HandlingMissingValues.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randint(0,10,size=(20,4)), columns = list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>11</th>\n",
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       "      <th>14</th>\n",
       "      <td>7</td>\n",
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       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
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       "      <th>16</th>\n",
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      "text/plain": [
       "    A  B  C  D\n",
       "0   3  6  7  7\n",
       "1   3  7  9  1\n",
       "2   7  2  9  0\n",
       "3   3  8  5  6\n",
       "4   4  1  1  6\n",
       "5   6  9  7  9\n",
       "6   9  0  5  8\n",
       "7   2  0  5  5\n",
       "8   7  6  6  7\n",
       "9   4  4  6  6\n",
       "10  2  6  7  4\n",
       "11  4  8  9  0\n",
       "12  0  0  7  8\n",
       "13  8  9  6  1\n",
       "14  7  8  8  8\n",
       "15  5  6  5  3\n",
       "16  1  9  7  7\n",
       "17  4  1  5  9\n",
       "18  3  3  0  6\n",
       "19  5  0  2  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randint(0,100,size=(200,1)), columns = list('T'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>19</th>\n",
       "      <td>17</td>\n",
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       "      <th>20</th>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
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       "      <th>22</th>\n",
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       "      <th>23</th>\n",
       "      <td>87</td>\n",
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       "      <th>24</th>\n",
       "      <td>39</td>\n",
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       "      <th>25</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>38</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
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       "      <th>170</th>\n",
       "      <td>84</td>\n",
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       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>61</td>\n",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>48</td>\n",
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       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "      T\n",
       "0    10\n",
       "1    76\n",
       "2    98\n",
       "3     3\n",
       "4     9\n",
       "5    69\n",
       "6    33\n",
       "7    85\n",
       "8     3\n",
       "9    93\n",
       "10   95\n",
       "11   13\n",
       "12   36\n",
       "13   68\n",
       "14   89\n",
       "15   82\n",
       "16   28\n",
       "17   47\n",
       "18   21\n",
       "19   17\n",
       "20   71\n",
       "21   14\n",
       "22   68\n",
       "23   87\n",
       "24   39\n",
       "25    5\n",
       "26   12\n",
       "27   70\n",
       "28   38\n",
       "29   12\n",
       "..   ..\n",
       "170  84\n",
       "171  19\n",
       "172  46\n",
       "173  35\n",
       "174  61\n",
       "175  79\n",
       "176  37\n",
       "177  85\n",
       "178  32\n",
       "179  13\n",
       "180  96\n",
       "181  94\n",
       "182  52\n",
       "183  98\n",
       "184  48\n",
       "185  11\n",
       "186  17\n",
       "187  15\n",
       "188  40\n",
       "189  71\n",
       "190  57\n",
       "191  68\n",
       "192  46\n",
       "193  42\n",
       "194  97\n",
       "195  78\n",
       "196  74\n",
       "197  67\n",
       "198   1\n",
       "199   9\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "rand = random.randint(0,100)\n",
    "rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[74, 7, 67, 38, 48, 86, 97, 85, 4, 62, 8, 93, 11, 19, 6, 20, 36, 3, 23, 76]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_list = random.sample(range(100),20)\n",
    "random_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
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       "      <th>9</th>\n",
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      ],
      "text/plain": [
       "    A\n",
       "0  81\n",
       "1  79\n",
       "2   2\n",
       "3  94\n",
       "4  64\n",
       "5  91\n",
       "6  76\n",
       "7  28\n",
       "8  20\n",
       "9   6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1 = pd.DataFrame(np.random.randint(0,100,size=(10,1)), columns = ['A'])\n",
    "df1\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
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       "    A\n",
       "0  81\n",
       "1  79\n",
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       "5  91\n",
       "6  76\n",
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    "df1['A'].replace(29, np.nan, inplace= True)\n",
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   "execution_count": 10,
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       "      <th>186</th>\n",
       "      <td>17</td>\n",
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       "      <th>188</th>\n",
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       "      <th>189</th>\n",
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       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>57</td>\n",
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       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>97</td>\n",
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       "      <th>195</th>\n",
       "      <td>78</td>\n",
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       "      <th>196</th>\n",
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       "..   ..\n",
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       "196  74\n",
       "197  67\n",
       "198   1\n",
       "199   9\n",
       "\n",
       "[200 rows x 1 columns]"
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     "execution_count": 10,
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    "random_list\n",
    "df.count()"
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  },
  {
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   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>188</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        T\n",
       "0    10.0\n",
       "1     NaN\n",
       "2    98.0\n",
       "3     NaN\n",
       "4     9.0\n",
       "5    69.0\n",
       "6    33.0\n",
       "7     NaN\n",
       "8     NaN\n",
       "9     NaN\n",
       "10   95.0\n",
       "11   13.0\n",
       "12    NaN\n",
       "13   68.0\n",
       "14   89.0\n",
       "15   82.0\n",
       "16   28.0\n",
       "17   47.0\n",
       "18   21.0\n",
       "19   17.0\n",
       "20   71.0\n",
       "21   14.0\n",
       "22   68.0\n",
       "23   87.0\n",
       "24   39.0\n",
       "25    5.0\n",
       "26   12.0\n",
       "27   70.0\n",
       "28    NaN\n",
       "29   12.0\n",
       "..    ...\n",
       "170  84.0\n",
       "171   NaN\n",
       "172  46.0\n",
       "173  35.0\n",
       "174  61.0\n",
       "175  79.0\n",
       "176  37.0\n",
       "177   NaN\n",
       "178  32.0\n",
       "179  13.0\n",
       "180  96.0\n",
       "181  94.0\n",
       "182  52.0\n",
       "183  98.0\n",
       "184   NaN\n",
       "185   NaN\n",
       "186  17.0\n",
       "187  15.0\n",
       "188  40.0\n",
       "189  71.0\n",
       "190  57.0\n",
       "191  68.0\n",
       "192  46.0\n",
       "193  42.0\n",
       "194   NaN\n",
       "195  78.0\n",
       "196   NaN\n",
       "197   NaN\n",
       "198   1.0\n",
       "199   9.0\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIRTY_DATASET_1 = df.copy()\n",
    "for i in range(20):\n",
    "    DIRTY_DATASET_1['T'].replace(random_list[i], np.nan, inplace= True)\n",
    "    \n",
    "    \n",
    "DB_A = DIRTY_DATASET_1.copy()\n",
    "print(DB_A.count())\n",
    "DB_B = DIRTY_DATASET_1.copy()\n",
    "print(DB_B.count())\n",
    "DB_C = DIRTY_DATASET_1.copy()\n",
    "print(DB_C.count())\n",
    "DB_D = DIRTY_DATASET_1.copy()\n",
    "print(DB_D.count())\n",
    "DB_E = DIRTY_DATASET_1.copy()\n",
    "print(DB_E.count())\n",
    "\n",
    "DIRTY_DATASET_1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T    155\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIRTY_DATASET_1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        T\n",
      "0    10.0\n",
      "1     NaN\n",
      "2    98.0\n",
      "3     NaN\n",
      "4     9.0\n",
      "5    69.0\n",
      "6    33.0\n",
      "7     NaN\n",
      "8     NaN\n",
      "9     NaN\n",
      "10   95.0\n",
      "11   13.0\n",
      "12    NaN\n",
      "13   68.0\n",
      "14   89.0\n",
      "15   82.0\n",
      "16   28.0\n",
      "17   47.0\n",
      "18   21.0\n",
      "19   17.0\n",
      "20   71.0\n",
      "21   14.0\n",
      "22   68.0\n",
      "23   87.0\n",
      "24   39.0\n",
      "25    5.0\n",
      "26   12.0\n",
      "27   70.0\n",
      "28    NaN\n",
      "29   12.0\n",
      "..    ...\n",
      "170  84.0\n",
      "171   NaN\n",
      "172  46.0\n",
      "173  35.0\n",
      "174  61.0\n",
      "175  79.0\n",
      "176  37.0\n",
      "177   NaN\n",
      "178  32.0\n",
      "179  13.0\n",
      "180  96.0\n",
      "181  94.0\n",
      "182  52.0\n",
      "183  98.0\n",
      "184   NaN\n",
      "185   NaN\n",
      "186  17.0\n",
      "187  15.0\n",
      "188  40.0\n",
      "189  71.0\n",
      "190  57.0\n",
      "191  68.0\n",
      "192  46.0\n",
      "193  42.0\n",
      "194   NaN\n",
      "195  78.0\n",
      "196   NaN\n",
      "197   NaN\n",
      "198   1.0\n",
      "199   9.0\n",
      "\n",
      "[200 rows x 1 columns]\n",
      "T    155\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(DB_A)\n",
    "print(DB_A.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting all observations with missing values\n",
    "DB_A = DB_A.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>68.0</td>\n",
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       "      <th>193</th>\n",
       "      <td>42.0</td>\n",
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       "      <th>195</th>\n",
       "      <td>78.0</td>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9.0</td>\n",
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       "..    ...\n",
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       "175  79.0\n",
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       "178  32.0\n",
       "179  13.0\n",
       "180  96.0\n",
       "181  94.0\n",
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       "\n",
       "[155 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DB_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T    155\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_constant = 0\n",
    "DB_B.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        T\n",
       "0    10.0\n",
       "1     0.0\n",
       "2    98.0\n",
       "3     0.0\n",
       "4     9.0\n",
       "5    69.0\n",
       "6    33.0\n",
       "7     0.0\n",
       "8     0.0\n",
       "9     0.0\n",
       "10   95.0\n",
       "11   13.0\n",
       "12    0.0\n",
       "13   68.0\n",
       "14   89.0\n",
       "15   82.0\n",
       "16   28.0\n",
       "17   47.0\n",
       "18   21.0\n",
       "19   17.0\n",
       "20   71.0\n",
       "21   14.0\n",
       "22   68.0\n",
       "23   87.0\n",
       "24   39.0\n",
       "25    5.0\n",
       "26   12.0\n",
       "27   70.0\n",
       "28    0.0\n",
       "29   12.0\n",
       "..    ...\n",
       "170  84.0\n",
       "171   0.0\n",
       "172  46.0\n",
       "173  35.0\n",
       "174  61.0\n",
       "175  79.0\n",
       "176  37.0\n",
       "177   0.0\n",
       "178  32.0\n",
       "179  13.0\n",
       "180  96.0\n",
       "181  94.0\n",
       "182  52.0\n",
       "183  98.0\n",
       "184   0.0\n",
       "185   0.0\n",
       "186  17.0\n",
       "187  15.0\n",
       "188  40.0\n",
       "189  71.0\n",
       "190  57.0\n",
       "191  68.0\n",
       "192  46.0\n",
       "193  42.0\n",
       "194   0.0\n",
       "195  78.0\n",
       "196   0.0\n",
       "197   0.0\n",
       "198   1.0\n",
       "199   9.0\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing missing values with a global constant\n",
    "DB_B['T'].replace(np.nan,global_constant, inplace= True)\n",
    "DB_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T    200\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(DB_B.count())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T    155\n",
      "dtype: int64\n",
      "T    200\n",
      "dtype: int64\n",
      "51.21935483870968\n",
      "             T\n",
      "0    10.000000\n",
      "1    51.219355\n",
      "2    98.000000\n",
      "3    51.219355\n",
      "4     9.000000\n",
      "5    69.000000\n",
      "6    33.000000\n",
      "7    51.219355\n",
      "8    51.219355\n",
      "9    51.219355\n",
      "10   95.000000\n",
      "11   13.000000\n",
      "12   51.219355\n",
      "13   68.000000\n",
      "14   89.000000\n",
      "15   82.000000\n",
      "16   28.000000\n",
      "17   47.000000\n",
      "18   21.000000\n",
      "19   17.000000\n",
      "20   71.000000\n",
      "21   14.000000\n",
      "22   68.000000\n",
      "23   87.000000\n",
      "24   39.000000\n",
      "25    5.000000\n",
      "26   12.000000\n",
      "27   70.000000\n",
      "28   51.219355\n",
      "29   12.000000\n",
      "..         ...\n",
      "170  84.000000\n",
      "171  51.219355\n",
      "172  46.000000\n",
      "173  35.000000\n",
      "174  61.000000\n",
      "175  79.000000\n",
      "176  37.000000\n",
      "177  51.219355\n",
      "178  32.000000\n",
      "179  13.000000\n",
      "180  96.000000\n",
      "181  94.000000\n",
      "182  52.000000\n",
      "183  98.000000\n",
      "184  51.219355\n",
      "185  51.219355\n",
      "186  17.000000\n",
      "187  15.000000\n",
      "188  40.000000\n",
      "189  71.000000\n",
      "190  57.000000\n",
      "191  68.000000\n",
      "192  46.000000\n",
      "193  42.000000\n",
      "194  51.219355\n",
      "195  78.000000\n",
      "196  51.219355\n",
      "197  51.219355\n",
      "198   1.000000\n",
      "199   9.000000\n",
      "\n",
      "[200 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "#Replacing missing values with mean\n",
    "print(DB_C.count())\n",
    "mymean = DB_C['T'].mean()\n",
    "#DB_C['T'].fillna(DB_C['T'].mean())\n",
    "DB_C['T'].replace(np.nan,mymean, inplace= True)\n",
    "print(DB_C.count())\n",
    "print(mymean)\n",
    "print(DB_C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T    155\n",
      "dtype: int64\n",
      "T    200\n",
      "dtype: int64\n",
      "49.0\n",
      "        T\n",
      "0    10.0\n",
      "1    49.0\n",
      "2    98.0\n",
      "3    49.0\n",
      "4     9.0\n",
      "5    69.0\n",
      "6    33.0\n",
      "7    49.0\n",
      "8    49.0\n",
      "9    49.0\n",
      "10   95.0\n",
      "11   13.0\n",
      "12   49.0\n",
      "13   68.0\n",
      "14   89.0\n",
      "15   82.0\n",
      "16   28.0\n",
      "17   47.0\n",
      "18   21.0\n",
      "19   17.0\n",
      "20   71.0\n",
      "21   14.0\n",
      "22   68.0\n",
      "23   87.0\n",
      "24   39.0\n",
      "25    5.0\n",
      "26   12.0\n",
      "27   70.0\n",
      "28   49.0\n",
      "29   12.0\n",
      "..    ...\n",
      "170  84.0\n",
      "171  49.0\n",
      "172  46.0\n",
      "173  35.0\n",
      "174  61.0\n",
      "175  79.0\n",
      "176  37.0\n",
      "177  49.0\n",
      "178  32.0\n",
      "179  13.0\n",
      "180  96.0\n",
      "181  94.0\n",
      "182  52.0\n",
      "183  98.0\n",
      "184  49.0\n",
      "185  49.0\n",
      "186  17.0\n",
      "187  15.0\n",
      "188  40.0\n",
      "189  71.0\n",
      "190  57.0\n",
      "191  68.0\n",
      "192  46.0\n",
      "193  42.0\n",
      "194  49.0\n",
      "195  78.0\n",
      "196  49.0\n",
      "197  49.0\n",
      "198   1.0\n",
      "199   9.0\n",
      "\n",
      "[200 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "#Replacing missing values with median\n",
    "print(DB_D.count())\n",
    "mymedian = DB_D['T'].median()\n",
    "#DB_C['T'].fillna(DB_C['T'].mean())\n",
    "DB_D['T'].replace(np.nan,mymedian, inplace= True)\n",
    "print(DB_D.count())\n",
    "print(mymedian)\n",
    "print(DB_D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
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       "  <tbody>\n",
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       "      <th>173</th>\n",
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       "      <th>54</th>\n",
       "      <td>77</td>\n",
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       "      <th>103</th>\n",
       "      <td>26</td>\n",
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       "      <th>193</th>\n",
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       "      <th>126</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
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       "      <th>132</th>\n",
       "      <td>59</td>\n",
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       "      <th>56</th>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>52</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      T\n",
       "173  35\n",
       "128  85\n",
       "54   77\n",
       "103  26\n",
       "60    7\n",
       "193  42\n",
       "126  56\n",
       "132  59\n",
       "56   85\n",
       "100  31\n",
       "150  88\n",
       "154  59\n",
       "119  21\n",
       "75   99\n",
       "145  25\n",
       "163  60\n",
       "43   55\n",
       "105  97\n",
       "21   14\n",
       "110  52"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = df.sample(n=20,replace=False)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      T\n",
      "57    3\n",
      "190  57\n",
      "163  60\n",
      "99   84\n",
      "91   76\n",
      "112  76\n",
      "117  81\n",
      "181  94\n",
      "146  67\n",
      "42    4\n",
      "135  45\n",
      "11   13\n",
      "191  68\n",
      "113  85\n",
      "77   95\n",
      "131  15\n",
      "195  78\n",
      "0    10\n",
      "20   71\n",
      "25    5\n",
      "      T\n",
      "94   26\n",
      "1    76\n",
      "158  89\n",
      "132  59\n",
      "96   18\n",
      "187  15\n",
      "133  66\n",
      "111  61\n",
      "157  99\n",
      "161  70\n",
      "91   76\n",
      "84   83\n",
      "42    4\n",
      "182  52\n",
      "144  45\n",
      "199   9\n",
      "71   66\n",
      "32   41\n",
      "145  25\n",
      "13   68\n",
      "      T\n",
      "22   68\n",
      "118   8\n",
      "114  56\n",
      "122  65\n",
      "73   37\n",
      "77   95\n",
      "52   72\n",
      "109  10\n",
      "138  67\n",
      "169  50\n",
      "59   82\n",
      "3     3\n",
      "197  67\n",
      "162  16\n",
      "125  10\n",
      "67   49\n",
      "65   48\n",
      "178  32\n",
      "166  74\n",
      "89   44\n",
      "      T\n",
      "112  76\n",
      "193  42\n",
      "158  89\n",
      "73   37\n",
      "115  11\n",
      "32   41\n",
      "86   40\n",
      "180  96\n",
      "31   87\n",
      "196  74\n",
      "116  90\n",
      "14   89\n",
      "101  86\n",
      "194  97\n",
      "77   95\n",
      "123  34\n",
      "104  41\n",
      "136  76\n",
      "159  28\n",
      "134  37\n",
      "      T\n",
      "54   77\n",
      "192  46\n",
      "76   49\n",
      "82   85\n",
      "132  59\n",
      "134  37\n",
      "122  65\n",
      "116  90\n",
      "150  88\n",
      "48   91\n",
      "97   52\n",
      "100  31\n",
      "141  82\n",
      "156  24\n",
      "118   8\n",
      "157  99\n",
      "135  45\n",
      "52   72\n",
      "65   48\n",
      "189  71\n",
      "      T\n",
      "36   53\n",
      "51   36\n",
      "94   26\n",
      "144  45\n",
      "160  12\n",
      "33   36\n",
      "26   12\n",
      "30    6\n",
      "43   55\n",
      "44   39\n",
      "73   37\n",
      "109  10\n",
      "31   87\n",
      "176  37\n",
      "154  59\n",
      "101  86\n",
      "34   64\n",
      "68   39\n",
      "87   66\n",
      "146  67\n",
      "      T\n",
      "131  15\n",
      "90   55\n",
      "149  53\n",
      "160  12\n",
      "187  15\n",
      "154  59\n",
      "18   21\n",
      "132  59\n",
      "75   99\n",
      "119  21\n",
      "4     9\n",
      "107  78\n",
      "114  56\n",
      "195  78\n",
      "42    4\n",
      "139  81\n",
      "186  17\n",
      "51   36\n",
      "152  28\n",
      "20   71\n",
      "      T\n",
      "80   47\n",
      "119  21\n",
      "172  46\n",
      "41    2\n",
      "69   49\n",
      "93   93\n",
      "1    76\n",
      "17   47\n",
      "183  98\n",
      "37    4\n",
      "97   52\n",
      "63   68\n",
      "151  63\n",
      "44   39\n",
      "86   40\n",
      "160  12\n",
      "134  37\n",
      "48   91\n",
      "99   84\n",
      "135  45\n",
      "      T\n",
      "175  79\n",
      "153  15\n",
      "18   21\n",
      "56   85\n",
      "3     3\n",
      "37    4\n",
      "75   99\n",
      "116  90\n",
      "11   13\n",
      "139  81\n",
      "194  97\n",
      "166  74\n",
      "154  59\n",
      "63   68\n",
      "17   47\n",
      "7    85\n",
      "4     9\n",
      "157  99\n",
      "103  26\n",
      "66   32\n",
      "      T\n",
      "110  52\n",
      "26   12\n",
      "90   55\n",
      "148  34\n",
      "55   18\n",
      "124  81\n",
      "4     9\n",
      "114  56\n",
      "0    10\n",
      "122  65\n",
      "118   8\n",
      "48   91\n",
      "103  26\n",
      "186  17\n",
      "137  70\n",
      "181  94\n",
      "73   37\n",
      "142  25\n",
      "65   48\n",
      "121  85\n"
     ]
    }
   ],
   "source": [
    "samplelist = []\n",
    "for i in range(10):\n",
    "    samplelist.append(df.sample(n=20,replace=False))\n",
    "    print(samplelist[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[54.35, 52.4, 47.65, 63.3, 60.95, 43.6, 43.35, 50.7, 54.3, 44.65]\n"
     ]
    }
   ],
   "source": [
    "mean10 = []\n",
    "for i in range(10):\n",
    "    mean10.append(samplelist[i]['T'].mean())\n",
    "print(mean10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.525"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statistics\n",
    "whole_mean = statistics.mean(mean10)\n",
    "whole_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T    155\n",
      "dtype: int64\n",
      "T    200\n",
      "dtype: int64\n",
      "          T\n",
      "0    10.000\n",
      "1    51.525\n",
      "2    98.000\n",
      "3    51.525\n",
      "4     9.000\n",
      "5    69.000\n",
      "6    33.000\n",
      "7    51.525\n",
      "8    51.525\n",
      "9    51.525\n",
      "10   95.000\n",
      "11   13.000\n",
      "12   51.525\n",
      "13   68.000\n",
      "14   89.000\n",
      "15   82.000\n",
      "16   28.000\n",
      "17   47.000\n",
      "18   21.000\n",
      "19   17.000\n",
      "20   71.000\n",
      "21   14.000\n",
      "22   68.000\n",
      "23   87.000\n",
      "24   39.000\n",
      "25    5.000\n",
      "26   12.000\n",
      "27   70.000\n",
      "28   51.525\n",
      "29   12.000\n",
      "..      ...\n",
      "170  84.000\n",
      "171  51.525\n",
      "172  46.000\n",
      "173  35.000\n",
      "174  61.000\n",
      "175  79.000\n",
      "176  37.000\n",
      "177  51.525\n",
      "178  32.000\n",
      "179  13.000\n",
      "180  96.000\n",
      "181  94.000\n",
      "182  52.000\n",
      "183  98.000\n",
      "184  51.525\n",
      "185  51.525\n",
      "186  17.000\n",
      "187  15.000\n",
      "188  40.000\n",
      "189  71.000\n",
      "190  57.000\n",
      "191  68.000\n",
      "192  46.000\n",
      "193  42.000\n",
      "194  51.525\n",
      "195  78.000\n",
      "196  51.525\n",
      "197  51.525\n",
      "198   1.000\n",
      "199   9.000\n",
      "\n",
      "[200 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "#Replacing missing values with mean of 10 samples of 20 size each\n",
    "print(DB_E.count())\n",
    "DB_E['T'].replace(np.nan,whole_mean, inplace= True)\n",
    "print(DB_E.count())\n",
    "\n",
    "print(DB_E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standard Deviation of all dataframes\n",
    "stddev_df = statistics.stdev(df['T'])\n",
    "stddev_DIRTY_DATASET_1 = DIRTY_DATASET_1['T'].std()\n",
    "stddev_DBA = statistics.stdev(DB_A['T'])\n",
    "stddev_DBB = statistics.stdev(DB_B['T'])\n",
    "stddev_DBC = statistics.stdev(DB_C['T'])\n",
    "stddev_DBD = statistics.stdev(DB_D['T'])\n",
    "stddev_DBE = statistics.stdev(DB_E['T'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.60767546441666\n",
      "26.396396587940124\n",
      "26.39639658794013\n",
      "31.606436196528488\n",
      "23.220868920965092\n",
      "23.239448354016385\n",
      "23.221221441508042\n"
     ]
    }
   ],
   "source": [
    "print(stddev_df)\n",
    "print(stddev_DIRTY_DATASET_1)\n",
    "print(stddev_DBA)\n",
    "print(stddev_DBB)\n",
    "print(stddev_DBC)\n",
    "print(stddev_DBD)\n",
    "print(stddev_DBE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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