e-commerce.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8be4608-725a-4df4-b25d-1b9078ba1553",
   "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 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 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 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",
    "    ('sellers.csv', 'sellers'),\n",
    "    ('products.csv', 'products'),\n",
    "    ('geolocation.csv', 'geolocation'),\n",
    "    ('order_items.csv','order_items'),\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='mypassword',\n",
    "    database='e-commerce'\n",
    ")\n",
    "cursor = conn.cursor()\n",
    "\n",
    "# Folder containing the CSV files\n",
    "folder_path = 'C:\\\\Users\\\\Aishwarya\\\\Desktop\\\\E-commerce'\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": 134,
   "id": "71c1ab78-712b-43fb-9582-339d39b39485",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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 = \"mypassword\",\n",
    "                            database = \"e-commerce\")\n",
    "cur = db.cursor()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421640b2-54d1-4fdf-9438-cd0159f1b443",
   "metadata": {},
   "source": [
    "#1. List all unique cities where customers are located. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0750e0d8-d726-4f18-b3e7-2f8aa1e32006",
   "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>0</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": [
       "                       0\n",
       "0                 franca\n",
       "1  sao bernardo do campo\n",
       "2              sao paulo\n",
       "3        mogi das cruzes\n",
       "4               campinas"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" select distinct customer_city from customers \"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d7174d-a8b7-4589-a38d-523dfd61a3fc",
   "metadata": {},
   "source": [
    "#2. Count the number of orders placed in 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b02bee19-1cd8-4a00-aea5-86ae3b2ebf71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('total orders placed in 2017 are', 135303)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" select count(order_id) from orders where year(order_purchase_timestamp) = 2017 \"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "\"total orders placed in 2017 are\",data[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66485598-0a4a-40c1-b973-41b47affafe5",
   "metadata": {},
   "source": [
    "#3. Find the total sales per category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "19ac86ff-04e4-4256-a477-f08a367bf303",
   "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>1520215.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Furniture Decoration</td>\n",
       "      <td>4290529.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>telephony</td>\n",
       "      <td>1460646.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fashion Bags and Accessories</td>\n",
       "      <td>654474.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bed table bath</td>\n",
       "      <td>5137661.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>cds music dvds</td>\n",
       "      <td>3598.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>La Cuisine</td>\n",
       "      <td>8740.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Fashion Children's Clothing</td>\n",
       "      <td>2357.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>PC Gamer</td>\n",
       "      <td>6523.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>insurance and services</td>\n",
       "      <td>973.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Category       Sales\n",
       "0                      perfumery  1520215.98\n",
       "1           Furniture Decoration  4290529.18\n",
       "2                      telephony  1460646.15\n",
       "3   Fashion Bags and Accessories   654474.84\n",
       "4                 bed table bath  5137661.01\n",
       "..                           ...         ...\n",
       "69                cds music dvds     3598.29\n",
       "70                    La Cuisine     8740.59\n",
       "71   Fashion Children's Clothing     2357.01\n",
       "72                      PC Gamer     6523.29\n",
       "73        insurance and services      973.53\n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" select products.productcategory,\n",
    "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 productcategory\n",
    "\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = [\"Category\",\"Sales\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab9d2a9-dac0-4df2-919f-a2a51a87ea09",
   "metadata": {},
   "source": [
    "#4. Calculate the percentage of orders that were paid in installments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4369a57-0208-42bc-93dd-9922342431df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('the percentage of orders that were paid in installments is ',\n",
       " Decimal('99.9981'))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" select sum(case when payment_installments >= 1 then 1\n",
    "else 0 end)/count(*)*100 from payments \"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\"the percentage of orders that were paid in installments is \",data[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df07e64-3b15-4562-bfde-0be591f9e2e7",
   "metadata": {},
   "source": [
    "#5. Count the number of customers from each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "268eebde-405b-4856-b2e0-248326b62c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = \"\"\" select customer_state ,count(customer_id)\n",
    "from customers group by customer_state\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data,columns = [\"state\",\"customer_count\"])\n",
    "df =df.sort_values(by = \"customer_count\", ascending = False)\n",
    "plt.figure(figsize=(8,3))\n",
    "plt.bar(df[\"state\"],df[\"customer_count\"])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel(\"states\")\n",
    "plt.ylabel(\"customer_count\")\n",
    "plt.title(\"Count Of Customers By States\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364d84d5-6a77-40ac-ac26-92d3f55731c7",
   "metadata": {},
   "source": [
    "#Intermediate Queries\n",
    "1. Calculate the number of orders per month in 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bbc2a7f5-cb56-4da0-9fe6-c927ce634cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = \"\"\" select monthname(order_purchase_timestamp) months,count(order_id) \n",
    "order_count from orders where year(order_purchase_timestamp) = 2018 group by months \"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data,columns = [\"months\",\"order_count\"])\n",
    "o = [\"January\",\"February\",\"March\",\"April\",\"June\",\"July\",\"August\",\"September\",\"October\"]\n",
    "ax = sns.barplot(x = df[\"months\"],y = df[\"order_count\"],data = df , order = o, color = \"blue\")\n",
    "plt.xticks(rotation = 45)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.title(\"Count Of Orders By Months is 2018\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ed34f2-ae61-437f-b153-9cf70b51df24",
   "metadata": {},
   "source": [
    "#2. Find the average number of products per order, grouped by customer city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "74f32147-a082-4fcb-a2e4-21c06eda2663",
   "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 city</th>\n",
       "      <th>average products/orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>padre carvalho</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>celso ramos</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>datas</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>candido godoi</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>matias olimpio</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cidelandia</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>curralinho</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>picarra</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>morro de sao paulo</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>teixeira soares</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customer city average products/orders\n",
       "0      padre carvalho                   21.00\n",
       "1         celso ramos                   19.50\n",
       "2               datas                   18.00\n",
       "3       candido godoi                   18.00\n",
       "4      matias olimpio                   15.00\n",
       "5          cidelandia                   12.00\n",
       "6          curralinho                   12.00\n",
       "7             picarra                   12.00\n",
       "8  morro de sao paulo                   12.00\n",
       "9     teixeira soares                   12.00"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" with count_per_order as\n",
    "(select orders.order_id,orders.customer_id,count(order_items.order_id) as oc\n",
    "from orders join order_items\n",
    "on orders.order_id = order_items.order_id\n",
    "group by orders.order_id,orders.customer_id)\n",
    "\n",
    "select customers.customer_city,round(avg(count_per_order.oc),2) average_orders\n",
    "from customers join count_per_order\n",
    "on customers.customer_id = count_per_order.customer_id\n",
    "group by customers.customer_city order by average_orders desc\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"customer city\",\"average products/orders\"])\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4a6e0f-ec96-490c-b4dc-c8b8c357ff9d",
   "metadata": {},
   "source": [
    "#3. Calculate the percentage of total revenue contributed by each product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "8576ce66-42e9-42e3-b8dd-777462b73f37",
   "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>Percentage distribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BED TABLE BATH</td>\n",
       "      <td>32.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HEALTH BEAUTY</td>\n",
       "      <td>31.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COMPUTER ACCESSORIES</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FURNITURE DECORATION</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WATCHES PRESENT</td>\n",
       "      <td>26.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Category  Percentage distribution\n",
       "0        BED TABLE BATH                    32.09\n",
       "1         HEALTH BEAUTY                    31.06\n",
       "2  COMPUTER ACCESSORIES                    29.71\n",
       "3  FURNITURE DECORATION                    26.80\n",
       "4       WATCHES PRESENT                    26.78"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" select upper(products.productcategory) category,\n",
    "round((sum(payments.payment_value)/(select sum(payment_value) from payments))*100,2)sales_percentage\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 productcategory order by sales_percentage desc\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data,columns = [\"Category\",\"Percentage distribution\"])\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdce6a88-9163-42ee-80ff-66e04e0e6952",
   "metadata": {},
   "source": [
    "#. Identify the correlation between product price and the number of times a product has been purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "31e7da40-9701-4fb9-95cf-94740f3d849c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the correlation is -0.1063151416715756\n"
     ]
    }
   ],
   "source": [
    "cur = db.cursor()\n",
    "query = \"\"\"select products.productcategory, \n",
    "count(order_items.product_id),\n",
    "round(avg(order_items.price),2)\n",
    "from products join order_items\n",
    "on products.product_id = order_items.product_id\n",
    "group by products.productcategory\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data,columns = [\"Category\", \"order_count\",\"price\"])\n",
    "\n",
    "arr1 = df[\"order_count\"]\n",
    "arr2 = df[\"price\"]\n",
    "\n",
    "a = np.corrcoef([arr1,arr2])\n",
    "print(\"the correlation is\", a[0][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6eb922-0fec-4eb9-a9a0-0bd35d8ad360",
   "metadata": {},
   "source": [
    "#5. Calculate the total revenue generated by each seller, and rank them by revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d1cee5fa-01b1-4b48-9289-8d010590f0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = \"\"\" select *, dense_rank() over(order by revenue desc) as rn from\n",
    "(select order_items.seller_id, sum(payments.payment_value)\n",
    "revenue from order_items join payments\n",
    "on order_items.order_id = payments.order_id\n",
    "group by order_items.seller_id) as a \"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"seller_id\", \"revenue\", \"rank\"])\n",
    "df = df.head()\n",
    "sns.barplot(x = \"seller_id\", y = \"revenue\", data = df)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e669e7-a8e4-435b-a3be-6a52de7f59e5",
   "metadata": {},
   "source": [
    "#Advanced Queries\n",
    "1. Calculate the moving average of order values for each customer over their order history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "04b4332b-ebfc-4b08-9cbf-3be073b7e035",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
       "      <td>2017-11-14 16:08:26</td>\n",
       "      <td>114.74</td>\n",
       "      <td>114.739998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
       "      <td>2017-11-14 16:08:26</td>\n",
       "      <td>114.74</td>\n",
       "      <td>114.739998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
       "      <td>2017-11-14 16:08:26</td>\n",
       "      <td>114.74</td>\n",
       "      <td>114.739998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000161a058600d5901f007fab4c27140</td>\n",
       "      <td>2017-07-16 09:40:32</td>\n",
       "      <td>67.41</td>\n",
       "      <td>67.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000161a058600d5901f007fab4c27140</td>\n",
       "      <td>2017-07-16 09:40:32</td>\n",
       "      <td>67.41</td>\n",
       "      <td>67.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311653</th>\n",
       "      <td>ffffa3172527f765de70084a7e53aae8</td>\n",
       "      <td>2017-09-02 11:53:32</td>\n",
       "      <td>45.50</td>\n",
       "      <td>45.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311654</th>\n",
       "      <td>ffffa3172527f765de70084a7e53aae8</td>\n",
       "      <td>2017-09-02 11:53:32</td>\n",
       "      <td>45.50</td>\n",
       "      <td>45.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311655</th>\n",
       "      <td>ffffe8b65bbe3087b653a978c870db99</td>\n",
       "      <td>2017-09-29 14:07:03</td>\n",
       "      <td>18.37</td>\n",
       "      <td>18.370001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311656</th>\n",
       "      <td>ffffe8b65bbe3087b653a978c870db99</td>\n",
       "      <td>2017-09-29 14:07:03</td>\n",
       "      <td>18.37</td>\n",
       "      <td>18.370001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311657</th>\n",
       "      <td>ffffe8b65bbe3087b653a978c870db99</td>\n",
       "      <td>2017-09-29 14:07:03</td>\n",
       "      <td>18.37</td>\n",
       "      <td>18.370001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311658 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0                    1       2  \\\n",
       "0       00012a2ce6f8dcda20d059ce98491703  2017-11-14 16:08:26  114.74   \n",
       "1       00012a2ce6f8dcda20d059ce98491703  2017-11-14 16:08:26  114.74   \n",
       "2       00012a2ce6f8dcda20d059ce98491703  2017-11-14 16:08:26  114.74   \n",
       "3       000161a058600d5901f007fab4c27140  2017-07-16 09:40:32   67.41   \n",
       "4       000161a058600d5901f007fab4c27140  2017-07-16 09:40:32   67.41   \n",
       "...                                  ...                  ...     ...   \n",
       "311653  ffffa3172527f765de70084a7e53aae8  2017-09-02 11:53:32   45.50   \n",
       "311654  ffffa3172527f765de70084a7e53aae8  2017-09-02 11:53:32   45.50   \n",
       "311655  ffffe8b65bbe3087b653a978c870db99  2017-09-29 14:07:03   18.37   \n",
       "311656  ffffe8b65bbe3087b653a978c870db99  2017-09-29 14:07:03   18.37   \n",
       "311657  ffffe8b65bbe3087b653a978c870db99  2017-09-29 14:07:03   18.37   \n",
       "\n",
       "                 3  \n",
       "0       114.739998  \n",
       "1       114.739998  \n",
       "2       114.739998  \n",
       "3        67.410004  \n",
       "4        67.410004  \n",
       "...            ...  \n",
       "311653   45.500000  \n",
       "311654   45.500000  \n",
       "311655   18.370001  \n",
       "311656   18.370001  \n",
       "311657   18.370001  \n",
       "\n",
       "[311658 rows x 4 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"select customer_id, order_purchase_timestamp, payment,\n",
    "avg(payment) over(partition by customer_id order by order_purchase_timestamp\n",
    "rows between 2 preceding and current row) as mov_avg\n",
    "from\n",
    "(select orders.customer_id, orders.order_purchase_timestamp, \n",
    "payments.payment_value as payment\n",
    "from payments join orders\n",
    "on payments.order_id = orders.order_id) as a\"\"\"\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0de326-3815-4516-a854-819d1f729d7f",
   "metadata": {},
   "source": [
    "#2. Calculate the cumulative sales per month for each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "4da48de1-e933-40ab-a7e4-f4eb3ebcd871",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>756.72</td>\n",
       "      <td>756.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>177271.44</td>\n",
       "      <td>178028.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>58.86</td>\n",
       "      <td>178087.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>415464.12</td>\n",
       "      <td>593551.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>875724.03</td>\n",
       "      <td>1469275.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>1349590.80</td>\n",
       "      <td>2818865.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1253364.09</td>\n",
       "      <td>4072230.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>1778756.46</td>\n",
       "      <td>5850986.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>1533829.14</td>\n",
       "      <td>7384815.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>1777148.76</td>\n",
       "      <td>9161964.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2023188.96</td>\n",
       "      <td>11185153.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>2183287.35</td>\n",
       "      <td>13368440.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>2339033.64</td>\n",
       "      <td>15707474.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>3584648.40</td>\n",
       "      <td>19292122.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>2635204.44</td>\n",
       "      <td>21927327.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>3345012.54</td>\n",
       "      <td>25272339.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2977390.02</td>\n",
       "      <td>28249729.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>3478956.36</td>\n",
       "      <td>31728686.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>3482356.44</td>\n",
       "      <td>35211042.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>3461946.45</td>\n",
       "      <td>38672989.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>3071641.50</td>\n",
       "      <td>41744630.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>3199622.25</td>\n",
       "      <td>44944252.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>3067275.96</td>\n",
       "      <td>48011528.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>13318.62</td>\n",
       "      <td>48024847.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>1769.01</td>\n",
       "      <td>48026616.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0   1           2            3\n",
       "0   2016   9      756.72       756.72\n",
       "1   2016  10   177271.44    178028.16\n",
       "2   2016  12       58.86    178087.02\n",
       "3   2017   1   415464.12    593551.14\n",
       "4   2017   2   875724.03   1469275.17\n",
       "5   2017   3  1349590.80   2818865.97\n",
       "6   2017   4  1253364.09   4072230.06\n",
       "7   2017   5  1778756.46   5850986.52\n",
       "8   2017   6  1533829.14   7384815.66\n",
       "9   2017   7  1777148.76   9161964.42\n",
       "10  2017   8  2023188.96  11185153.38\n",
       "11  2017   9  2183287.35  13368440.73\n",
       "12  2017  10  2339033.64  15707474.37\n",
       "13  2017  11  3584648.40  19292122.77\n",
       "14  2017  12  2635204.44  21927327.21\n",
       "15  2018   1  3345012.54  25272339.75\n",
       "16  2018   2  2977390.02  28249729.77\n",
       "17  2018   3  3478956.36  31728686.13\n",
       "18  2018   4  3482356.44  35211042.57\n",
       "19  2018   5  3461946.45  38672989.02\n",
       "20  2018   6  3071641.50  41744630.52\n",
       "21  2018   7  3199622.25  44944252.77\n",
       "22  2018   8  3067275.96  48011528.73\n",
       "23  2018   9    13318.62  48024847.35\n",
       "24  2018  10     1769.01  48026616.36"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"select years, months , payment, sum(payment)\n",
    "over(order by years, months) 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 orders join payments\n",
    "on orders.order_id = payments.order_id\n",
    "group by years, months order by years, months) as a\n",
    "\"\"\"\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d0f29e-b71f-4b50-9c9c-587d67104767",
   "metadata": {},
   "source": [
    "#3. Calculate the year-over-year growth rate of total sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e35ec489-eb36-4129-aab8-857dc0036cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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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>years</th>\n",
       "      <th>yoy % growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>12112.703756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>20.000924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  yoy % growth\n",
       "0   2016           NaN\n",
       "1   2017  12112.703756\n",
       "2   2018     20.000924"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"with a as(select year(orders.order_purchase_timestamp) as years,\n",
    "round(sum(payments.payment_value),2) as payment from orders join payments\n",
    "on orders.order_id = payments.order_id\n",
    "group by years order by years)\n",
    "\n",
    "select years, ((payment - lag(payment, 1) over(order by years))/\n",
    "lag(payment, 1) over(order by years)) * 100 from a\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"years\", \"yoy % growth\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7197cba-26c8-493b-8d63-b190b12598a5",
   "metadata": {},
   "source": [
    "#4. Calculate the retention rate of customers, defined as the percentage of customers who make another purchase within 6 months of their first purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1bc9a023-9567-479e-a03a-3d14cef7b92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(None,)]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"with a as (select customers.customer_id,\n",
    "min(orders.order_purchase_timestamp) first_order\n",
    "from customers join orders\n",
    "on customers.customer_id = orders.customer_id\n",
    "group by customers.customer_id),\n",
    "\n",
    "b as (select a.customer_id, count(distinct orders.order_purchase_timestamp) next_order\n",
    "from a join orders\n",
    "on orders.customer_id = a.customer_id\n",
    "and orders.order_purchase_timestamp > first_order\n",
    "and orders.order_purchase_timestamp < \n",
    "date_add(first_order, interval 6 month)\n",
    "group by a.customer_id) \n",
    "\n",
    "select 100 * (count( distinct a.customer_id)/ count(distinct b.customer_id)) \n",
    "from a left join b \n",
    "on a.customer_id = b.customer_id ;\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4fd77e-ce7b-474b-8137-7ffb349a2b0f",
   "metadata": {},
   "source": [
    "#5. Identify the top 3 customers who spent the most money in each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d50a1dc9-853b-4276-867a-b91e61bdec4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = \"\"\"select years, customer_id, payment, d_rank\n",
    "from\n",
    "(select year(orders.order_purchase_timestamp) years,\n",
    "orders.customer_id,\n",
    "sum(payments.payment_value) payment,\n",
    "dense_rank() over(partition by year(orders.order_purchase_timestamp)\n",
    "order by sum(payments.payment_value) desc) d_rank\n",
    "from orders join payments \n",
    "on payments.order_id = orders.order_id\n",
    "group by year(orders.order_purchase_timestamp),\n",
    "orders.customer_id) as a\n",
    "where d_rank <= 3 ;\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "data = cur.fetchall()\n",
    "df = pd.DataFrame(data, columns = [\"years\",\"id\",\"payment\",\"rank\"])\n",
    "sns.barplot(x = \"id\", y = \"payment\", data = df, hue = \"years\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e9673c2-d88e-4261-8085-758e5a99d309",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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