Python_SQL_Ecommerce_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05861240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing customers.csv\n",
      "NaN values before replacement:\n",
      "customer_id                 0\n",
      "customer_unique_id          0\n",
      "customer_zip_code_prefix    0\n",
      "customer_city               0\n",
      "customer_state              0\n",
      "dtype: int64\n",
      "\n",
      "Processing orders.csv\n",
      "NaN values before replacement:\n",
      "order_id                            0\n",
      "customer_id                         0\n",
      "order_status                        0\n",
      "order_purchase_timestamp            0\n",
      "order_approved_at                 160\n",
      "order_delivered_carrier_date     1783\n",
      "order_delivered_customer_date    2965\n",
      "order_estimated_delivery_date       0\n",
      "dtype: int64\n",
      "\n",
      "Processing order_items.csv\n",
      "NaN values before replacement:\n",
      "order_id               0\n",
      "order_item_id          0\n",
      "product_id             0\n",
      "seller_id              0\n",
      "shipping_limit_date    0\n",
      "price                  0\n",
      "freight_value          0\n",
      "dtype: int64\n",
      "\n",
      "Processing sellers.csv\n",
      "NaN values before replacement:\n",
      "seller_id                 0\n",
      "seller_zip_code_prefix    0\n",
      "seller_city               0\n",
      "seller_state              0\n",
      "dtype: int64\n",
      "\n",
      "Processing products.csv\n",
      "NaN values before replacement:\n",
      "product_id                      0\n",
      "product category              610\n",
      "product_name_length           610\n",
      "product_description_length    610\n",
      "product_photos_qty            610\n",
      "product_weight_g                2\n",
      "product_length_cm               2\n",
      "product_height_cm               2\n",
      "product_width_cm                2\n",
      "dtype: int64\n",
      "\n",
      "Processing geolocation.csv\n",
      "NaN values before replacement:\n",
      "geolocation_zip_code_prefix    0\n",
      "geolocation_lat                0\n",
      "geolocation_lng                0\n",
      "geolocation_city               0\n",
      "geolocation_state              0\n",
      "dtype: int64\n",
      "\n",
      "Processing payments.csv\n",
      "NaN values before replacement:\n",
      "order_id                0\n",
      "payment_sequential      0\n",
      "payment_type            0\n",
      "payment_installments    0\n",
      "payment_value           0\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import mysql.connector\n",
    "import os\n",
    "\n",
    "# List of CSV files and their corresponding table names\n",
    "csv_files = [\n",
    "    ('customers.csv', 'customers'),\n",
    "    ('orders.csv', 'orders'),\n",
    "    ('order_items.csv', 'order_items'),\n",
    "    ('sellers.csv', 'sellers'),\n",
    "    ('products.csv', 'products'),\n",
    "    ('geolocation.csv', 'geolocation'),\n",
    "    ('payments.csv', 'payments')  # Added payments.csv for specific handling\n",
    "]\n",
    "\n",
    "# Connect to the MySQL database\n",
    "conn = mysql.connector.connect(\n",
    "    host='localhost',\n",
    "    user='root',\n",
    "    password='Yash@2002',\n",
    "    database='ecommerce'\n",
    ")\n",
    "cursor = conn.cursor()\n",
    "\n",
    "# Folder containing the CSV files\n",
    "folder_path = 'D:/OneDrive/Desktop/Ecommerce/archive (2)'\n",
    "\n",
    "def get_sql_type(dtype):\n",
    "    if pd.api.types.is_integer_dtype(dtype):\n",
    "        return 'INT'\n",
    "    elif pd.api.types.is_float_dtype(dtype):\n",
    "        return 'FLOAT'\n",
    "    elif pd.api.types.is_bool_dtype(dtype):\n",
    "        return 'BOOLEAN'\n",
    "    elif pd.api.types.is_datetime64_any_dtype(dtype):\n",
    "        return 'DATETIME'\n",
    "    else:\n",
    "        return 'TEXT'\n",
    "\n",
    "for csv_file, table_name in csv_files:\n",
    "    file_path = os.path.join(folder_path, csv_file)\n",
    "    \n",
    "    # Read the CSV file into a pandas DataFrame\n",
    "    df = pd.read_csv(file_path)\n",
    "    \n",
    "    # Replace NaN with None to handle SQL NULL\n",
    "    df = df.where(pd.notnull(df), None)\n",
    "    \n",
    "    # Debugging: Check for NaN values\n",
    "    print(f\"Processing {csv_file}\")\n",
    "    print(f\"NaN values before replacement:\\n{df.isnull().sum()}\\n\")\n",
    "\n",
    "    # Clean column names\n",
    "    df.columns = [col.replace(' ', '_').replace('-', '_').replace('.', '_') for col in df.columns]\n",
    "\n",
    "    # Generate the CREATE TABLE statement with appropriate data types\n",
    "    columns = ', '.join([f'`{col}` {get_sql_type(df[col].dtype)}' for col in df.columns])\n",
    "    create_table_query = f'CREATE TABLE IF NOT EXISTS `{table_name}` ({columns})'\n",
    "    cursor.execute(create_table_query)\n",
    "\n",
    "    # Insert DataFrame data into the MySQL table\n",
    "    for _, row in df.iterrows():\n",
    "        # Convert row to tuple and handle NaN/None explicitly\n",
    "        values = tuple(None if pd.isna(x) else x for x in row)\n",
    "        sql = f\"INSERT INTO `{table_name}` ({', '.join(['`' + col + '`' for col in df.columns])}) VALUES ({', '.join(['%s'] * len(row))})\"\n",
    "        cursor.execute(sql, values)\n",
    "\n",
    "    # Commit the transaction for the current CSV file\n",
    "    conn.commit()\n",
    "\n",
    "# Close the connection\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64e797be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: mysql-connector-python\n",
      "Successfully installed mysql-connector-python-9.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mysql-connector-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bc5eafe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mysql.connector \n",
    "\n",
    "db = mysql.connector.connect(host = \"localhost\",\n",
    "                            username = \"root\",\n",
    "                            password = \"Yash@2002\",\n",
    "                            database = \"ecommerce\"\n",
    "                            )\n",
    "\n",
    "cur = db.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df428237",
   "metadata": {},
   "source": [
    "## Basic Queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abf43360",
   "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>unique_cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>franca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sao bernardo do campo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sao paulo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mogi das cruzes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>campinas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unique_cities\n",
       "0                 franca\n",
       "1  sao bernardo do campo\n",
       "2              sao paulo\n",
       "3        mogi das cruzes\n",
       "4               campinas"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. List all unique cities where customers are located.\n",
    "\n",
    "query = \"\"\"select distinct(customer_city) from customers\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data,columns=[\"unique_cities\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46676396",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total orders placed in 2017 are: 45101\n"
     ]
    }
   ],
   "source": [
    "# 2. Count the number of orders placed in 2017.\n",
    "\n",
    "query = \"\"\"select count(order_id) from orders where year(order_purchase_timestamp)=2017\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "print(\"Total orders placed in 2017 are:\", data[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28ad4224",
   "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>category</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>perfumery</td>\n",
       "      <td>506738.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Furniture Decoration</td>\n",
       "      <td>1430176.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>telephony</td>\n",
       "      <td>486882.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bed table bath</td>\n",
       "      <td>1712553.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>automotive</td>\n",
       "      <td>852294.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               category       sales\n",
       "0             perfumery   506738.66\n",
       "1  Furniture Decoration  1430176.39\n",
       "2             telephony   486882.05\n",
       "3        bed table bath  1712553.67\n",
       "4            automotive   852294.33"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Find the total sales per category.\n",
    "\n",
    "query = \"\"\"select products.product_category category, round(sum(payments.payment_value),2) sales \n",
    "from products join order_items \n",
    "on products.product_id = order_items.product_id \n",
    "join payments\n",
    "on payments.order_id = order_items.order_id\n",
    "group by product_category\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"category\",\"sales\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "935246c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the percentage of orders that were paid in installments: 99.9981\n"
     ]
    }
   ],
   "source": [
    "# 4. Calculate the percentage of orders that were paid in installments.\n",
    "\n",
    "query = \"\"\"select (sum(case when payment_installments >= 1 then 1 else 0 end))/count(*)*100 from payments\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "print(\"the percentage of orders that were paid in installments:\",data[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8d1bb668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   state  orders\n",
      "0     SP   41746\n",
      "1     SC    3637\n",
      "2     MG   11635\n",
      "3     PR    5045\n",
      "4     RJ   12852\n",
      "5     RS    5466\n",
      "6     PA     975\n",
      "7     GO    2020\n",
      "8     ES    2033\n",
      "9     BA    3380\n",
      "10    MA     747\n",
      "11    MS     715\n",
      "12    CE    1336\n",
      "13    DF    2140\n",
      "14    RN     485\n",
      "15    PE    1652\n",
      "16    MT     907\n",
      "17    AM     148\n",
      "18    AP      68\n",
      "19    AL     413\n",
      "20    RO     253\n",
      "21    PB     536\n",
      "22    TO     280\n",
      "23    PI     495\n",
      "24    AC      81\n",
      "25    SE     350\n",
      "26    RR      46\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Count the number of customers from each state. \n",
    "\n",
    "query = \"\"\"select customer_state, count(customer_id) from customers group by customer_state\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns=[\"state\",\"orders\"])\n",
    "print(df)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "ax = sns.barplot(x=df['state'],y=df['orders'])\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Count of Costumers Per State\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95cbebb",
   "metadata": {},
   "source": [
    "## Intermediate Queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae96abd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Months  Order_count\n",
      "0       July         6292\n",
      "1     August         6512\n",
      "2   February         6728\n",
      "3       June         6167\n",
      "4      March         7211\n",
      "5    January         7269\n",
      "6        May         6873\n",
      "7      April         6939\n",
      "8  September           16\n",
      "9    October            4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Calculate the number of orders per month in 2018.\n",
    "\n",
    "query = \"\"\"select monthname(order_purchase_timestamp) months,count(order_id) order_count \n",
    "from orders where year(order_purchase_timestamp)=2018\n",
    "group by months\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data,columns=[\"Months\",\"Order_count\"])\n",
    "print(df)\n",
    "\n",
    "o = [\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\"]\n",
    "plt.figure(figsize=(9,5))\n",
    "ax = sns.barplot(x=df['Months'],y=df['Order_count'],order=o)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Count of Orders Per Month in Year-2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "508516da",
   "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>category</th>\n",
       "      <th>sales_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>perfumery</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Furniture Decoration</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>telephony</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bed table bath</td>\n",
       "      <td>10.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>automotive</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               category  sales_percentage\n",
       "0             perfumery              3.17\n",
       "1  Furniture Decoration              8.93\n",
       "2             telephony              3.04\n",
       "3        bed table bath             10.70\n",
       "4            automotive              5.32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Calculate the percentage of total revenue contributed by each product category.\n",
    "\n",
    "query = \"\"\"select products.product_category category, \n",
    "round(sum(payments.payment_value)/(select sum(payment_value) from payments)*100,2) sales \n",
    "from products join order_items \n",
    "on products.product_id = order_items.product_id \n",
    "join payments\n",
    "on payments.order_id = order_items.order_id\n",
    "group by product_category\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"category\",\"sales_percentage\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81b2dcd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between the product price and the number of times product has been purchased:  -0.10631552237549534\n"
     ]
    }
   ],
   "source": [
    "# 4. Identify the correlation between product price and the number of times a product has been purchased.\n",
    "import numpy as np\n",
    "query = \"\"\"select products.product_category, count(order_items.product_id), avg(order_items.price) \n",
    "from order_items join products\n",
    "on order_items.product_id = products.product_id\n",
    "group by products.product_category\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns=[\"Category\",\"Number of Products Purchased\",\"Price\"])\n",
    "\n",
    "arr1 = df[\"Number of Products Purchased\"]\n",
    "arr2 = df[\"Price\"]\n",
    "\n",
    "a = np.corrcoef([arr1,arr2])\n",
    "print(\"The correlation between the product price and the number of times product has been purchased: \",a[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e52541f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Seller_id</th>\n",
       "      <th>Sellers_Revenue</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>507166.91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1025f0e2d44d7041d6cf58b6550e0bfa</td>\n",
       "      <td>308222.04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4a3ca9315b744ce9f8e9374361493884</td>\n",
       "      <td>301245.27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1f50f920176fa81dab994f9023523100</td>\n",
       "      <td>290253.42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53243585a1d6dc2643021fd1853d8905</td>\n",
       "      <td>284903.08</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Seller_id  Sellers_Revenue  Rank\n",
       "0  7c67e1448b00f6e969d365cea6b010ab        507166.91     1\n",
       "1  1025f0e2d44d7041d6cf58b6550e0bfa        308222.04     2\n",
       "2  4a3ca9315b744ce9f8e9374361493884        301245.27     3\n",
       "3  1f50f920176fa81dab994f9023523100        290253.42     4\n",
       "4  53243585a1d6dc2643021fd1853d8905        284903.08     5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Calculate the total revenue generated by each seller, and rank them by revenue.\n",
    "\n",
    "query = \"\"\"select *, dense_rank() over(order by rev desc) as rn from\n",
    "(select sellers.seller_id, \n",
    "round(sum(payments.payment_value),2) rev \n",
    "from sellers join order_items \n",
    "on sellers.seller_id = order_items.seller_id \n",
    "join payments\n",
    "on payments.order_id = order_items.order_id\n",
    "group by sellers.seller_id) as a\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"Seller_id\",\"Sellers_Revenue\",\"Rank\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aedcf036",
   "metadata": {},
   "source": [
    "## Advanced Queries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b4e7db3c",
   "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>Customer_id</th>\n",
       "      <th>Order_Purchase_Timestamp</th>\n",
       "      <th>Payment_value</th>\n",
       "      <th>Mov_Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103876</th>\n",
       "      <td>fffb97495f78be80e2759335275df2aa</td>\n",
       "      <td>2018-01-16 14:51:35</td>\n",
       "      <td>61.01</td>\n",
       "      <td>61.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103877</th>\n",
       "      <td>fffc22669ca576ae3f654ea64c8f36be</td>\n",
       "      <td>2017-06-30 11:21:11</td>\n",
       "      <td>101.56</td>\n",
       "      <td>101.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103878</th>\n",
       "      <td>fffcb937e9dd47a13f05ecb8290f4d3e</td>\n",
       "      <td>2018-03-17 00:55:27</td>\n",
       "      <td>91.91</td>\n",
       "      <td>91.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103879</th>\n",
       "      <td>fffecc9f79fd8c764f843e9951b11341</td>\n",
       "      <td>2018-03-29 16:59:26</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103880</th>\n",
       "      <td>fffecc9f79fd8c764f843e9951b11341</td>\n",
       "      <td>2018-03-29 16:59:26</td>\n",
       "      <td>9.49</td>\n",
       "      <td>5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103881</th>\n",
       "      <td>fffecc9f79fd8c764f843e9951b11341</td>\n",
       "      <td>2018-03-29 16:59:26</td>\n",
       "      <td>71.23</td>\n",
       "      <td>27.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103882</th>\n",
       "      <td>fffeda5b6d849fbd39689bb92087f431</td>\n",
       "      <td>2018-05-22 13:36:02</td>\n",
       "      <td>63.13</td>\n",
       "      <td>63.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103883</th>\n",
       "      <td>ffff42319e9b2d713724ae527742af25</td>\n",
       "      <td>2018-06-13 16:57:05</td>\n",
       "      <td>214.13</td>\n",
       "      <td>214.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103884</th>\n",
       "      <td>ffffa3172527f765de70084a7e53aae8</td>\n",
       "      <td>2017-09-02 11:53:32</td>\n",
       "      <td>45.50</td>\n",
       "      <td>45.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103885</th>\n",
       "      <td>ffffe8b65bbe3087b653a978c870db99</td>\n",
       "      <td>2017-09-29 14:07:03</td>\n",
       "      <td>18.37</td>\n",
       "      <td>18.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Customer_id Order_Purchase_Timestamp  \\\n",
       "103876  fffb97495f78be80e2759335275df2aa      2018-01-16 14:51:35   \n",
       "103877  fffc22669ca576ae3f654ea64c8f36be      2017-06-30 11:21:11   \n",
       "103878  fffcb937e9dd47a13f05ecb8290f4d3e      2018-03-17 00:55:27   \n",
       "103879  fffecc9f79fd8c764f843e9951b11341      2018-03-29 16:59:26   \n",
       "103880  fffecc9f79fd8c764f843e9951b11341      2018-03-29 16:59:26   \n",
       "103881  fffecc9f79fd8c764f843e9951b11341      2018-03-29 16:59:26   \n",
       "103882  fffeda5b6d849fbd39689bb92087f431      2018-05-22 13:36:02   \n",
       "103883  ffff42319e9b2d713724ae527742af25      2018-06-13 16:57:05   \n",
       "103884  ffffa3172527f765de70084a7e53aae8      2017-09-02 11:53:32   \n",
       "103885  ffffe8b65bbe3087b653a978c870db99      2017-09-29 14:07:03   \n",
       "\n",
       "        Payment_value  Mov_Avg  \n",
       "103876          61.01    61.01  \n",
       "103877         101.56   101.56  \n",
       "103878          91.91    91.91  \n",
       "103879           0.64     0.64  \n",
       "103880           9.49     5.06  \n",
       "103881          71.23    27.12  \n",
       "103882          63.13    63.13  \n",
       "103883         214.13   214.13  \n",
       "103884          45.50    45.50  \n",
       "103885          18.37    18.37  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Calculate the moving average of order values for each customer over their order history.\n",
    "\n",
    "query = \"\"\"select *,round(avg(payment) \n",
    "over(partition by customer_id order by \n",
    "order_purchase_timestamp \n",
    "rows between 2 preceding and current row),2) \n",
    "as mov_avg from\n",
    "(select orders.customer_id,\n",
    "orders.order_purchase_timestamp,\n",
    "payments.payment_value as payment\n",
    "from orders join payments \n",
    "on orders.order_id = payments.order_id) as a\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"Customer_id\",\"Order_Purchase_Timestamp\",\"Payment_value\",\"Mov_Avg\"])\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41a44453",
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Cumulative_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>252.24</td>\n",
       "      <td>252.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>59090.48</td>\n",
       "      <td>59342.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>19.62</td>\n",
       "      <td>59362.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>138488.04</td>\n",
       "      <td>197850.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>291908.01</td>\n",
       "      <td>489758.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>449863.60</td>\n",
       "      <td>939621.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>417788.03</td>\n",
       "      <td>1357410.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>592918.82</td>\n",
       "      <td>1950328.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>511276.38</td>\n",
       "      <td>2461605.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>592382.92</td>\n",
       "      <td>3053988.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>674396.32</td>\n",
       "      <td>3728384.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>727762.45</td>\n",
       "      <td>4456146.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>779677.88</td>\n",
       "      <td>5235824.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>1194882.80</td>\n",
       "      <td>6430707.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>878401.48</td>\n",
       "      <td>7309109.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1115004.18</td>\n",
       "      <td>8424113.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>992463.34</td>\n",
       "      <td>9416576.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1159652.12</td>\n",
       "      <td>10576228.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>1160785.48</td>\n",
       "      <td>11737014.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1153982.15</td>\n",
       "      <td>12890996.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1023880.50</td>\n",
       "      <td>13914876.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>1066540.75</td>\n",
       "      <td>14981417.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>1022425.32</td>\n",
       "      <td>16003842.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>4439.54</td>\n",
       "      <td>16008282.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>589.67</td>\n",
       "      <td>16008872.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Month     Payment  Cumulative_sale\n",
       "0   2016      9      252.24           252.24\n",
       "1   2016     10    59090.48         59342.72\n",
       "2   2016     12       19.62         59362.34\n",
       "3   2017      1   138488.04        197850.38\n",
       "4   2017      2   291908.01        489758.39\n",
       "5   2017      3   449863.60        939621.99\n",
       "6   2017      4   417788.03       1357410.02\n",
       "7   2017      5   592918.82       1950328.84\n",
       "8   2017      6   511276.38       2461605.22\n",
       "9   2017      7   592382.92       3053988.14\n",
       "10  2017      8   674396.32       3728384.46\n",
       "11  2017      9   727762.45       4456146.91\n",
       "12  2017     10   779677.88       5235824.79\n",
       "13  2017     11  1194882.80       6430707.59\n",
       "14  2017     12   878401.48       7309109.07\n",
       "15  2018      1  1115004.18       8424113.25\n",
       "16  2018      2   992463.34       9416576.59\n",
       "17  2018      3  1159652.12      10576228.71\n",
       "18  2018      4  1160785.48      11737014.19\n",
       "19  2018      5  1153982.15      12890996.34\n",
       "20  2018      6  1023880.50      13914876.84\n",
       "21  2018      7  1066540.75      14981417.59\n",
       "22  2018      8  1022425.32      16003842.91\n",
       "23  2018      9     4439.54      16008282.45\n",
       "24  2018     10      589.67      16008872.12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Calculate the cumulative sales per month for each year.\n",
    "\n",
    "query = \"\"\"select *, round(sum(payment) over(order by years,months),2) as cumulative_sales from\n",
    "(select year(orders.order_purchase_timestamp) as years,\n",
    "month(orders.order_purchase_timestamp) as months,\n",
    "round(sum(payments.payment_value),2) as payment from payments join orders\n",
    "on payments.order_id = orders.order_id\n",
    "group by years,months order by years,months) as a;\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"Year\",\"Month\",\"Payment\",\"Cumulative_sale\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f074bc84",
   "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>Year</th>\n",
       "      <th>Total_sale</th>\n",
       "      <th>growth_rate_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>59362.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>7249746.73</td>\n",
       "      <td>12112.703761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>8699763.05</td>\n",
       "      <td>20.000924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Total_sale  growth_rate_%\n",
       "0  2016    59362.34            NaN\n",
       "1  2017  7249746.73   12112.703761\n",
       "2  2018  8699763.05      20.000924"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Calculate the year-over-year growth rate of total sales.\n",
    "\n",
    "query = \"\"\"select years,payment,\n",
    "((payment - lag(payment,1) over(order by years))/lag(payment,1) over(order by years)) * 100\n",
    "from (select year(orders.order_purchase_timestamp) as years,\n",
    "round(sum(payments.payment_value),2) as payment from payments join orders\n",
    "on payments.order_id = orders.order_id\n",
    "group by years order by years) as a\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns=['Year','Total_sale','growth_rate_%'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f8bb62d",
   "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>customer_id</th>\n",
       "      <th>Year</th>\n",
       "      <th>Total_payment</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a9dc96b027d1252bbac0a9b72d837fc6</td>\n",
       "      <td>2016</td>\n",
       "      <td>1423.550049</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1d34ed25963d5aae4cf3d7f3a4cda173</td>\n",
       "      <td>2016</td>\n",
       "      <td>1400.739990</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4a06381959b6670756de02e07b83815f</td>\n",
       "      <td>2016</td>\n",
       "      <td>1227.780029</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1617b1357756262bfa56ab541c47bc16</td>\n",
       "      <td>2017</td>\n",
       "      <td>13664.080078</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c6e2731c5b391845f6800c97401a43a9</td>\n",
       "      <td>2017</td>\n",
       "      <td>6929.310059</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3fd6777bbce08a352fddd04e4a7cc8f6</td>\n",
       "      <td>2017</td>\n",
       "      <td>6726.660156</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ec5b2ba62e574342386871631fafd3fc</td>\n",
       "      <td>2018</td>\n",
       "      <td>7274.879883</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>f48d464a0baaea338cb25f816991ab1f</td>\n",
       "      <td>2018</td>\n",
       "      <td>6922.209961</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>e0a2412720e9ea4f26c1ac985f6a7358</td>\n",
       "      <td>2018</td>\n",
       "      <td>4809.439941</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id  Year  Total_payment  Rank\n",
       "0  a9dc96b027d1252bbac0a9b72d837fc6  2016    1423.550049     1\n",
       "1  1d34ed25963d5aae4cf3d7f3a4cda173  2016    1400.739990     2\n",
       "2  4a06381959b6670756de02e07b83815f  2016    1227.780029     3\n",
       "3  1617b1357756262bfa56ab541c47bc16  2017   13664.080078     1\n",
       "4  c6e2731c5b391845f6800c97401a43a9  2017    6929.310059     2\n",
       "5  3fd6777bbce08a352fddd04e4a7cc8f6  2017    6726.660156     3\n",
       "6  ec5b2ba62e574342386871631fafd3fc  2018    7274.879883     1\n",
       "7  f48d464a0baaea338cb25f816991ab1f  2018    6922.209961     2\n",
       "8  e0a2412720e9ea4f26c1ac985f6a7358  2018    4809.439941     3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Identify the top 3 customers who spent the most money in each year.\n",
    "\n",
    "query = \"\"\"select customer_id,years,payment,Ranking from\n",
    "(select orders.customer_id, year(orders.order_purchase_timestamp) years, \n",
    "sum(payments.payment_value) as payment,\n",
    "dense_rank() over(partition by year(orders.order_purchase_timestamp) order by sum(payments.payment_value) desc) as Ranking\n",
    "from orders join payments on payments.order_id = orders.order_id\n",
    "group by years,orders.customer_id) as a\n",
    "where ranking <= 3;\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns=['customer_id','Year','Total_payment','Rank'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bd958cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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