russian-invasion-of-ukraine.ipynb

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   "source": [
    "<a id=\"0\"></a>\n",
    "# <p style=\"padding:8px;background-color:#005bbb;margin:0;color:gold;font-family:Bank Gothic MD BT;font-size:140%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Assessment of Damage Associated with Russia's Invasion of Ukraine in February 2022</p>"
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   "source": [
    "# Download all libraries needed for this project\n",
    "\n",
    "%matplotlib inline \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import folium as F\n",
    "import json\n",
    "import requests\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import plotly.graph_objects as go\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from math import log10, floor\n",
    "\n",
    "# print('Matplotlib version: ', mpl.__version__)\n",
    "# print(plt.style.available)\n",
    "mpl.style.use(['ggplot'])"
   ]
  },
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   "source": [
    "<div style=\"color:white;display:fill;border-radius:12px;\n",
    "            background-color:#005bbb;font-size:150%;\n",
    "            font-family:Georgia;letter-spacing:0.5px\">\n",
    "    <p style=\"padding: 8px;color:gold;\"><b> 1. Data Collection</b></p>\n",
    "</div>"
   ]
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    "tags": []
   },
   "source": [
    "Read the CSV files into a Pandas DataFrame"
   ]
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    {
     "name": "stdout",
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     "text": [
      "Import Complete\n"
     ]
    }
   ],
   "source": [
    "russian_personnel = pd.read_csv('../input/2022-ukraine-russian-war/russia_losses_personnel.csv')\n",
    "russian_equipment = pd.read_csv('../input/2022-ukraine-russian-war/russia_losses_equipment.csv')\n",
    "russian_equipment_adj = pd.read_csv('../input/2022-ukraine-russian-war/russia_losses_equipment_correction.csv')\n",
    "print('import complete'.title())"
   ]
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   "source": [
    "## Acronyms\n",
    "* POW -  Prisoner of War\n",
    "* MRL -  Multiple Rocket Launcher\n",
    "* APC -  Armored Personnel Carrier\n",
    "* SRBM - Short-range ballistic missile\n",
    "<!-- * UAV -  Unmanned Aerial Vehicle -->\n",
    "<!-- * RPA -  Remotely Piloted Vehicle -->"
   ]
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   "cell_type": "markdown",
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   "source": [
    "<div style=\"color:white;display:fill;border-radius:12px;\n",
    "            background-color:#005bbb;font-size:150%;\n",
    "            font-family:Georgia;letter-spacing:0.5px\">\n",
    "    <p style=\"padding: 8px;color:gold;\"><b> 2. Data Wrangling</b></p>\n",
    "</div>"
   ]
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   "source": [
    "### Let's start by getting a basic understanding of the DataFrames and clean the data for analysis"
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <th>personnel</th>\n",
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       "      <th>0</th>\n",
       "      <td>2024-05-06</td>\n",
       "      <td>803</td>\n",
       "      <td>475300</td>\n",
       "      <td>about</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>2024-05-05</td>\n",
       "      <td>802</td>\n",
       "      <td>474260</td>\n",
       "      <td>about</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2024-05-04</td>\n",
       "      <td>801</td>\n",
       "      <td>473400</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>799</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>797</th>\n",
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       "      <td>6</td>\n",
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       "      <th>798</th>\n",
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       "      <th>799</th>\n",
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       "      <td>4</td>\n",
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       "      <td>about</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>800</th>\n",
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       "      <td>about</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>801</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>2800</td>\n",
       "      <td>about</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 5 columns</p>\n",
       "</div>"
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       "           date  day  personnel personnel*    POW\n",
       "0    2024-05-06  803     475300      about    NaN\n",
       "1    2024-05-05  802     474260      about    NaN\n",
       "2    2024-05-04  801     473400      about    NaN\n",
       "3    2024-05-03  800     472140      about    NaN\n",
       "4    2024-05-02  799     470870      about    NaN\n",
       "..          ...  ...        ...        ...    ...\n",
       "797  2022-03-01    6       5710      about  200.0\n",
       "798  2022-02-28    5       5300      about    0.0\n",
       "799  2022-02-27    4       4500      about    0.0\n",
       "800  2022-02-26    3       4300      about    0.0\n",
       "801  2022-02-25    2       2800      about    0.0\n",
       "\n",
       "[802 rows x 5 columns]"
      ]
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     "execution_count": 3,
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   "source": [
    "russian_personnel"
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>personnel</th>\n",
       "      <th>personnel*</th>\n",
       "      <th>POW</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>802</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802</td>\n",
       "      <td>62.000000</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>802</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>top</th>\n",
       "      <td>2024-05-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>about</td>\n",
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       "      <th>freq</th>\n",
       "      <td>1</td>\n",
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       "      <td>796</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>131.440363</td>\n",
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       "      <th>min</th>\n",
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       "      <th>25%</th>\n",
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       "              date         day      personnel personnel*         POW\n",
       "count          802  802.000000     802.000000        802   62.000000\n",
       "unique         802         NaN            NaN          2         NaN\n",
       "top     2024-05-06         NaN            NaN      about         NaN\n",
       "freq             1         NaN            NaN        796         NaN\n",
       "mean           NaN  402.500000  187896.826683        NaN  386.387097\n",
       "std            NaN  231.661751  138912.723831        NaN  131.440363\n",
       "min            NaN    2.000000    2800.000000        NaN    0.000000\n",
       "25%            NaN  202.250000   53387.500000        NaN  389.000000\n",
       "50%            NaN  402.500000  174270.000000        NaN  421.000000\n",
       "75%            NaN  602.750000  290522.500000        NaN  474.500000\n",
       "max            NaN  803.000000  475300.000000        NaN  496.000000"
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     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "russian_personnel.describe(include='all')"
   ]
  },
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    {
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     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "#remove confusing columns\n",
    "russian_personnel.drop(['personnel', 'personnel*'], axis=1, inplace=True)\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4590b82b",
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     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# Set index to date\n",
    "russian_personnel.set_index('date', inplace=True)\n",
    "print(\"Done\")"
   ]
  },
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   "id": "43be0927",
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     "shell.execute_reply": "2024-05-08T15:01:56.784665Z",
     "shell.execute_reply.started": "2024-05-08T15:00:21.553720Z"
    },
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day    False\n",
       "POW     True\n",
       "Name: top, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if there are blank cells in the data\n",
    "russian_personnel.isnull().describe().loc['top']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58a1730d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:01:56.901455Z",
     "iopub.status.busy": "2024-05-08T15:01:56.900561Z",
     "iopub.status.idle": "2024-05-08T15:01:56.903629Z",
     "shell.execute_reply": "2024-05-08T15:01:56.904135Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# fill empty rows with 0 and change data type\n",
    "russian_personnel.fillna(0, inplace=True)\n",
    "russian_personnel = russian_personnel.astype(int)[::-1]\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8859d88b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:01:57.041206Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 802 entries, 2022-02-25 to 2024-05-06\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   day     802 non-null    int64\n",
      " 1   POW     802 non-null    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 18.8+ KB\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>POW</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "      <th>2022-02-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-26</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-27</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <th>2022-03-01</th>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-02</th>\n",
       "      <td>799</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-03</th>\n",
       "      <td>800</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-04</th>\n",
       "      <td>801</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-05</th>\n",
       "      <td>802</td>\n",
       "      <td>496</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-06</th>\n",
       "      <td>803</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            day  POW\n",
       "date                \n",
       "2022-02-25    2    0\n",
       "2022-02-26    3    0\n",
       "2022-02-27    4    0\n",
       "2022-02-28    5    0\n",
       "2022-03-01    6  200\n",
       "...         ...  ...\n",
       "2024-05-02  799  496\n",
       "2024-05-03  800  496\n",
       "2024-05-04  801  496\n",
       "2024-05-05  802  496\n",
       "2024-05-06  803  496\n",
       "\n",
       "[802 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "# replace blanks at the end of POW column with continuous count\n",
    "last = 0\n",
    "newpow = []\n",
    "for i in russian_personnel['POW'].values:\n",
    "    if last <= i:\n",
    "        last = i\n",
    "    elif last > i:\n",
    "        i = last\n",
    "    newpow.append(i)\n",
    "russian_personnel['POW'] = newpow\n",
    "\n",
    "# view clean data\n",
    "russian_personnel.info()\n",
    "russian_personnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6308cdb",
   "metadata": {
    "execution": {
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     "status": "completed"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 802 entries, 0 to 801\n",
      "Data columns (total 19 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   date                       802 non-null    object \n",
      " 1   day                        802 non-null    int64  \n",
      " 2   aircraft                   802 non-null    int64  \n",
      " 3   helicopter                 802 non-null    int64  \n",
      " 4   tank                       802 non-null    int64  \n",
      " 5   APC                        802 non-null    int64  \n",
      " 6   field artillery            802 non-null    int64  \n",
      " 7   MRL                        802 non-null    int64  \n",
      " 8   military auto              65 non-null     float64\n",
      " 9   fuel tank                  65 non-null     float64\n",
      " 10  drone                      802 non-null    int64  \n",
      " 11  naval ship                 802 non-null    int64  \n",
      " 12  anti-aircraft warfare      802 non-null    int64  \n",
      " 13  special equipment          783 non-null    float64\n",
      " 14  mobile SRBM system         36 non-null     float64\n",
      " 15  greatest losses direction  203 non-null    object \n",
      " 16  vehicles and fuel tanks    737 non-null    float64\n",
      " 17  cruise missiles            737 non-null    float64\n",
      " 18  submarines                 236 non-null    float64\n",
      "dtypes: float64(7), int64(10), object(2)\n",
      "memory usage: 119.2+ KB\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <td>2024-05-05</td>\n",
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       "      <td>16337.0</td>\n",
       "      <td>2127.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2024-05-03</td>\n",
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       "      <td>12102</td>\n",
       "      <td>1053</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>9580</td>\n",
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       "      <td>786</td>\n",
       "      <td>1993.0</td>\n",
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       "      <td>784</td>\n",
       "      <td>1988.0</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
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       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  day  aircraft  helicopter  tank    APC  field artillery  \\\n",
       "0    2024-05-06  803       349         325  7380  14213            12250   \n",
       "1    2024-05-05  802       349         325  7375  14179            12212   \n",
       "2    2024-05-04  801       348         325  7366  14156            12148   \n",
       "3    2024-05-03  800       348         325  7354  14129            12102   \n",
       "4    2024-05-02  799       348         325  7332  14096            12044   \n",
       "..          ...  ...       ...         ...   ...    ...              ...   \n",
       "797  2022-03-01    6        29          29   198    846               77   \n",
       "798  2022-02-28    5        29          29   150    816               74   \n",
       "799  2022-02-27    4        27          26   150    706               50   \n",
       "800  2022-02-26    3        27          26   146    706               49   \n",
       "801  2022-02-25    2        10           7    80    516               49   \n",
       "\n",
       "      MRL  military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "0    1057            NaN        NaN   9683          26                    791   \n",
       "1    1057            NaN        NaN   9636          26                    790   \n",
       "2    1055            NaN        NaN   9611          26                    788   \n",
       "3    1053            NaN        NaN   9580          26                    786   \n",
       "4    1053            NaN        NaN   9561          26                    784   \n",
       "..    ...            ...        ...    ...         ...                    ...   \n",
       "797    24          305.0       60.0      3           2                      7   \n",
       "798    21          291.0       60.0      3           2                      5   \n",
       "799     4          130.0       60.0      2           2                      0   \n",
       "800     4          130.0       60.0      2           2                      0   \n",
       "801     4          100.0       60.0      0           2                      0   \n",
       "\n",
       "     special equipment  mobile SRBM system greatest losses direction  \\\n",
       "0               2008.0                 NaN                       NaN   \n",
       "1               2002.0                 NaN                       NaN   \n",
       "2               2001.0                 NaN                       NaN   \n",
       "3               1993.0                 NaN                       NaN   \n",
       "4               1988.0                 NaN                       NaN   \n",
       "..                 ...                 ...                       ...   \n",
       "797                NaN                 NaN                       NaN   \n",
       "798                NaN                 NaN                       NaN   \n",
       "799                NaN                 NaN                       NaN   \n",
       "800                NaN                 NaN                       NaN   \n",
       "801                NaN                 NaN                       NaN   \n",
       "\n",
       "     vehicles and fuel tanks  cruise missiles  submarines  \n",
       "0                    16477.0           2148.0         1.0  \n",
       "1                    16374.0           2147.0         1.0  \n",
       "2                    16337.0           2127.0         1.0  \n",
       "3                    16266.0           2126.0         1.0  \n",
       "4                    16224.0           2126.0         1.0  \n",
       "..                       ...              ...         ...  \n",
       "797                      NaN              NaN         NaN  \n",
       "798                      NaN              NaN         NaN  \n",
       "799                      NaN              NaN         NaN  \n",
       "800                      NaN              NaN         NaN  \n",
       "801                      NaN              NaN         NaN  \n",
       "\n",
       "[802 rows x 19 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "russian_equipment.info()\n",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   date                     5 non-null      object\n",
      " 1   day                      5 non-null      int64 \n",
      " 2   aircraft                 5 non-null      int64 \n",
      " 3   helicopter               5 non-null      int64 \n",
      " 4   tank                     5 non-null      int64 \n",
      " 5   APC                      5 non-null      int64 \n",
      " 6   field artillery          5 non-null      int64 \n",
      " 7   MRL                      5 non-null      int64 \n",
      " 8   drone                    5 non-null      int64 \n",
      " 9   naval ship               5 non-null      int64 \n",
      " 10  submarines               5 non-null      int64 \n",
      " 11  anti-aircraft warfare    5 non-null      int64 \n",
      " 12  special equipment        5 non-null      int64 \n",
      " 13  vehicles and fuel tanks  5 non-null      int64 \n",
      " 14  cruise missiles          5 non-null      int64 \n",
      "dtypes: int64(14), object(1)\n",
      "memory usage: 728.0+ bytes\n"
     ]
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       "      <td>2023-10-03</td>\n",
       "      <td>587</td>\n",
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      "text/plain": [
       "         date  day  aircraft  helicopter  tank  APC  field artillery  MRL  \\\n",
       "0  2022-10-13  231         0           0     0  -25               32    0   \n",
       "1  2023-05-27  458         3           2    -5    7                8    2   \n",
       "2  2023-10-03  587        -1           0     5    0                0    0   \n",
       "3  2024-03-13  749         0           0     0    0                0    0   \n",
       "4  2024-05-05  802         0           0     0    5               43    1   \n",
       "\n",
       "   drone  naval ship  submarines  anti-aircraft warfare  special equipment  \\\n",
       "0     20           1           0                      0                  0   \n",
       "1     49           0           0                      0                  0   \n",
       "2     -1           0           0                      0                  0   \n",
       "3     -1           0           0                      0                  0   \n",
       "4      0           0           0                      0                 -3   \n",
       "\n",
       "   vehicles and fuel tanks  cruise missiles  \n",
       "0                        0                0  \n",
       "1                       11               31  \n",
       "2                        0                0  \n",
       "3                        0               -1  \n",
       "4                       44               19  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "russian_equipment_adj.info()\n",
    "russian_equipment_adj"
   ]
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      "text/plain": [
       "           date  day  aircraft  helicopter  tank    APC  field artillery  \\\n",
       "571  2022-10-13  232       268         240  2511   5167             1556   \n",
       "345  2023-05-27  458       310         296  3799   7442             3406   \n",
       "216  2023-10-03  587       315         316  4732   9008             6565   \n",
       "54   2024-03-13  749       347         325  6752  12921            10554   \n",
       "1    2024-05-05  802       349         325  7375  14179            12212   \n",
       "\n",
       "      MRL  military auto  fuel tank  drone  naval ship  anti-aircraft warfare  \\\n",
       "571   357            NaN        NaN   1182          16                    183   \n",
       "345   572            NaN        NaN   2941          18                    329   \n",
       "216   801            NaN        NaN   5080          20                    540   \n",
       "54   1017            NaN        NaN   8205          26                    715   \n",
       "1    1057            NaN        NaN   9636          26                    790   \n",
       "\n",
       "     special equipment  mobile SRBM system  greatest losses direction  \\\n",
       "571              140.0                 NaN  Kramatorsk and Kryvyi Rih   \n",
       "345              449.0                 NaN                        NaN   \n",
       "216              943.0                 NaN                        NaN   \n",
       "54              1699.0                 NaN                        NaN   \n",
       "1               2002.0                 NaN                        NaN   \n",
       "\n",
       "     vehicles and fuel tanks  cruise missiles  submarines  \n",
       "571                   3935.0            316.0         NaN  \n",
       "345                   6172.0           1025.0         NaN  \n",
       "216                   8932.0           1529.0         1.0  \n",
       "54                   13932.0           1919.0         1.0  \n",
       "1                    16374.0           2147.0         1.0  "
      ]
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   "source": [
    "# checking the rows to be corrected in the original dataset\n",
    "russian_equipment[russian_equipment['date'].isin(list(russian_equipment_adj['date']))][::-1]"
   ]
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Kramatorsk and Kryvyi Rih</td>\n",
       "      <td>268.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>5142.0</td>\n",
       "      <td>1588.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3935.0</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2023-05-27</td>\n",
       "      <td>458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>3794.0</td>\n",
       "      <td>7449.0</td>\n",
       "      <td>3414.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>2990.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>6183.0</td>\n",
       "      <td>1056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2023-10-03</td>\n",
       "      <td>587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>4737.0</td>\n",
       "      <td>9008.0</td>\n",
       "      <td>6565.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>5079.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>8932.0</td>\n",
       "      <td>1529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2024-03-13</td>\n",
       "      <td>749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>6752.0</td>\n",
       "      <td>12921.0</td>\n",
       "      <td>10554.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>8204.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>1699.0</td>\n",
       "      <td>13932.0</td>\n",
       "      <td>1918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-05-05</td>\n",
       "      <td>802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>7375.0</td>\n",
       "      <td>14184.0</td>\n",
       "      <td>12255.0</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>9636.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>16418.0</td>\n",
       "      <td>2166.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  day  military auto  fuel tank  mobile SRBM system  \\\n",
       "571  2022-10-13  232            0.0        0.0                 0.0   \n",
       "345  2023-05-27  458            0.0        0.0                 0.0   \n",
       "216  2023-10-03  587            0.0        0.0                 0.0   \n",
       "54   2024-03-13  749            0.0        0.0                 0.0   \n",
       "1    2024-05-05  802            0.0        0.0                 0.0   \n",
       "\n",
       "     greatest losses direction  aircraft  helicopter    tank      APC  \\\n",
       "571  Kramatorsk and Kryvyi Rih     268.0       240.0  2511.0   5142.0   \n",
       "345                          0     313.0       298.0  3794.0   7449.0   \n",
       "216                          0     314.0       316.0  4737.0   9008.0   \n",
       "54                           0     347.0       325.0  6752.0  12921.0   \n",
       "1                            0     349.0       325.0  7375.0  14184.0   \n",
       "\n",
       "     field artillery     MRL   drone  naval ship  submarines  \\\n",
       "571           1588.0   357.0  1202.0        17.0         0.0   \n",
       "345           3414.0   574.0  2990.0        18.0         0.0   \n",
       "216           6565.0   801.0  5079.0        20.0         1.0   \n",
       "54           10554.0  1017.0  8204.0        26.0         1.0   \n",
       "1            12255.0  1058.0  9636.0        26.0         1.0   \n",
       "\n",
       "     anti-aircraft warfare  special equipment  vehicles and fuel tanks  \\\n",
       "571                  183.0              140.0                   3935.0   \n",
       "345                  329.0              449.0                   6183.0   \n",
       "216                  540.0              943.0                   8932.0   \n",
       "54                   715.0             1699.0                  13932.0   \n",
       "1                    790.0             1999.0                  16418.0   \n",
       "\n",
       "     cruise missiles  \n",
       "571            316.0  \n",
       "345           1056.0  \n",
       "216           1529.0  \n",
       "54            1918.0  \n",
       "1             2166.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge equipment tables to account for corrections\n",
    "merged_df=pd.merge(russian_equipment, russian_equipment_adj, on='date', how='left')\n",
    "merged_df.fillna(0, inplace=True)\n",
    "\n",
    "# Add the numerical values from common columns together\n",
    "common_columns=list(russian_equipment_adj.columns.drop(['day','date']))\n",
    "for col in common_columns:\n",
    "    if col + '_x' in merged_df.columns and col + '_y' in merged_df.columns:\n",
    "        merged_df[col] = merged_df[col + '_x'] + merged_df[col + '_y']\n",
    "\n",
    "# Drop the original columns after adding their values\n",
    "merged_df.drop([col + '_x' for col in common_columns] \n",
    "               + [col + '_y' for col in common_columns]\n",
    "               + ['day_y'], axis=1, inplace=True)\n",
    "\n",
    "merged_df.rename({'day_x':'day'}, axis=1, inplace=True)\n",
    "\n",
    "# Check to see if data is corrected\n",
    "merged_df[merged_df['date'].isin(list(russian_equipment_adj['date']))][::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49515fa5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:01:57.822145Z",
     "iopub.status.busy": "2024-05-08T15:01:57.821196Z",
     "iopub.status.idle": "2024-05-08T15:01:57.824922Z",
     "shell.execute_reply": "2024-05-08T15:01:57.825441Z",
     "shell.execute_reply.started": "2024-05-08T15:00:21.739853Z"
    },
    "papermill": {
     "duration": 0.099999,
     "end_time": "2024-05-08T15:01:57.825616",
     "exception": false,
     "start_time": "2024-05-08T15:01:57.725617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>greatest losses direction</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>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>submarines</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>3</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>5</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>6</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>846.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>2022-03-02</td>\n",
       "      <td>7</td>\n",
       "      <td>355.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>2022-03-03</td>\n",
       "      <td>8</td>\n",
       "      <td>374.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>2022-03-04</td>\n",
       "      <td>9</td>\n",
       "      <td>404.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>2022-03-05</td>\n",
       "      <td>10</td>\n",
       "      <td>409.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>2022-03-06</td>\n",
       "      <td>11</td>\n",
       "      <td>447.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>985.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  day  military auto  fuel tank  mobile SRBM system  \\\n",
       "801  2022-02-25    2          100.0       60.0                 0.0   \n",
       "800  2022-02-26    3          130.0       60.0                 0.0   \n",
       "799  2022-02-27    4          130.0       60.0                 0.0   \n",
       "798  2022-02-28    5          291.0       60.0                 0.0   \n",
       "797  2022-03-01    6          305.0       60.0                 0.0   \n",
       "796  2022-03-02    7          355.0       60.0                 0.0   \n",
       "795  2022-03-03    8          374.0       60.0                 0.0   \n",
       "794  2022-03-04    9          404.0       60.0                 0.0   \n",
       "793  2022-03-05   10          409.0       60.0                 0.0   \n",
       "792  2022-03-06   11          447.0       60.0                 0.0   \n",
       "\n",
       "    greatest losses direction  aircraft  helicopter   tank    APC  \\\n",
       "801                         0      10.0         7.0   80.0  516.0   \n",
       "800                         0      27.0        26.0  146.0  706.0   \n",
       "799                         0      27.0        26.0  150.0  706.0   \n",
       "798                         0      29.0        29.0  150.0  816.0   \n",
       "797                         0      29.0        29.0  198.0  846.0   \n",
       "796                         0      30.0        31.0  211.0  862.0   \n",
       "795                         0      30.0        31.0  217.0  900.0   \n",
       "794                         0      33.0        37.0  251.0  939.0   \n",
       "793                         0      39.0        40.0  269.0  945.0   \n",
       "792                         0      44.0        48.0  285.0  985.0   \n",
       "\n",
       "     field artillery   MRL  drone  naval ship  submarines  \\\n",
       "801             49.0   4.0    0.0         2.0         0.0   \n",
       "800             49.0   4.0    2.0         2.0         0.0   \n",
       "799             50.0   4.0    2.0         2.0         0.0   \n",
       "798             74.0  21.0    3.0         2.0         0.0   \n",
       "797             77.0  24.0    3.0         2.0         0.0   \n",
       "796             85.0  40.0    3.0         2.0         0.0   \n",
       "795             90.0  42.0    3.0         2.0         0.0   \n",
       "794            105.0  50.0    3.0         2.0         0.0   \n",
       "793            105.0  50.0    3.0         2.0         0.0   \n",
       "792            109.0  50.0    4.0         2.0         0.0   \n",
       "\n",
       "     anti-aircraft warfare  special equipment  vehicles and fuel tanks  \\\n",
       "801                    0.0                0.0                      0.0   \n",
       "800                    0.0                0.0                      0.0   \n",
       "799                    0.0                0.0                      0.0   \n",
       "798                    5.0                0.0                      0.0   \n",
       "797                    7.0                0.0                      0.0   \n",
       "796                    9.0                0.0                      0.0   \n",
       "795                   11.0                0.0                      0.0   \n",
       "794                   18.0                0.0                      0.0   \n",
       "793                   19.0                0.0                      0.0   \n",
       "792                   21.0                0.0                      0.0   \n",
       "\n",
       "     cruise missiles  \n",
       "801              0.0  \n",
       "800              0.0  \n",
       "799              0.0  \n",
       "798              0.0  \n",
       "797              0.0  \n",
       "796              0.0  \n",
       "795              0.0  \n",
       "794              0.0  \n",
       "793              0.0  \n",
       "792              0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corrected data used to replace original equipment data\n",
    "russian_equipment = merged_df[::-1]\n",
    "\n",
    "russian_equipment.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1b9d4c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:01:57.952521Z",
     "iopub.status.busy": "2024-05-08T15:01:57.951834Z",
     "iopub.status.idle": "2024-05-08T15:01:58.014991Z",
     "shell.execute_reply": "2024-05-08T15:01:58.015495Z",
     "shell.execute_reply.started": "2024-05-08T15:00:21.777539Z"
    },
    "papermill": {
     "duration": 0.129205,
     "end_time": "2024-05-08T15:01:58.015662",
     "exception": false,
     "start_time": "2024-05-08T15:01:57.886457",
     "status": "completed"
    },
    "tags": []
   },
   "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>date</th>\n",
       "      <th>day</th>\n",
       "      <th>military auto</th>\n",
       "      <th>fuel tank</th>\n",
       "      <th>mobile SRBM system</th>\n",
       "      <th>greatest losses direction</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>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>submarines</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</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>802</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.0</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>802.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>599.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>402.500000</td>\n",
       "      <td>84.897756</td>\n",
       "      <td>5.618454</td>\n",
       "      <td>0.177057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278.729426</td>\n",
       "      <td>264.226933</td>\n",
       "      <td>3654.102244</td>\n",
       "      <td>7193.104738</td>\n",
       "      <td>4110.406484</td>\n",
       "      <td>558.266833</td>\n",
       "      <td>3263.230673</td>\n",
       "      <td>17.461347</td>\n",
       "      <td>0.294264</td>\n",
       "      <td>345.442643</td>\n",
       "      <td>579.490025</td>\n",
       "      <td>6477.211970</td>\n",
       "      <td>939.319202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>231.661751</td>\n",
       "      <td>314.941759</td>\n",
       "      <td>19.050404</td>\n",
       "      <td>0.820202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.745640</td>\n",
       "      <td>67.836337</td>\n",
       "      <td>1879.316688</td>\n",
       "      <td>3335.089768</td>\n",
       "      <td>3471.726323</td>\n",
       "      <td>296.065141</td>\n",
       "      <td>2698.992048</td>\n",
       "      <td>5.108718</td>\n",
       "      <td>0.455996</td>\n",
       "      <td>224.166036</td>\n",
       "      <td>580.102079</td>\n",
       "      <td>4128.892864</td>\n",
       "      <td>672.078657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</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>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>202.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244.500000</td>\n",
       "      <td>213.500000</td>\n",
       "      <td>2176.250000</td>\n",
       "      <td>4662.750000</td>\n",
       "      <td>1281.750000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>165.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>3477.000000</td>\n",
       "      <td>233.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>402.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>3617.000000</td>\n",
       "      <td>6983.500000</td>\n",
       "      <td>2685.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>2248.500000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>5532.500000</td>\n",
       "      <td>911.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>602.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.750000</td>\n",
       "      <td>323.750000</td>\n",
       "      <td>4989.250000</td>\n",
       "      <td>9432.750000</td>\n",
       "      <td>6973.250000</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>5313.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>547.750000</td>\n",
       "      <td>984.250000</td>\n",
       "      <td>9328.500000</td>\n",
       "      <td>1534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>1701.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>7380.000000</td>\n",
       "      <td>14213.000000</td>\n",
       "      <td>12255.000000</td>\n",
       "      <td>1058.000000</td>\n",
       "      <td>9683.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>791.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>16477.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date         day  military auto   fuel tank  mobile SRBM system  \\\n",
       "count          802  802.000000     802.000000  802.000000          802.000000   \n",
       "unique         802         NaN            NaN         NaN                 NaN   \n",
       "top     2022-02-25         NaN            NaN         NaN                 NaN   \n",
       "freq             1         NaN            NaN         NaN                 NaN   \n",
       "mean           NaN  402.500000      84.897756    5.618454            0.177057   \n",
       "std            NaN  231.661751     314.941759   19.050404            0.820202   \n",
       "min            NaN    2.000000       0.000000    0.000000            0.000000   \n",
       "25%            NaN  202.250000       0.000000    0.000000            0.000000   \n",
       "50%            NaN  402.500000       0.000000    0.000000            0.000000   \n",
       "75%            NaN  602.750000       0.000000    0.000000            0.000000   \n",
       "max            NaN  803.000000    1701.000000   76.000000            4.000000   \n",
       "\n",
       "        greatest losses direction    aircraft  helicopter         tank  \\\n",
       "count                       802.0  802.000000  802.000000   802.000000   \n",
       "unique                       65.0         NaN         NaN          NaN   \n",
       "top                           0.0         NaN         NaN          NaN   \n",
       "freq                        599.0         NaN         NaN          NaN   \n",
       "mean                          NaN  278.729426  264.226933  3654.102244   \n",
       "std                           NaN   64.745640   67.836337  1879.316688   \n",
       "min                           NaN   10.000000    7.000000    80.000000   \n",
       "25%                           NaN  244.500000  213.500000  2176.250000   \n",
       "50%                           NaN  306.000000  291.000000  3617.000000   \n",
       "75%                           NaN  319.750000  323.750000  4989.250000   \n",
       "max                           NaN  349.000000  325.000000  7380.000000   \n",
       "\n",
       "                 APC  field artillery          MRL        drone  naval ship  \\\n",
       "count     802.000000       802.000000   802.000000   802.000000  802.000000   \n",
       "unique           NaN              NaN          NaN          NaN         NaN   \n",
       "top              NaN              NaN          NaN          NaN         NaN   \n",
       "freq             NaN              NaN          NaN          NaN         NaN   \n",
       "mean     7193.104738      4110.406484   558.266833  3263.230673   17.461347   \n",
       "std      3335.089768      3471.726323   296.065141  2698.992048    5.108718   \n",
       "min       516.000000        49.000000     4.000000     0.000000    2.000000   \n",
       "25%      4662.750000      1281.750000   311.000000   905.000000   15.000000   \n",
       "50%      6983.500000      2685.000000   527.000000  2248.500000   18.000000   \n",
       "75%      9432.750000      6973.250000   817.000000  5313.750000   20.000000   \n",
       "max     14213.000000     12255.000000  1058.000000  9683.000000   26.000000   \n",
       "\n",
       "        submarines  anti-aircraft warfare  special equipment  \\\n",
       "count   802.000000             802.000000         802.000000   \n",
       "unique         NaN                    NaN                NaN   \n",
       "top            NaN                    NaN                NaN   \n",
       "freq           NaN                    NaN                NaN   \n",
       "mean      0.294264             345.442643         579.490025   \n",
       "std       0.455996             224.166036         580.102079   \n",
       "min       0.000000               0.000000           0.000000   \n",
       "25%       0.000000             165.500000         117.750000   \n",
       "50%       0.000000             279.000000         296.000000   \n",
       "75%       1.000000             547.750000         984.250000   \n",
       "max       1.000000             791.000000        2008.000000   \n",
       "\n",
       "        vehicles and fuel tanks  cruise missiles  \n",
       "count                802.000000       802.000000  \n",
       "unique                      NaN              NaN  \n",
       "top                         NaN              NaN  \n",
       "freq                        NaN              NaN  \n",
       "mean                6477.211970       939.319202  \n",
       "std                 4128.892864       672.078657  \n",
       "min                    0.000000         0.000000  \n",
       "25%                 3477.000000       233.000000  \n",
       "50%                 5532.500000       911.000000  \n",
       "75%                 9328.500000      1534.000000  \n",
       "max                16477.000000      2166.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "russian_equipment.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3c7459d6",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 802 entries, 2022-02-25 to 2024-05-06\n",
      "Data columns (total 18 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   day                        802 non-null    int64  \n",
      " 1   military auto              802 non-null    float64\n",
      " 2   fuel tank                  802 non-null    float64\n",
      " 3   mobile SRBM system         802 non-null    float64\n",
      " 4   greatest losses direction  802 non-null    object \n",
      " 5   aircraft                   802 non-null    float64\n",
      " 6   helicopter                 802 non-null    float64\n",
      " 7   tank                       802 non-null    float64\n",
      " 8   APC                        802 non-null    float64\n",
      " 9   field artillery            802 non-null    float64\n",
      " 10  MRL                        802 non-null    float64\n",
      " 11  drone                      802 non-null    float64\n",
      " 12  naval ship                 802 non-null    float64\n",
      " 13  submarines                 802 non-null    float64\n",
      " 14  anti-aircraft warfare      802 non-null    float64\n",
      " 15  special equipment          802 non-null    float64\n",
      " 16  vehicles and fuel tanks    802 non-null    float64\n",
      " 17  cruise missiles            802 non-null    float64\n",
      "dtypes: float64(16), int64(1), object(1)\n",
      "memory usage: 119.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#set index for sorting by date.\n",
    "russian_equipment.set_index('date', inplace=True)\n",
    "russian_equipment.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6b17f2a",
   "metadata": {
    "execution": {
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     "start_time": "2024-05-08T15:01:58.222495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 'Sievierodonetsk', 'Kurakhove and Izyum',\n",
       "       'Zaporizhzhia and Izyum', 'Izyum', 'Izyum, Novopavlivsk',\n",
       "       'Popasna', 'Lyman and Kurakhove', 'Slobozhanskyi', 'Novopavlivsk',\n",
       "       'Avdiivka', 'Novopavlivsk, Kurakhove and Sievierodonetsk',\n",
       "       'Kurakhove', 'Kurakhove and Avdiivka', 'Bakhmut and Zaporizhzhia',\n",
       "       'Lyman and Zaporizhzhia', 'Sloviansk, Kryvyi Rih and Zaporizhzhia',\n",
       "       'Bakhmut', 'Lyman', 'Avdiivka and Kryvyi Rih', 'Zaporizhzhia',\n",
       "       'Kryvyi Rih and Zaporizhzhia', 'Kryvyi Rih and Bakhmut',\n",
       "       'Sloviansk', 'Kharkiv and Bakhmut', 'Sievierodonetsk and Bakhmut',\n",
       "       'Bakhmut and Sievierodonetsk', 'Sloviansk, Bakhmut and Kryvyi Rih',\n",
       "       'Bakhmut and Avdiivka', 'Sloviansk, Bakhmut and Avdiivka',\n",
       "       'Sloviansk and Bakhmut', 'Bakhmut and Kurakhove',\n",
       "       'Kramatorsk and Bakhmut', 'Sloviansk and Donetsk',\n",
       "       'Avdiivka and Bakhmut', 'Kramatorsk', 'Mykolaiv',\n",
       "       'Kramatorsk, Kryvyi Rih and Bakhmut', 'Kryvyi Rih', 'Donetsk',\n",
       "       'Donetsk and Kryvyi Rih', 'Bakhmut and Kryvyi Rih',\n",
       "       'Bakhmut and Donetsk', 'Kharkiv and Donetsk',\n",
       "       'Donetsk and Mykolaiv', 'Donetsk and Kurakhove',\n",
       "       'Kryvyi Rih and Donetsk', 'Kryvyi Rih and Mykolaiv',\n",
       "       'Kramatorsk and Donetsk', 'Kramatorsk and Kryvyi Rih',\n",
       "       'Kramatorsk, Avdiivka and Kryvyi Rih',\n",
       "       'Donetsk, Bakhmut and Kramatorsk', 'Kryvyi Rih and Kramatorsk',\n",
       "       'Bakhmut, Avdiivka and Kramatorsk', 'Bakhmut and Kramatorsk',\n",
       "       'Donetsk and Lyman', 'Avdiivka and Lyman', 'Lyman and Avdiivka',\n",
       "       'Lyman, Bakhmut and Avdiivka', 'Lyman, Avdiivka and Bakhmut',\n",
       "       'Bakhmut and Lyman', 'Avdiivka, Bakhmut and Lyman',\n",
       "       'Bakhmut, Lyman and Avdiivka', 'Lyman and Bakhmut',\n",
       "       'Kupiansk, Avdiivka and Bakhmut'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a closer look at column data to assess its usefullness\n",
    "russian_equipment['greatest losses direction'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31dc9b82",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 802 entries, 2022-02-25 to 2024-05-06\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype\n",
      "---  ------                   --------------  -----\n",
      " 0   day                      802 non-null    int64\n",
      " 1   aircraft                 802 non-null    int64\n",
      " 2   helicopter               802 non-null    int64\n",
      " 3   tank                     802 non-null    int64\n",
      " 4   APC                      802 non-null    int64\n",
      " 5   field artillery          802 non-null    int64\n",
      " 6   MRL                      802 non-null    int64\n",
      " 7   drone                    802 non-null    int64\n",
      " 8   naval ship               802 non-null    int64\n",
      " 9   submarines               802 non-null    int64\n",
      " 10  anti-aircraft warfare    802 non-null    int64\n",
      " 11  special equipment        802 non-null    int64\n",
      " 12  vehicles and fuel tanks  802 non-null    int64\n",
      " 13  cruise missiles          802 non-null    int64\n",
      "dtypes: int64(14)\n",
      "memory usage: 94.0+ KB\n"
     ]
    },
    {
     "data": {
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       "<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>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>drone</th>\n",
       "      <th>naval ship</th>\n",
       "      <th>submarines</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <th>special equipment</th>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-25</th>\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>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-26</th>\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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-27</th>\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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\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>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\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>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>365</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-02</th>\n",
       "      <td>799</td>\n",
       "      <td>348</td>\n",
       "      <td>325</td>\n",
       "      <td>7332</td>\n",
       "      <td>14096</td>\n",
       "      <td>12044</td>\n",
       "      <td>1053</td>\n",
       "      <td>9561</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>784</td>\n",
       "      <td>1988</td>\n",
       "      <td>16224</td>\n",
       "      <td>2126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-03</th>\n",
       "      <td>800</td>\n",
       "      <td>348</td>\n",
       "      <td>325</td>\n",
       "      <td>7354</td>\n",
       "      <td>14129</td>\n",
       "      <td>12102</td>\n",
       "      <td>1053</td>\n",
       "      <td>9580</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>786</td>\n",
       "      <td>1993</td>\n",
       "      <td>16266</td>\n",
       "      <td>2126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-04</th>\n",
       "      <td>801</td>\n",
       "      <td>348</td>\n",
       "      <td>325</td>\n",
       "      <td>7366</td>\n",
       "      <td>14156</td>\n",
       "      <td>12148</td>\n",
       "      <td>1055</td>\n",
       "      <td>9611</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>788</td>\n",
       "      <td>2001</td>\n",
       "      <td>16337</td>\n",
       "      <td>2127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-05</th>\n",
       "      <td>802</td>\n",
       "      <td>349</td>\n",
       "      <td>325</td>\n",
       "      <td>7375</td>\n",
       "      <td>14184</td>\n",
       "      <td>12255</td>\n",
       "      <td>1058</td>\n",
       "      <td>9636</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>790</td>\n",
       "      <td>1999</td>\n",
       "      <td>16418</td>\n",
       "      <td>2166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-06</th>\n",
       "      <td>803</td>\n",
       "      <td>349</td>\n",
       "      <td>325</td>\n",
       "      <td>7380</td>\n",
       "      <td>14213</td>\n",
       "      <td>12250</td>\n",
       "      <td>1057</td>\n",
       "      <td>9683</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>791</td>\n",
       "      <td>2008</td>\n",
       "      <td>16477</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            day  aircraft  helicopter  tank    APC  field artillery   MRL  \\\n",
       "date                                                                        \n",
       "2022-02-25    2        10           7    80    516               49     4   \n",
       "2022-02-26    3        27          26   146    706               49     4   \n",
       "2022-02-27    4        27          26   150    706               50     4   \n",
       "2022-02-28    5        29          29   150    816               74    21   \n",
       "2022-03-01    6        29          29   198    846               77    24   \n",
       "...         ...       ...         ...   ...    ...              ...   ...   \n",
       "2024-05-02  799       348         325  7332  14096            12044  1053   \n",
       "2024-05-03  800       348         325  7354  14129            12102  1053   \n",
       "2024-05-04  801       348         325  7366  14156            12148  1055   \n",
       "2024-05-05  802       349         325  7375  14184            12255  1058   \n",
       "2024-05-06  803       349         325  7380  14213            12250  1057   \n",
       "\n",
       "            drone  naval ship  submarines  anti-aircraft warfare  \\\n",
       "date                                                               \n",
       "2022-02-25      0           2           0                      0   \n",
       "2022-02-26      2           2           0                      0   \n",
       "2022-02-27      2           2           0                      0   \n",
       "2022-02-28      3           2           0                      5   \n",
       "2022-03-01      3           2           0                      7   \n",
       "...           ...         ...         ...                    ...   \n",
       "2024-05-02   9561          26           1                    784   \n",
       "2024-05-03   9580          26           1                    786   \n",
       "2024-05-04   9611          26           1                    788   \n",
       "2024-05-05   9636          26           1                    790   \n",
       "2024-05-06   9683          26           1                    791   \n",
       "\n",
       "            special equipment  vehicles and fuel tanks  cruise missiles  \n",
       "date                                                                     \n",
       "2022-02-25                  0                      160                0  \n",
       "2022-02-26                  0                      190                0  \n",
       "2022-02-27                  0                      190                0  \n",
       "2022-02-28                  0                      351                0  \n",
       "2022-03-01                  0                      365                0  \n",
       "...                       ...                      ...              ...  \n",
       "2024-05-02               1988                    16224             2126  \n",
       "2024-05-03               1993                    16266             2126  \n",
       "2024-05-04               2001                    16337             2127  \n",
       "2024-05-05               1999                    16418             2166  \n",
       "2024-05-06               2008                    16477             2148  \n",
       "\n",
       "[802 rows x 14 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the pandas option to suppress the warning *update later*\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "\n",
    "# Consolidating repetitive columns (according to the dataset updates) and deleting blank ones\n",
    "russian_equipment.loc[:,'vehicles and fuel tanks'] += (russian_equipment.loc[:,'fuel tank'] \n",
    "                                             + russian_equipment.loc[:,'military auto'])\n",
    "russian_equipment.loc[:,'cruise missiles'] += russian_equipment.loc[:,'mobile SRBM system']\n",
    "\n",
    "russian_equipment.drop(columns={'fuel tank', 'military auto', 'mobile SRBM system'\n",
    "                                , 'greatest losses direction'}, inplace=True)\n",
    "\n",
    "#Change all remaining numeric data to type integer\n",
    "russian_equipment = russian_equipment.astype(int)\n",
    "russian_equipment.info()\n",
    "russian_equipment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11eab3d5",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Categorizing equipment based on type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30153274",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Grouping equipment in dataframe\n",
    "air_units = ['drone','aircraft','helicopter', 'cruise missiles']\n",
    "water_units = ['naval ship', 'submarines']\n",
    "ground_units = ['APC','vehicles and fuel tanks', 'tank', 'field artillery', 'MRL',  'anti-aircraft warfare', 'special equipment']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ede2a12",
   "metadata": {
    "papermill": {
     "duration": 0.063754,
     "end_time": "2024-05-08T15:01:58.858335",
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   "source": [
    "<div style=\"color:white;display:fill;border-radius:12px;\n",
    "            background-color:#005bbb;font-size:150%;\n",
    "            font-family:Georgia;letter-spacing:0.5px\">\n",
    "    <p style=\"padding: 8px;color:gold;\"><b> 3. Exploratory Data Analysis and Visualization</b></p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9e0c5e",
   "metadata": {
    "papermill": {
     "duration": 0.063775,
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    },
    "tags": []
   },
   "source": [
    "# Problem 1\n",
    "##### Visually represent the region being invaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31c75679",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAVIGABLE WORLD MAP WITH KYIV (UKRAINE'S CAPITAL) HIGHLIGHTED\n"
     ]
    },
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7a2607f0ec90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the coordinates for Ukraine and create the map\n",
    "ukraine_lat = 48.383022\n",
    "ukraine_long = 31.1828699\n",
    "ukraine_map = F.Map(location=[ukraine_lat, ukraine_long], \n",
    "                    tiles=\"cartodbpositron\", zoom_start=7)\n",
    "\n",
    "# Create a feature group for Kiev\n",
    "kiev = F.FeatureGroup()\n",
    "\n",
    "kiev_lat = 50.4500336\n",
    "kiev_long = 30.5241361\n",
    "kiev.add_child(F.CircleMarker([kiev_lat, kiev_long], radius=5, color=\"red\", fill_color=\"blue\"))\n",
    "ukraine_map.add_child(kiev)\n",
    "\n",
    "F.Marker([kiev_lat, kiev_long], popup=\"Ukraine's Capital City\").add_to(ukraine_map)\n",
    "\n",
    "# Read GeoJSON data for Ukraine from file\n",
    "with open('/kaggle/input/world-countries/world-countries.json', 'r') as geojson_file:\n",
    "    ukraine_geojson = json.load(geojson_file)\n",
    "\n",
    "# Add the GeoJSON data to the map\n",
    "F.GeoJson(ukraine_geojson, name='ukraine', style_function=\n",
    "          lambda x: {\"fillColor\": \"gold\", \"color\": \"blue\"}).add_to(ukraine_map)\n",
    "\n",
    "print(\"Navigable World Map with Kyiv (Ukraine's capital) highlighted\".upper())\n",
    "ukraine_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55ac71e",
   "metadata": {
    "papermill": {
     "duration": 0.076408,
     "end_time": "2024-05-08T15:01:59.485635",
     "exception": false,
     "start_time": "2024-05-08T15:01:59.409227",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 2\n",
    "##### Show the total Prisoners of War per day recorded in the Personel table.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "87c44aec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:01:59.650329Z",
     "iopub.status.busy": "2024-05-08T15:01:59.646618Z",
     "iopub.status.idle": "2024-05-08T15:01:59.919076Z",
     "shell.execute_reply": "2024-05-08T15:01:59.919605Z",
     "shell.execute_reply.started": "2024-05-08T15:00:22.097078Z"
    },
    "papermill": {
     "duration": 0.358214,
     "end_time": "2024-05-08T15:01:59.919797",
     "exception": false,
     "start_time": "2024-05-08T15:01:59.561583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
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       "        "
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   "source": [
    "# Refine data to extract on the columns needed for this table\n",
    "pow_data = russian_personnel[[\"day\", \"POW\"]]\n",
    "\n",
    "# create figure\n",
    "fig=go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(x=pow_data.index, y=pow_data['POW']))\n",
    "\n",
    "# set title\n",
    "fig.update_layout(title={'text':'Total POW Held to Date',\n",
    "                         'y':0.93,'x':0.5,'xanchor':'center','yanchor':'top'})\n",
    "\n",
    "# Add range slider for customized search results\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        rangeselector=dict(\n",
    "            buttons=list([\n",
    "                dict(count=1, label=\"1 D\", step=\"day\", stepmode=\"todate\"),\n",
    "                dict(count=7, label=\"1 WK\", step=\"day\", stepmode=\"todate\"),\n",
    "                dict(count=30, label=\"1 MTH\", step=\"day\",stepmode=\"todate\"),\n",
    "                dict(count=3, label=\"1 QTR\", step=\"month\",stepmode=\"todate\"),\n",
    "                dict(label='ALL', step=\"all\")]), \n",
    "             activecolor='gold'),\n",
    "        automargin=True, rangeslider=dict(visible=True), type=\"date\"),\n",
    "    autosize=True)\n",
    "\n",
    "fig.update_layout(plot_bgcolor='#f1f3f4')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Problem 3\n",
    "##### What is the daily change in POW and compared to Total POW?"
   ]
  },
  {
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   "id": "2be7fc32",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Total POW</th>\n",
       "      <th>Daily Increase in POW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>216</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>232</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>259</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>284</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>360</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>371</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>389</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>389</td>\n",
       "      <td>0</td>\n",
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       "      <th>18</th>\n",
       "      <td>389</td>\n",
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       "      <th>19</th>\n",
       "      <td>389</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>389</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>389</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>405</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>405</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>405</td>\n",
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       "      <td>405</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>405</td>\n",
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      "text/plain": [
       "     Daily Total POW  Daily Increase in POW\n",
       "day                                        \n",
       "2                  0                      0\n",
       "3                  0                      0\n",
       "4                  0                      0\n",
       "5                  0                      0\n",
       "6                200                    200\n",
       "7                200                      0\n",
       "8                200                      0\n",
       "9                200                      0\n",
       "10               216                     16\n",
       "11               232                     16\n",
       "12               259                     27\n",
       "13               284                     25\n",
       "14               360                     76\n",
       "15               371                     11\n",
       "16               389                     18\n",
       "17               389                      0\n",
       "18               389                      0\n",
       "19               389                      0\n",
       "20               389                      0\n",
       "21               389                      0\n",
       "22               405                     16\n",
       "23               405                      0\n",
       "24               405                      0\n",
       "25               405                      0\n",
       "26               405                      0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "# Helper Variables\n",
    "last = 0\n",
    "increase = []\n",
    "\n",
    "# Calculate daily change in POW\n",
    "for i in (pow_data['POW'].values):\n",
    "    count = i - last\n",
    "    increase.append(count)\n",
    "    last = i\n",
    "    \n",
    "# Assign new values to table    \n",
    "pow_data['Daily Increase in POW'] = increase\n",
    "\n",
    "# Style table\n",
    "pow_data_ = pow_data[['day', 'POW', 'Daily Increase in POW']].set_index('day')\n",
    "pow_data_.rename(columns={'POW':'Daily Total POW'}, inplace=True)\n",
    "\n",
    "pow_data_.head(25)"
   ]
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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set graph style\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "# Create graph\n",
    "pow_data_.plot(figsize=(20,6))\n",
    "plt.xlabel('Days since Invasion'.upper())\n",
    "plt.ylabel('Count of POW'.upper())\n",
    "plt.title(\"Total Daily POW VS. Daily Increase in POW\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed97b364",
   "metadata": {
    "papermill": {
     "duration": 0.075363,
     "end_time": "2024-05-08T15:02:00.965824",
     "exception": false,
     "start_time": "2024-05-08T15:02:00.890461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 4\n",
    "##### Visualize the daily change in POW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03380fff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:01.169827Z",
     "iopub.status.busy": "2024-05-08T15:02:01.143483Z",
     "iopub.status.idle": "2024-05-08T15:02:01.499135Z",
     "shell.execute_reply": "2024-05-08T15:02:01.499686Z",
     "shell.execute_reply.started": "2024-05-08T15:00:22.670884Z"
    },
    "papermill": {
     "duration": 0.458362,
     "end_time": "2024-05-08T15:02:01.499864",
     "exception": false,
     "start_time": "2024-05-08T15:02:01.041502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    18.000000\n",
      "mean     16.444444\n",
      "std      16.905350\n",
      "min       1.000000\n",
      "25%       7.500000\n",
      "50%      11.500000\n",
      "75%      17.500000\n",
      "max      76.000000\n",
      "Name: Daily Increase in POW, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "\n",
    "# Create Plot Object\n",
    "fig = plt.figure()\n",
    "ax0 = fig.add_subplot(1, 2, 1)\n",
    "ax1 = fig.add_subplot(1, 2, 2)\n",
    "\n",
    "# Assessing data after day 10 when POW is within a more narrow/normalized range. See above graph\n",
    "plot_data1=pow_data_['Daily Increase in POW'][pow_data_['Daily Increase in POW'] > 0][1:]\n",
    "print(plot_data1.describe())\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "plot_data1.plot(kind='box', color='blue', figsize=(20, 6), vert=False, ax=ax0, notch=True, label=\"\", \n",
    "                grid=True, patch_artist=False) \n",
    "ax0.set_title(f'Statistical Avgs for Daily Increase in POW Since Day {plot_data1.index[0]} of Invasion')\n",
    "ax0.set_xlabel('Count of POW'.upper())\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "plot_data1.plot(color='blue', figsize=(20,6), ax=ax1, marker=\"o\", grid=False)\n",
    "ax1.set_xlabel('Days since Invasion'.upper(), loc='left')\n",
    "ax1.set_ylabel('Count of New POW'.upper(), loc='bottom')\n",
    "ax1.set_title(f\"Daily Increase in POW Since Day {plot_data1.index[0]} of Invasion\")\n",
    "\n",
    "# Print graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e761684a",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:01.669553Z",
     "iopub.status.busy": "2024-05-08T15:02:01.668833Z",
     "iopub.status.idle": "2024-05-08T15:02:01.671802Z",
     "shell.execute_reply": "2024-05-08T15:02:01.672304Z",
     "shell.execute_reply.started": "2024-05-08T15:00:23.022044Z"
    },
    "papermill": {
     "duration": 0.090431,
     "end_time": "2024-05-08T15:02:01.672487",
     "exception": false,
     "start_time": "2024-05-08T15:02:01.582056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 16 average daily POW between days 10 and 63 of the invasion. No additional prisoners have been reported since.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {round(plot_data1.describe()[1])} average daily POW between days {plot_data1.index.min()} and {plot_data1.index.max()} of the invasion. No additional prisoners have been reported since.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a2997d",
   "metadata": {
    "papermill": {
     "duration": 0.078761,
     "end_time": "2024-05-08T15:02:01.829743",
     "exception": false,
     "start_time": "2024-05-08T15:02:01.750982",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 5\n",
    "##### What Is the total daily loss for all equipment used in the invasion?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f6dd1b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:01.993180Z",
     "iopub.status.busy": "2024-05-08T15:02:01.992210Z",
     "iopub.status.idle": "2024-05-08T15:02:02.005638Z",
     "shell.execute_reply": "2024-05-08T15:02:02.005089Z",
     "shell.execute_reply.started": "2024-05-08T15:00:23.030416Z"
    },
    "papermill": {
     "duration": 0.096996,
     "end_time": "2024-05-08T15:02:02.005785",
     "exception": false,
     "start_time": "2024-05-08T15:02:01.908789",
     "status": "completed"
    },
    "tags": []
   },
   "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>Daily Total Equipment Loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>65908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>66089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>66289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>66582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>66708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Daily Total Equipment Loss\n",
       "day                            \n",
       "2                           828\n",
       "3                          1152\n",
       "4                          1157\n",
       "5                          1480\n",
       "6                          1580\n",
       "..                          ...\n",
       "799                       65908\n",
       "800                       66089\n",
       "801                       66289\n",
       "802                       66582\n",
       "803                       66708\n",
       "\n",
       "[802 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Structure data from dataset\n",
    "daily_total_equip_loss = russian_equipment.reset_index()\n",
    "daily_total_equip_loss.drop(columns={'date'}, inplace=True)\n",
    "daily_total_equip_loss.set_index('day', inplace=True)\n",
    "\n",
    "# Create new dataframe for graph object\n",
    "daily_total_equip_loss['Daily Total Equipment Loss'] = pd.DataFrame(daily_total_equip_loss.sum(axis=1))\n",
    "daily_total_equip_loss[['Daily Total Equipment Loss']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4fa31ae3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:02.173141Z",
     "iopub.status.busy": "2024-05-08T15:02:02.172361Z",
     "iopub.status.idle": "2024-05-08T15:02:02.943191Z",
     "shell.execute_reply": "2024-05-08T15:02:02.942679Z",
     "shell.execute_reply.started": "2024-05-08T15:00:23.054171Z"
    },
    "papermill": {
     "duration": 0.858366,
     "end_time": "2024-05-08T15:02:02.943333",
     "exception": false,
     "start_time": "2024-05-08T15:02:02.084967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and show graph\n",
    "plot_data2 = daily_total_equip_loss[['Daily Total Equipment Loss']]\n",
    "\n",
    "fig=px.scatter(plot_data2, x=plot_data2.index, y='Daily Total Equipment Loss', marginal_x='box', \n",
    "               color='Daily Total Equipment Loss', labels={\n",
    "                   'Daily Total Equipment Loss':'Daily Total Equipment Loss'.upper()\n",
    "                   ,'day':'days since invasion'.upper()})\n",
    "\n",
    "fig.update_layout(plot_bgcolor='rgb(241,246,255)',\n",
    "                  title={'text':f'Total Equipment Lost since day {plot_data2.index[0]} of the invasion'\n",
    "                         .title(),'y':0.93,'x':0.45,'xanchor':'center','yanchor':'top'})\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dc544b",
   "metadata": {
    "papermill": {
     "duration": 0.080481,
     "end_time": "2024-05-08T15:02:03.102594",
     "exception": false,
     "start_time": "2024-05-08T15:02:03.022113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 6\n",
    "##### Create a word map to show the equipment types with the highest total loss of all the equipment used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "37429d13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:03.283302Z",
     "iopub.status.busy": "2024-05-08T15:02:03.282506Z",
     "iopub.status.idle": "2024-05-08T15:02:03.285793Z",
     "shell.execute_reply": "2024-05-08T15:02:03.286272Z",
     "shell.execute_reply.started": "2024-05-08T15:00:23.390829Z"
    },
    "papermill": {
     "duration": 0.102609,
     "end_time": "2024-05-08T15:02:03.286464",
     "exception": false,
     "start_time": "2024-05-08T15:02:03.183855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equipment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>submarines</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naval ship</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>helicopter</th>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aircraft</th>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRL</th>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>special equipment</th>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cruise missiles</th>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tank</th>\n",
       "      <td>7380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drone</th>\n",
       "      <td>9683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>field artillery</th>\n",
       "      <td>12250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APC</th>\n",
       "      <td>14213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <td>16477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Count\n",
       "Equipment                     \n",
       "submarines                   1\n",
       "naval ship                  26\n",
       "helicopter                 325\n",
       "aircraft                   349\n",
       "anti-aircraft warfare      791\n",
       "MRL                       1057\n",
       "special equipment         2008\n",
       "cruise missiles           2148\n",
       "tank                      7380\n",
       "drone                     9683\n",
       "field artillery          12250\n",
       "APC                      14213\n",
       "vehicles and fuel tanks  16477"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "\n",
    "# Get the most recent loss data\n",
    "total = int(daily_total_equip_loss.iloc[-1: , -1:].values)\n",
    "\n",
    "# gather data pertaining to the total equipment lost and generate a count of the equipment lost by type\n",
    "total_loss = daily_total_equip_loss.iloc[-1: , :-1]\n",
    "total_loss.reset_index(inplace=True)\n",
    "total_loss = pd.DataFrame(total_loss.drop('day', axis=1).transpose()).reset_index()\n",
    "total_loss.rename(columns={'index':'Equipment', 0:'Count'}, inplace=True)\n",
    "total_loss.set_index('Equipment', inplace=True)\n",
    "total_loss.sort_values(by='Count', inplace=True)\n",
    "\n",
    "total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e36bdcc8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:03.465925Z",
     "iopub.status.busy": "2024-05-08T15:02:03.460795Z",
     "iopub.status.idle": "2024-05-08T15:02:05.644185Z",
     "shell.execute_reply": "2024-05-08T15:02:05.644734Z",
     "shell.execute_reply.started": "2024-05-08T15:00:23.412153Z"
    },
    "papermill": {
     "duration": 2.275926,
     "end_time": "2024-05-08T15:02:05.644914",
     "exception": false,
     "start_time": "2024-05-08T15:02:03.368988",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# duplicate equipment name based on how much they contribute to total loss in the shape of the Ukraine map\n",
    "word_string=\"\"\n",
    "max_words = total_loss.mean()\n",
    "for equipment in total_loss.index:\n",
    "     times_repeat = int(total_loss.loc[equipment, 'Count']/total * max_words)\n",
    "     word_string = word_string.upper() + ((equipment + \" \") * times_repeat)\n",
    "\n",
    "# create image of Ukraine map\n",
    "response = requests.get(\"https://www.freeworldmaps.net/europe/ukraine/map-ukraine.jpg\")\n",
    "ukraine_mask = np.array(Image.open(BytesIO(response.content)))\n",
    "\n",
    "# create word cloud\n",
    "word_cloud = WordCloud(background_color = 'white', colormap='brg', mask=ukraine_mask).generate(word_string)\n",
    "\n",
    "# Display cloud\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.imshow(word_cloud, interpolation='bessel', alpha=1)\n",
    "plt.imshow(ukraine_mask, cmap=plt.cm.gray, interpolation='bessel', alpha=0.1)\n",
    "plt.title('Most Russian Equipment Lost Since Invasion', fontweight='bold', color='black')\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93651d8d",
   "metadata": {
    "papermill": {
     "duration": 0.089375,
     "end_time": "2024-05-08T15:02:05.822843",
     "exception": false,
     "start_time": "2024-05-08T15:02:05.733468",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 7\n",
    "##### How much equipment was lost by Russia in the first week of the invasion?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a1fde39e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:06.013935Z",
     "iopub.status.busy": "2024-05-08T15:02:06.013252Z",
     "iopub.status.idle": "2024-05-08T15:02:06.038230Z",
     "shell.execute_reply": "2024-05-08T15:02:06.037760Z",
     "shell.execute_reply.started": "2024-05-08T15:00:25.094332Z"
    },
    "papermill": {
     "duration": 0.125111,
     "end_time": "2024-05-08T15:02:06.038361",
     "exception": false,
     "start_time": "2024-05-08T15:02:05.913250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>vehicles and fuel tanks</th>\n",
       "      <th>cruise missiles</th>\n",
       "      <th>Daily Total Equipment Loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>1152</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>1157</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\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>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>1580</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>211</td>\n",
       "      <td>862</td>\n",
       "      <td>85</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>415</td>\n",
       "      <td>0</td>\n",
       "      <td>1688</td>\n",
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      "text/plain": [
       "     aircraft  helicopter  tank  APC  field artillery  MRL  drone  naval ship  \\\n",
       "day                                                                             \n",
       "2          10           7    80  516               49    4      0           2   \n",
       "3          27          26   146  706               49    4      2           2   \n",
       "4          27          26   150  706               50    4      2           2   \n",
       "5          29          29   150  816               74   21      3           2   \n",
       "6          29          29   198  846               77   24      3           2   \n",
       "7          30          31   211  862               85   40      3           2   \n",
       "\n",
       "     submarines  anti-aircraft warfare  special equipment  \\\n",
       "day                                                         \n",
       "2             0                      0                  0   \n",
       "3             0                      0                  0   \n",
       "4             0                      0                  0   \n",
       "5             0                      5                  0   \n",
       "6             0                      7                  0   \n",
       "7             0                      9                  0   \n",
       "\n",
       "     vehicles and fuel tanks  cruise missiles  Daily Total Equipment Loss  \n",
       "day                                                                        \n",
       "2                        160                0                         828  \n",
       "3                        190                0                        1152  \n",
       "4                        190                0                        1157  \n",
       "5                        351                0                        1480  \n",
       "6                        365                0                        1580  \n",
       "7                        415                0                        1688  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "# Getting count of total looses in the first week\n",
    "dt7 = daily_total_equip_loss.head(6).drop('Daily Total Equipment Loss', axis=1)\n",
    "\n",
    "# Create pie chart chowing loss ratio\n",
    "dt7_sum = dt7.sum(axis=0)[1:]\n",
    "fig = go.Figure(data=[go.Pie(labels=dt7_sum.keys()\n",
    "                             , values=dt7_sum.values\n",
    "                             , textinfo='label'\n",
    "                             , insidetextorientation='auto'\n",
    "                )])\n",
    "chart_title = ('Cumulative Equipment Loss in the First Week: '\n",
    "               + russian_equipment.index[0] + \" to \" + russian_equipment.index[5])\n",
    "fig.update_layout(title={'text':chart_title,'y':0.85,'x':0.5,'xanchor':'center','yanchor':'top'})\n",
    "\n",
    "fig.show()\n",
    "daily_total_equip_loss.head(6)"
   ]
  },
  {
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     "exception": false,
     "start_time": "2024-05-08T15:02:06.126945",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 8\n",
    "##### What is the most recent count of Russian equipment loss?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5157fec8",
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    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:06.419972Z",
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     "start_time": "2024-05-08T15:02:06.307962",
     "status": "completed"
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    "tags": []
   },
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Equipment Loss As At: 2024-05-06 (Most Recent Data)\",\"y\":0.9,\"x\":0.5,\"xanchor\":\"center\",\"yanchor\":\"top\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>unit_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_type</th>\n",
       "      <th>equipment_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Water Units</th>\n",
       "      <th>submarines</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naval ship</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Ground Units</th>\n",
       "      <th>anti-aircraft warfare</th>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRL</th>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>special equipment</th>\n",
       "      <td>2008</td>\n",
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       "    <tr>\n",
       "      <th>tank</th>\n",
       "      <td>7380</td>\n",
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       "    <tr>\n",
       "      <th>field artillery</th>\n",
       "      <td>12250</td>\n",
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       "    <tr>\n",
       "      <th>APC</th>\n",
       "      <td>14213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicles and fuel tanks</th>\n",
       "      <td>16477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Air Units</th>\n",
       "      <th>helicopter</th>\n",
       "      <td>325</td>\n",
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       "    <tr>\n",
       "      <th>aircraft</th>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cruise missiles</th>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drone</th>\n",
       "      <td>9683</td>\n",
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      "text/plain": [
       "                                      unit_amount\n",
       "unit_type    equipment_name                      \n",
       "Water Units  submarines                         1\n",
       "             naval ship                        26\n",
       "Ground Units anti-aircraft warfare            791\n",
       "             MRL                             1057\n",
       "             special equipment               2008\n",
       "             tank                            7380\n",
       "             field artillery                12250\n",
       "             APC                            14213\n",
       "             vehicles and fuel tanks        16477\n",
       "Air Units    helicopter                       325\n",
       "             aircraft                         349\n",
       "             cruise missiles                 2148\n",
       "             drone                           9683"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preparing data for charts\n",
    "def input_unit(unit_name):\n",
    "    if unit_name in air_units:\n",
    "        return \"Air Units\"\n",
    "    elif unit_name in water_units:\n",
    "        return \"Water Units\"\n",
    "    else:\n",
    "        return \"Ground Units\"\n",
    "\n",
    "new_df = russian_equipment.tail(1).transpose().reset_index()\n",
    "new_df.columns = ['equipment_name', 'unit_amount']\n",
    "new_df = new_df.drop([0], axis=0)\n",
    "\n",
    "# performing data manipulation, inputing categorized unit type into a new column\n",
    "new_df['unit_type'] = new_df['equipment_name'].apply(lambda x: input_unit(x))\n",
    "\n",
    "# Creating Sunburst chart based on air, ground or naval unit type\n",
    "fig = px.sunburst(\n",
    "        new_df, path=['unit_type', 'equipment_name', 'unit_amount'], maxdepth=2\n",
    "        , color='unit_type', values='unit_amount', color_continuous_scale='BdRu'\n",
    "        , title='Total Equipment Loss As At: ' + russian_equipment.index[-1] + ' (Most Recent Data)'\n",
    "                )\n",
    "fig.update_layout(title={'y':.9,'x':0.5,'xanchor':'center','yanchor':'top'})\n",
    "\n",
    "fig.show()\n",
    "\n",
    "new_df.groupby(['unit_type', 'equipment_name']).sum().sort_values(by=['unit_type','unit_amount'], ascending=[False, True])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8d2efd",
   "metadata": {
    "papermill": {
     "duration": 0.090386,
     "end_time": "2024-05-08T15:02:06.732242",
     "exception": false,
     "start_time": "2024-05-08T15:02:06.641856",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 9\n",
    "##### Is there any correlation between the POW and equipment loss?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a540522",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:06.923399Z",
     "iopub.status.busy": "2024-05-08T15:02:06.922740Z",
     "iopub.status.idle": "2024-05-08T15:02:07.287679Z",
     "shell.execute_reply": "2024-05-08T15:02:07.288113Z",
     "shell.execute_reply.started": "2024-05-08T15:00:25.249615Z"
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    "papermill": {
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     "end_time": "2024-05-08T15:02:07.288280",
     "exception": false,
     "start_time": "2024-05-08T15:02:06.820329",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "\n",
    "merge = pd.merge(russian_equipment, russian_personnel).drop(['day'], axis=1)\n",
    "merged = merge.corr()['POW'].sort_values(ascending=False)\n",
    "merged.drop('POW', inplace=True)\n",
    "\n",
    "merged.plot.barh(title='Correlation between POW and Equipment Loss',\n",
    "                 figsize=(10,7), alpha=.55, color='gold'\n",
    "                ).axvline(x=0, color='grey',linestyle='--', linewidth=1, alpha=0.2)\n",
    "\n",
    "for index, value in enumerate(merged):\n",
    "    value = round(value, 3)\n",
    "    label = format(value)\n",
    "    if value>0:\n",
    "        value-=.04\n",
    "        color='darkblue'\n",
    "    else:\n",
    "        value+=.003\n",
    "        color='crimson'\n",
    "    plt.annotate(label, xy=(value, index-.1), color=color, alpha=1)\n",
    "    \n",
    "plt.xlabel('Correlation Coefficient'.upper(), loc='left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "62adc9ff",
   "metadata": {
    "_kg_hide-input": false,
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:07.484505Z",
     "iopub.status.busy": "2024-05-08T15:02:07.483739Z",
     "iopub.status.idle": "2024-05-08T15:02:07.491823Z",
     "shell.execute_reply": "2024-05-08T15:02:07.491184Z",
     "shell.execute_reply.started": "2024-05-08T15:00:25.589751Z"
    },
    "papermill": {
     "duration": 0.111297,
     "end_time": "2024-05-08T15:02:07.491993",
     "exception": false,
     "start_time": "2024-05-08T15:02:07.380696",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORRELATION STATS FOR POW AND EQUIPMENT\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    13.000000\n",
       "mean      0.319189\n",
       "std       0.149172\n",
       "min       0.117432\n",
       "25%       0.218707\n",
       "50%       0.271335\n",
       "75%       0.333106\n",
       "max       0.619643\n",
       "Name: POW, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Correlation stats for POW and Equipment'.upper())\n",
    "merged.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac516bf9",
   "metadata": {
    "papermill": {
     "duration": 0.095238,
     "end_time": "2024-05-08T15:02:07.685599",
     "exception": false,
     "start_time": "2024-05-08T15:02:07.590361",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"color:white;display:fill;border-radius:12px;\n",
    "            background-color:#005bbb;font-size:150%;\n",
    "            font-family:Georgia;letter-spacing:0.5px\">\n",
    "    <p style=\"padding: 8px;color:gold;\"><b> 4. Summary of Findings</b></p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c4dee2",
   "metadata": {
    "papermill": {
     "duration": 0.092465,
     "end_time": "2024-05-08T15:02:07.871919",
     "exception": false,
     "start_time": "2024-05-08T15:02:07.779454",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Problem 10\n",
    "##### What inferences can you make from the relationship between POW and Equipment lost?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "359330da",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:08.067882Z",
     "iopub.status.busy": "2024-05-08T15:02:08.067196Z",
     "iopub.status.idle": "2024-05-08T15:02:08.069731Z",
     "shell.execute_reply": "2024-05-08T15:02:08.070215Z",
     "shell.execute_reply.started": "2024-05-08T15:00:25.600807Z"
    },
    "papermill": {
     "duration": 0.106689,
     "end_time": "2024-05-08T15:02:08.070425",
     "exception": false,
     "start_time": "2024-05-08T15:02:07.963736",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "m = merged.describe()\n",
    "k = merged.keys()\n",
    "v = merged.values\n",
    "rank = total_loss.set_index([pd.Index(range(1, len(total_loss) +1))])[total_loss.index.str.startswith(k[1])].index[0]\n",
    "sd=round(m[2], 3)\n",
    "\n",
    "s=''\n",
    "if sd>1:\n",
    "    s='high, and indicates that the there is much variability in the data; all points seem to be far from the mean or average correlation.'\n",
    "else:\n",
    "    s='low, and indicates that the there is not much variability in the data; all points seem to be close to the mean or average correlation.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "55e70353",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-05-08T15:02:08.266025Z",
     "iopub.status.busy": "2024-05-08T15:02:08.265317Z",
     "iopub.status.idle": "2024-05-08T15:02:08.268757Z",
     "shell.execute_reply": "2024-05-08T15:02:08.268111Z",
     "shell.execute_reply.started": "2024-05-08T15:00:25.613693Z"
    },
    "papermill": {
     "duration": 0.103976,
     "end_time": "2024-05-08T15:02:08.268897",
     "exception": false,
     "start_time": "2024-05-08T15:02:08.164921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t There is an overall strong, positive, direct corralation between Prisoners of War (POW) and overall Equipment Loss.  From the above data, it is evident that as POW increases so does the overall equipment loss. The strength in  correlation, ranges from 0.536 (helicopter) to 0.117 (submarines). It should be noted that the  strongest correlation exists not between the equipmet type with the highest loss (vehicles and fuel tanks) but with  the type that ranked as having the 3th highest loss of all 13 equipment types assessed.  \n",
      " The average correlation is 0.319; falling between MRL and tank. The standard deviation is 0.149  which is low, and indicates that the there is not much variability in the data; all points seem to be close to the mean or average correlation. The last record of prisoners of war was on day 63 of the invasion, and the last  record of equipment lost was on day 803. \n",
      "\n",
      "\t  It is then valid to conclude that as the invasion continues, and POW increases, so will helicopters lost at a  minimum rate of 0.3:1.0 when compared to Prisoners of War. \n",
      "\n",
      "\t  #PeaceForUkraine\n"
     ]
    }
   ],
   "source": [
    "conclusion = print(\n",
    " f\"\\t There is an overall strong, positive, direct corralation between Prisoners of War (POW) and overall Equipment Loss. \\\n",
    " From the above data, it is evident that as POW increases so does the overall equipment loss. The strength in \\\n",
    " correlation, ranges from {round(v[1], 3)} ({k[1]}) to {round(v[-1], 3)} ({k[-1]}). It should be noted that the \\\n",
    " strongest correlation exists not between the equipmet type with the highest loss ({total_loss.index[-1]}) but with \\\n",
    " the type that ranked as having the {rank}th highest loss of all {len(total_loss.index)} equipment types assessed. \\\n",
    " \\n The average correlation is {round(m[1], 3)}; falling between {k[5]} and {k[4]}. The standard deviation is {sd} \\\n",
    " which is {s} The last record of prisoners of war was on day {plot_data1.index.max()} of the invasion, and the last \\\n",
    " record of equipment lost was on day {pow_data_.index[-1]}. \\n\\n\\t \\\n",
    " It is then valid to conclude that as the invasion continues, and POW increases, so will {k[1]}s lost at a \\\n",
    " minimum rate of {round(m[1], 1)}:1.0 when compared to Prisoners of War. \\n\\n\\t \\\n",
    " #PeaceForUkraine\"\n",
    "    )\n",
    "\n",
    "conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d6cc6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T19:10:51.895898Z",
     "iopub.status.busy": "2022-05-05T19:10:51.895499Z",
     "iopub.status.idle": "2022-05-05T19:10:51.919286Z",
     "shell.execute_reply": "2022-05-05T19:10:51.918598Z",
     "shell.execute_reply.started": "2022-05-05T19:10:51.895788Z"
    },
    "papermill": {
     "duration": 0.095008,
     "end_time": "2024-05-08T15:02:08.457199",
     "exception": false,
     "start_time": "2024-05-08T15:02:08.362191",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"color:white;display:fill;border-radius:12px;\n",
    "            background-color:#005bbb;font-size:150%;\n",
    "            font-family:Georgia;letter-spacing:0.5px\">\n",
    "    <p style=\"color:gold;text-align:center\"><b>- Thank you -</b></p>\n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "datasetId": 4456,
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