
      "3  NaN  6.5  3.0\n"
     ]
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
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       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
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     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "print(data)\n",
    "data.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "     0    1    2   4\n",
       "0  1.0  6.5  3.0 NaN\n",
       "1  1.0  NaN  NaN NaN\n",
       "2  NaN  NaN  NaN NaN\n",
       "3  NaN  6.5  3.0 NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4] = NA\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(axis=1, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
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       "Index: [0, 1, 2, 3]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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    }
   ],
   "source": [
    "data.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>-0.895626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.974236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.989479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.027522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.587219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912       NaN       NaN\n",
       "1 -0.895626       NaN       NaN\n",
       "2  0.974236       NaN -1.989479\n",
       "3  1.027522       NaN -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(7, 3))\n",
    "df.iloc[:4, 1] = NA\n",
    "df.iloc[:2, 2] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0 -0.572912       NaN       NaN\n",
      "1 -0.895626       NaN       NaN\n",
      "2  0.974236       NaN -1.989479\n",
      "3  1.027522       NaN -0.587219\n",
      "4 -0.278506 -0.773305 -0.779209\n",
      "5 -1.607480  0.428635 -0.327336\n",
      "6  0.121959 -0.133635  1.071005\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>5</th>\n",
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       "      <td>-0.327336</td>\n",
       "    </tr>\n",
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       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
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       "      <td>1.071005</td>\n",
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       "</div>"
      ],
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       "          0         1         2\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0 -0.572912       NaN       NaN\n",
      "1 -0.895626       NaN       NaN\n",
      "2  0.974236       NaN -1.989479\n",
      "3  1.027522       NaN -0.587219\n",
      "4 -0.278506 -0.773305 -0.779209\n",
      "5 -1.607480  0.428635 -0.327336\n",
      "6  0.121959 -0.133635  1.071005\n"
     ]
    },
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "2  0.974236       NaN -1.989479\n",
       "3  1.027522       NaN -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[1].notnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling In Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.895626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.974236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.989479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.027522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.587219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912       NaN       NaN\n",
       "1 -0.895626       NaN       NaN\n",
       "2  0.974236       NaN -1.989479\n",
       "3  1.027522       NaN -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0 -0.572912       NaN       NaN\n",
      "1 -0.895626       NaN       NaN\n",
      "2  0.974236       NaN -1.989479\n",
      "3  1.027522       NaN -0.587219\n",
      "4 -0.278506 -0.773305 -0.779209\n",
      "5 -1.607480  0.428635 -0.327336\n",
      "6  0.121959 -0.133635  1.071005\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
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       "      <td>1.027522</td>\n",
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       "      <td>-0.587219</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912  0.000000  0.000000\n",
       "1 -0.895626  0.000000  0.000000\n",
       "2  0.974236  0.000000 -1.989479\n",
       "3  1.027522  0.000000 -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.895626</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.974236</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-1.989479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.027522</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-0.587219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912  5.000000  7.000000\n",
       "1 -0.895626  5.000000  7.000000\n",
       "2  0.974236  5.000000 -1.989479\n",
       "3  1.027522  5.000000 -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({1:5, 2:7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -0.175829\n",
      "1   -0.159435\n",
      "2   -0.522448\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <td>-0.159435</td>\n",
       "      <td>-1.989479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.027522</td>\n",
       "      <td>-0.159435</td>\n",
       "      <td>-0.587219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912 -0.159435 -0.522448\n",
       "1 -0.895626 -0.159435 -0.522448\n",
       "2  0.974236 -0.159435 -1.989479\n",
       "3  1.027522 -0.159435 -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.mean())\n",
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>-0.895626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.974236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.989479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.027522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.587219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912       NaN       NaN\n",
       "1 -0.895626       NaN       NaN\n",
       "2  0.974236       NaN -1.989479\n",
       "3  1.027522       NaN -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(df.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>-1.989479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.027522</td>\n",
       "      <td>-0.159435</td>\n",
       "      <td>-0.587219</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>-0.278506</td>\n",
       "      <td>-0.773305</td>\n",
       "      <td>-0.779209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.607480</td>\n",
       "      <td>0.428635</td>\n",
       "      <td>-0.327336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.121959</td>\n",
       "      <td>-0.133635</td>\n",
       "      <td>1.071005</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.572912 -0.159435 -0.522448\n",
       "1 -0.895626 -0.159435 -0.522448\n",
       "2  0.974236 -0.159435 -1.989479\n",
       "3  1.027522 -0.159435 -0.587219\n",
       "4 -0.278506 -0.773305 -0.779209\n",
       "5 -1.607480  0.428635 -0.327336\n",
       "6  0.121959 -0.133635  1.071005"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.519422</td>\n",
       "      <td>0.160851</td>\n",
       "      <td>0.704004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.451904</td>\n",
       "      <td>0.041461</td>\n",
       "      <td>-1.902933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.783414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.525482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.138853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.361466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.790641</td>\n",
       "      <td>-1.625916</td>\n",
       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414       NaN -1.525482\n",
       "3  2.138853       NaN       NaN\n",
       "4  0.361466       NaN       NaN\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6, 3))\n",
    "df.iloc[2:-1, 1] = NA\n",
    "df.iloc[3:-1, 2] = NA\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.519422  0.160851  0.704004\n",
      "1  0.451904  0.041461 -1.902933\n",
      "2 -1.783414       NaN -1.525482\n",
      "3  2.138853       NaN       NaN\n",
      "4  0.361466       NaN       NaN\n",
      "5 -1.790641 -1.625916  0.024016\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
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       "    <tr>\n",
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       "      <td>-1.790641</td>\n",
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       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414  0.041461 -1.525482\n",
       "3  2.138853  0.041461 -1.525482\n",
       "4  0.361466  0.041461 -1.525482\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.519422  0.160851  0.704004\n",
      "1  0.451904  0.041461 -1.902933\n",
      "2 -1.783414       NaN -1.525482\n",
      "3  2.138853       NaN       NaN\n",
      "4  0.361466       NaN       NaN\n",
      "5 -1.790641 -1.625916  0.024016\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
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       "      <th>3</th>\n",
       "      <td>2.138853</td>\n",
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       "    <tr>\n",
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       "      <td>0.361466</td>\n",
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       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.790641</td>\n",
       "      <td>-1.625916</td>\n",
       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414 -1.625916 -1.525482\n",
       "3  2.138853 -1.625916  0.024016\n",
       "4  0.361466 -1.625916  0.024016\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.519422  0.160851  0.704004\n",
      "1  0.451904  0.041461 -1.902933\n",
      "2 -1.783414       NaN -1.525482\n",
      "3  2.138853       NaN       NaN\n",
      "4  0.361466       NaN       NaN\n",
      "5 -1.790641 -1.625916  0.024016\n"
     ]
    },
    {
     "data": {
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       "      <td>-1.525482</td>\n",
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       "      <th>3</th>\n",
       "      <td>2.138853</td>\n",
       "      <td>2.138853</td>\n",
       "      <td>2.138853</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.361466</td>\n",
       "      <td>0.361466</td>\n",
       "      <td>0.361466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.790641</td>\n",
       "      <td>-1.625916</td>\n",
       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414 -1.783414 -1.525482\n",
       "3  2.138853  2.138853  2.138853\n",
       "4  0.361466  0.361466  0.361466\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(method='ffill', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.519422  0.160851  0.704004\n",
      "1  0.451904  0.041461 -1.902933\n",
      "2 -1.783414       NaN -1.525482\n",
      "3  2.138853       NaN       NaN\n",
      "4  0.361466       NaN       NaN\n",
      "5 -1.790641 -1.625916  0.024016\n"
     ]
    },
    {
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       "      <th>2</th>\n",
       "      <td>-1.783414</td>\n",
       "      <td>-1.525482</td>\n",
       "      <td>-1.525482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.138853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.361466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.790641</td>\n",
       "      <td>-1.625916</td>\n",
       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414 -1.525482 -1.525482\n",
       "3  2.138853       NaN       NaN\n",
       "4  0.361466       NaN       NaN\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(method='bfill', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.519422  0.160851  0.704004\n",
      "1  0.451904  0.041461 -1.902933\n",
      "2 -1.783414       NaN -1.525482\n",
      "3  2.138853       NaN       NaN\n",
      "4  0.361466       NaN       NaN\n",
      "5 -1.790641 -1.625916  0.024016\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.783414</td>\n",
       "      <td>0.041461</td>\n",
       "      <td>-1.525482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.138853</td>\n",
       "      <td>0.041461</td>\n",
       "      <td>-1.525482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.361466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.525482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.790641</td>\n",
       "      <td>-1.625916</td>\n",
       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414  0.041461 -1.525482\n",
       "3  2.138853  0.041461 -1.525482\n",
       "4  0.361466       NaN -1.525482\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(method='ffill', limit=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    3.833333\n",
       "2    3.500000\n",
       "3    3.833333\n",
       "4    7.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1., NA, 3.5, NA, 7])\n",
    "data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.519422  0.160851  0.704004\n",
      "1  0.451904  0.041461 -1.902933\n",
      "2 -1.783414       NaN -1.525482\n",
      "3  2.138853       NaN       NaN\n",
      "4  0.361466       NaN       NaN\n",
      "5 -1.790641 -1.625916  0.024016\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>0.704004</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.451904</td>\n",
       "      <td>0.041461</td>\n",
       "      <td>-1.902933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.783414</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.525482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.138853</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.361466</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.790641</td>\n",
       "      <td>-1.625916</td>\n",
       "      <td>0.024016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.519422  0.160851  0.704004\n",
       "1  0.451904  0.041461 -1.902933\n",
       "2 -1.783414  2.000000 -1.525482\n",
       "3  2.138853  2.000000  2.000000\n",
       "4  0.361466  2.000000  2.000000\n",
       "5 -1.790641 -1.625916  0.024016"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.fillna(2)\n",
    "df.fillna(value=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2].isna().sum() / len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.2 Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Duplicates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  two   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  one   3\n",
       "5  two   4\n",
       "6  two   4"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'k1': ['one', 'two'] * 3 + ['two'],\n",
    "                     'k2': [1, 1, 2, 3, 3, 4, 4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "6  two   4"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>k1</th>\n",
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    "data[~data.duplicated()]"
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    "data.drop_duplicates()"
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   "cell_type": "code",
   "execution_count": 67,
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    "data.drop_duplicates(subset=\"k1\")"
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   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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    "data.drop_duplicates(subset=[\"k1\",'k2'])"
   ]
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   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "    k1  k2  v1\n",
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       "5  two   4   5\n",
       "6  two   4   6"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data['v1'] = range(7)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "    k1  k2  v1\n",
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       "1  two   1   1"
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     "execution_count": 70,
     "metadata": {},
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    "data.drop_duplicates(['k1'])"
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   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "    k1  k2  v1\n",
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       "3  two   3   3\n",
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       "6  two   4   6"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(['k1', 'k2'], keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>nova lox</td>\n",
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      ],
      "text/plain": [
       "          food  ounces\n",
       "0        bacon     4.0\n",
       "1  pulled pork     3.0\n",
       "2        bacon    12.0\n",
       "3     Pastrami     6.0\n",
       "4  corned beef     7.5\n",
       "5        Bacon     8.0\n",
       "6     pastrami     3.0\n",
       "7    honey ham     5.0\n",
       "8     nova lox     6.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'food': ['bacon', 'pulled pork', 'bacon',\n",
    "                              'Pastrami', 'corned beef', 'Bacon',\n",
    "                              'pastrami', 'honey ham', 'nova lox'],\n",
    "                     'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bacon': 'pig',\n",
       " 'pulled pork': 'pig',\n",
       " 'pastrami': 'cow',\n",
       " 'corned beef': 'cow',\n",
       " 'honey ham': 'pig',\n",
       " 'nova lox': 'salmon'}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meat_to_animal = {\n",
    " 'bacon': 'pig',\n",
    " 'pulled pork': 'pig',\n",
    " 'pastrami': 'cow',\n",
    " 'corned beef': 'cow',\n",
    " 'honey ham': 'pig',\n",
    " 'nova lox': 'salmon'\n",
    "}\n",
    "meat_to_animal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          bacon\n",
       "1    pulled pork\n",
       "2          bacon\n",
       "3       pastrami\n",
       "4    corned beef\n",
       "5          bacon\n",
       "6       pastrami\n",
       "7      honey ham\n",
       "8       nova lox\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowercased = data['food'].str.lower()\n",
    "lowercased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces  animal\n",
       "0        bacon     4.0     pig\n",
       "1  pulled pork     3.0     pig\n",
       "2        bacon    12.0     pig\n",
       "3     Pastrami     6.0     cow\n",
       "4  corned beef     7.5     cow\n",
       "5        Bacon     8.0     pig\n",
       "6     pastrami     3.0     cow\n",
       "7    honey ham     5.0     pig\n",
       "8     nova lox     6.0  salmon"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['animal'] = lowercased.map(meat_to_animal)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       pig\n",
       "1       pig\n",
       "2       pig\n",
       "3       cow\n",
       "4       cow\n",
       "5       pig\n",
       "6       cow\n",
       "7       pig\n",
       "8    salmon\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['food'].map(lambda x: meat_to_animal[x.lower()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replacing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1    -999.0\n",
       "2       2.0\n",
       "3    -999.0\n",
       "4   -1000.0\n",
       "5       3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1., -999., 2., -999., -1000., 3.])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       NaN\n",
       "2       2.0\n",
       "3       NaN\n",
       "4   -1000.0\n",
       "5       3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace(-999, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    0.0\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], [np.nan, 0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    0.0\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace({-999: np.nan, -1000: 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renaming Axis Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.arange(12).reshape((3, 4)),\n",
    "                    index=['Ohio', 'Colorado', 'New York'],\n",
    "                    columns=['one', 'two', 'three', 'four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = lambda x: x[:4].upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OHIO', 'COLO', 'NEW '], dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.map(transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OHIO</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLO</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      one  two  three  four\n",
       "OHIO    0    1      2     3\n",
       "COLO    4    5      6     7\n",
       "NEW     8    9     10    11"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = data.index.map(transform)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ONE</th>\n",
       "      <th>TWO</th>\n",
       "      <th>THREE</th>\n",
       "      <th>FOUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colo</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ONE  TWO  THREE  FOUR\n",
       "Ohio    0    1      2     3\n",
       "Colo    4    5      6     7\n",
       "New     8    9     10    11"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index=str.title, columns=str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>peekaboo</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INDIANA</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLO</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         one  two  peekaboo  four\n",
       "INDIANA    0    1         2     3\n",
       "COLO       4    5         6     7\n",
       "NEW        8    9        10    11"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index={'OHIO': 'INDIANA'},\n",
    "            columns={'three': 'peekaboo'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discretization and Binning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 25], (18, 25], (18, 25], (25, 40], (18, 25], ..., (25, 40], (60, 120], (40, 60], (40, 60], (25, 40]]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [(18, 25] < (25, 40] < (40, 60] < (60, 120]]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]\n",
    "slab = [18,25,40,60,120]\n",
    "\n",
    "cat = pd.cut(ages, slab)\n",
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 1, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(18, 25], (25, 40], (40, 60], (60, 120]],\n",
       "              closed='right',\n",
       "              dtype='interval[int64]')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 25]     5\n",
       "(25, 40]     4\n",
       "(40, 60]     2\n",
       "(60, 120]    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[18, 25), [18, 25), [25, 40), [25, 40), [18, 25), ..., [25, 40), [60, 120), [40, 60), [40, 60), [25, 40)]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [[18, 25) < [25, 40) < [40, 60) < [60, 120)]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(ages, slab,right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Youth, Youth, YoungAdult, YoungAdult, Youth, ..., YoungAdult, Senior, MiddleAged, MiddleAged, YoungAdult]\n",
       "Length: 12\n",
       "Categories (4, object): [Youth < YoungAdult < MiddleAged < Senior]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']\n",
    "pd.cut(ages, slab,right=False, labels=group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0432, 0.279]    3\n",
       "(0.279, 0.514]     6\n",
       "(0.514, 0.748]     3\n",
       "(0.748, 0.983]     8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.rand(20)\n",
    "pd.cut(data,4).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.6719999999999997, -0.634]    250\n",
       "(-0.634, 0.0128]                 250\n",
       "(0.0128, 0.656]                  250\n",
       "(0.656, 3.087]                   250\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(1000) # Normally distribut\n",
    "cats = pd.qcut(data, 4) # Cut into quartiles\n",
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.6719999999999997, -1.32]    100\n",
       "(-1.32, -0.245]                 300\n",
       "(-0.245, 0.501]                 300\n",
       "(0.501, 3.087]                  300\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(data, [0,.1, .4, .7, 1]).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting and Filtering Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.026852</td>\n",
       "      <td>0.004152</td>\n",
       "      <td>0.045912</td>\n",
       "      <td>-0.000521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.996633</td>\n",
       "      <td>1.002402</td>\n",
       "      <td>0.982907</td>\n",
       "      <td>1.013698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.814995</td>\n",
       "      <td>-3.179195</td>\n",
       "      <td>-2.801545</td>\n",
       "      <td>-3.306253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.623434</td>\n",
       "      <td>-0.673773</td>\n",
       "      <td>-0.644449</td>\n",
       "      <td>-0.674137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.005256</td>\n",
       "      <td>0.008046</td>\n",
       "      <td>0.045728</td>\n",
       "      <td>0.009155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.681104</td>\n",
       "      <td>0.631754</td>\n",
       "      <td>0.706802</td>\n",
       "      <td>0.673209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.125245</td>\n",
       "      <td>3.352365</td>\n",
       "      <td>3.211923</td>\n",
       "      <td>3.476086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.026852     0.004152     0.045912    -0.000521\n",
       "std       0.996633     1.002402     0.982907     1.013698\n",
       "min      -3.814995    -3.179195    -2.801545    -3.306253\n",
       "25%      -0.623434    -0.673773    -0.644449    -0.674137\n",
       "50%       0.005256     0.008046     0.045728     0.009155\n",
       "75%       0.681104     0.631754     0.706802     0.673209\n",
       "max       3.125245     3.352365     3.211923     3.476086"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.random.randn(1000, 4))\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -1.666930\n",
       "1      0.098160\n",
       "2      0.889703\n",
       "3     -0.712264\n",
       "4     -0.036302\n",
       "         ...   \n",
       "995   -0.922939\n",
       "996   -0.030337\n",
       "997   -1.628738\n",
       "998   -0.396577\n",
       "999    1.320330\n",
       "Name: 2, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = data[2]\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "340    3.211923\n",
       "347    3.159968\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col[np.abs(col) > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>3.125245</td>\n",
       "      <td>-0.511147</td>\n",
       "      <td>-1.204394</td>\n",
       "      <td>-0.762351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>1.310247</td>\n",
       "      <td>0.496506</td>\n",
       "      <td>-1.065768</td>\n",
       "      <td>-3.292343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>-0.176473</td>\n",
       "      <td>0.434266</td>\n",
       "      <td>0.872243</td>\n",
       "      <td>-3.108708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>-3.044199</td>\n",
       "      <td>-0.162408</td>\n",
       "      <td>-1.036904</td>\n",
       "      <td>1.152428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>1.669273</td>\n",
       "      <td>-0.068263</td>\n",
       "      <td>3.211923</td>\n",
       "      <td>-2.048441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2.155585</td>\n",
       "      <td>-0.387555</td>\n",
       "      <td>3.159968</td>\n",
       "      <td>-0.703138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>3.057494</td>\n",
       "      <td>0.322947</td>\n",
       "      <td>2.660515</td>\n",
       "      <td>-0.502255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>1.682280</td>\n",
       "      <td>3.040520</td>\n",
       "      <td>0.820684</td>\n",
       "      <td>0.460881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>-3.814995</td>\n",
       "      <td>0.137341</td>\n",
       "      <td>1.406870</td>\n",
       "      <td>-2.398559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>1.011214</td>\n",
       "      <td>-1.245577</td>\n",
       "      <td>0.669296</td>\n",
       "      <td>3.476086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>1.035967</td>\n",
       "      <td>-0.539081</td>\n",
       "      <td>-0.794766</td>\n",
       "      <td>-3.306253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>-0.468953</td>\n",
       "      <td>3.352365</td>\n",
       "      <td>1.477727</td>\n",
       "      <td>-0.098911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>-0.683350</td>\n",
       "      <td>-3.179195</td>\n",
       "      <td>-0.877304</td>\n",
       "      <td>2.479147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.233757</td>\n",
       "      <td>3.037499</td>\n",
       "      <td>-0.392639</td>\n",
       "      <td>-0.511670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "171  3.125245 -0.511147 -1.204394 -0.762351\n",
       "221  1.310247  0.496506 -1.065768 -3.292343\n",
       "272 -0.176473  0.434266  0.872243 -3.108708\n",
       "300 -3.044199 -0.162408 -1.036904  1.152428\n",
       "340  1.669273 -0.068263  3.211923 -2.048441\n",
       "347  2.155585 -0.387555  3.159968 -0.703138\n",
       "652  3.057494  0.322947  2.660515 -0.502255\n",
       "670  1.682280  3.040520  0.820684  0.460881\n",
       "828 -3.814995  0.137341  1.406870 -2.398559\n",
       "885  1.011214 -1.245577  0.669296  3.476086\n",
       "913  1.035967 -0.539081 -0.794766 -3.306253\n",
       "939 -0.468953  3.352365  1.477727 -0.098911\n",
       "955 -0.683350 -3.179195 -0.877304  2.479147\n",
       "975  0.233757  3.037499 -0.392639 -0.511670"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(np.abs(data)>3).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.185997</td>\n",
       "      <td>-1.984483</td>\n",
       "      <td>-1.666930</td>\n",
       "      <td>0.930354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.518777</td>\n",
       "      <td>0.401362</td>\n",
       "      <td>0.098160</td>\n",
       "      <td>0.730277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.103558</td>\n",
       "      <td>-0.113518</td>\n",
       "      <td>0.889703</td>\n",
       "      <td>-0.246225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.979450</td>\n",
       "      <td>-0.080273</td>\n",
       "      <td>-0.712264</td>\n",
       "      <td>-0.162991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.395216</td>\n",
       "      <td>-1.207483</td>\n",
       "      <td>-0.036302</td>\n",
       "      <td>-1.549586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>-1.032182</td>\n",
       "      <td>0.686207</td>\n",
       "      <td>-0.922939</td>\n",
       "      <td>-0.788503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-0.160061</td>\n",
       "      <td>1.516310</td>\n",
       "      <td>-0.030337</td>\n",
       "      <td>0.332903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-0.411915</td>\n",
       "      <td>0.963837</td>\n",
       "      <td>-1.628738</td>\n",
       "      <td>-1.740135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.108912</td>\n",
       "      <td>-0.221179</td>\n",
       "      <td>-0.396577</td>\n",
       "      <td>-0.013862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>-1.766656</td>\n",
       "      <td>1.058305</td>\n",
       "      <td>1.320330</td>\n",
       "      <td>-0.510937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "0   -0.185997 -1.984483 -1.666930  0.930354\n",
       "1   -0.518777  0.401362  0.098160  0.730277\n",
       "2    0.103558 -0.113518  0.889703 -0.246225\n",
       "3    0.979450 -0.080273 -0.712264 -0.162991\n",
       "4    0.395216 -1.207483 -0.036302 -1.549586\n",
       "..        ...       ...       ...       ...\n",
       "995 -1.032182  0.686207 -0.922939 -0.788503\n",
       "996 -0.160061  1.516310 -0.030337  0.332903\n",
       "997 -0.411915  0.963837 -1.628738 -1.740135\n",
       "998  0.108912 -0.221179 -0.396577 -0.013862\n",
       "999 -1.766656  1.058305  1.320330 -0.510937\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[np.abs(data) > 3] = np.sign(data) * 3\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.027529</td>\n",
       "      <td>0.003901</td>\n",
       "      <td>0.045540</td>\n",
       "      <td>-0.000289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.993125</td>\n",
       "      <td>1.000495</td>\n",
       "      <td>0.981751</td>\n",
       "      <td>1.009979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-2.801545</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.623434</td>\n",
       "      <td>-0.673773</td>\n",
       "      <td>-0.644449</td>\n",
       "      <td>-0.674137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.005256</td>\n",
       "      <td>0.008046</td>\n",
       "      <td>0.045728</td>\n",
       "      <td>0.009155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.681104</td>\n",
       "      <td>0.631754</td>\n",
       "      <td>0.706802</td>\n",
       "      <td>0.673209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.027529     0.003901     0.045540    -0.000289\n",
       "std       0.993125     1.000495     0.981751     1.009979\n",
       "min      -3.000000    -3.000000    -2.801545    -3.000000\n",
       "25%      -0.623434    -0.673773    -0.644449    -0.674137\n",
       "50%       0.005256     0.008046     0.045728     0.009155\n",
       "75%       0.681104     0.631754     0.706802     0.673209\n",
       "max       3.000000     3.000000     3.000000     3.000000"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>4</th>\n",
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       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "     0    1    2    3\n",
       "0 -1.0 -1.0 -1.0  1.0\n",
       "1 -1.0  1.0  1.0  1.0\n",
       "2  1.0 -1.0  1.0 -1.0\n",
       "3  1.0 -1.0 -1.0 -1.0\n",
       "4  1.0 -1.0 -1.0 -1.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sign(data).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permutation and Random Sampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    0   1   2   3\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15\n",
       "4  16  17  18  19"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.arange(5 * 4).reshape((5, 4)))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 0, 4, 2])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler = np.random.permutation(5)\n",
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    0   1   2   3\n",
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       "3  12  13  14  15\n",
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       "4  16  17  18  19\n",
       "2   8   9  10  11"
      ]
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     "execution_count": 118,
     "metadata": {},
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   "source": [
    "df.take(sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    0   1   2   3\n",
       "3  12  13  14  15\n",
       "0   0   1   2   3\n",
       "4  16  17  18  19"
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     "execution_count": 119,
     "metadata": {},
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   ],
   "source": [
    "df.sample(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -1\n",
       "3    6\n",
       "0    5\n",
       "0    5\n",
       "2   -1\n",
       "0    5\n",
       "2   -1\n",
       "2   -1\n",
       "0    5\n",
       "1    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices = pd.Series([5, 7, -1, 6, 4])\n",
    "draws = choices.sample(n=10, replace=True)\n",
    "draws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    7\n",
       "0    5\n",
       "4    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices = pd.Series([5, 7, -1, 6, 4])\n",
    "draws = choices.sample(n=3)\n",
    "draws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Indicator/Dummy Variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>key</th>\n",
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       "      <td>C</td>\n",
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       "      <th>4</th>\n",
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       "      <td>C</td>\n",
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       "      <td>A</td>\n",
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       "      <th>7</th>\n",
       "      <td>B</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  key\n",
       "0   A\n",
       "1   A\n",
       "2   B\n",
       "3   C\n",
       "4   A\n",
       "5   C\n",
       "6   A\n",
       "7   B"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame({\"key\":[\"A\",\"A\",\"B\",\"C\",\"A\",\"C\",\"A\",\"B\"]})\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   A  B  C\n",
       "0  1  0  0\n",
       "1  1  0  0\n",
       "2  0  1  0\n",
       "3  0  0  1\n",
       "4  1  0  0\n",
       "5  0  0  1\n",
       "6  1  0  0\n",
       "7  0  1  0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(a.key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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