Army.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Army Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>activeDuty</th>\n",
       "      <th>paramilitary</th>\n",
       "      <th>reserves</th>\n",
       "      <th>total</th>\n",
       "      <th>population2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>2185000</td>\n",
       "      <td>660000</td>\n",
       "      <td>1170000</td>\n",
       "      <td>4015000</td>\n",
       "      <td>1448471.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1455550</td>\n",
       "      <td>2526950</td>\n",
       "      <td>1155000</td>\n",
       "      <td>5137500</td>\n",
       "      <td>1406631.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States</td>\n",
       "      <td>1388100</td>\n",
       "      <td>0</td>\n",
       "      <td>844950</td>\n",
       "      <td>2233050</td>\n",
       "      <td>334805.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Korea</td>\n",
       "      <td>1280000</td>\n",
       "      <td>5889000</td>\n",
       "      <td>600000</td>\n",
       "      <td>7769000</td>\n",
       "      <td>25990.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Russia</td>\n",
       "      <td>1014000</td>\n",
       "      <td>554000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>3568000</td>\n",
       "      <td>145805.947</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>11680.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>345.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>0</td>\n",
       "      <td>2550</td>\n",
       "      <td>0</td>\n",
       "      <td>2550</td>\n",
       "      <td>1274.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "      <td>9800</td>\n",
       "      <td>0</td>\n",
       "      <td>9800</td>\n",
       "      <td>5182.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Panama</td>\n",
       "      <td>0</td>\n",
       "      <td>26000</td>\n",
       "      <td>0</td>\n",
       "      <td>26000</td>\n",
       "      <td>4446.964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  activeDuty  paramilitary  reserves    total  \\\n",
       "0            China     2185000        660000   1170000  4015000   \n",
       "1            India     1455550       2526950   1155000  5137500   \n",
       "2    United States     1388100             0    844950  2233050   \n",
       "3      North Korea     1280000       5889000    600000  7769000   \n",
       "4           Russia     1014000        554000   2000000  3568000   \n",
       "..             ...         ...           ...       ...      ...   \n",
       "163          Haiti         150            50         0      200   \n",
       "164        Iceland           0           250         0      250   \n",
       "165      Mauritius           0          2550         0     2550   \n",
       "166     Costa Rica           0          9800         0     9800   \n",
       "167         Panama           0         26000         0    26000   \n",
       "\n",
       "     population2022  \n",
       "0       1448471.400  \n",
       "1       1406631.776  \n",
       "2        334805.269  \n",
       "3         25990.679  \n",
       "4        145805.947  \n",
       "..              ...  \n",
       "163       11680.283  \n",
       "164         345.393  \n",
       "165        1274.727  \n",
       "166        5182.354  \n",
       "167        4446.964  \n",
       "\n",
       "[168 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "Army=pd.read_csv(\"C:/Users/91843/Desktop/Research/Dataset/army-total-world.csv\")\n",
    "Army"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>activeDuty</th>\n",
       "      <th>paramilitary</th>\n",
       "      <th>reserves</th>\n",
       "      <th>total</th>\n",
       "      <th>population2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>2185000</td>\n",
       "      <td>660000</td>\n",
       "      <td>1170000</td>\n",
       "      <td>4015000</td>\n",
       "      <td>1448471.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1455550</td>\n",
       "      <td>2526950</td>\n",
       "      <td>1155000</td>\n",
       "      <td>5137500</td>\n",
       "      <td>1406631.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States</td>\n",
       "      <td>1388100</td>\n",
       "      <td>0</td>\n",
       "      <td>844950</td>\n",
       "      <td>2233050</td>\n",
       "      <td>334805.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Korea</td>\n",
       "      <td>1280000</td>\n",
       "      <td>5889000</td>\n",
       "      <td>600000</td>\n",
       "      <td>7769000</td>\n",
       "      <td>25990.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Russia</td>\n",
       "      <td>1014000</td>\n",
       "      <td>554000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>3568000</td>\n",
       "      <td>145805.947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  activeDuty  paramilitary  reserves    total  population2022\n",
       "0          China     2185000        660000   1170000  4015000     1448471.400\n",
       "1          India     1455550       2526950   1155000  5137500     1406631.776\n",
       "2  United States     1388100             0    844950  2233050      334805.269\n",
       "3    North Korea     1280000       5889000    600000  7769000       25990.679\n",
       "4         Russia     1014000        554000   2000000  3568000      145805.947"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prints first n rows of the DataFrame\n",
    "Army.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>activeDuty</th>\n",
       "      <th>paramilitary</th>\n",
       "      <th>reserves</th>\n",
       "      <th>total</th>\n",
       "      <th>population2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>11680.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>345.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>0</td>\n",
       "      <td>2550</td>\n",
       "      <td>0</td>\n",
       "      <td>2550</td>\n",
       "      <td>1274.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "      <td>9800</td>\n",
       "      <td>0</td>\n",
       "      <td>9800</td>\n",
       "      <td>5182.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Panama</td>\n",
       "      <td>0</td>\n",
       "      <td>26000</td>\n",
       "      <td>0</td>\n",
       "      <td>26000</td>\n",
       "      <td>4446.964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  activeDuty  paramilitary  reserves  total  population2022\n",
       "163       Haiti         150            50         0    200       11680.283\n",
       "164     Iceland           0           250         0    250         345.393\n",
       "165   Mauritius           0          2550         0   2550        1274.727\n",
       "166  Costa Rica           0          9800         0   9800        5182.354\n",
       "167      Panama           0         26000         0  26000        4446.964"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the last 5 rows\n",
    "Army.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prints the number of rows as well as columns in a data frame\n",
    "Army.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168 entries, 0 to 167\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   country         168 non-null    object \n",
      " 1   activeDuty      168 non-null    int64  \n",
      " 2   paramilitary    168 non-null    int64  \n",
      " 3   reserves        168 non-null    int64  \n",
      " 4   total           168 non-null    int64  \n",
      " 5   population2022  168 non-null    float64\n",
      "dtypes: float64(1), int64(4), object(1)\n",
      "memory usage: 8.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Index, Datatype, and Memory details\n",
    "Army.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activeDuty</th>\n",
       "      <th>paramilitary</th>\n",
       "      <th>reserves</th>\n",
       "      <th>total</th>\n",
       "      <th>population2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>1.680000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.199320e+05</td>\n",
       "      <td>1.437658e+05</td>\n",
       "      <td>1.494214e+05</td>\n",
       "      <td>4.131192e+05</td>\n",
       "      <td>4.713149e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.733041e+05</td>\n",
       "      <td>6.704519e+05</td>\n",
       "      <td>5.244440e+05</td>\n",
       "      <td>1.272817e+06</td>\n",
       "      <td>1.601753e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>9.942600e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.412500e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.441250e+04</td>\n",
       "      <td>4.300066e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.732500e+04</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>5.745000e+04</td>\n",
       "      <td>1.118101e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.233250e+05</td>\n",
       "      <td>3.131250e+04</td>\n",
       "      <td>5.270000e+04</td>\n",
       "      <td>2.521250e+05</td>\n",
       "      <td>3.454340e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.185000e+06</td>\n",
       "      <td>5.889000e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>1.052200e+07</td>\n",
       "      <td>1.448471e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         activeDuty  paramilitary      reserves         total  population2022\n",
       "count  1.680000e+02  1.680000e+02  1.680000e+02  1.680000e+02    1.680000e+02\n",
       "mean   1.199320e+05  1.437658e+05  1.494214e+05  4.131192e+05    4.713149e+04\n",
       "std    2.733041e+05  6.704519e+05  5.244440e+05  1.272817e+06    1.601753e+05\n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  2.000000e+02    9.942600e+01\n",
       "25%    9.412500e+03  0.000000e+00  0.000000e+00  1.441250e+04    4.300066e+03\n",
       "50%    2.732500e+04  5.400000e+03  4.000000e+01  5.745000e+04    1.118101e+04\n",
       "75%    1.233250e+05  3.131250e+04  5.270000e+04  2.521250e+05    3.454340e+04\n",
       "max    2.185000e+06  5.889000e+06  5.000000e+06  1.052200e+07    1.448471e+06"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics for numerical columns\n",
    "Army.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of 'activeDuty' column: 119931.96428571429\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(Army)\n",
    "\n",
    "# Check the mean of the 'activeDuty' column\n",
    "mean_active_duty = df['activeDuty'].mean()\n",
    "\n",
    "# Print the mean value\n",
    "print(\"Mean of 'activeDuty' column:\", mean_active_duty)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean values for selected columns:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "activeDuty      119931.964286\n",
       "paramilitary    143765.833333\n",
       "reserves        149421.369048\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean for the selected columns\n",
    "df=pd.DataFrame(Army)\n",
    "\n",
    "Mean_Values=df[['activeDuty','paramilitary','reserves']].mean()\n",
    "# Print mean values\n",
    "print(\"Mean values for selected columns:\")\n",
    "Mean_Values\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEDIAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of 'activeDuty' column: 27325.0\n"
     ]
    }
   ],
   "source": [
    "# Check the median of the 'activeDuty' column\n",
    "median_active_duty = df['activeDuty'].median()\n",
    "\n",
    "# Print the median value\n",
    "print(\"Median of 'activeDuty' column:\", median_active_duty)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median values for selected columns:\n",
      "activeDuty      27325.0\n",
      "paramilitary     5400.0\n",
      "reserves           40.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check the median for multiple columns\n",
    "median_values = df[['activeDuty', 'paramilitary', 'reserves']].median()\n",
    "\n",
    "# Print median values\n",
    "print(\"Median values for selected columns:\")\n",
    "print(median_values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activeDuty    273304.117234\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the median of the 'activeDuty' column\n",
    "df=pd.DataFrame(Army)\n",
    "\n",
    "standard_deviation =df[['activeDuty']].std()\n",
    "standard_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard deviation values for selected columns:\n",
      "activeDuty      273304.117234\n",
      "paramilitary    670451.912302\n",
      "reserves        524444.033175\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check the standard deviation for multiple columns\n",
    "std_values = df[['activeDuty', 'paramilitary', 'reserves']].std()\n",
    "\n",
    "# Print standard deviation values\n",
    "print(\"Standard deviation values for selected columns:\")\n",
    "print(std_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of non-null values in each column:\n",
      "country           168\n",
      "activeDuty        168\n",
      "paramilitary      168\n",
      "reserves          168\n",
      "total             168\n",
      "population2022    168\n",
      "dtype: int64\n",
      "\n",
      "Count of non-null values in 'activeDuty' column: 168\n",
      "\n",
      "Count of non-null values in each row:\n",
      "0      6\n",
      "1      6\n",
      "2      6\n",
      "3      6\n",
      "4      6\n",
      "      ..\n",
      "163    6\n",
      "164    6\n",
      "165    6\n",
      "166    6\n",
      "167    6\n",
      "Length: 168, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Here's a simple breakdown:\n",
    "\n",
    "# Null Value: A value that is missing or undefined. In pandas, this is often represented by NaN or None.\n",
    "\n",
    "# Non-Null Value: A value that is present in the dataset and is not missing or undefined.\n",
    "\n",
    "# When you use the count() method in pandas, it counts the number of non-null values in a DataFrame or Series. For example:\n",
    "\n",
    "# Count non-null values in each column\n",
    "column_counts = Army.count()\n",
    "print(\"Count of non-null values in each column:\")\n",
    "print(column_counts)\n",
    "\n",
    "# Count non-null values in a specific column\n",
    "active_duty_count = Army['activeDuty'].count()\n",
    "print(\"\\nCount of non-null values in 'activeDuty' column:\", active_duty_count)\n",
    "\n",
    "# Count non-null values in each row\n",
    "row_counts = Army.count(axis=1)\n",
    "print(\"\\nCount of non-null values in each row:\")\n",
    "print(row_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Zimbabwe'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns the biggest value in every column\n",
    "Army['country'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Afghanistan'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns the lowest value in every column\n",
    "Army['country'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample DataFrame\n",
    "data = {\n",
    "    'country':        ['China', 'India', 'United States', 'North Korea', 'Russia'],\n",
    "    'activeDuty':     [2185000, 1455550, 1388100, 1280000, 1014000],\n",
    "    'paramilitary':   [660000, 2526950, 0, 5889000, 554000],\n",
    "    'reserves':       [1170000, 1155000, 844950, 600000, 2000000],\n",
    "    'total':          [4015000, 5137500, 2233050, 7769000, 3568000],\n",
    "    'population2022': [1448471.4, 1406631.776, 334805.269, 25990.679, 145805.947]\n",
    "}\n",
    "\n",
    "Army = pd.DataFrame(data)\n",
    "\n",
    "# Plotting a pie chart based on the 'activeDuty' column\n",
    "plt.figure(figsize=(4, 4))\n",
    "Army.set_index('country')['activeDuty'].plot.pie(autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "plt.title('Active Duty Military Personnel by Country')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}