orders_details.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:1: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:1: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\d'\n",
      "C:\\Users\\gowth\\AppData\\Local\\Temp\\ipykernel_3228\\1789384759.py:1: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  df=pd.read_csv(\"D:\\dataset for  practice\\orders_details\\Orders.csv\")\n",
      "C:\\Users\\gowth\\AppData\\Local\\Temp\\ipykernel_3228\\1789384759.py:2: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  dfv=pd.read_csv(\"D:\\dataset for  practice\\orders_details\\Details.csv\")\n"
     ]
    },
    {
     "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>Amount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>PaymentMode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B-25681</td>\n",
       "      <td>1096</td>\n",
       "      <td>658</td>\n",
       "      <td>7</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Electronic Games</td>\n",
       "      <td>COD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B-26055</td>\n",
       "      <td>5729</td>\n",
       "      <td>64</td>\n",
       "      <td>14</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>EMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B-25955</td>\n",
       "      <td>2927</td>\n",
       "      <td>146</td>\n",
       "      <td>8</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>EMI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID  Amount  Profit  Quantity     Category      Sub-Category  \\\n",
       "0  B-25681    1096     658         7  Electronics  Electronic Games   \n",
       "1  B-26055    5729      64        14    Furniture            Chairs   \n",
       "2  B-25955    2927     146         8    Furniture         Bookcases   \n",
       "\n",
       "  PaymentMode  \n",
       "0         COD  \n",
       "1         EMI  \n",
       "2         EMI  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"D:\\dataset for  practice\\orders_details\\Orders.csv\")\n",
    "dfv=pd.read_csv(\"D:\\dataset for  practice\\orders_details\\Details.csv\")\n",
    "dfv.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Amount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>PaymentMode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B-25681</td>\n",
       "      <td>1096</td>\n",
       "      <td>658</td>\n",
       "      <td>7</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Electronic Games</td>\n",
       "      <td>COD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B-26055</td>\n",
       "      <td>5729</td>\n",
       "      <td>64</td>\n",
       "      <td>14</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>EMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B-25955</td>\n",
       "      <td>2927</td>\n",
       "      <td>146</td>\n",
       "      <td>8</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>EMI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID  Amount  Profit  Quantity     Category      Sub-Category  \\\n",
       "0  B-25681    1096     658         7  Electronics  Electronic Games   \n",
       "1  B-26055    5729      64        14    Furniture            Chairs   \n",
       "2  B-25955    2927     146         8    Furniture         Bookcases   \n",
       "\n",
       "  PaymentMode  \n",
       "0         COD  \n",
       "1         EMI  \n",
       "2         EMI  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before merge: <class 'pandas.core.frame.DataFrame'> <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Before merge:\", type(df), type(dfv))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Order ID  Order Date CustomerName           State       City  Amount  \\\n",
      "0     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad      80   \n",
      "1     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad      66   \n",
      "2     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad    1275   \n",
      "3     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad       8   \n",
      "4     B-25602  01-04-2018       Vrinda     Maharashtra       Pune    2617   \n",
      "...       ...         ...          ...             ...        ...     ...   \n",
      "1495  B-26099  30-03-2018       Bhishm     Maharashtra     Mumbai    2366   \n",
      "1496  B-26099  30-03-2018       Bhishm     Maharashtra     Mumbai       9   \n",
      "1497  B-26100  31-03-2018       Hitika  Madhya Pradesh     Indore     828   \n",
      "1498  B-26100  31-03-2018       Hitika  Madhya Pradesh     Indore      34   \n",
      "1499  B-26100  31-03-2018       Hitika  Madhya Pradesh     Indore      72   \n",
      "\n",
      "      Profit  Quantity     Category      Sub-Category  PaymentMode  \n",
      "0        -56         4  Electronics  Electronic Games          UPI  \n",
      "1        -12         5     Clothing             Stole          UPI  \n",
      "2       1148         7    Furniture         Bookcases          EMI  \n",
      "3         -2         3     Clothing       Hankerchief  Credit Card  \n",
      "4       1151         4  Electronics            Phones  Credit Card  \n",
      "...      ...       ...          ...               ...          ...  \n",
      "1495     552         5     Clothing          Trousers          UPI  \n",
      "1496       3         1     Clothing             Skirt          COD  \n",
      "1497     230         2    Furniture            Chairs          COD  \n",
      "1498      10         2     Clothing           T-shirt   Debit Card  \n",
      "1499      16         2     Clothing             Shirt          COD  \n",
      "\n",
      "[1500 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "merged_df=pd.merge(df,dfv,on=\"Order ID\", how=\"outer\")\n",
    "print(merged_df)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Amount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>291.847333</td>\n",
       "      <td>24.64200</td>\n",
       "      <td>3.743333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>461.924620</td>\n",
       "      <td>168.55881</td>\n",
       "      <td>2.184942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>-1981.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.750000</td>\n",
       "      <td>-12.00000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>122.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>326.250000</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5729.000000</td>\n",
       "      <td>1864.00000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Amount      Profit     Quantity\n",
       "count  1500.000000  1500.00000  1500.000000\n",
       "mean    291.847333    24.64200     3.743333\n",
       "std     461.924620   168.55881     2.184942\n",
       "min       4.000000 -1981.00000     1.000000\n",
       "25%      47.750000   -12.00000     2.000000\n",
       "50%     122.000000     8.00000     3.000000\n",
       "75%     326.250000    38.00000     5.000000\n",
       "max    5729.000000  1864.00000    14.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Order ID', 'Order Date', 'CustomerName', 'State', 'City', 'Amount',\n",
      "       'Profit', 'Quantity', 'Category', 'Sub-Category', 'PaymentMode'],\n",
      "      dtype='object')\n",
      "Order ID, Order Date, CustomerName, State, City, Amount, Profit, Quantity, Category, Sub-Category, PaymentMode\n"
     ]
    }
   ],
   "source": [
    "print(merged_df.columns)\n",
    "column_list=list(merged_df.columns)\n",
    "formatted_columns = ', '.join(column_list)\n",
    "print(formatted_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID        object\n",
       "Order Date      object\n",
       "CustomerName    object\n",
       "State           object\n",
       "City            object\n",
       "Amount           int64\n",
       "Profit           int64\n",
       "Quantity         int64\n",
       "Category        object\n",
       "Sub-Category    object\n",
       "PaymentMode     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.shape\n",
    "merged_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "     Order ID  Order Date CustomerName           State       City  Amount  \\\n",
      "0     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad      80   \n",
      "1     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad      66   \n",
      "2     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad    1275   \n",
      "3     B-25601  01-04-2018       Bharat         Gujarat  Ahmedabad       8   \n",
      "4     B-25602  01-04-2018       Vrinda     Maharashtra       Pune    2617   \n",
      "...       ...         ...          ...             ...        ...     ...   \n",
      "1495  B-26099  30-03-2018       Bhishm     Maharashtra     Mumbai    2366   \n",
      "1496  B-26099  30-03-2018       Bhishm     Maharashtra     Mumbai       9   \n",
      "1497  B-26100  31-03-2018       Hitika  Madhya Pradesh     Indore     828   \n",
      "1498  B-26100  31-03-2018       Hitika  Madhya Pradesh     Indore      34   \n",
      "1499  B-26100  31-03-2018       Hitika  Madhya Pradesh     Indore      72   \n",
      "\n",
      "      Profit  Quantity     Category      Sub-Category  \n",
      "0        -56         4  Electronics  Electronic Games  \n",
      "1        -12         5     Clothing             Stole  \n",
      "2       1148         7    Furniture         Bookcases  \n",
      "3         -2         3     Clothing       Hankerchief  \n",
      "4       1151         4  Electronics            Phones  \n",
      "...      ...       ...          ...               ...  \n",
      "1495     552         5     Clothing          Trousers  \n",
      "1496       3         1     Clothing             Skirt  \n",
      "1497     230         2    Furniture            Chairs  \n",
      "1498      10         2     Clothing           T-shirt  \n",
      "1499      16         2     Clothing             Shirt  \n",
      "\n",
      "[1500 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(merged_df.ndim)\n",
    "merged_df_iloc=merged_df.iloc[:,:-1]# remove last column in table or dataset\n",
    "print(merged_df_iloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "0              Gujarat\n",
      "1              Gujarat\n",
      "2              Gujarat\n",
      "3              Gujarat\n",
      "4          Maharashtra\n",
      "             ...      \n",
      "1495       Maharashtra\n",
      "1496       Maharashtra\n",
      "1497    Madhya Pradesh\n",
      "1498    Madhya Pradesh\n",
      "1499    Madhya Pradesh\n",
      "Name: State, Length: 1500, dtype: string\n"
     ]
    }
   ],
   "source": [
    "print(merged_df['State'].dtypes)\n",
    "merged_df['State']=merged_df['State'].astype(pd.StringDtype())\n",
    "\n",
    "\n",
    "merged_df['State'].dtypes\n",
    "print(merged_df['State'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     01-04-2018\n",
       "1     01-04-2018\n",
       "2     01-04-2018\n",
       "3     01-04-2018\n",
       "4     01-04-2018\n",
       "5     01-04-2018\n",
       "6     01-04-2018\n",
       "7     01-04-2018\n",
       "8     01-04-2018\n",
       "9     03-04-2018\n",
       "10    03-04-2018\n",
       "11    03-04-2018\n",
       "12    03-04-2018\n",
       "13    03-04-2018\n",
       "14    03-04-2018\n",
       "15    03-04-2018\n",
       "16    03-04-2018\n",
       "17    03-04-2018\n",
       "18    03-04-2018\n",
       "Name: Order Date, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['Order Date'].dtypes\n",
    "merged_df['Order Date'].head(19)\n",
    "#merged_df['Order Date'] = pd.to_datetime(df['Order Date'], format='%d-%m-%y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "plt.bar(merged_df['State'],merged_df['Profit'], color='maroon')\n",
    "plt.title(\"Profit by State\")\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Profit')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gowth\\AppData\\Local\\Temp\\ipykernel_3228\\2834568450.py:1: FutureWarning: <class 'pandas.core.arrays.string_.StringArray'>._reduce will require a `keepdims` parameter in the future\n",
      "  merged_df.max()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Order ID            B-26100\n",
       "Order Date       31-12-2018\n",
       "CustomerName         Yohann\n",
       "State           West Bengal\n",
       "City                Udaipur\n",
       "Amount                 5729\n",
       "Profit                 1864\n",
       "Quantity                 14\n",
       "Category          Furniture\n",
       "Sub-Category       Trousers\n",
       "PaymentMode             UPI\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gowth\\AppData\\Local\\Temp\\ipykernel_3228\\1159104173.py:1: FutureWarning: <class 'pandas.core.arrays.string_.StringArray'>._reduce will require a `keepdims` parameter in the future\n",
      "  merged_df.min()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Order ID               B-25601\n",
       "Order Date          01-01-2018\n",
       "CustomerName         Aakanksha\n",
       "State           Andhra Pradesh\n",
       "City                 Ahmedabad\n",
       "Amount                       4\n",
       "Profit                   -1981\n",
       "Quantity                     1\n",
       "Category              Clothing\n",
       "Sub-Category       Accessories\n",
       "PaymentMode                COD\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.min()"
   ]
  }
 ],
 "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}