Pandas_InClass_6 (Groupby & Missing Values) 22.03.24.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Importing Libraries </b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-04T19:06:15.654368Z",
     "start_time": "2023-04-04T19:06:10.963607Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"diamonds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype   \n",
      "---  ------   --------------  -----   \n",
      " 0   carat    53940 non-null  float64 \n",
      " 1   cut      53940 non-null  category\n",
      " 2   color    53940 non-null  category\n",
      " 3   clarity  53940 non-null  category\n",
      " 4   depth    53940 non-null  float64 \n",
      " 5   table    53940 non-null  float64 \n",
      " 6   price    53940 non-null  int64   \n",
      " 7   x        53940 non-null  float64 \n",
      " 8   y        53940 non-null  float64 \n",
      " 9   z        53940 non-null  float64 \n",
      "dtypes: category(3), float64(6), int64(1)\n",
      "memory usage: 3.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat         depth         table         price             x  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "                  y             z  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()   #istatiksel verileri getirir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
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       "      <th>min</th>\n",
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       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>carat</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>0.797940</td>\n",
       "      <td>0.474011</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.04</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>43.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>61.80</td>\n",
       "      <td>62.50</td>\n",
       "      <td>79.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>43.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>326.0</td>\n",
       "      <td>950.00</td>\n",
       "      <td>2401.00</td>\n",
       "      <td>5324.25</td>\n",
       "      <td>18823.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.71</td>\n",
       "      <td>5.70</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.72</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.54</td>\n",
       "      <td>58.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>3.538734</td>\n",
       "      <td>0.705699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.53</td>\n",
       "      <td>4.04</td>\n",
       "      <td>31.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std    min     25%      50%      75%  \\\n",
       "carat  53940.0     0.797940     0.474011    0.2    0.40     0.70     1.04   \n",
       "depth  53940.0    61.749405     1.432621   43.0   61.00    61.80    62.50   \n",
       "table  53940.0    57.457184     2.234491   43.0   56.00    57.00    59.00   \n",
       "price  53940.0  3932.799722  3989.439738  326.0  950.00  2401.00  5324.25   \n",
       "x      53940.0     5.731157     1.121761    0.0    4.71     5.70     6.54   \n",
       "y      53940.0     5.734526     1.142135    0.0    4.72     5.71     6.54   \n",
       "z      53940.0     3.538734     0.705699    0.0    2.91     3.53     4.04   \n",
       "\n",
       "            max  \n",
       "carat      5.01  \n",
       "depth     79.00  \n",
       "table     95.00  \n",
       "price  18823.00  \n",
       "x         10.74  \n",
       "y         58.90  \n",
       "z         31.80  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T  #another way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>carat</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79794</td>\n",
       "      <td>0.474011</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.04</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <td>53940</td>\n",
       "      <td>5</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>21551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <td>53940</td>\n",
       "      <td>7</td>\n",
       "      <td>G</td>\n",
       "      <td>11292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clarity</th>\n",
       "      <td>53940</td>\n",
       "      <td>8</td>\n",
       "      <td>SI1</td>\n",
       "      <td>13065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>43.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>43.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>326.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>5324.25</td>\n",
       "      <td>18823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.71</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.72</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.54</td>\n",
       "      <td>58.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.538734</td>\n",
       "      <td>0.705699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.53</td>\n",
       "      <td>4.04</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count unique    top   freq         mean          std    min    25%  \\\n",
       "carat    53940.0    NaN    NaN    NaN      0.79794     0.474011    0.2    0.4   \n",
       "cut        53940      5  Ideal  21551          NaN          NaN    NaN    NaN   \n",
       "color      53940      7      G  11292          NaN          NaN    NaN    NaN   \n",
       "clarity    53940      8    SI1  13065          NaN          NaN    NaN    NaN   \n",
       "depth    53940.0    NaN    NaN    NaN    61.749405     1.432621   43.0   61.0   \n",
       "table    53940.0    NaN    NaN    NaN    57.457184     2.234491   43.0   56.0   \n",
       "price    53940.0    NaN    NaN    NaN  3932.799722  3989.439738  326.0  950.0   \n",
       "x        53940.0    NaN    NaN    NaN     5.731157     1.121761    0.0   4.71   \n",
       "y        53940.0    NaN    NaN    NaN     5.734526     1.142135    0.0   4.72   \n",
       "z        53940.0    NaN    NaN    NaN     3.538734     0.705699    0.0   2.91   \n",
       "\n",
       "            50%      75%      max  \n",
       "carat       0.7     1.04     5.01  \n",
       "cut         NaN      NaN      NaN  \n",
       "color       NaN      NaN      NaN  \n",
       "clarity     NaN      NaN      NaN  \n",
       "depth      61.8     62.5     79.0  \n",
       "table      57.0     59.0     95.0  \n",
       "price    2401.0  5324.25  18823.0  \n",
       "x           5.7     6.54    10.74  \n",
       "y          5.71     6.54     58.9  \n",
       "z          3.53     4.04     31.8  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th colspan=\"3\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
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       "      <th>mean</th>\n",
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       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <td>326</td>\n",
       "      <td>18806</td>\n",
       "      <td>3457.541970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>326</td>\n",
       "      <td>18823</td>\n",
       "      <td>4584.257704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <td>336</td>\n",
       "      <td>18818</td>\n",
       "      <td>3981.759891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <td>327</td>\n",
       "      <td>18788</td>\n",
       "      <td>3928.864452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <td>337</td>\n",
       "      <td>18574</td>\n",
       "      <td>4358.757764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          price                    \n",
       "            min    max         mean\n",
       "cut                                \n",
       "Ideal       326  18806  3457.541970\n",
       "Premium     326  18823  4584.257704\n",
       "Very Good   336  18818  3981.759891\n",
       "Good        327  18788  3928.864452\n",
       "Fair        337  18574  4358.757764"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"cut\")[[\"price\"]].agg([min, max, np.mean])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>carat</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">0.20</th>\n",
       "      <th>D</th>\n",
       "      <td>367.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>367.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>345.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>364.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>367.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>367.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">0.21</th>\n",
       "      <th>D</th>\n",
       "      <td>386.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>386.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>326.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>375.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">0.22</th>\n",
       "      <th>D</th>\n",
       "      <td>404.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>337.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>370.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>342.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>406.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.23</th>\n",
       "      <th>D</th>\n",
       "      <td>357.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>493.355556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>326.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>497.133858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>357.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>493.013158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>354.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>447.931034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price                   \n",
       "               min    max        mean\n",
       "carat color                          \n",
       "0.20  D      367.0  367.0  367.000000\n",
       "      E      345.0  367.0  364.250000\n",
       "      F      367.0  367.0  367.000000\n",
       "      G        NaN    NaN         NaN\n",
       "      H        NaN    NaN         NaN\n",
       "      I        NaN    NaN         NaN\n",
       "      J        NaN    NaN         NaN\n",
       "0.21  D      386.0  386.0  386.000000\n",
       "      E      326.0  394.0  375.600000\n",
       "      F        NaN    NaN         NaN\n",
       "      G        NaN    NaN         NaN\n",
       "      H        NaN    NaN         NaN\n",
       "      I        NaN    NaN         NaN\n",
       "      J        NaN    NaN         NaN\n",
       "0.22  D      404.0  404.0  404.000000\n",
       "      E      337.0  404.0  370.500000\n",
       "      F      342.0  470.0  406.000000\n",
       "      G        NaN    NaN         NaN\n",
       "      H        NaN    NaN         NaN\n",
       "      I        NaN    NaN         NaN\n",
       "      J        NaN    NaN         NaN\n",
       "0.23  D      357.0  688.0  493.355556\n",
       "      E      326.0  682.0  497.133858\n",
       "      F      357.0  650.0  493.013158\n",
       "      G      354.0  550.0  447.931034"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"carat\", \"color\"])[[\"price\"]].agg([min, max, np.mean]).head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "               price                  \n",
       "                 min      max     mean\n",
       "carat color                           \n",
       "4.01  G          NaN      NaN      NaN\n",
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       "      I          NaN      NaN      NaN\n",
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       "      J      18531.0  18531.0  18531.0\n",
       "5.01  D          NaN      NaN      NaN\n",
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       "      J      18018.0  18018.0  18018.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "df.groupby([\"carat\", \"color\"])[[\"price\"]].agg([min, max, np.mean]).tail(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "          carat     depth     table     price         x         y         z\n",
       "carat  1.000000  0.028224  0.181618  0.921591  0.975094  0.951722  0.953387\n",
       "depth  0.028224  1.000000 -0.295779 -0.010647 -0.025289 -0.029341  0.094924\n",
       "table  0.181618 -0.295779  1.000000  0.127134  0.195344  0.183760  0.150929\n",
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       "z      0.953387  0.094924  0.150929  0.861249  0.970772  0.952006  1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "df.select_dtypes(include=\"number\").corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        1\n",
       "2        2\n",
       "3        1\n",
       "4        2\n",
       "        ..\n",
       "53935    0\n",
       "53936    2\n",
       "53937    3\n",
       "53938    1\n",
       "53939    0\n",
       "Name: cut, Length: 53940, dtype: category\n",
       "Categories (5, int64): [0, 1, 3, 2, 4]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cut.map({\"Ideal\":0, \"Premium\":1, \"Good\":2, \"Very Good\":3, \"Fair\":4})  #Not permanent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
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       "      <td>61.5</td>\n",
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       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
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       "      <td>3.84</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
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       "      <td>58.0</td>\n",
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       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
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       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
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       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th rowspan=\"7\" valign=\"top\">Ideal</th>\n",
       "      <th>D</th>\n",
       "      <td>2834</td>\n",
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       "      <th>E</th>\n",
       "      <td>3903</td>\n",
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       "      <th>F</th>\n",
       "      <td>3826</td>\n",
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       "      <th>G</th>\n",
       "      <td>4884</td>\n",
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       "      <td>3115</td>\n",
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       "      <td>896</td>\n",
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       "      <th rowspan=\"7\" valign=\"top\">Premium</th>\n",
       "      <th>D</th>\n",
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       "      <th>E</th>\n",
       "      <td>2337</td>\n",
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       "      <th>F</th>\n",
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       "      <td>2924</td>\n",
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       "      <td>2360</td>\n",
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       "      <td>808</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Very Good</th>\n",
       "      <th>D</th>\n",
       "      <td>1513</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>2400</td>\n",
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       "      <th>F</th>\n",
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       "      <td>2299</td>\n",
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       "      <th rowspan=\"7\" valign=\"top\">Good</th>\n",
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       "      <td>662</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>933</td>\n",
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       "      <th>F</th>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
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       "      <th>G</th>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>522</td>\n",
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       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Fair</th>\n",
       "      <th>D</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>312</td>\n",
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       "      <th>G</th>\n",
       "      <td>314</td>\n",
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       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price\n",
       "cut       color       \n",
       "Ideal     D       2834\n",
       "          E       3903\n",
       "          F       3826\n",
       "          G       4884\n",
       "          H       3115\n",
       "          I       2093\n",
       "          J        896\n",
       "Premium   D       1603\n",
       "          E       2337\n",
       "          F       2331\n",
       "          G       2924\n",
       "          H       2360\n",
       "          I       1428\n",
       "          J        808\n",
       "Very Good D       1513\n",
       "          E       2400\n",
       "          F       2164\n",
       "          G       2299\n",
       "          H       1824\n",
       "          I       1204\n",
       "          J        678\n",
       "Good      D        662\n",
       "          E        933\n",
       "          F        909\n",
       "          G        871\n",
       "          H        702\n",
       "          I        522\n",
       "          J        307\n",
       "Fair      D        163\n",
       "          E        224\n",
       "          F        312\n",
       "          G        314\n",
       "          H        303\n",
       "          I        175\n",
       "          J        119"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"cut\", \"color\"])[[\"price\"]].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <th>F</th>\n",
       "      <td>3826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <th>F</th>\n",
       "      <td>2164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <th>G</th>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <th>G</th>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <th>G</th>\n",
       "      <td>2299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <th>G</th>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>G</th>\n",
       "      <td>2924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <th>H</th>\n",
       "      <td>1824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <th>H</th>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <th>H</th>\n",
       "      <td>3115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>H</th>\n",
       "      <td>2360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <th>H</th>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <th>I</th>\n",
       "      <td>2093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <th>I</th>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>I</th>\n",
       "      <td>1428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <th>I</th>\n",
       "      <td>1204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <th>I</th>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <th>J</th>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ideal</th>\n",
       "      <th>J</th>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>J</th>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <th>J</th>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <th>J</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price\n",
       "cut       color       \n",
       "Ideal     D       2834\n",
       "Very Good D       1513\n",
       "Good      D        662\n",
       "Premium   D       1603\n",
       "Fair      D        163\n",
       "Good      E        933\n",
       "Very Good E       2400\n",
       "Premium   E       2337\n",
       "Ideal     E       3903\n",
       "Fair      E        224\n",
       "          F        312\n",
       "Premium   F       2331\n",
       "Good      F        909\n",
       "Ideal     F       3826\n",
       "Very Good F       2164\n",
       "Good      G        871\n",
       "Fair      G        314\n",
       "Very Good G       2299\n",
       "Ideal     G       4884\n",
       "Premium   G       2924\n",
       "Very Good H       1824\n",
       "Fair      H        303\n",
       "Ideal     H       3115\n",
       "Premium   H       2360\n",
       "Good      H        702\n",
       "Ideal     I       2093\n",
       "Good      I        522\n",
       "Premium   I       1428\n",
       "Very Good I       1204\n",
       "Fair      I        175\n",
       "Good      J        307\n",
       "Ideal     J        896\n",
       "Premium   J        808\n",
       "Very Good J        678\n",
       "Fair      J        119"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"cut\", \"color\"])[[\"price\"]].count().sort_values(by=\"color\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Missing Values</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Type of NaN Values</b><font color='black'>\n",
    "    \n",
    "[SOURCE01](https://www.educative.io/edpresso/what-is-the-none-keyword-in-python), \n",
    "[SOURCE02](https://stackoverflow.com/questions/21095654/what-is-a-nonetype-object),\n",
    "[SOURCE03](https://www.skytowner.com/explore/difference_between_none_and_nan_in_pandas), \n",
    "[SOURCE04](https://kegui.medium.com/what-is-the-difference-between-nan-none-pd-nan-and-np-nan-a8ee0532e2eb),\n",
    "[SOURCE05](https://stackoverflow.com/questions/53436339/difference-between-np-nan-and-np-nan),\n",
    "[SOURCE06](https://stackoverflow.com/questions/40309789/difference-between-nan-and-nan-in-python),\n",
    "[SOURCE07](https://towardsdatascience.com/navigating-the-hell-of-nans-in-python-71b12558895b)\n",
    "    \n",
    "**None**:\n",
    "\n",
    "   -It means \"nothing\" in Python.\n",
    "\n",
    "   -It is used to indicate that an object has no value in Python.\n",
    "\n",
    "   -Its type is NoneType and it has only a single value: None.\n",
    "\n",
    "   -For example, if a function does not return any value, Python automatically returns None.\n",
    "\n",
    "   \n",
    "**nan ve NaN**:\n",
    "\n",
    "   - It stands for \"Not a Number.\"\n",
    "\n",
    "   -It represents a value of the float type.\n",
    "\n",
    "   -It is used to indicate non-numeric values.\n",
    "\n",
    "   -In libraries such as Pandas and Numpy, it is also used to represent missing data.\n",
    "\n",
    "   -When comparing nan values with equality, it always returns False. That is, the expression nan == nan results in False.\n",
    "\n",
    "   -There is no technical difference between nan and NaN. Different sources or libraries may prefer different spellings, but they mean the same thing.\n",
    "\n",
    "**summary**:\n",
    "\n",
    "   -None is a unique value in Python and means \"no value.\"\n",
    "   \n",
    "   -nan or NaN represents non-numeric values and is often used for missing data or mathematically undefined operations.\n",
    "\n",
    "These two values belong to different types and are used for different situations.\n",
    "\n",
    "In practice, you often encounter NaN values in Pandas when performing data analysis. However, None is more common in general Python programming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_no = [\"P001\", \"P002\", \"P003\", \"P004\", \"P005\", \"P006\", \"P007\", \"P008\", \"P009\", \"P010\", \"P011\"]\n",
    "gender = [\"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"M\"]\n",
    "status = [\"FT\", \"PT\", \"-\", \"FT\", \"PT\", \"PT\", \"FT\", \"-\", \"PT\", \"FT\", np.nan]\n",
    "dept = [\"DS\", \"FS\", \"AWS\", \"AWS\", \"DS\", None, \"FS\", \"FS\", np.nan, \"DS\", \"AWS\"]\n",
    "V1 = np.array([2, 3, 5, np.nan, 7, 1, np.nan, 10, 14, \"-\", 6])\n",
    "V2 = np.array([8, np.nan, 5, 8, 11, np.nan, np.nan, 2, 3, 7, 9])\n",
    "salary = np.array([np.nan, 54, 59, 120, 58, 75, None, 136, 60, 125, np.nan])\n",
    "\n",
    "df0 = pd.DataFrame({\n",
    "                   \"id\" : id_no,\n",
    "                   \"gender\": gender,\n",
    "                   \"status\": status,\n",
    "                   \"dept\": dept,\n",
    "                   \"var1\" : V1,\n",
    "                   \"var2\" : V2,\n",
    "                   \"salary\" : salary\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df0.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>dept</th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P001</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P002</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>FS</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P003</td>\n",
       "      <td>M</td>\n",
       "      <td>-</td>\n",
       "      <td>AWS</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P004</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>AWS</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P005</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>DS</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P006</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P007</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>FS</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P008</td>\n",
       "      <td>F</td>\n",
       "      <td>-</td>\n",
       "      <td>FS</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P009</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P010</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>-</td>\n",
       "      <td>7.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P011</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWS</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gender status  dept var1  var2 salary\n",
       "0   P001      M     FT    DS    2   8.0    NaN\n",
       "1   P002      F     PT    FS    3   NaN     54\n",
       "2   P003      M      -   AWS    5   5.0     59\n",
       "3   P004      F     FT   AWS  nan   8.0    120\n",
       "4   P005      M     PT    DS    7  11.0     58\n",
       "5   P006      F     PT  None    1   NaN     75\n",
       "6   P007      M     FT    FS  nan   NaN   None\n",
       "7   P008      F      -    FS   10   2.0    136\n",
       "8   P009      M     PT   NaN   14   3.0     60\n",
       "9   P010      F     FT    DS    -   7.0    125\n",
       "10  P011      M    NaN   AWS    6   9.0    NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   id      11 non-null     object \n",
      " 1   gender  11 non-null     object \n",
      " 2   status  10 non-null     object \n",
      " 3   dept    9 non-null      object \n",
      " 4   var1    11 non-null     object \n",
      " 5   var2    8 non-null      float64\n",
      " 6   salary  8 non-null      object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 748.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    2.0\n",
       "4    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,np.nan, np.NaN, 2, None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 nan\n",
      "2 nan\n",
      "3 nan\n",
      "4 nan\n",
      "5 nan\n",
      "6 False\n",
      "7 False\n",
      "8 False\n",
      "9 False\n",
      "10 True\n"
     ]
    }
   ],
   "source": [
    "print(\"1\", 0 * np.nan)\n",
    "print(\"2\", np.nan - np.nan)\n",
    "print(\"3\", np.nan + np.nan)\n",
    "print(\"4\", np.nan - 10)\n",
    "print(\"5\", np.nan + 10)\n",
    "print(\"6\", np.nan == np.nan) # not equal in value, because it's undefined.\n",
    "print(\"7\", 10 > np.nan)\n",
    "print(\"8\", np.inf > np.nan) # even infinity > nan returns false because the value is undefined\n",
    "print(\"9\", float('-inf') < np.nan < float('inf')) # nan is also not a value that falls between negative and positive infinity.\n",
    "print(\"10\", np.nan is np.nan) # when queried with \"is\", it checks the id values, i.e.;\n",
    "# memory location, which are the same.\n",
    "# However, this does not mean the values are the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Detecting Missing Values</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>dept</th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P001</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P002</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>FS</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P003</td>\n",
       "      <td>M</td>\n",
       "      <td>-</td>\n",
       "      <td>AWS</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P004</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>AWS</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P005</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
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       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
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       "    <tr>\n",
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       "      <td>P006</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
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       "      <th>6</th>\n",
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       "      <td>None</td>\n",
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       "      <th>7</th>\n",
       "      <td>P008</td>\n",
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       "      <td>P009</td>\n",
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       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P010</td>\n",
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       "      <td>AWS</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "      id gender status  dept var1  var2 salary\n",
       "0   P001      M     FT    DS    2   8.0    NaN\n",
       "1   P002      F     PT    FS    3   NaN     54\n",
       "2   P003      M      -   AWS    5   5.0     59\n",
       "3   P004      F     FT   AWS  nan   8.0    120\n",
       "4   P005      M     PT    DS    7  11.0     58\n",
       "5   P006      F     PT  None    1   NaN     75\n",
       "6   P007      M     FT    FS  nan   NaN   None\n",
       "7   P008      F      -    FS   10   2.0    136\n",
       "8   P009      M     PT   NaN   14   3.0     60\n",
       "9   P010      F     FT    DS    -   7.0    125\n",
       "10  P011      M    NaN   AWS    6   9.0    NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
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      "text/plain": [
       "       id  gender  status   dept   var1   var2  salary\n",
       "0   False   False   False  False  False  False    True\n",
       "1   False   False   False  False  False   True   False\n",
       "2   False   False   False  False  False  False   False\n",
       "3   False   False   False  False  False  False   False\n",
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       "8   False   False   False   True  False  False   False\n",
       "9   False   False   False  False  False  False   False\n",
       "10  False   False    True  False  False  False    True"
      ]
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.isnull()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
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      "text/plain": [
       "       id  gender  status   dept   var1   var2  salary\n",
       "0   False   False   False  False  False  False    True\n",
       "1   False   False   False  False  False   True   False\n",
       "2   False   False   False  False  False  False   False\n",
       "3   False   False   False  False  False  False   False\n",
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       "8   False   False   False   True  False  False   False\n",
       "9   False   False   False  False  False  False   False\n",
       "10  False   False    True  False  False  False    True"
      ]
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     "execution_count": 40,
     "metadata": {},
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    }
   ],
   "source": [
    "df.isna()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "      id  gender  status   dept  var1   var2  salary\n",
       "0   True    True    True   True  True   True   False\n",
       "1   True    True    True   True  True  False    True\n",
       "2   True    True    True   True  True   True    True\n",
       "3   True    True    True   True  True   True    True\n",
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       "8   True    True    True  False  True   True    True\n",
       "9   True    True    True   True  True   True    True\n",
       "10  True    True   False   True  True   True   False"
      ]
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>dept</th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  gender  status   dept   var1   var2  salary\n",
       "0   False   False   False  False  False  False    True\n",
       "1   False   False   False  False  False   True   False\n",
       "2   False   False   False  False  False  False   False\n",
       "3   False   False   False  False  False  False   False\n",
       "4   False   False   False  False  False  False   False\n",
       "5   False   False   False   True  False   True   False\n",
       "6   False   False   False  False  False   True    True\n",
       "7   False   False   False  False  False  False   False\n",
       "8   False   False   False   True  False  False   False\n",
       "9   False   False   False  False  False  False   False\n",
       "10  False   False    True  False  False  False    True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        False\n",
       "gender    False\n",
       "status     True\n",
       "dept       True\n",
       "var1      False\n",
       "var2       True\n",
       "salary     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any() #It returns True if there is a null value in the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5      True\n",
       "6      True\n",
       "7     False\n",
       "8      True\n",
       "9     False\n",
       "10     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any(axis = 1)  #It examines the nan values in the row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        0\n",
       "gender    0\n",
       "status    1\n",
       "dept      2\n",
       "var1      0\n",
       "var2      3\n",
       "salary    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  #It determines how many nan values are in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()  #It determines how many nan values are in the entire DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_no = [\"P001\", \"P002\", \"P003\", \"P004\", \"P005\", \"P006\", \"P007\", \"P008\", \"P009\", \"P010\", \"P011\"]\n",
    "gender = [\"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"M\"]\n",
    "status = [\"FT\", \"PT\", \"-\", \"FT\", \"PT\", \"PT\", \"FT\", \"-\", \"PT\", \"FT\", np.nan]\n",
    "dept = [\"DS\", \"FS\", \"AWS\", \"AWS\", \"DS\", None, \"FS\", \"FS\", np.nan, \"DS\", \"AWS\"]\n",
    "V1 = np.array([2, 3, 5, np.nan, 7, 1, np.nan, 10, 14, \"-\", 6])\n",
    "V2 = np.array([8, np.nan, 5, 8, 11, np.nan, np.nan, 2, 3, 7, 9])\n",
    "salary = np.array([np.nan, 54, 59, 120, 58, 75, None, 136, 60, 125, np.nan])\n",
    "\n",
    "df0 = pd.DataFrame({\n",
    "                   \"id\" : id_no,\n",
    "                   \"gender\": gender,\n",
    "                   \"status\": status,\n",
    "                   \"dept\": dept,\n",
    "                   \"var1\" : V1,\n",
    "                   \"var2\" : V2,\n",
    "                   \"salary\" : salary\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   id      11 non-null     object \n",
      " 1   gender  11 non-null     object \n",
      " 2   status  10 non-null     object \n",
      " 3   dept    9 non-null      object \n",
      " 4   var1    11 non-null     object \n",
      " 5   var2    8 non-null      float64\n",
      " 6   salary  8 non-null      object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 748.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>dept</th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P001</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P002</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>FS</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P003</td>\n",
       "      <td>M</td>\n",
       "      <td>-</td>\n",
       "      <td>AWS</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P004</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>AWS</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P005</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>DS</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P006</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P007</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>FS</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P008</td>\n",
       "      <td>F</td>\n",
       "      <td>-</td>\n",
       "      <td>FS</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P009</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P010</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>-</td>\n",
       "      <td>7.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P011</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWS</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gender status  dept var1  var2 salary\n",
       "0   P001      M     FT    DS    2   8.0    NaN\n",
       "1   P002      F     PT    FS    3   NaN     54\n",
       "2   P003      M      -   AWS    5   5.0     59\n",
       "3   P004      F     FT   AWS  nan   8.0    120\n",
       "4   P005      M     PT    DS    7  11.0     58\n",
       "5   P006      F     PT  None    1   NaN     75\n",
       "6   P007      M     FT    FS  nan   NaN   None\n",
       "7   P008      F      -    FS   10   2.0    136\n",
       "8   P009      M     PT   NaN   14   3.0     60\n",
       "9   P010      F     FT    DS    -   7.0    125\n",
       "10  P011      M    NaN   AWS    6   9.0    NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Converting Improper Values to NaN Values</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       3\n",
       "2       5\n",
       "3     nan\n",
       "4       7\n",
       "5       1\n",
       "6     nan\n",
       "7      10\n",
       "8      14\n",
       "9       -\n",
       "10      6\n",
       "Name: var1, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"var1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       3\n",
       "2       5\n",
       "3     nan\n",
       "4       7\n",
       "5       1\n",
       "6     nan\n",
       "7      10\n",
       "8      14\n",
       "9     NaN\n",
       "10      6\n",
       "Name: var1, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"var1\"].replace(to_replace=\"-\", value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"var1\"] = df[\"var1\"].replace(to_replace=\"-\", value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   id      11 non-null     object \n",
      " 1   gender  11 non-null     object \n",
      " 2   status  10 non-null     object \n",
      " 3   dept    9 non-null      object \n",
      " 4   var1    10 non-null     object \n",
      " 5   var2    8 non-null      float64\n",
      " 6   salary  8 non-null      object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 748.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"var1\"] = df[\"var1\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2.0\n",
       "1      3.0\n",
       "2      5.0\n",
       "3      NaN\n",
       "4      7.0\n",
       "5      1.0\n",
       "6      NaN\n",
       "7     10.0\n",
       "8     14.0\n",
       "9      NaN\n",
       "10     6.0\n",
       "Name: var1, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.var1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   id      11 non-null     object \n",
      " 1   gender  11 non-null     object \n",
      " 2   status  10 non-null     object \n",
      " 3   dept    9 non-null      object \n",
      " 4   var1    8 non-null      float64\n",
      " 5   var2    8 non-null      float64\n",
      " 6   salary  8 non-null      object \n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 748.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      FT\n",
       "1      PT\n",
       "2     NaN\n",
       "3      FT\n",
       "4      PT\n",
       "5      PT\n",
       "6      FT\n",
       "7     NaN\n",
       "8      PT\n",
       "9      FT\n",
       "10    NaN\n",
       "Name: status, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"status\"] = df[\"status\"].replace(to_replace=\"-\", value=np.nan)\n",
    "df[\"status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>dept</th>\n",
       "      <th>var1</th>\n",
       "      <th>var2</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P001</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P002</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>FS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P003</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P004</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>AWS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P005</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>DS</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P006</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P007</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>FS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P008</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P009</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P010</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P011</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWS</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gender status  dept  var1  var2 salary\n",
       "0   P001      M     FT    DS   2.0   8.0    NaN\n",
       "1   P002      F     PT    FS   3.0   NaN     54\n",
       "2   P003      M    NaN   AWS   5.0   5.0     59\n",
       "3   P004      F     FT   AWS   NaN   8.0    120\n",
       "4   P005      M     PT    DS   7.0  11.0     58\n",
       "5   P006      F     PT  None   1.0   NaN     75\n",
       "6   P007      M     FT    FS   NaN   NaN   None\n",
       "7   P008      F    NaN    FS  10.0   2.0    136\n",
       "8   P009      M     PT   NaN  14.0   3.0     60\n",
       "9   P010      F     FT    DS   NaN   7.0    125\n",
       "10  P011      M    NaN   AWS   6.0   9.0    NaN"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        0\n",
       "gender    0\n",
       "status    3\n",
       "dept      2\n",
       "var1      3\n",
       "var2      3\n",
       "salary    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Missing Value Handling Methods</b><font color='black'>\n",
    "    \n",
    "[SOURCE01](https://towardsdatascience.com/7-ways-to-handle-missing-values-in-machine-learning-1a6326adf79e), \n",
    "[SOURCE02](https://www.shiksha.com/online-courses/articles/handling-missing-values-beginners-tutorial/),\n",
    "[SOURCE03](https://medium.com/geekculture/how-to-deal-with-missing-values-in-machine-learning-98e47f025b9c), \n",
    "[SOURCE04](https://yigitsener.medium.com/veri-biliminde-eksik-kay%C4%B1p-verilere-yakla%C5%9F%C4%B1m-stratejileri-ve-python-pandas-uygulamas%C4%B1-501fbf643795)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>dropna() & drop()</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.0</td>\n",
       "      <td>120</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P005</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>DS</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P006</td>\n",
       "      <td>F</td>\n",
       "      <td>PT</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>P007</td>\n",
       "      <td>M</td>\n",
       "      <td>FT</td>\n",
       "      <td>FS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P008</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P009</td>\n",
       "      <td>M</td>\n",
       "      <td>PT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P010</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P011</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWS</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gender status  dept  var1  var2 salary\n",
       "0   P001      M     FT    DS   2.0   8.0    NaN\n",
       "1   P002      F     PT    FS   3.0   NaN     54\n",
       "2   P003      M    NaN   AWS   5.0   5.0     59\n",
       "3   P004      F     FT   AWS   NaN   8.0    120\n",
       "4   P005      M     PT    DS   7.0  11.0     58\n",
       "5   P006      F     PT  None   1.0   NaN     75\n",
       "6   P007      M     FT    FS   NaN   NaN   None\n",
       "7   P008      F    NaN    FS  10.0   2.0    136\n",
       "8   P009      M     PT   NaN  14.0   3.0     60\n",
       "9   P010      F     FT    DS   NaN   7.0    125\n",
       "10  P011      M    NaN   AWS   6.0   9.0    NaN"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     id gender status dept  var1  var2 salary\n",
       "4  P005      M     PT   DS   7.0  11.0     58"
      ]
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     "execution_count": 77,
     "metadata": {},
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   ],
   "source": [
    "df.dropna() #It deletes rows that contain nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     id gender status dept  var1  var2 salary\n",
       "4  P005      M     PT   DS   7.0  11.0     58"
      ]
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     "execution_count": 80,
     "metadata": {},
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   ],
   "source": [
    "df.dropna(axis = 0, how= \"any\", inplace= False) #An \"any\" axis method will delete any rows with at least one NaN value, and if inplace=True, the change will be permanent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P010</td>\n",
       "      <td>F</td>\n",
       "      <td>FT</td>\n",
       "      <td>DS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P011</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AWS</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gender status  dept  var1  var2 salary\n",
       "0   P001      M     FT    DS   2.0   8.0    NaN\n",
       "1   P002      F     PT    FS   3.0   NaN     54\n",
       "2   P003      M    NaN   AWS   5.0   5.0     59\n",
       "3   P004      F     FT   AWS   NaN   8.0    120\n",
       "4   P005      M     PT    DS   7.0  11.0     58\n",
       "5   P006      F     PT  None   1.0   NaN     75\n",
       "6   P007      M     FT    FS   NaN   NaN   None\n",
       "7   P008      F    NaN    FS  10.0   2.0    136\n",
       "8   P009      M     PT   NaN  14.0   3.0     60\n",
       "9   P010      F     FT    DS   NaN   7.0    125\n",
       "10  P011      M    NaN   AWS   6.0   9.0    NaN"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 0, how= \"all\", inplace= False) #The \"all\" axis method will delete rows where all values are NaN, and with inplace=True, the change will be permanent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      id gender\n",
       "0   P001      M\n",
       "1   P002      F\n",
       "2   P003      M\n",
       "3   P004      F\n",
       "4   P005      M\n",
       "5   P006      F\n",
       "6   P007      M\n",
       "7   P008      F\n",
       "8   P009      M\n",
       "9   P010      F\n",
       "10  P011      M"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 1, how= \"any\", inplace= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
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       "      <td>M</td>\n",
       "      <td>AWS</td>\n",
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      ],
      "text/plain": [
       "      id gender  dept\n",
       "0   P001      M    DS\n",
       "1   P002      F    FS\n",
       "2   P003      M   AWS\n",
       "3   P004      F   AWS\n",
       "4   P005      M    DS\n",
       "5   P006      F  None\n",
       "6   P007      M    FS\n",
       "7   P008      F    FS\n",
       "8   P009      M   NaN\n",
       "9   P010      F    DS\n",
       "10  P011      M   AWS"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 1, thresh= 9, inplace= False) #I want it to return columns with at least 9 non-NaN values. I specify the number of values with 'thresh'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop() #Index and column should be included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
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       "      <td>14.0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>DS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>P011</td>\n",
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       "      <td>AWS</td>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id gender status  dept  var1  var2 salary\n",
       "0   P001      M     FT    DS   2.0   8.0    NaN\n",
       "1   P002      F     PT    FS   3.0   NaN     54\n",
       "2   P003      M    NaN   AWS   5.0   5.0     59\n",
       "3   P004      F     FT   AWS   NaN   8.0    120\n",
       "4   P005      M     PT    DS   7.0  11.0     58\n",
       "5   P006      F     PT  None   1.0   NaN     75\n",
       "6   P007      M     FT    FS   NaN   NaN   None\n",
       "7   P008      F    NaN    FS  10.0   2.0    136\n",
       "8   P009      M     PT   NaN  14.0   3.0     60\n",
       "9   P010      F     FT    DS   NaN   7.0    125\n",
       "10  P011      M    NaN   AWS   6.0   9.0    NaN"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "      <td>P009</td>\n",
       "      <td>M</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>125</td>\n",
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      ],
      "text/plain": [
       "     id gender status dept  var1  var2 salary\n",
       "0  P001      M     FT   DS   2.0   8.0    NaN\n",
       "1  P002      F     PT   FS   3.0   NaN     54\n",
       "2  P003      M    NaN  AWS   5.0   5.0     59\n",
       "3  P004      F     FT  AWS   NaN   8.0    120\n",
       "4  P005      M     PT   DS   7.0  11.0     58\n",
       "7  P008      F    NaN   FS  10.0   2.0    136\n",
       "8  P009      M     PT  NaN  14.0   3.0     60\n",
       "9  P010      F     FT   DS   NaN   7.0    125"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([5,6,10]) #index deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>NaN</td>\n",
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       "      id gender status  dept  var1 salary\n",
       "0   P001      M     FT    DS   2.0    NaN\n",
       "1   P002      F     PT    FS   3.0     54\n",
       "2   P003      M    NaN   AWS   5.0     59\n",
       "3   P004      F     FT   AWS   NaN    120\n",
       "4   P005      M     PT    DS   7.0     58\n",
       "5   P006      F     PT  None   1.0     75\n",
       "6   P007      M     FT    FS   NaN   None\n",
       "7   P008      F    NaN    FS  10.0    136\n",
       "8   P009      M     PT   NaN  14.0     60\n",
       "9   P010      F     FT    DS   NaN    125\n",
       "10  P011      M    NaN   AWS   6.0    NaN"
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       "      id status  dept  var2 salary\n",
       "0   P001     FT    DS   8.0    NaN\n",
       "1   P002     PT    FS   NaN     54\n",
       "2   P003    NaN   AWS   5.0     59\n",
       "3   P004     FT   AWS   8.0    120\n",
       "4   P005     PT    DS  11.0     58\n",
       "5   P006     PT  None   NaN     75\n",
       "6   P007     FT    FS   NaN   None\n",
       "7   P008    NaN    FS   2.0    136\n",
       "8   P009     PT   NaN   3.0     60\n",
       "9   P010     FT    DS   7.0    125\n",
       "10  P011    NaN   AWS   9.0    NaN"
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       "      id status  dept  var2 salary\n",
       "0   P001     FT    DS   8.0    NaN\n",
       "1   P002     PT    FS   NaN     54\n",
       "2   P003    NaN   AWS   5.0     59\n",
       "3   P004     FT   AWS   8.0    120\n",
       "4   P005     PT    DS  11.0     58\n",
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       "7   P008    NaN    FS   2.0    136\n",
       "8   P009     PT   NaN   3.0     60\n",
       "9   P010     FT    DS   7.0    125\n",
       "10  P011    NaN   AWS   9.0    NaN"
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    "df.drop(columns= [\"var1\", \"gender\"]) #Different method without axis. "
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