09 - Repairing Missing Data Using Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/09_dummy_missing_data.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CURVE A</th>\n",
       "      <th>CURVE B</th>\n",
       "      <th>CURVE C</th>\n",
       "      <th>CURVE C1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101.0</th>\n",
       "      <td>145.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101.5</th>\n",
       "      <td>96.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.0</th>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.5</th>\n",
       "      <td>51.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.0</th>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.5</th>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.5</th>\n",
       "      <td>117.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.5</th>\n",
       "      <td>139.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "100.0      NaN     48.0     15.2      15.2\n",
       "100.5      NaN     15.0     26.1      26.1\n",
       "101.0    145.0     51.0     16.2      16.2\n",
       "101.5     96.0     46.0     18.9      18.9\n",
       "102.0    137.0      NaN     17.9      17.9\n",
       "102.5     51.0     53.0     24.3      24.3\n",
       "103.0      2.0     35.0     22.4      22.4\n",
       "103.5     15.0     17.0     17.4      17.4\n",
       "104.0    104.0      NaN     19.8       NaN\n",
       "104.5     10.0     36.0     24.4       NaN\n",
       "105.0     32.0      3.0     15.2       NaN\n",
       "105.5    117.0     26.0     21.1       NaN\n",
       "106.0     19.0     97.0     22.6      22.6\n",
       "106.5      6.0      NaN     22.8      22.8\n",
       "107.0     55.0     88.0     22.4      22.4\n",
       "107.5    139.0      4.0     15.6      15.6\n",
       "108.0     95.0      NaN     29.7      29.7\n",
       "108.5     37.0     97.0     22.6       NaN\n",
       "109.0    108.0      NaN     22.3      22.3\n",
       "109.5      NaN     95.0     17.5      17.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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AQA+45ppr8thjj6W/vz+llNZzNohQDQAAAADQ5YaGhjJ//vzstddeOeigg1rP2WBCNQAAAABAl/vqV7+ahx9+OLNnz+66q6kToRoAAAAAoKsNDQ1l3rx5ec1rXpNDDz209ZyNIlQDAAAAAHSxr33ta3nwwQe78t7UawnVAAAAAABdas2aNZk3b1523XXXHH744a3nbLTJrQcATHTTp0/PokWLWs8AGFODg4OtJwAAQE+68cYbc9999+WP//iPM2lS916X3L3LAQAAAAAmsFprBgYG8iu/8is58sgjW8/ZJEI1AAAAAEAX+sY3vpG77747p59+evr6+lrP2SRCNQAAAABAl1l7NfVOO+2Uo48+uvWcTSZUAwAAAAB0mW9961u58847c9ppp2Xy5O5/KUKhGgAAAACgywwMDGSHHXbIscce23rKqBCqAQAAAAC6yODgYG677baceuqpmTJlSus5o0KoBgAAAADoIgMDA9luu+3y7ne/u/WUUSNUAwAAAAB0idtvvz3f/va3c+qpp2bq1Kmt54waoRoAAAAAoEsMDAxk2223zfHHH996yqgSqgEAAAAAusD3v//9fPOb38xJJ52ULbbYovWcUSVUAwAAAAB0gYGBgWy99dY58cQTW08ZdUI1AAAAAECHu/fee3PjjTdm5syZ2WqrrVrPGXVCNQAAAABAhxsYGMiWW26Z973vfa2njAmhGgAAAACgg91///25/vrr8973vjdbb7116zljQqgGAAAAAOhg8+bNy9SpU3PSSSe1njJmhGoAAAAAgA710EMP5dprr8173vOebLvttq3njBmhGgAAAACgQy1YsCCTJ0/OySef3HrKmBKqAQAAAAA61JIlSzJt2rRMmzat9ZQxJVQDAAAAAHSomTNn5sknn8yXv/zl1lPGlFANAAAAANCh3vjGN+Z1r3td5s+fn9WrV7eeM2aEagAAAACADlVKSX9/fx577LFcc801reeMGaEaAAAAAKCD/eqv/mpe+9rX5oILLsjQ0FDrOWNCqAYAAAAA6GBrr6p++OGH89WvfrX1nDEhVAMAAAAAdLgZM2bkNa95TebNm9eTV1UL1QAAAAAAHW7SpEmZPXt2HnzwwXzta19rPWfUCdUAAAAAAF3g8MMPz6te9arMmzcva9asaT1nVE1uPQBgohscHMzcuXNbzwAYM+edd17rCQAA0BP6+voye/bsfOITn8iNN96Yww47rPWkUeOKagAAAACALnHkkUdm5513zsDAQGqtreeMGqEaAAAAAKBL9PX15fTTT8/dd9+db3zjG63njBqhGgAAAACgixxzzDHZcccde+qqaqEaAAAAAKCLTJ48ObNmzcqdd96Zb33rW63njAqhGgAAAACgyxx77LHZfvvtMzAw0HrKqBCqAQAAAAC6zJQpU3Lqqafmtttuy+DgYOs5m0yoBgAAAADoQscdd1y22267nriqWqgGAAAAAOhCU6dOzSmnnJJvf/vbuf3221vP2SRCNQAAAABAlzrhhBOyzTbbdP1V1UI1AAAAAECX2mKLLXLSSSflm9/8Zr7//e+3nrPRhGoAAAAAgC723ve+N9OmTevqq6qFagAAAACALrbVVltl5syZufHGG3Pvvfe2nrNRhGoAAAAAgC43c+bMbLnlll17VbVQDQAAAADQ5bbeeuuceOKJuf7663P//fe3nrPBhGoAAAAAgB5w0kknZerUqZk3b17rKRtMqAYAAAAA6AEve9nLcsIJJ+Taa6/NQw891HrOBhGqAQAAAAB6xCmnnJLJkyfnggsuaD1lgwjVAAAAAAA94uUvf3ne/e535ytf+UoeffTR1nPWm1ANAAAAANBDTjvttJRSctlll7West6EagAAAACAHrLDDjtk++23z5IlS1pPWW9CNQAAAAAATQnVAAAAAAA0JVQDAAAAANCUUA0AAAAAQFNCNQAAAAAATQnVAAAAAAA0JVQDAAAAANCUUA0AAAAAQFNCNQAAAAAATQnVAAAAAAA0JVQDAAAAANCUUA0AAAAAQFNCNQAAAAAATQnVAAAAAAA0JVQDAAAAANCUUA0AAAAAQFNCNQAAAAAATQnVAAAAAAA0JVQDAAAAANCUUA0AAAAAQFNCNQAAAAAATQnVAAAAAAA0JVQDAAAAADDqSil/VEqppZS/+UUfK1QDAAAAADCqSikHJ/nPSW5bn48XqgEAAAAAesjQ0FBWrFiRe++9N1//+tczNDQ0rl+/lLJtkguS/HqSJevzOUI1AAAAAECPGBoaytlnn52nnnoq9957bz7+8Y/n7LPPHu9Y/b+SfKHWet36foJQDQAAAADQIxYvXpw777zz395evnx57rjjjixevHhcvn4p5TeS7JHkwxvyeUI1AAAAAECPuPvuu7NixYqfeWzlypW55557xvxrl1L2SvKJJLNqrc9vyOcK1QAAAAAAPWLPPfdMX1/fzzw2derU7LHHHuPx5Q9Jsn2S20spq0spq5MckeS3R96e+vM+cfJ4rAMAAAAAYOzts88+WbNmTSZNmpRaa6ZOnZrXve51Oeigg8bjy1+W5JYXPPbPSe7O8JXWq37eJwrVAAAAAAA94vLLL8+aNWvyu7/7u3nuueeyxx575KCDDvp3V1mPhVrrU0meWvexUsqzSZ6stX7vpT5XqAYAAAAA6AHPPvtsvvCFL2TGjBmZOXNm6zkbRKgGAAAAAOgBl112WZYtW5Y5c+a0nvJvaq1vXZ+PE6oBGps+fXoWLVrUegbAmBocHGw9AQAAetry5cvz+c9/PgcddFD22muv1nM22KTWAwAAAAAA2DRXXHFFli5dmv7+/tZTNopQDQAAAADQxVauXJkLL7wwBxxwQPbbb7/WczaKUA0AAAAA0MWuvPLKLFmypKPuTb2hhGoAAAAAgC61atWqLFiwIPvvv3/e8IY3tJ6z0YRqAAAAAIAudfXVV+fxxx9Pf39/Simt52w0oRoAAAAAoAutXr068+fPz9577503v/nNredsEqEaAAAAAKALLVy4MI8++mjmzJnT1VdTJ0I1AAAAAEDXGRoaygUXXJA999wzBx98cOs5m0yoBgAAAADoMtddd10eeuihzJ49u+uvpk6EagAAAACArrJmzZrMmzcvu+22W97ylre0njMqhGoAAAAAgC5yww035P7778/s2bMzaVJvJN7e+FMAAAAAAEwAtdYMDAxkl112yRFHHNF6zqgRqgEAAAAAusRNN92Ue++9N6effnr6+vpazxk1QjUAAAAAQBdYezX1zjvvnKOOOqr1nFElVAMAAAAAdIGbb7453//+9zNr1qyeupo6EaoBAAAAADperTXnn39+dtxxxxxzzDGt54w6oRoAAAAAoMN95zvfye23357TTjstm222Wes5o06oBgAAAADocAMDA9l+++3zzne+s/WUMSFUAwAAAAB0sO9+97sZHBzMKaeckilTprSeMyaEagAAAACADnb++ednu+22y3HHHdd6ypgRqgEAAAAAOtSdd96ZW265JSeffHI233zz1nPGjFANAAAAANCh5s+fn2222Sbvec97Wk8ZU0I1AAAAAECHuv/++3PAAQdkiy22aD1lTAnVAAAAAAAdbNKk3s+4vf8nBAAAAACgownVAAAAAAA0Nbn1AICJbnBwMHPnzm09A2DMnHfeea0nAIy56dOnZ9GiRa1nAEDXckU1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0NTk1gMAJrrp06dn0aJFrWcAjKnBwcHWEwDG1ODgYObOndt6BsCY8t+ujCVXVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU5NbD/hFFi1a1HoCwJgaHBzM3LlzW88AGDPnnXde6wkAAECHc0U1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU0I1AAAAAABNCdUAAAAAADS1XqG6lPKKUsp/KqV8sZRyTylleSllaSnlX0spHyilvOjvU0qZUUq5qpTy5Mjn3FZKmVtK6RvdPwYAAAAAAN1q8np+3ElJ/j7JI0muS/JAkh2TvC/J/0nyzlLKSbXWuvYTSinvSXJJkhVJLkryZJLjk3wmyaEjvycAAAAAABPc+obqHyQ5IcmVtdY1ax8spfxxksVJZmY4Wl8y8vg2Sf53kqEkb6213jLy+EeSXJvk/aWUU2utF47WHwQAAAAAgO60Xrf+qLVeW2u9Yt1IPfL4o0n+YeTNt67zrvcn2SHJhWsj9cjHr0jy4ZE3f2tjRwMAAAAA0DtG48UUnx/5dfU6jx058uuXX+Tjr0/yXJIZpZSpo/D1AQAAAADoYpsUqkspk5PMGXlz3Si918ivP3jh59RaVye5L8O3HXnNpnx9AAAAAAC636ZeUf3JJPsluarWevU6j2878uvSn/N5ax9/2SZ+fQAAAAAAutxGh+pSyu8lOTPJXUn6R20RAAAAAAATykaF6lLK7yb5qyR3JHlbrfXJF3zI2iumt82LW/v4Uxvz9QEAAAAA6B0bHKpLKXOT/HWS72U4Uj/6Ih/2/ZFfX/sinz85yW4ZfvHFH27o1wcAAAAAoLdsUKgupfxhks8kGcxwpP7Jz/nQa0d+PfZF3nd4ki2T3FRrXbkhXx8AAAAAgN6z3qG6lPKRDL944reSvL3W+vhLfPgXkjye5NRSypvX+T02T/JnI2/+/YbPBQAAAACg10xenw8qpfxakj9NMpTkhiS/V0p54Yf9qNb62SSptT5dSvmNDAfrRaWUC5M8meSEJHuNPH7RaPwBAAAAAADobusVqjN8T+kk6Usy9+d8zNeSfHbtG7XWy0opRyT5UJKZSTZPck+SM5L8z1pr3Yi9AAAAAAD0mPUK1bXWP0nyJxv6m9dab0zyrg39PAAAAAAAJo4NejFFAAAAAAAYbUI1AAAAAABNCdUAAAAAADQlVAMAAAAA0JRQDQAAAABAU6XW2noDAAAAAAATmCuqAQAAAABoSqgGAAAAAKApoRoAAAAAgKaEagAAAAAAmhKqAQAAAABoSqgGAAAAAKCp/x9HVuRSHL8t8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CURVE A     3\n",
       "CURVE B     5\n",
       "CURVE C     0\n",
       "CURVE C1    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>CURVE A</th>\n",
       "      <th>CURVE B</th>\n",
       "      <th>CURVE C</th>\n",
       "      <th>CURVE C1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.0</th>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.0</th>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.5</th>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.5</th>\n",
       "      <td>117.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "100.0      NaN     48.0     15.2      15.2\n",
       "100.5      NaN     15.0     26.1      26.1\n",
       "102.0    137.0      NaN     17.9      17.9\n",
       "104.0    104.0      NaN     19.8       NaN\n",
       "104.5     10.0     36.0     24.4       NaN\n",
       "105.0     32.0      3.0     15.2       NaN\n",
       "105.5    117.0     26.0     21.1       NaN\n",
       "106.5      6.0      NaN     22.8      22.8\n",
       "108.0     95.0      NaN     29.7      29.7\n",
       "108.5     37.0     97.0     22.6       NaN\n",
       "109.0    108.0      NaN     22.3      22.3\n",
       "109.5      NaN     95.0     17.5      17.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CURVE A</th>\n",
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       "      <th>103.5</th>\n",
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       "      <td>17.4</td>\n",
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       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.5</th>\n",
       "      <td>139.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "101.0    145.0     51.0     16.2      16.2\n",
       "101.5     96.0     46.0     18.9      18.9\n",
       "102.5     51.0     53.0     24.3      24.3\n",
       "103.0      2.0     35.0     22.4      22.4\n",
       "103.5     15.0     17.0     17.4      17.4\n",
       "106.0     19.0     97.0     22.6      22.6\n",
       "107.0     55.0     88.0     22.4      22.4\n",
       "107.5    139.0      4.0     15.6      15.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows containing NaN\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DEPTH</th>\n",
       "      <th></th>\n",
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       "      <th>101.0</th>\n",
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       "      <td>18.9</td>\n",
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       "      <th>106.0</th>\n",
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       "      <td>22.8</td>\n",
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       "      <th>107.0</th>\n",
       "      <td>22.4</td>\n",
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       "      <th>107.5</th>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>29.7</td>\n",
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       "    <tr>\n",
       "      <th>108.5</th>\n",
       "      <td>22.6</td>\n",
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       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>22.3</td>\n",
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       "      <th>109.5</th>\n",
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       "</div>"
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      "text/plain": [
       "       CURVE C\n",
       "DEPTH         \n",
       "100.0     15.2\n",
       "100.5     26.1\n",
       "101.0     16.2\n",
       "101.5     18.9\n",
       "102.0     17.9\n",
       "102.5     24.3\n",
       "103.0     22.4\n",
       "103.5     17.4\n",
       "104.0     19.8\n",
       "104.5     24.4\n",
       "105.0     15.2\n",
       "105.5     21.1\n",
       "106.0     22.6\n",
       "106.5     22.8\n",
       "107.0     22.4\n",
       "107.5     15.6\n",
       "108.0     29.7\n",
       "108.5     22.6\n",
       "109.0     22.3\n",
       "109.5     17.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop columns containing NaN\n",
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill the NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>103.5</th>\n",
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       "      <th>104.5</th>\n",
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       "      <th>105.0</th>\n",
       "      <td>32.0</td>\n",
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       "      <td>15.2</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.5</th>\n",
       "      <td>117.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
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       "    <tr>\n",
       "      <th>106.5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.8</td>\n",
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       "    <tr>\n",
       "      <th>107.0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.5</th>\n",
       "      <td>139.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>95.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>108.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.5</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "100.0   1000.0     48.0     15.2      15.2\n",
       "100.5   1000.0     15.0     26.1      26.1\n",
       "101.0    145.0     51.0     16.2      16.2\n",
       "101.5     96.0     46.0     18.9      18.9\n",
       "102.0    137.0   1000.0     17.9      17.9\n",
       "102.5     51.0     53.0     24.3      24.3\n",
       "103.0      2.0     35.0     22.4      22.4\n",
       "103.5     15.0     17.0     17.4      17.4\n",
       "104.0    104.0   1000.0     19.8    1000.0\n",
       "104.5     10.0     36.0     24.4    1000.0\n",
       "105.0     32.0      3.0     15.2    1000.0\n",
       "105.5    117.0     26.0     21.1    1000.0\n",
       "106.0     19.0     97.0     22.6      22.6\n",
       "106.5      6.0   1000.0     22.8      22.8\n",
       "107.0     55.0     88.0     22.4      22.4\n",
       "107.5    139.0      4.0     15.6      15.6\n",
       "108.0     95.0   1000.0     29.7      29.7\n",
       "108.5     37.0     97.0     22.6    1000.0\n",
       "109.0    108.0   1000.0     22.3      22.3\n",
       "109.5   1000.0     95.0     17.5      17.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill with a fixed value\n",
    "df.fillna(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101.0</th>\n",
       "      <td>145.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101.5</th>\n",
       "      <td>96.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.0</th>\n",
       "      <td>137.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.5</th>\n",
       "      <td>51.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.0</th>\n",
       "      <td>104.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.5</th>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.5</th>\n",
       "      <td>117.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.5</th>\n",
       "      <td>139.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>108.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.5</th>\n",
       "      <td>108.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "100.0      NaN     48.0     15.2      15.2\n",
       "100.5      NaN     15.0     26.1      26.1\n",
       "101.0    145.0     51.0     16.2      16.2\n",
       "101.5     96.0     46.0     18.9      18.9\n",
       "102.0    137.0     46.0     17.9      17.9\n",
       "102.5     51.0     53.0     24.3      24.3\n",
       "103.0      2.0     35.0     22.4      22.4\n",
       "103.5     15.0     17.0     17.4      17.4\n",
       "104.0    104.0     17.0     19.8      17.4\n",
       "104.5     10.0     36.0     24.4      17.4\n",
       "105.0     32.0      3.0     15.2      17.4\n",
       "105.5    117.0     26.0     21.1      17.4\n",
       "106.0     19.0     97.0     22.6      22.6\n",
       "106.5      6.0     97.0     22.8      22.8\n",
       "107.0     55.0     88.0     22.4      22.4\n",
       "107.5    139.0      4.0     15.6      15.6\n",
       "108.0     95.0      4.0     29.7      29.7\n",
       "108.5     37.0     97.0     22.6      29.7\n",
       "109.0    108.0     97.0     22.3      22.3\n",
       "109.5    108.0     95.0     17.5      17.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill Forward\n",
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>103.0</th>\n",
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       "      <th>103.5</th>\n",
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       "      <th>105.5</th>\n",
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       "      <td>21.1</td>\n",
       "      <td>22.6</td>\n",
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       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
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       "    <tr>\n",
       "      <th>106.5</th>\n",
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       "      <th>107.0</th>\n",
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       "      <td>88.0</td>\n",
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       "      <th>107.5</th>\n",
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       "      <th>108.0</th>\n",
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       "      <td>97.0</td>\n",
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       "      <th>108.5</th>\n",
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       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>108.0</td>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "100.0    145.0     48.0     15.2      15.2\n",
       "100.5    145.0     15.0     26.1      26.1\n",
       "101.0    145.0     51.0     16.2      16.2\n",
       "101.5     96.0     46.0     18.9      18.9\n",
       "102.0    137.0     53.0     17.9      17.9\n",
       "102.5     51.0     53.0     24.3      24.3\n",
       "103.0      2.0     35.0     22.4      22.4\n",
       "103.5     15.0     17.0     17.4      17.4\n",
       "104.0    104.0     36.0     19.8      22.6\n",
       "104.5     10.0     36.0     24.4      22.6\n",
       "105.0     32.0      3.0     15.2      22.6\n",
       "105.5    117.0     26.0     21.1      22.6\n",
       "106.0     19.0     97.0     22.6      22.6\n",
       "106.5      6.0     88.0     22.8      22.8\n",
       "107.0     55.0     88.0     22.4      22.4\n",
       "107.5    139.0      4.0     15.6      15.6\n",
       "108.0     95.0     97.0     29.7      29.7\n",
       "108.5     37.0     97.0     22.6      22.3\n",
       "109.0    108.0     95.0     22.3      22.3\n",
       "109.5      NaN     95.0     17.5      17.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill Backward\n",
    "df.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>105.5</th>\n",
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       "      <th>109.5</th>\n",
       "      <td>108.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CURVE A  CURVE B  CURVE C  CURVE C1\n",
       "DEPTH                                     \n",
       "100.0      NaN     48.0     15.2     15.20\n",
       "100.5      NaN     15.0     26.1     26.10\n",
       "101.0    145.0     51.0     16.2     16.20\n",
       "101.5     96.0     46.0     18.9     18.90\n",
       "102.0    137.0     49.5     17.9     17.90\n",
       "102.5     51.0     53.0     24.3     24.30\n",
       "103.0      2.0     35.0     22.4     22.40\n",
       "103.5     15.0     17.0     17.4     17.40\n",
       "104.0    104.0     26.5     19.8     18.44\n",
       "104.5     10.0     36.0     24.4     19.48\n",
       "105.0     32.0      3.0     15.2     20.52\n",
       "105.5    117.0     26.0     21.1     21.56\n",
       "106.0     19.0     97.0     22.6     22.60\n",
       "106.5      6.0     92.5     22.8     22.80\n",
       "107.0     55.0     88.0     22.4     22.40\n",
       "107.5    139.0      4.0     15.6     15.60\n",
       "108.0     95.0     50.5     29.7     29.70\n",
       "108.5     37.0     97.0     22.6     26.00\n",
       "109.0    108.0     96.0     22.3     22.30\n",
       "109.5    108.0     95.0     17.5     17.50"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interpolate values\n",
    "df.interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CURVE A</th>\n",
       "      <th>CURVE B</th>\n",
       "      <th>CURVE C</th>\n",
       "      <th>CURVE C1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>68.705882</td>\n",
       "      <td>48.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.5</th>\n",
       "      <td>68.705882</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101.0</th>\n",
       "      <td>145.000000</td>\n",
       "      <td>51.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.200000</td>\n",
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       "    <tr>\n",
       "      <th>101.5</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>46.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.0</th>\n",
       "      <td>137.000000</td>\n",
       "      <td>47.4</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.5</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>24.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.0</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.5</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.0</th>\n",
       "      <td>104.000000</td>\n",
       "      <td>47.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>20.753333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.5</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>20.753333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.0</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>20.753333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.5</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>20.753333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.5</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>47.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.0</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.5</th>\n",
       "      <td>139.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>47.4</td>\n",
       "      <td>29.7</td>\n",
       "      <td>29.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.5</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>20.753333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>47.4</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.5</th>\n",
       "      <td>68.705882</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CURVE A  CURVE B  CURVE C   CURVE C1\n",
       "DEPTH                                         \n",
       "100.0   68.705882     48.0     15.2  15.200000\n",
       "100.5   68.705882     15.0     26.1  26.100000\n",
       "101.0  145.000000     51.0     16.2  16.200000\n",
       "101.5   96.000000     46.0     18.9  18.900000\n",
       "102.0  137.000000     47.4     17.9  17.900000\n",
       "102.5   51.000000     53.0     24.3  24.300000\n",
       "103.0    2.000000     35.0     22.4  22.400000\n",
       "103.5   15.000000     17.0     17.4  17.400000\n",
       "104.0  104.000000     47.4     19.8  20.753333\n",
       "104.5   10.000000     36.0     24.4  20.753333\n",
       "105.0   32.000000      3.0     15.2  20.753333\n",
       "105.5  117.000000     26.0     21.1  20.753333\n",
       "106.0   19.000000     97.0     22.6  22.600000\n",
       "106.5    6.000000     47.4     22.8  22.800000\n",
       "107.0   55.000000     88.0     22.4  22.400000\n",
       "107.5  139.000000      4.0     15.6  15.600000\n",
       "108.0   95.000000     47.4     29.7  29.700000\n",
       "108.5   37.000000     97.0     22.6  20.753333\n",
       "109.0  108.000000     47.4     22.3  22.300000\n",
       "109.5   68.705882     95.0     17.5  17.500000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill with mean\n",
    "df.fillna(value=df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CURVE A</th>\n",
       "      <th>CURVE B</th>\n",
       "      <th>CURVE C</th>\n",
       "      <th>CURVE C1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68.705882</td>\n",
       "      <td>47.400000</td>\n",
       "      <td>20.720000</td>\n",
       "      <td>20.753333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51.247884</td>\n",
       "      <td>33.221766</td>\n",
       "      <td>3.951762</td>\n",
       "      <td>4.210169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>15.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>17.475000</td>\n",
       "      <td>17.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>22.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>22.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>29.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CURVE A    CURVE B    CURVE C   CURVE C1\n",
       "count   17.000000  15.000000  20.000000  15.000000\n",
       "mean    68.705882  47.400000  20.720000  20.753333\n",
       "std     51.247884  33.221766   3.951762   4.210169\n",
       "min      2.000000   3.000000  15.200000  15.200000\n",
       "25%     19.000000  21.500000  17.475000  17.450000\n",
       "50%     55.000000  46.000000  21.700000  22.300000\n",
       "75%    108.000000  70.500000  22.650000  22.700000\n",
       "max    145.000000  97.000000  29.700000  29.700000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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