equipment_losses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0fc66a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statistics as stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d372410f",
   "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>date</th>\n",
       "      <th>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
       "      <th>MRL</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>516</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>146</td>\n",
       "      <td>706</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>706</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>150</td>\n",
       "      <td>816</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>198</td>\n",
       "      <td>846</td>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>86</th>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>88</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>1285</td>\n",
       "      <td>3141</td>\n",
       "      <td>599</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470</td>\n",
       "      <td>13</td>\n",
       "      <td>93</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2194.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2022-05-23</td>\n",
       "      <td>89</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>1293</td>\n",
       "      <td>3166</td>\n",
       "      <td>604</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>476</td>\n",
       "      <td>13</td>\n",
       "      <td>93</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2022-05-24</td>\n",
       "      <td>90</td>\n",
       "      <td>205</td>\n",
       "      <td>170</td>\n",
       "      <td>1302</td>\n",
       "      <td>3194</td>\n",
       "      <td>606</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480</td>\n",
       "      <td>13</td>\n",
       "      <td>93</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2213.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2022-05-25</td>\n",
       "      <td>91</td>\n",
       "      <td>206</td>\n",
       "      <td>170</td>\n",
       "      <td>1305</td>\n",
       "      <td>3213</td>\n",
       "      <td>606</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491</td>\n",
       "      <td>13</td>\n",
       "      <td>93</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2217.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>92</td>\n",
       "      <td>206</td>\n",
       "      <td>170</td>\n",
       "      <td>1315</td>\n",
       "      <td>3235</td>\n",
       "      <td>617</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>502</td>\n",
       "      <td>13</td>\n",
       "      <td>93</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  day  aircraft  helicopter  tank   APC  field artillery  MRL  \\\n",
       "0   2022-02-25    2        10           7    80   516               49    4   \n",
       "1   2022-02-26    3        27          26   146   706               49    4   \n",
       "2   2022-02-27    4        27          26   150   706               50    4   \n",
       "3   2022-02-28    5        29          29   150   816               74   21   \n",
       "4   2022-03-01    6        29          29   198   846               77   24   \n",
       "..         ...  ...       ...         ...   ...   ...              ...  ...   \n",
       "86  2022-05-22   88       204         170  1285  3141              599  201   \n",
       "87  2022-05-23   89       204         170  1293  3166              604  201   \n",
       "88  2022-05-24   90       205         170  1302  3194              606  201   \n",
       "89  2022-05-25   91       206         170  1305  3213              606  201   \n",
       "90  2022-05-26   92       206         170  1315  3235              617  201   \n",
       "\n",
       "    military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0           100.0       60.0      0           2                      0   \n",
       "1           130.0       60.0      2           2                      0   \n",
       "2           130.0       60.0      2           2                      0   \n",
       "3           291.0       60.0      3           2                      5   \n",
       "4           305.0       60.0      3           2                      7   \n",
       "..            ...        ...    ...         ...                    ...   \n",
       "86            NaN        NaN    470          13                     93   \n",
       "87            NaN        NaN    476          13                     93   \n",
       "88            NaN        NaN    480          13                     93   \n",
       "89            NaN        NaN    491          13                     93   \n",
       "90            NaN        NaN    502          13                     93   \n",
       "\n",
       "    special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0                 NaN                 NaN                      NaN   \n",
       "1                 NaN                 NaN                      NaN   \n",
       "2                 NaN                 NaN                      NaN   \n",
       "3                 NaN                 NaN                      NaN   \n",
       "4                 NaN                 NaN                      NaN   \n",
       "..                ...                 ...                      ...   \n",
       "86               43.0                 NaN                   2194.0   \n",
       "87               43.0                 NaN                   2206.0   \n",
       "88               43.0                 NaN                   2213.0   \n",
       "89               44.0                 NaN                   2217.0   \n",
       "90               47.0                 NaN                   2225.0   \n",
       "\n",
       "    cruise missiles  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "..              ...  \n",
       "86            107.0  \n",
       "87            110.0  \n",
       "88            112.0  \n",
       "89            112.0  \n",
       "90            114.0  \n",
       "\n",
       "[91 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOAD DATASET\n",
    "df = pd.read_csv('russia_losses_equipment.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b538e84",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
       "      <th>MRL</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>91.00000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>139.483516</td>\n",
       "      <td>126.945055</td>\n",
       "      <td>747.450549</td>\n",
       "      <td>1983.131868</td>\n",
       "      <td>345.967033</td>\n",
       "      <td>118.131868</td>\n",
       "      <td>1047.507692</td>\n",
       "      <td>69.323077</td>\n",
       "      <td>170.351648</td>\n",
       "      <td>7.252747</td>\n",
       "      <td>58.395604</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>3.944444</td>\n",
       "      <td>2042.730769</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.41338</td>\n",
       "      <td>59.639167</td>\n",
       "      <td>41.893612</td>\n",
       "      <td>351.079487</td>\n",
       "      <td>723.008179</td>\n",
       "      <td>164.651649</td>\n",
       "      <td>55.197868</td>\n",
       "      <td>466.162060</td>\n",
       "      <td>7.545917</td>\n",
       "      <td>161.032321</td>\n",
       "      <td>3.710626</td>\n",
       "      <td>25.683492</td>\n",
       "      <td>10.110501</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>133.944035</td>\n",
       "      <td>8.935323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>516.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.50000</td>\n",
       "      <td>95.500000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>471.000000</td>\n",
       "      <td>1478.500000</td>\n",
       "      <td>221.500000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1940.750000</td>\n",
       "      <td>90.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>1178.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2050.500000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.50000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>1069.500000</td>\n",
       "      <td>2588.500000</td>\n",
       "      <td>483.000000</td>\n",
       "      <td>162.500000</td>\n",
       "      <td>1437.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2160.750000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.00000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>3235.000000</td>\n",
       "      <td>617.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>1701.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "      <td>114.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            day    aircraft  helicopter         tank          APC  \\\n",
       "count  91.00000   91.000000   91.000000    91.000000    91.000000   \n",
       "mean   47.00000  139.483516  126.945055   747.450549  1983.131868   \n",
       "std    26.41338   59.639167   41.893612   351.079487   723.008179   \n",
       "min     2.00000   10.000000    7.000000    80.000000   516.000000   \n",
       "25%    24.50000   95.500000  116.500000   471.000000  1478.500000   \n",
       "50%    47.00000  154.000000  137.000000   725.000000  1923.000000   \n",
       "75%    69.50000  194.000000  155.000000  1069.500000  2588.500000   \n",
       "max    92.00000  206.000000  170.000000  1315.000000  3235.000000   \n",
       "\n",
       "       field artillery         MRL  military auto  fuel tank       drone  \\\n",
       "count        91.000000   91.000000      65.000000  65.000000   91.000000   \n",
       "mean        345.967033  118.131868    1047.507692  69.323077  170.351648   \n",
       "std         164.651649   55.197868     466.162060   7.545917  161.032321   \n",
       "min          49.000000    4.000000     100.000000  60.000000    0.000000   \n",
       "25%         221.500000   73.000000     600.000000  60.000000   19.000000   \n",
       "50%         347.000000  111.000000    1178.000000  73.000000  119.000000   \n",
       "75%         483.000000  162.500000    1437.000000  76.000000  297.000000   \n",
       "max         617.000000  201.000000    1701.000000  76.000000  502.000000   \n",
       "\n",
       "       naval ship  anti-aircraft warfare  special equipment  \\\n",
       "count   91.000000              91.000000          72.000000   \n",
       "mean     7.252747              58.395604          29.444444   \n",
       "std      3.710626              25.683492          10.110501   \n",
       "min      2.000000               0.000000          10.000000   \n",
       "25%      3.000000              44.000000          24.000000   \n",
       "50%      7.000000              55.000000          27.000000   \n",
       "75%     10.000000              80.500000          39.500000   \n",
       "max     13.000000              93.000000          47.000000   \n",
       "\n",
       "       mobile SRBM system  vehicles and fuel tanks  cruise missiles  \n",
       "count           36.000000                26.000000        26.000000  \n",
       "mean             3.944444              2042.730769        97.000000  \n",
       "std              0.333333               133.944035         8.935323  \n",
       "min              2.000000              1796.000000        84.000000  \n",
       "25%              4.000000              1940.750000        90.500000  \n",
       "50%              4.000000              2050.500000        95.000000  \n",
       "75%              4.000000              2160.750000       103.000000  \n",
       "max              4.000000              2225.000000       114.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4920513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 91 entries, 0 to 90\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   date                     91 non-null     object \n",
      " 1   day                      91 non-null     int64  \n",
      " 2   aircraft                 91 non-null     int64  \n",
      " 3   helicopter               91 non-null     int64  \n",
      " 4   tank                     91 non-null     int64  \n",
      " 5   APC                      91 non-null     int64  \n",
      " 6   field artillery          91 non-null     int64  \n",
      " 7   MRL                      91 non-null     int64  \n",
      " 8   military auto            65 non-null     float64\n",
      " 9   fuel tank                65 non-null     float64\n",
      " 10  drone                    91 non-null     int64  \n",
      " 11  naval ship               91 non-null     int64  \n",
      " 12  anti-aircraft warfare    91 non-null     int64  \n",
      " 13  special equipment        72 non-null     float64\n",
      " 14  mobile SRBM system       36 non-null     float64\n",
      " 15  vehicles and fuel tanks  26 non-null     float64\n",
      " 16  cruise missiles          26 non-null     float64\n",
      "dtypes: float64(6), int64(10), object(1)\n",
      "memory usage: 12.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9306785",
   "metadata": {},
   "source": [
    "## Changes NaN Values in 'mobile SRBM system' column with The Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb340ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29    2.0\n",
       "30    4.0\n",
       "31    4.0\n",
       "32    4.0\n",
       "33    4.0\n",
       "34    4.0\n",
       "35    4.0\n",
       "36    4.0\n",
       "37    4.0\n",
       "38    4.0\n",
       "39    4.0\n",
       "40    4.0\n",
       "41    4.0\n",
       "42    4.0\n",
       "43    4.0\n",
       "44    4.0\n",
       "45    4.0\n",
       "46    4.0\n",
       "47    4.0\n",
       "48    4.0\n",
       "49    4.0\n",
       "50    4.0\n",
       "51    4.0\n",
       "52    4.0\n",
       "53    4.0\n",
       "54    4.0\n",
       "55    4.0\n",
       "56    4.0\n",
       "57    4.0\n",
       "58    4.0\n",
       "59    4.0\n",
       "60    4.0\n",
       "61    4.0\n",
       "62    4.0\n",
       "63    4.0\n",
       "64    4.0\n",
       "Name: mobile SRBM system, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['mobile SRBM system'].notnull(), 'mobile SRBM system']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb3f1fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['mobile SRBM system'] == 4, 'mobile SRBM system'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cfe0b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['mobile SRBM system'] == 2, 'mobile SRBM system'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "daf70cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobile_mode = stat.mode(df[\"mobile SRBM system\"])\n",
    "mobile_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cec86ae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
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       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
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      ],
      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-02-25    2        10           7    80  516               49    4   \n",
       "1  2022-02-26    3        27          26   146  706               49    4   \n",
       "2  2022-02-27    4        27          26   150  706               50    4   \n",
       "3  2022-02-28    5        29          29   150  816               74   21   \n",
       "4  2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0                NaN                 4.0                      NaN   \n",
       "1                NaN                 4.0                      NaN   \n",
       "2                NaN                 4.0                      NaN   \n",
       "3                NaN                 4.0                      NaN   \n",
       "4                NaN                 4.0                      NaN   \n",
       "\n",
       "   cruise missiles  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mobile SRBM system\"] = df[\"mobile SRBM system\"].fillna(mobile_mode)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6be19b",
   "metadata": {},
   "source": [
    "## Changes NaN Values in 'vehicles and fuel tanks' column with The Median Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2425c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65    1796.0\n",
       "66    1824.0\n",
       "67    1843.0\n",
       "68    1867.0\n",
       "69    1907.0\n",
       "70    1926.0\n",
       "71    1934.0\n",
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       "73    1970.0\n",
       "74    1980.0\n",
       "75    1997.0\n",
       "76    2019.0\n",
       "77    2042.0\n",
       "78    2059.0\n",
       "79    2087.0\n",
       "80    2101.0\n",
       "81    2109.0\n",
       "82    2137.0\n",
       "83    2157.0\n",
       "84    2162.0\n",
       "85    2178.0\n",
       "86    2194.0\n",
       "87    2206.0\n",
       "88    2213.0\n",
       "89    2217.0\n",
       "90    2225.0\n",
       "Name: vehicles and fuel tanks, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['vehicles and fuel tanks'].notnull(), 'vehicles and fuel tanks']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a45e36eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_med = df['vehicles and fuel tanks'].median()\n",
    "vehicle_med "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f69b1cf1",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-02-25    2        10           7    80  516               49    4   \n",
       "1  2022-02-26    3        27          26   146  706               49    4   \n",
       "2  2022-02-27    4        27          26   150  706               50    4   \n",
       "3  2022-02-28    5        29          29   150  816               74   21   \n",
       "4  2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0                NaN                 4.0                   2050.5   \n",
       "1                NaN                 4.0                   2050.5   \n",
       "2                NaN                 4.0                   2050.5   \n",
       "3                NaN                 4.0                   2050.5   \n",
       "4                NaN                 4.0                   2050.5   \n",
       "\n",
       "   cruise missiles  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vehicles and fuel tanks'] = df['vehicles and fuel tanks'].fillna(vehicle_med)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc085910",
   "metadata": {},
   "source": [
    "## Changes NaN Values in 'cruise missiles' column with The Mean Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97a1575c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65     84.0\n",
       "66     84.0\n",
       "67     84.0\n",
       "68     87.0\n",
       "69     89.0\n",
       "70     90.0\n",
       "71     90.0\n",
       "72     92.0\n",
       "73     94.0\n",
       "74     94.0\n",
       "75     94.0\n",
       "76     94.0\n",
       "77     95.0\n",
       "78     95.0\n",
       "79     95.0\n",
       "80     97.0\n",
       "81     97.0\n",
       "82    102.0\n",
       "83    103.0\n",
       "84    103.0\n",
       "85    104.0\n",
       "86    107.0\n",
       "87    110.0\n",
       "88    112.0\n",
       "89    112.0\n",
       "90    114.0\n",
       "Name: cruise missiles, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['cruise missiles'].notnull(), 'cruise missiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a204173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cruise_mean = df['cruise missiles'].mean()\n",
    "cruise_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1af1320",
   "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>date</th>\n",
       "      <th>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
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       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
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       "      <td>49</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>146</td>\n",
       "      <td>706</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>706</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>150</td>\n",
       "      <td>816</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>198</td>\n",
       "      <td>846</td>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-02-25    2        10           7    80  516               49    4   \n",
       "1  2022-02-26    3        27          26   146  706               49    4   \n",
       "2  2022-02-27    4        27          26   150  706               50    4   \n",
       "3  2022-02-28    5        29          29   150  816               74   21   \n",
       "4  2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0                NaN                 4.0                   2050.5   \n",
       "1                NaN                 4.0                   2050.5   \n",
       "2                NaN                 4.0                   2050.5   \n",
       "3                NaN                 4.0                   2050.5   \n",
       "4                NaN                 4.0                   2050.5   \n",
       "\n",
       "   cruise missiles  \n",
       "0             97.0  \n",
       "1             97.0  \n",
       "2             97.0  \n",
       "3             97.0  \n",
       "4             97.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cruise missiles'] = df['cruise missiles'].fillna(cruise_mean)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec85ee46",
   "metadata": {},
   "source": [
    "## Changes NaN Values in 'special equipment' column with The Mean Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f16b903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    10.0\n",
       "20    10.0\n",
       "21    11.0\n",
       "22    11.0\n",
       "23    12.0\n",
       "      ... \n",
       "86    43.0\n",
       "87    43.0\n",
       "88    43.0\n",
       "89    44.0\n",
       "90    47.0\n",
       "Name: special equipment, Length: 72, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['special equipment'].notnull(), 'special equipment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56956bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.444444444444443"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "special_mean = df['special equipment'].mean()\n",
    "special_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f4b1a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>97.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>146</td>\n",
       "      <td>706</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2</td>\n",
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       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>706</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>150</td>\n",
       "      <td>816</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>198</td>\n",
       "      <td>846</td>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-02-25    2        10           7    80  516               49    4   \n",
       "1  2022-02-26    3        27          26   146  706               49    4   \n",
       "2  2022-02-27    4        27          26   150  706               50    4   \n",
       "3  2022-02-28    5        29          29   150  816               74   21   \n",
       "4  2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0          29.444444                 4.0                   2050.5   \n",
       "1          29.444444                 4.0                   2050.5   \n",
       "2          29.444444                 4.0                   2050.5   \n",
       "3          29.444444                 4.0                   2050.5   \n",
       "4          29.444444                 4.0                   2050.5   \n",
       "\n",
       "   cruise missiles  \n",
       "0             97.0  \n",
       "1             97.0  \n",
       "2             97.0  \n",
       "3             97.0  \n",
       "4             97.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['special equipment'] = df['special equipment'].fillna(special_mean)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3b83e9",
   "metadata": {},
   "source": [
    "## Changes NaN Values in 'Fuel Tank' column with The Mean Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "51138fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     60.0\n",
       "1     60.0\n",
       "2     60.0\n",
       "3     60.0\n",
       "4     60.0\n",
       "      ... \n",
       "60    76.0\n",
       "61    76.0\n",
       "62    76.0\n",
       "63    76.0\n",
       "64    76.0\n",
       "Name: fuel tank, Length: 65, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['fuel tank'].notnull(), 'fuel tank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0cd1c236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.32307692307693"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_mean = df['fuel tank'].mean()\n",
    "fuel_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5d45143f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
       "      <th>MRL</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>516</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>146</td>\n",
       "      <td>706</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>706</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>150</td>\n",
       "      <td>816</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>198</td>\n",
       "      <td>846</td>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-02-25    2        10           7    80  516               49    4   \n",
       "1  2022-02-26    3        27          26   146  706               49    4   \n",
       "2  2022-02-27    4        27          26   150  706               50    4   \n",
       "3  2022-02-28    5        29          29   150  816               74   21   \n",
       "4  2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0          29.444444                 4.0                   2050.5   \n",
       "1          29.444444                 4.0                   2050.5   \n",
       "2          29.444444                 4.0                   2050.5   \n",
       "3          29.444444                 4.0                   2050.5   \n",
       "4          29.444444                 4.0                   2050.5   \n",
       "\n",
       "   cruise missiles  \n",
       "0             97.0  \n",
       "1             97.0  \n",
       "2             97.0  \n",
       "3             97.0  \n",
       "4             97.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuel tank'] = df['fuel tank'].fillna(fuel_mean)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dbbe5145",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
       "      <th>MRL</th>\n",
       "      <th>military auto</th>\n",
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       "      <th>special equipment</th>\n",
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       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>91.00000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
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       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>91.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>139.483516</td>\n",
       "      <td>126.945055</td>\n",
       "      <td>747.450549</td>\n",
       "      <td>1983.131868</td>\n",
       "      <td>345.967033</td>\n",
       "      <td>118.131868</td>\n",
       "      <td>1047.507692</td>\n",
       "      <td>69.323077</td>\n",
       "      <td>170.351648</td>\n",
       "      <td>7.252747</td>\n",
       "      <td>58.395604</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>3.978022</td>\n",
       "      <td>2048.280220</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.41338</td>\n",
       "      <td>59.639167</td>\n",
       "      <td>41.893612</td>\n",
       "      <td>351.079487</td>\n",
       "      <td>723.008179</td>\n",
       "      <td>164.651649</td>\n",
       "      <td>55.197868</td>\n",
       "      <td>466.162060</td>\n",
       "      <td>6.363276</td>\n",
       "      <td>161.032321</td>\n",
       "      <td>3.710626</td>\n",
       "      <td>25.683492</td>\n",
       "      <td>8.980088</td>\n",
       "      <td>0.209657</td>\n",
       "      <td>70.682868</td>\n",
       "      <td>4.709329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>516.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.50000</td>\n",
       "      <td>95.500000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>471.000000</td>\n",
       "      <td>1478.500000</td>\n",
       "      <td>221.500000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2050.500000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>1178.000000</td>\n",
       "      <td>69.323077</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2050.500000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.50000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>1069.500000</td>\n",
       "      <td>2588.500000</td>\n",
       "      <td>483.000000</td>\n",
       "      <td>162.500000</td>\n",
       "      <td>1437.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2050.500000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.00000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>1315.000000</td>\n",
       "      <td>3235.000000</td>\n",
       "      <td>617.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>1701.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "      <td>114.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            day    aircraft  helicopter         tank          APC  \\\n",
       "count  91.00000   91.000000   91.000000    91.000000    91.000000   \n",
       "mean   47.00000  139.483516  126.945055   747.450549  1983.131868   \n",
       "std    26.41338   59.639167   41.893612   351.079487   723.008179   \n",
       "min     2.00000   10.000000    7.000000    80.000000   516.000000   \n",
       "25%    24.50000   95.500000  116.500000   471.000000  1478.500000   \n",
       "50%    47.00000  154.000000  137.000000   725.000000  1923.000000   \n",
       "75%    69.50000  194.000000  155.000000  1069.500000  2588.500000   \n",
       "max    92.00000  206.000000  170.000000  1315.000000  3235.000000   \n",
       "\n",
       "       field artillery         MRL  military auto  fuel tank       drone  \\\n",
       "count        91.000000   91.000000      65.000000  91.000000   91.000000   \n",
       "mean        345.967033  118.131868    1047.507692  69.323077  170.351648   \n",
       "std         164.651649   55.197868     466.162060   6.363276  161.032321   \n",
       "min          49.000000    4.000000     100.000000  60.000000    0.000000   \n",
       "25%         221.500000   73.000000     600.000000  60.000000   19.000000   \n",
       "50%         347.000000  111.000000    1178.000000  69.323077  119.000000   \n",
       "75%         483.000000  162.500000    1437.000000  76.000000  297.000000   \n",
       "max         617.000000  201.000000    1701.000000  76.000000  502.000000   \n",
       "\n",
       "       naval ship  anti-aircraft warfare  special equipment  \\\n",
       "count   91.000000              91.000000          91.000000   \n",
       "mean     7.252747              58.395604          29.444444   \n",
       "std      3.710626              25.683492           8.980088   \n",
       "min      2.000000               0.000000          10.000000   \n",
       "25%      3.000000              44.000000          25.000000   \n",
       "50%      7.000000              55.000000          29.444444   \n",
       "75%     10.000000              80.500000          38.000000   \n",
       "max     13.000000              93.000000          47.000000   \n",
       "\n",
       "       mobile SRBM system  vehicles and fuel tanks  cruise missiles  \n",
       "count           91.000000                91.000000        91.000000  \n",
       "mean             3.978022              2048.280220        97.000000  \n",
       "std              0.209657                70.682868         4.709329  \n",
       "min              2.000000              1796.000000        84.000000  \n",
       "25%              4.000000              2050.500000        97.000000  \n",
       "50%              4.000000              2050.500000        97.000000  \n",
       "75%              4.000000              2050.500000        97.000000  \n",
       "max              4.000000              2225.000000       114.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cce3046",
   "metadata": {},
   "source": [
    "## Changes NaN Values in 'military auto' column with The Median Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "521eaeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      100.0\n",
       "1      130.0\n",
       "2      130.0\n",
       "3      291.0\n",
       "4      305.0\n",
       "       ...  \n",
       "60    1643.0\n",
       "61    1666.0\n",
       "62    1688.0\n",
       "63    1695.0\n",
       "64    1701.0\n",
       "Name: military auto, Length: 65, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['military auto'].notnull(), 'military auto']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a76a3ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1178.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "military_med = df['military auto'].median()\n",
    "military_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed0d8c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 91 entries, 0 to 90\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   date                     91 non-null     object \n",
      " 1   day                      91 non-null     int64  \n",
      " 2   aircraft                 91 non-null     int64  \n",
      " 3   helicopter               91 non-null     int64  \n",
      " 4   tank                     91 non-null     int64  \n",
      " 5   APC                      91 non-null     int64  \n",
      " 6   field artillery          91 non-null     int64  \n",
      " 7   MRL                      91 non-null     int64  \n",
      " 8   military auto            65 non-null     float64\n",
      " 9   fuel tank                91 non-null     float64\n",
      " 10  drone                    91 non-null     int64  \n",
      " 11  naval ship               91 non-null     int64  \n",
      " 12  anti-aircraft warfare    91 non-null     int64  \n",
      " 13  special equipment        91 non-null     float64\n",
      " 14  mobile SRBM system       91 non-null     float64\n",
      " 15  vehicles and fuel tanks  91 non-null     float64\n",
      " 16  cruise missiles          91 non-null     float64\n",
      "dtypes: float64(6), int64(10), object(1)\n",
      "memory usage: 12.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fbdd6304",
   "metadata": {},
   "outputs": [
    {
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       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>516</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>146</td>\n",
       "      <td>706</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>706</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>150</td>\n",
       "      <td>816</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>198</td>\n",
       "      <td>846</td>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-02-25    2        10           7    80  516               49    4   \n",
       "1  2022-02-26    3        27          26   146  706               49    4   \n",
       "2  2022-02-27    4        27          26   150  706               50    4   \n",
       "3  2022-02-28    5        29          29   150  816               74   21   \n",
       "4  2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0          29.444444                 4.0                   2050.5   \n",
       "1          29.444444                 4.0                   2050.5   \n",
       "2          29.444444                 4.0                   2050.5   \n",
       "3          29.444444                 4.0                   2050.5   \n",
       "4          29.444444                 4.0                   2050.5   \n",
       "\n",
       "   cruise missiles  \n",
       "0             97.0  \n",
       "1             97.0  \n",
       "2             97.0  \n",
       "3             97.0  \n",
       "4             97.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['military auto'] = df['military auto'].fillna(military_med)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37f2b8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a5881f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday'] = pd.DatetimeIndex(df.date).weekday\n",
    "df['date day'] = pd.DatetimeIndex(df.date).day\n",
    "df['month'] = pd.DatetimeIndex(df.date).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9336e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
       "      <th>MRL</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "      <th>weekday</th>\n",
       "      <th>date day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>516</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>146</td>\n",
       "      <td>706</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>706</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>150</td>\n",
       "      <td>816</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>198</td>\n",
       "      <td>846</td>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2050.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0 2022-02-25    2        10           7    80  516               49    4   \n",
       "1 2022-02-26    3        27          26   146  706               49    4   \n",
       "2 2022-02-27    4        27          26   150  706               50    4   \n",
       "3 2022-02-28    5        29          29   150  816               74   21   \n",
       "4 2022-03-01    6        29          29   198  846               77   24   \n",
       "\n",
       "   military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0          100.0       60.0      0           2                      0   \n",
       "1          130.0       60.0      2           2                      0   \n",
       "2          130.0       60.0      2           2                      0   \n",
       "3          291.0       60.0      3           2                      5   \n",
       "4          305.0       60.0      3           2                      7   \n",
       "\n",
       "   special equipment  mobile SRBM system  vehicles and fuel tanks  \\\n",
       "0          29.444444                 4.0                   2050.5   \n",
       "1          29.444444                 4.0                   2050.5   \n",
       "2          29.444444                 4.0                   2050.5   \n",
       "3          29.444444                 4.0                   2050.5   \n",
       "4          29.444444                 4.0                   2050.5   \n",
       "\n",
       "   cruise missiles  weekday  date day  month  \n",
       "0             97.0        4        25      2  \n",
       "1             97.0        5        26      2  \n",
       "2             97.0        6        27      2  \n",
       "3             97.0        0        28      2  \n",
       "4             97.0        1         1      3  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0484abbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "813b96db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Aircraft')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['aircraft'])\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "plt.yticks(np.arange(0,211, step=20))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan Aircraft dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Aircraft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21a4ad3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Jumlah Kehilangan')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.plot(df['day'], df['aircraft'], label = 'Aircraft')\n",
    "plt.plot(df['day'], df['helicopter'], label = 'Helicopter')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "plt.legend()\n",
    "fig.suptitle('Grafik Jumlah Kehilangan Aircraft & Helicopter dalam 91 hari')\n",
    "plt.xlabel('Hari')\n",
    "plt.ylabel('Jumlah Kehilangan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "160565f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, *, edgecolors=None, plotnonfinite=False, data=None, **kwargs)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,5), dpi=100)\n",
    "plt.scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "32066a25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['tank'])\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "plt.yticks(np.arange(0,1410, step=200))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan Tank dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c10a24db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['APC'], c = 'orange')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan APC dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fbdca087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['field artillery'], c = 'purple')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan field artillery dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2a205186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'day', 'aircraft', 'helicopter', 'tank', 'APC',\n",
       "       'field artillery', 'MRL', 'military auto', 'fuel tank', 'drone',\n",
       "       'naval ship', 'anti-aircraft warfare', 'special equipment',\n",
       "       'mobile SRBM system', 'vehicles and fuel tanks', 'cruise missiles',\n",
       "       'weekday', 'date day', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "40e6790a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['MRL'], c = 'darkblue')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan MRL dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c516577c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['military auto'], c = 'green')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan Military Auto dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d79c3159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['fuel tank'], c = 'lightgreen')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan fuel tank dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d2083380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['drone'], c = 'magenta')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan drone dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "af10191f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['naval ship'], c = 'maroon')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan naval ship dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1772b825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['anti-aircraft warfare'], c = 'navy')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan anti-aircraft warfare ship dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "053a5b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['special equipment'], c = 'gray')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan special equipment dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "11e054cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['mobile SRBM system'], c = 'black')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan mobile SRBM system dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "09586024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['vehicles and fuel tanks'], c = 'black')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan vehicles and fuel tanks dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "26d839a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Losses')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5), dpi = 100)\n",
    "plt.scatter(x = df['day'], y = df['cruise missiles'], c = 'black')\n",
    "plt.xticks(np.arange(1,92, step=10))\n",
    "for txt in fig.texts:\n",
    "    txt.set_visible(False)\n",
    "fig.suptitle('Grafik Jumlah Kehilangan cruise missiles dalam 91 hari')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fd9d55df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Date Column\n",
    "df.drop('date', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f8dca90c",
   "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>day</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>helicopter</th>\n",
       "      <th>tank</th>\n",
       "      <th>APC</th>\n",
       "      <th>field artillery</th>\n",
       "      <th>MRL</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "      <th>weekday</th>\n",
       "      <th>date day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.004712</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.037694</td>\n",
       "      <td>0.243128</td>\n",
       "      <td>0.023088</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.047118</td>\n",
       "      <td>0.028271</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013874</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.966152</td>\n",
       "      <td>0.045704</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.011779</td>\n",
       "      <td>0.000942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.012376</td>\n",
       "      <td>0.011918</td>\n",
       "      <td>0.066922</td>\n",
       "      <td>0.323607</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.059588</td>\n",
       "      <td>0.027502</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013496</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.939881</td>\n",
       "      <td>0.044462</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.011918</td>\n",
       "      <td>0.000917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.012374</td>\n",
       "      <td>0.011916</td>\n",
       "      <td>0.068745</td>\n",
       "      <td>0.323561</td>\n",
       "      <td>0.022915</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.059579</td>\n",
       "      <td>0.027498</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013494</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.939747</td>\n",
       "      <td>0.044455</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.012374</td>\n",
       "      <td>0.000917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.012969</td>\n",
       "      <td>0.012969</td>\n",
       "      <td>0.067083</td>\n",
       "      <td>0.364929</td>\n",
       "      <td>0.033094</td>\n",
       "      <td>0.009392</td>\n",
       "      <td>0.130140</td>\n",
       "      <td>0.026833</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.013168</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>0.917018</td>\n",
       "      <td>0.043380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012522</td>\n",
       "      <td>0.000894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002663</td>\n",
       "      <td>0.012873</td>\n",
       "      <td>0.012873</td>\n",
       "      <td>0.087894</td>\n",
       "      <td>0.375548</td>\n",
       "      <td>0.034181</td>\n",
       "      <td>0.010654</td>\n",
       "      <td>0.135393</td>\n",
       "      <td>0.026635</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.000888</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>0.013071</td>\n",
       "      <td>0.001776</td>\n",
       "      <td>0.910239</td>\n",
       "      <td>0.043059</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  aircraft  helicopter      tank       APC  field artillery  \\\n",
       "0  0.000942  0.004712    0.003298  0.037694  0.243128         0.023088   \n",
       "1  0.001375  0.012376    0.011918  0.066922  0.323607         0.022460   \n",
       "2  0.001833  0.012374    0.011916  0.068745  0.323561         0.022915   \n",
       "3  0.002236  0.012969    0.012969  0.067083  0.364929         0.033094   \n",
       "4  0.002663  0.012873    0.012873  0.087894  0.375548         0.034181   \n",
       "\n",
       "        MRL  military auto  fuel tank     drone  naval ship  \\\n",
       "0  0.001885       0.047118   0.028271  0.000000    0.000942   \n",
       "1  0.001833       0.059588   0.027502  0.000917    0.000917   \n",
       "2  0.001833       0.059579   0.027498  0.000917    0.000917   \n",
       "3  0.009392       0.130140   0.026833  0.001342    0.000894   \n",
       "4  0.010654       0.135393   0.026635  0.001332    0.000888   \n",
       "\n",
       "   anti-aircraft warfare  special equipment  mobile SRBM system  \\\n",
       "0               0.000000           0.013874            0.001885   \n",
       "1               0.000000           0.013496            0.001833   \n",
       "2               0.000000           0.013494            0.001833   \n",
       "3               0.002236           0.013168            0.001789   \n",
       "4               0.003107           0.013071            0.001776   \n",
       "\n",
       "   vehicles and fuel tanks  cruise missiles   weekday  date day     month  \n",
       "0                 0.966152         0.045704  0.001885  0.011779  0.000942  \n",
       "1                 0.939881         0.044462  0.002292  0.011918  0.000917  \n",
       "2                 0.939747         0.044455  0.002750  0.012374  0.000917  \n",
       "3                 0.917018         0.043380  0.000000  0.012522  0.000894  \n",
       "4                 0.910239         0.043059  0.000444  0.000444  0.001332  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "column_names = df.columns.to_list()\n",
    "normal = preprocessing.normalize(df)\n",
    "normalized_df = pd.DataFrame(normal, columns = column_names)\n",
    "normalized_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "109f0c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_df.to_csv('normalize_losses_equipment', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3503f44d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "name": "python",
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