Amazon Sales Report (Project).ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b96e9068",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e02d8e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\RAJESH KUMAR\\Desktop\\Python projects\\Python_Amazon_Sales_Analysis-main\\Python_Amazon_Sales_Analysis-main\\Amazon Sale Report.csv', encoding='unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7f9a02a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128976, 21)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50518392",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>ship-service-level</th>\n",
       "      <th>Category</th>\n",
       "      <th>Size</th>\n",
       "      <th>Courier Status</th>\n",
       "      <th>...</th>\n",
       "      <th>currency</th>\n",
       "      <th>Amount</th>\n",
       "      <th>ship-city</th>\n",
       "      <th>ship-state</th>\n",
       "      <th>ship-postal-code</th>\n",
       "      <th>ship-country</th>\n",
       "      <th>B2B</th>\n",
       "      <th>fulfilled-by</th>\n",
       "      <th>New</th>\n",
       "      <th>PendingS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>405-8078784-5731545</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>T-shirt</td>\n",
       "      <td>S</td>\n",
       "      <td>On the Way</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>647.62</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>400081.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
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       "      <td>04-30-22</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>3XL</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>406.00</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>560085.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>404-0687676-7273146</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>XL</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>329.00</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>410210.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>403-9615377-8133951</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Blazzer</td>\n",
       "      <td>L</td>\n",
       "      <td>On the Way</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>753.33</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>605008.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>407-1069790-7240320</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>Trousers</td>\n",
       "      <td>3XL</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>574.00</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>600073.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "   index             Order ID      Date                        Status  \\\n",
       "0      0  405-8078784-5731545  04-30-22                     Cancelled   \n",
       "1      1  171-9198151-1101146  04-30-22  Shipped - Delivered to Buyer   \n",
       "2      2  404-0687676-7273146  04-30-22                       Shipped   \n",
       "3      3  403-9615377-8133951  04-30-22                     Cancelled   \n",
       "4      4  407-1069790-7240320  04-30-22                       Shipped   \n",
       "\n",
       "  Fulfilment Sales Channel ship-service-level  Category Size Courier Status  \\\n",
       "0   Merchant     Amazon.in           Standard   T-shirt    S     On the Way   \n",
       "1   Merchant     Amazon.in           Standard     Shirt  3XL        Shipped   \n",
       "2     Amazon     Amazon.in          Expedited     Shirt   XL        Shipped   \n",
       "3   Merchant     Amazon.in           Standard   Blazzer    L     On the Way   \n",
       "4     Amazon     Amazon.in          Expedited  Trousers  3XL        Shipped   \n",
       "\n",
       "   ...  currency  Amount    ship-city   ship-state ship-postal-code  \\\n",
       "0  ...       INR  647.62       MUMBAI  MAHARASHTRA         400081.0   \n",
       "1  ...       INR  406.00    BENGALURU    KARNATAKA         560085.0   \n",
       "2  ...       INR  329.00  NAVI MUMBAI  MAHARASHTRA         410210.0   \n",
       "3  ...       INR  753.33   PUDUCHERRY   PUDUCHERRY         605008.0   \n",
       "4  ...       INR  574.00      CHENNAI   TAMIL NADU         600073.0   \n",
       "\n",
       "   ship-country    B2B  fulfilled-by New  PendingS  \n",
       "0            IN  False     Easy Ship NaN       NaN  \n",
       "1            IN  False     Easy Ship NaN       NaN  \n",
       "2            IN   True           NaN NaN       NaN  \n",
       "3            IN  False     Easy Ship NaN       NaN  \n",
       "4            IN  False           NaN NaN       NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "543c9f4f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>Blazzer</td>\n",
       "      <td>XXL</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>690.0</td>\n",
       "      <td>HYDERABAD</td>\n",
       "      <td>TELANGANA</td>\n",
       "      <td>500049.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
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       "      <td>05-31-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>T-shirt</td>\n",
       "      <td>XS</td>\n",
       "      <td>Shipped</td>\n",
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       "      <td>1199.0</td>\n",
       "      <td>Halol</td>\n",
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       "      <td>05-31-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>T-shirt</td>\n",
       "      <td>S</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>696.0</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>492014.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "         index             Order ID      Date   Status Fulfilment  \\\n",
       "128971  128970  406-6001380-7673107  05-31-22  Shipped     Amazon   \n",
       "128972  128971  402-9551604-7544318  05-31-22  Shipped     Amazon   \n",
       "128973  128972  407-9547469-3152358  05-31-22  Shipped     Amazon   \n",
       "128974  128973  402-6184140-0545956  05-31-22  Shipped     Amazon   \n",
       "128975  128974  408-7436540-8728312  05-31-22  Shipped     Amazon   \n",
       "\n",
       "       Sales Channel ship-service-level Category Size Courier Status  ...  \\\n",
       "128971     Amazon.in          Expedited    Shirt   XL        Shipped  ...   \n",
       "128972     Amazon.in          Expedited  T-shirt    M        Shipped  ...   \n",
       "128973     Amazon.in          Expedited  Blazzer  XXL        Shipped  ...   \n",
       "128974     Amazon.in          Expedited  T-shirt   XS        Shipped  ...   \n",
       "128975     Amazon.in          Expedited  T-shirt    S        Shipped  ...   \n",
       "\n",
       "        currency  Amount  ship-city    ship-state ship-postal-code  \\\n",
       "128971       INR   517.0  HYDERABAD     TELANGANA         500013.0   \n",
       "128972       INR   999.0   GURUGRAM       HARYANA         122004.0   \n",
       "128973       INR   690.0  HYDERABAD     TELANGANA         500049.0   \n",
       "128974       INR  1199.0      Halol       Gujarat         389350.0   \n",
       "128975       INR   696.0     Raipur  CHHATTISGARH         492014.0   \n",
       "\n",
       "        ship-country    B2B  fulfilled-by New  PendingS  \n",
       "128971            IN  False           NaN NaN       NaN  \n",
       "128972            IN  False           NaN NaN       NaN  \n",
       "128973            IN  False           NaN NaN       NaN  \n",
       "128974            IN  False           NaN NaN       NaN  \n",
       "128975            IN  False           NaN NaN       NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() # Details about last 5 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66db0886",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 128976 entries, 0 to 128975\n",
      "Data columns (total 19 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   index               128976 non-null  int64  \n",
      " 1   Order ID            128976 non-null  object \n",
      " 2   Date                128976 non-null  object \n",
      " 3   Status              128976 non-null  object \n",
      " 4   Fulfilment          128976 non-null  object \n",
      " 5   Sales Channel       128976 non-null  object \n",
      " 6   ship-service-level  128976 non-null  object \n",
      " 7   Category            128976 non-null  object \n",
      " 8   Size                128976 non-null  object \n",
      " 9   Courier Status      128976 non-null  object \n",
      " 10  Qty                 128976 non-null  int64  \n",
      " 11  currency            121176 non-null  object \n",
      " 12  Amount              121176 non-null  float64\n",
      " 13  ship-city           128941 non-null  object \n",
      " 14  ship-state          128941 non-null  object \n",
      " 15  ship-postal-code    128941 non-null  float64\n",
      " 16  ship-country        128941 non-null  object \n",
      " 17  B2B                 128976 non-null  bool   \n",
      " 18  fulfilled-by        39263 non-null   object \n",
      "dtypes: bool(1), float64(2), int64(2), object(14)\n",
      "memory usage: 17.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c50720f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['New', 'PendingS'], axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f0b72b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                     0\n",
       "Order ID                  0\n",
       "Date                      0\n",
       "Status                    0\n",
       "Fulfilment                0\n",
       "Sales Channel             0\n",
       "ship-service-level        0\n",
       "Category                  0\n",
       "Size                      0\n",
       "Courier Status            0\n",
       "Qty                       0\n",
       "currency               7800\n",
       "Amount                 7800\n",
       "ship-city                35\n",
       "ship-state               35\n",
       "ship-postal-code         35\n",
       "ship-country             35\n",
       "B2B                       0\n",
       "fulfilled-by          89713\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c6e770e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop null values\n",
    "df.dropna( inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27b69f89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37514, 19)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "826efc89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel',\n",
       "       'ship-service-level', 'Category', 'Size', 'Courier Status', 'Qty',\n",
       "       'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code',\n",
       "       'ship-country', 'B2B', 'fulfilled-by'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2542c2a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ship-postal-code'] = df['ship-postal-code'].astype('int')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa590e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ship-postal-code'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "062e9b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] =pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d95b9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel',\n",
       "       'ship-service-level', 'Category', 'Size', 'Courier Status', 'Qty',\n",
       "       'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code',\n",
       "       'ship-country', 'B2B', 'fulfilled-by'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "04a6ac22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>ship-service-level</th>\n",
       "      <th>Category</th>\n",
       "      <th>Size</th>\n",
       "      <th>Courier Status</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>currency</th>\n",
       "      <th>Amount</th>\n",
       "      <th>ship-city</th>\n",
       "      <th>ship-state</th>\n",
       "      <th>ship-postal-code</th>\n",
       "      <th>ship-country</th>\n",
       "      <th>B2B</th>\n",
       "      <th>fulfilled-by</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>405-8078784-5731545</td>\n",
       "      <td>2022-04-30</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>T-shirt</td>\n",
       "      <td>S</td>\n",
       "      <td>On the Way</td>\n",
       "      <td>0</td>\n",
       "      <td>INR</td>\n",
       "      <td>647.62</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>400081</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>171-9198151-1101146</td>\n",
       "      <td>2022-04-30</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>3XL</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>406.00</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>560085</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>403-9615377-8133951</td>\n",
       "      <td>2022-04-30</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Blazzer</td>\n",
       "      <td>L</td>\n",
       "      <td>On the Way</td>\n",
       "      <td>0</td>\n",
       "      <td>INR</td>\n",
       "      <td>753.33</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>605008</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>406-7807733-3785945</td>\n",
       "      <td>2022-04-30</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>S</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>399.00</td>\n",
       "      <td>HYDERABAD</td>\n",
       "      <td>TELANGANA</td>\n",
       "      <td>500032</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>405-5513694-8146768</td>\n",
       "      <td>2022-04-30</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>XS</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>399.00</td>\n",
       "      <td>Amravati.</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>444606</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128875</th>\n",
       "      <td>128874</td>\n",
       "      <td>405-4724097-1016369</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>T-shirt</td>\n",
       "      <td>S</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>854.00</td>\n",
       "      <td>ALLUR</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>524315</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128876</th>\n",
       "      <td>128875</td>\n",
       "      <td>403-9524128-9243508</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Blazzer</td>\n",
       "      <td>XL</td>\n",
       "      <td>On the Way</td>\n",
       "      <td>0</td>\n",
       "      <td>INR</td>\n",
       "      <td>734.29</td>\n",
       "      <td>Barabanki</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>225001</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128888</th>\n",
       "      <td>128887</td>\n",
       "      <td>405-6493630-8542756</td>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Trousers</td>\n",
       "      <td>M</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>518.00</td>\n",
       "      <td>NOIDA</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>201301</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128891</th>\n",
       "      <td>128890</td>\n",
       "      <td>407-0116398-1810752</td>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Wallet</td>\n",
       "      <td>Free</td>\n",
       "      <td>On the Way</td>\n",
       "      <td>0</td>\n",
       "      <td>INR</td>\n",
       "      <td>398.10</td>\n",
       "      <td>MADURAI</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>625007</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128892</th>\n",
       "      <td>128891</td>\n",
       "      <td>403-0317423-9322704</td>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Blazzer</td>\n",
       "      <td>M</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>721.00</td>\n",
       "      <td>UTTAR BAGDOGRA</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>734014</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37514 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index             Order ID       Date                        Status  \\\n",
       "0            0  405-8078784-5731545 2022-04-30                     Cancelled   \n",
       "1            1  171-9198151-1101146 2022-04-30  Shipped - Delivered to Buyer   \n",
       "3            3  403-9615377-8133951 2022-04-30                     Cancelled   \n",
       "7            7  406-7807733-3785945 2022-04-30  Shipped - Delivered to Buyer   \n",
       "12          12  405-5513694-8146768 2022-04-30  Shipped - Delivered to Buyer   \n",
       "...        ...                  ...        ...                           ...   \n",
       "128875  128874  405-4724097-1016369 2022-06-01  Shipped - Delivered to Buyer   \n",
       "128876  128875  403-9524128-9243508 2022-06-01                     Cancelled   \n",
       "128888  128887  405-6493630-8542756 2022-05-31  Shipped - Delivered to Buyer   \n",
       "128891  128890  407-0116398-1810752 2022-05-31                     Cancelled   \n",
       "128892  128891  403-0317423-9322704 2022-05-31  Shipped - Delivered to Buyer   \n",
       "\n",
       "       Fulfilment Sales Channel ship-service-level  Category  Size  \\\n",
       "0        Merchant     Amazon.in           Standard   T-shirt     S   \n",
       "1        Merchant     Amazon.in           Standard     Shirt   3XL   \n",
       "3        Merchant     Amazon.in           Standard   Blazzer     L   \n",
       "7        Merchant     Amazon.in           Standard     Shirt     S   \n",
       "12       Merchant     Amazon.in           Standard     Shirt    XS   \n",
       "...           ...           ...                ...       ...   ...   \n",
       "128875   Merchant     Amazon.in           Standard   T-shirt     S   \n",
       "128876   Merchant     Amazon.in           Standard   Blazzer    XL   \n",
       "128888   Merchant     Amazon.in           Standard  Trousers     M   \n",
       "128891   Merchant     Amazon.in           Standard    Wallet  Free   \n",
       "128892   Merchant     Amazon.in           Standard   Blazzer     M   \n",
       "\n",
       "       Courier Status  Quantity currency  Amount       ship-city  \\\n",
       "0          On the Way         0      INR  647.62          MUMBAI   \n",
       "1             Shipped         1      INR  406.00       BENGALURU   \n",
       "3          On the Way         0      INR  753.33      PUDUCHERRY   \n",
       "7             Shipped         1      INR  399.00       HYDERABAD   \n",
       "12            Shipped         1      INR  399.00       Amravati.   \n",
       "...               ...       ...      ...     ...             ...   \n",
       "128875        Shipped         1      INR  854.00           ALLUR   \n",
       "128876     On the Way         0      INR  734.29       Barabanki   \n",
       "128888        Shipped         1      INR  518.00           NOIDA   \n",
       "128891     On the Way         0      INR  398.10         MADURAI   \n",
       "128892        Shipped         1      INR  721.00  UTTAR BAGDOGRA   \n",
       "\n",
       "            ship-state  ship-postal-code ship-country    B2B fulfilled-by  \n",
       "0          MAHARASHTRA            400081           IN  False    Easy Ship  \n",
       "1            KARNATAKA            560085           IN  False    Easy Ship  \n",
       "3           PUDUCHERRY            605008           IN  False    Easy Ship  \n",
       "7            TELANGANA            500032           IN  False    Easy Ship  \n",
       "12         MAHARASHTRA            444606           IN  False    Easy Ship  \n",
       "...                ...               ...          ...    ...          ...  \n",
       "128875  ANDHRA PRADESH            524315           IN  False    Easy Ship  \n",
       "128876   UTTAR PRADESH            225001           IN  False    Easy Ship  \n",
       "128888   UTTAR PRADESH            201301           IN  False    Easy Ship  \n",
       "128891      TAMIL NADU            625007           IN  False    Easy Ship  \n",
       "128892     WEST BENGAL            734014           IN  False    Easy Ship  \n",
       "\n",
       "[37514 rows x 19 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reanmes columns\n",
    "df.rename(columns={'Qty':'Quantity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c64f5dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>index</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Amount</th>\n",
       "      <th>ship-postal-code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37514.000000</td>\n",
       "      <td>37514.000000</td>\n",
       "      <td>37514.000000</td>\n",
       "      <td>37514.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60953.809858</td>\n",
       "      <td>0.867383</td>\n",
       "      <td>646.553960</td>\n",
       "      <td>463291.552754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36844.853039</td>\n",
       "      <td>0.354160</td>\n",
       "      <td>279.952414</td>\n",
       "      <td>194550.425637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27235.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>370465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>63470.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>500019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>91790.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>771.000000</td>\n",
       "      <td>600042.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>128891.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5495.000000</td>\n",
       "      <td>989898.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index           Qty        Amount  ship-postal-code\n",
       "count   37514.000000  37514.000000  37514.000000      37514.000000\n",
       "mean    60953.809858      0.867383    646.553960     463291.552754\n",
       "std     36844.853039      0.354160    279.952414     194550.425637\n",
       "min         0.000000      0.000000      0.000000     110001.000000\n",
       "25%     27235.250000      1.000000    458.000000     370465.000000\n",
       "50%     63470.500000      1.000000    629.000000     500019.000000\n",
       "75%     91790.750000      1.000000    771.000000     600042.000000\n",
       "max    128891.000000      5.000000   5495.000000     989898.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bc1808d6",
   "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>Order ID</th>\n",
       "      <th>Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>ship-service-level</th>\n",
       "      <th>Category</th>\n",
       "      <th>Size</th>\n",
       "      <th>Courier Status</th>\n",
       "      <th>currency</th>\n",
       "      <th>ship-city</th>\n",
       "      <th>ship-state</th>\n",
       "      <th>ship-country</th>\n",
       "      <th>fulfilled-by</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>34664</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4698</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>171-5057375-2831560</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>T-shirt</td>\n",
       "      <td>M</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>INR</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>IN</td>\n",
       "      <td>Easy Ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>12</td>\n",
       "      <td>28741</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "      <td>14062</td>\n",
       "      <td>6806</td>\n",
       "      <td>31859</td>\n",
       "      <td>37514</td>\n",
       "      <td>2839</td>\n",
       "      <td>6236</td>\n",
       "      <td>37514</td>\n",
       "      <td>37514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Order ID                        Status Fulfilment  \\\n",
       "count                 37514                         37514      37514   \n",
       "unique                34664                            11          1   \n",
       "top     171-5057375-2831560  Shipped - Delivered to Buyer   Merchant   \n",
       "freq                     12                         28741      37514   \n",
       "\n",
       "       Sales Channel ship-service-level Category   Size Courier Status  \\\n",
       "count          37514              37514    37514  37514          37514   \n",
       "unique             1                  1        8     11              3   \n",
       "top        Amazon.in           Standard  T-shirt      M        Shipped   \n",
       "freq           37514              37514    14062   6806          31859   \n",
       "\n",
       "       currency  ship-city   ship-state ship-country fulfilled-by  \n",
       "count     37514      37514        37514        37514        37514  \n",
       "unique        1       4698           58            1            1  \n",
       "top         INR  BENGALURU  MAHARASHTRA           IN    Easy Ship  \n",
       "freq      37514       2839         6236        37514        37514  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "56023167",
   "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>Qty</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37514.000000</td>\n",
       "      <td>37514.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.867383</td>\n",
       "      <td>646.553960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.354160</td>\n",
       "      <td>279.952414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>458.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>771.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5495.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Qty        Amount\n",
       "count  37514.000000  37514.000000\n",
       "mean       0.867383    646.553960\n",
       "std        0.354160    279.952414\n",
       "min        0.000000      0.000000\n",
       "25%        1.000000    458.000000\n",
       "50%        1.000000    629.000000\n",
       "75%        1.000000    771.000000\n",
       "max        5.000000   5495.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Qty','Amount']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1be07b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel',\n",
       "       'ship-service-level', 'Category', 'Size', 'Courier Status', 'Qty',\n",
       "       'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code',\n",
       "       'ship-country', 'B2B', 'fulfilled-by'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "97034c77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x='Size', data=df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3114df38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "# Create the countplot\n",
    "ax = sns.countplot(x='Size', data=df)\n",
    "\n",
    "# Add labels to the bars\n",
    "for bar in ax.containers:\n",
    "    ax.bar_label(bar)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da48b1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Note = from the above graph we can see that most of the people buys M-size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b4056f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "GROUP BY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "272dbd06",
   "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>Size</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>5905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L</td>\n",
       "      <td>5795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XL</td>\n",
       "      <td>5481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XXL</td>\n",
       "      <td>4465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3XL</td>\n",
       "      <td>3972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S</td>\n",
       "      <td>3896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XS</td>\n",
       "      <td>2191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6XL</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5XL</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4XL</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Size   Qty\n",
       "6      M  5905\n",
       "5      L  5795\n",
       "8     XL  5481\n",
       "10   XXL  4465\n",
       "0    3XL  3972\n",
       "7      S  3896\n",
       "9     XS  2191\n",
       "4   Free   467\n",
       "3    6XL   170\n",
       "2    5XL   104\n",
       "1    4XL    93"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Size'], as_index=False)['Qty'].sum().sort_values(by='Qty', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b12f74d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Grouping, summing, and sorting\n",
    "grouped_df = df.groupby(['Size'], as_index=False)['Qty'].sum().sort_values(by='Qty', ascending=False)\n",
    "\n",
    "# Creating a barplot\n",
    "plt.figure(figsize=(20, 8))  # corrected parameter name\n",
    "sns.barplot(x='Size', y='Qty', data=grouped_df)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af4d2bcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c7ac0b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "ax= sns.countplot(data=df, x='Courier Status', hue= 'Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fa06796",
   "metadata": {},
   "outputs": [],
   "source": [
    "Note: From above Graph the majority of the orders are shipped through the courier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2df9e263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "# Plot the histogram\n",
    "df['Size'].hist()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5d7e8dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Category'] = df['Category'].astype(str)\n",
    "column_data = df['Category']\n",
    "plt.figure(figsize=(20, ))\n",
    "plt.hist(column_data, bins=30, edgecolor='Black')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc158c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Note: From above Graph you can see that most of the buyers are T-shirt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6aa9f2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking B2B Data  by using pie chart \n",
    "B2B_Check = df['B2B'].value_counts()\n",
    "\n",
    "#  Plot the pie chart\n",
    "plt.pie(B2B_Check, labels=B2B_Check, autopct='%1.1f%%')\n",
    "#plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c5c9fb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking B2B Data  by using pie chart \n",
    "B2B_Check = df['B2B'].value_counts()\n",
    "\n",
    "#  Plot the pie chart\n",
    "plt.pie(B2B_Check, labels=B2B_Check.index, autopct='%1.1f%%')\n",
    "#plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c38626c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Note : From above chart we can see that maximum i.e. 99.3% of buyers are retailers and 0.7% are B2B buyers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "173e6b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare data for scatter plot\n",
    "x_data = df['Category']  \n",
    "y_data = df['Size'] \n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "# Plot the scatter plot\n",
    "plt.scatter(x_data, y_data)\n",
    "plt.xlabel('Category')  \n",
    "plt.ylabel('Size')  \n",
    "plt.title('Scatter Plot') \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "fa94f649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot count of cities by state\n",
    "\n",
    "plt.figure(figsize=(20, 8))\n",
    "sns.countplot(data=df, x='ship-state')\n",
    "plt.xlabel('ship-state')\n",
    "plt.ylabel('count')\n",
    "plt.title('Distribution of State')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1dd69364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top_10_States \n",
    "top_10_state = df['ship-state'].value_counts().head(10)\n",
    "# Plot count of cities by state\n",
    "plt.figure(figsize=(20, 8))\n",
    "sns.countplot(data=df[df['ship-state'].isin(top_10_state.index)], x='ship-state')\n",
    "plt.xlabel('ship-state')\n",
    "plt.ylabel('count')\n",
    "plt.title('Distribution of  State')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16775208",
   "metadata": {},
   "outputs": [],
   "source": [
    "Note: From above Graph you can see that most of the buyers are Maharashtra state\n",
    "Conclusion\n",
    "The data analysis reveals that the business has a significant customer base in Maharashtra state, mainly serves retailers,\n",
    "fulfills orders through Amazon, experiences high demand for T-shirts, and sees M-Size as the preferred choice among buyers.\n",
    " "
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}