Amazon sales analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76be33cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3483d35d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Amazon Sales data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbf4a63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Item Type</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Unit Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Cost</th>\n",
       "      <th>Total Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia and Oceania</td>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>Baby Food</td>\n",
       "      <td>Offline</td>\n",
       "      <td>H</td>\n",
       "      <td>5/28/2010</td>\n",
       "      <td>669165933</td>\n",
       "      <td>6/27/2010</td>\n",
       "      <td>9925</td>\n",
       "      <td>255.28</td>\n",
       "      <td>159.42</td>\n",
       "      <td>2533654.00</td>\n",
       "      <td>1582243.50</td>\n",
       "      <td>951410.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central America and the Caribbean</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>Cereal</td>\n",
       "      <td>Online</td>\n",
       "      <td>C</td>\n",
       "      <td>8/22/2012</td>\n",
       "      <td>963881480</td>\n",
       "      <td>9/15/2012</td>\n",
       "      <td>2804</td>\n",
       "      <td>205.70</td>\n",
       "      <td>117.11</td>\n",
       "      <td>576782.80</td>\n",
       "      <td>328376.44</td>\n",
       "      <td>248406.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Offline</td>\n",
       "      <td>L</td>\n",
       "      <td>5/2/2014</td>\n",
       "      <td>341417157</td>\n",
       "      <td>5/8/2014</td>\n",
       "      <td>1779</td>\n",
       "      <td>651.21</td>\n",
       "      <td>524.96</td>\n",
       "      <td>1158502.59</td>\n",
       "      <td>933903.84</td>\n",
       "      <td>224598.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>Fruits</td>\n",
       "      <td>Online</td>\n",
       "      <td>C</td>\n",
       "      <td>6/20/2014</td>\n",
       "      <td>514321792</td>\n",
       "      <td>7/5/2014</td>\n",
       "      <td>8102</td>\n",
       "      <td>9.33</td>\n",
       "      <td>6.92</td>\n",
       "      <td>75591.66</td>\n",
       "      <td>56065.84</td>\n",
       "      <td>19525.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Offline</td>\n",
       "      <td>L</td>\n",
       "      <td>2/1/2013</td>\n",
       "      <td>115456712</td>\n",
       "      <td>2/6/2013</td>\n",
       "      <td>5062</td>\n",
       "      <td>651.21</td>\n",
       "      <td>524.96</td>\n",
       "      <td>3296425.02</td>\n",
       "      <td>2657347.52</td>\n",
       "      <td>639077.50</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>95</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Mali</td>\n",
       "      <td>Clothes</td>\n",
       "      <td>Online</td>\n",
       "      <td>M</td>\n",
       "      <td>7/26/2011</td>\n",
       "      <td>512878119</td>\n",
       "      <td>9/3/2011</td>\n",
       "      <td>888</td>\n",
       "      <td>109.28</td>\n",
       "      <td>35.84</td>\n",
       "      <td>97040.64</td>\n",
       "      <td>31825.92</td>\n",
       "      <td>65214.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Fruits</td>\n",
       "      <td>Offline</td>\n",
       "      <td>L</td>\n",
       "      <td>11/11/2011</td>\n",
       "      <td>810711038</td>\n",
       "      <td>12/28/2011</td>\n",
       "      <td>6267</td>\n",
       "      <td>9.33</td>\n",
       "      <td>6.92</td>\n",
       "      <td>58471.11</td>\n",
       "      <td>43367.64</td>\n",
       "      <td>15103.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Vegetables</td>\n",
       "      <td>Offline</td>\n",
       "      <td>C</td>\n",
       "      <td>6/1/2016</td>\n",
       "      <td>728815257</td>\n",
       "      <td>6/29/2016</td>\n",
       "      <td>1485</td>\n",
       "      <td>154.06</td>\n",
       "      <td>90.93</td>\n",
       "      <td>228779.10</td>\n",
       "      <td>135031.05</td>\n",
       "      <td>93748.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Personal Care</td>\n",
       "      <td>Offline</td>\n",
       "      <td>M</td>\n",
       "      <td>7/30/2015</td>\n",
       "      <td>559427106</td>\n",
       "      <td>8/8/2015</td>\n",
       "      <td>5767</td>\n",
       "      <td>81.73</td>\n",
       "      <td>56.67</td>\n",
       "      <td>471336.91</td>\n",
       "      <td>326815.89</td>\n",
       "      <td>144521.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>Household</td>\n",
       "      <td>Offline</td>\n",
       "      <td>L</td>\n",
       "      <td>2/10/2012</td>\n",
       "      <td>665095412</td>\n",
       "      <td>2/15/2012</td>\n",
       "      <td>5367</td>\n",
       "      <td>668.27</td>\n",
       "      <td>502.54</td>\n",
       "      <td>3586605.09</td>\n",
       "      <td>2697132.18</td>\n",
       "      <td>889472.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Region                Country        Item Type  \\\n",
       "0               Australia and Oceania                 Tuvalu        Baby Food   \n",
       "1   Central America and the Caribbean                Grenada           Cereal   \n",
       "2                              Europe                 Russia  Office Supplies   \n",
       "3                  Sub-Saharan Africa  Sao Tome and Principe           Fruits   \n",
       "4                  Sub-Saharan Africa                 Rwanda  Office Supplies   \n",
       "..                                ...                    ...              ...   \n",
       "95                 Sub-Saharan Africa                   Mali          Clothes   \n",
       "96                               Asia               Malaysia           Fruits   \n",
       "97                 Sub-Saharan Africa           Sierra Leone       Vegetables   \n",
       "98                      North America                 Mexico    Personal Care   \n",
       "99                 Sub-Saharan Africa             Mozambique        Household   \n",
       "\n",
       "   Sales Channel Order Priority  Order Date   Order ID   Ship Date  \\\n",
       "0        Offline              H   5/28/2010  669165933   6/27/2010   \n",
       "1         Online              C   8/22/2012  963881480   9/15/2012   \n",
       "2        Offline              L    5/2/2014  341417157    5/8/2014   \n",
       "3         Online              C   6/20/2014  514321792    7/5/2014   \n",
       "4        Offline              L    2/1/2013  115456712    2/6/2013   \n",
       "..           ...            ...         ...        ...         ...   \n",
       "95        Online              M   7/26/2011  512878119    9/3/2011   \n",
       "96       Offline              L  11/11/2011  810711038  12/28/2011   \n",
       "97       Offline              C    6/1/2016  728815257   6/29/2016   \n",
       "98       Offline              M   7/30/2015  559427106    8/8/2015   \n",
       "99       Offline              L   2/10/2012  665095412   2/15/2012   \n",
       "\n",
       "    Units Sold  Unit Price  Unit Cost  Total Revenue  Total Cost  Total Profit  \n",
       "0         9925      255.28     159.42     2533654.00  1582243.50     951410.50  \n",
       "1         2804      205.70     117.11      576782.80   328376.44     248406.36  \n",
       "2         1779      651.21     524.96     1158502.59   933903.84     224598.75  \n",
       "3         8102        9.33       6.92       75591.66    56065.84      19525.82  \n",
       "4         5062      651.21     524.96     3296425.02  2657347.52     639077.50  \n",
       "..         ...         ...        ...            ...         ...           ...  \n",
       "95         888      109.28      35.84       97040.64    31825.92      65214.72  \n",
       "96        6267        9.33       6.92       58471.11    43367.64      15103.47  \n",
       "97        1485      154.06      90.93      228779.10   135031.05      93748.05  \n",
       "98        5767       81.73      56.67      471336.91   326815.89     144521.02  \n",
       "99        5367      668.27     502.54     3586605.09  2697132.18     889472.91  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0f0b03f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Region', 'Country', 'Item Type', 'Sales Channel', 'Order Priority',\n",
       "       'Order Date', 'Order ID', 'Ship Date', 'Units Sold', 'Unit Price',\n",
       "       'Unit Cost', 'Total Revenue', 'Total Cost', 'Total Profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2644a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Region          100 non-null    object \n",
      " 1   Country         100 non-null    object \n",
      " 2   Item Type       100 non-null    object \n",
      " 3   Sales Channel   100 non-null    object \n",
      " 4   Order Priority  100 non-null    object \n",
      " 5   Order Date      100 non-null    object \n",
      " 6   Order ID        100 non-null    int64  \n",
      " 7   Ship Date       100 non-null    object \n",
      " 8   Units Sold      100 non-null    int64  \n",
      " 9   Unit Price      100 non-null    float64\n",
      " 10  Unit Cost       100 non-null    float64\n",
      " 11  Total Revenue   100 non-null    float64\n",
      " 12  Total Cost      100 non-null    float64\n",
      " 13  Total Profit    100 non-null    float64\n",
      "dtypes: float64(5), int64(2), object(7)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdfde752",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Order Date'] = pd.to_datetime(data['Order Date'])\n",
    "data['Ship Date'] = pd.to_datetime(data['Ship Date'])\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b51bdc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Region'] = data['Region'].astype(str)\n",
    "data['Country'] = data['Country'].astype(str)\n",
    "data['Item Type'] = data['Item Type'].astype(str)\n",
    "data['Sales Channel'] = data['Sales Channel'].astype(str)\n",
    "data['Order Priority'] = data['Order Priority'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e942462d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Unit Cost</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Total Cost</th>\n",
       "      <th>Total Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5128.710000</td>\n",
       "      <td>276.761300</td>\n",
       "      <td>191.048000</td>\n",
       "      <td>1.373488e+06</td>\n",
       "      <td>9.318057e+05</td>\n",
       "      <td>4.416820e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2794.484562</td>\n",
       "      <td>235.592241</td>\n",
       "      <td>188.208181</td>\n",
       "      <td>1.460029e+06</td>\n",
       "      <td>1.083938e+06</td>\n",
       "      <td>4.385379e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>124.000000</td>\n",
       "      <td>9.330000</td>\n",
       "      <td>6.920000</td>\n",
       "      <td>4.870260e+03</td>\n",
       "      <td>3.612240e+03</td>\n",
       "      <td>1.258020e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2836.250000</td>\n",
       "      <td>81.730000</td>\n",
       "      <td>35.840000</td>\n",
       "      <td>2.687212e+05</td>\n",
       "      <td>1.688680e+05</td>\n",
       "      <td>1.214436e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5382.500000</td>\n",
       "      <td>179.880000</td>\n",
       "      <td>107.275000</td>\n",
       "      <td>7.523144e+05</td>\n",
       "      <td>3.635664e+05</td>\n",
       "      <td>2.907680e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7369.000000</td>\n",
       "      <td>437.200000</td>\n",
       "      <td>263.330000</td>\n",
       "      <td>2.212045e+06</td>\n",
       "      <td>1.613870e+06</td>\n",
       "      <td>6.358288e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9925.000000</td>\n",
       "      <td>668.270000</td>\n",
       "      <td>524.960000</td>\n",
       "      <td>5.997055e+06</td>\n",
       "      <td>4.509794e+06</td>\n",
       "      <td>1.719922e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Units Sold  Unit Price   Unit Cost  Total Revenue    Total Cost  \\\n",
       "count   100.000000  100.000000  100.000000   1.000000e+02  1.000000e+02   \n",
       "mean   5128.710000  276.761300  191.048000   1.373488e+06  9.318057e+05   \n",
       "std    2794.484562  235.592241  188.208181   1.460029e+06  1.083938e+06   \n",
       "min     124.000000    9.330000    6.920000   4.870260e+03  3.612240e+03   \n",
       "25%    2836.250000   81.730000   35.840000   2.687212e+05  1.688680e+05   \n",
       "50%    5382.500000  179.880000  107.275000   7.523144e+05  3.635664e+05   \n",
       "75%    7369.000000  437.200000  263.330000   2.212045e+06  1.613870e+06   \n",
       "max    9925.000000  668.270000  524.960000   5.997055e+06  4.509794e+06   \n",
       "\n",
       "       Total Profit  \n",
       "count  1.000000e+02  \n",
       "mean   4.416820e+05  \n",
       "std    4.385379e+05  \n",
       "min    1.258020e+03  \n",
       "25%    1.214436e+05  \n",
       "50%    2.907680e+05  \n",
       "75%    6.358288e+05  \n",
       "max    1.719922e+06  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Using describe function on dataframe for getting basic stats of numerical dataset \n",
    "data[['Units Sold', 'Unit Price',\t'Unit Cost', 'Total Revenue', 'Total Cost',\t'Total Profit']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2bff7370",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Adding extra column to dataframe which contain only month, year and month with year \n",
    "data['Order Month'] = data['Order Date'].dt.month\n",
    "data['Order Year'] = data['Order Date'].dt.year\n",
    "data['Order Date MonthYear'] = data['Order Date'].dt.strftime('%Y-%m')\n",
    "data = data.drop(columns=['Order Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6c35e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "839992f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                  0\n",
       "Country                 0\n",
       "Item Type               0\n",
       "Sales Channel           0\n",
       "Order Priority          0\n",
       "Order ID                0\n",
       "Ship Date               0\n",
       "Units Sold              0\n",
       "Unit Price              0\n",
       "Unit Cost               0\n",
       "Total Revenue           0\n",
       "Total Cost              0\n",
       "Total Profit            0\n",
       "Order Month             0\n",
       "Order Year              0\n",
       "Order Date MonthYear    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5991544a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The Gambia                          4\n",
       "Sierra Leone                        3\n",
       "Sao Tome and Principe               3\n",
       "Mexico                              3\n",
       "Australia                           3\n",
       "Djibouti                            3\n",
       "Switzerland                         2\n",
       "Myanmar                             2\n",
       "Norway                              2\n",
       "Turkmenistan                        2\n",
       "Cameroon                            2\n",
       "Bulgaria                            2\n",
       "Honduras                            2\n",
       "Azerbaijan                          2\n",
       "Libya                               2\n",
       "Rwanda                              2\n",
       "Mali                                2\n",
       "Gabon                               1\n",
       "Belize                              1\n",
       "Haiti                               1\n",
       "Lithuania                           1\n",
       "San Marino                          1\n",
       "United Kingdom                      1\n",
       "Austria                             1\n",
       "Fiji                                1\n",
       "Madagascar                          1\n",
       "Cote d'Ivoire                       1\n",
       "Tuvalu                              1\n",
       "Democratic Republic of the Congo    1\n",
       "Zambia                              1\n",
       "Malaysia                            1\n",
       "Nicaragua                           1\n",
       "Romania                             1\n",
       "Slovenia                            1\n",
       "Kuwait                              1\n",
       "Kenya                               1\n",
       "Iran                                1\n",
       "Pakistan                            1\n",
       "Lebanon                             1\n",
       "Spain                               1\n",
       "Samoa                               1\n",
       "Monaco                              1\n",
       "Laos                                1\n",
       "Saudi Arabia                        1\n",
       "Federated States of Micronesia      1\n",
       "Slovakia                            1\n",
       "Lesotho                             1\n",
       "Albania                             1\n",
       "Russia                              1\n",
       "Solomon Islands                     1\n",
       "Angola                              1\n",
       "Burkina Faso                        1\n",
       "Republic of the Congo               1\n",
       "Senegal                             1\n",
       "Kyrgyzstan                          1\n",
       "Cape Verde                          1\n",
       "Bangladesh                          1\n",
       "Mongolia                            1\n",
       "Sri Lanka                           1\n",
       "East Timor                          1\n",
       "Portugal                            1\n",
       "New Zealand                         1\n",
       "Moldova                             1\n",
       "France                              1\n",
       "Kiribati                            1\n",
       "South Sudan                         1\n",
       "Costa Rica                          1\n",
       "Syria                               1\n",
       "Brunei                              1\n",
       "Niger                               1\n",
       "Grenada                             1\n",
       "Comoros                             1\n",
       "Iceland                             1\n",
       "Macedonia                           1\n",
       "Mauritania                          1\n",
       "Mozambique                          1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows',None)\n",
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76e1564f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot of unit cost\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style='white')\n",
    "fig, ax = plt.subplots(figsize=(10, 2))\n",
    "sns.boxplot(data['Unit Cost'], color=\"plum\", width=.6)\n",
    "plt.title('Unit Cost Boxplot', fontsize=13)\n",
    "plt.xlabel('Unit Cost')\n",
    "plt.show()\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "020496d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box Plot of Total Cost\n",
    "sns.set(style='white')\n",
    "fig, ax = plt.subplots(figsize=(10, 2))\n",
    "sns.boxplot(data['Total Cost'], color=\"plum\", width=.6)\n",
    "\n",
    "plt.title('Total Cost Boxplot', fontsize=13)\n",
    "plt.xlabel('Cost')\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91786d4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}