ecommerce-sql-python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c80d6536",
   "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 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",
      "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"
     ]
    }
   ],
   "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",
    "    ('payments.csv', 'payments'),\n",
    "    ('order_items.csv','order_items')# 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='SQL3306Server!!',\n",
    "    database='ecommerce'\n",
    ")\n",
    "cursor = conn.cursor()\n",
    "\n",
    "# Folder containing the CSV files\n",
    "folder_path = 'C:/Users/Aayush/Documents/SQL Server Management Studio/ecommerce'\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": 4,
   "id": "3b734f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "db = mysql.connector.connect(host =\"localhost\",\n",
    "                            username =\"root\",\n",
    "                            password =\"SQL3306Server!!\",\n",
    "                            database = \"ecommerce\")\n",
    "\n",
    "cur = db.cursor()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb59529",
   "metadata": {},
   "source": [
    "## List all unique cities where customers are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0cdda0b7",
   "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>City</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>siriji</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>natividade da serra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4116</th>\n",
       "      <td>monte bonito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4117</th>\n",
       "      <td>sao rafael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4118</th>\n",
       "      <td>eugenio de castro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4119 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City\n",
       "0                    franca\n",
       "1     sao bernardo do campo\n",
       "2                 sao paulo\n",
       "3           mogi das cruzes\n",
       "4                  campinas\n",
       "...                     ...\n",
       "4114                 siriji\n",
       "4115    natividade da serra\n",
       "4116           monte bonito\n",
       "4117             sao rafael\n",
       "4118      eugenio de castro\n",
       "\n",
       "[4119 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "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,columns=['City'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b842ea47",
   "metadata": {},
   "source": [
    "## Count the number of orders placed in 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a3f42d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45101"
      ]
     },
     "execution_count": 22,
     "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",
    "data[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a084736",
   "metadata": {},
   "source": [
    "## Find the total sales per category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d06f8e03",
   "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>bed table bath</td>\n",
       "      <td>1712553.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HEALTH BEAUTY</td>\n",
       "      <td>1657373.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>computer accessories</td>\n",
       "      <td>1585330.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Furniture Decoration</td>\n",
       "      <td>1430176.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Watches present</td>\n",
       "      <td>1429216.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>PC Gamer</td>\n",
       "      <td>2174.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>House Comfort 2</td>\n",
       "      <td>1710.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>cds music dvds</td>\n",
       "      <td>1199.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Fashion Children's Clothing</td>\n",
       "      <td>785.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>insurance and services</td>\n",
       "      <td>324.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Category       Sales\n",
       "0                bed table bath  1712553.67\n",
       "1                 HEALTH BEAUTY  1657373.12\n",
       "2          computer accessories  1585330.45\n",
       "3          Furniture Decoration  1430176.39\n",
       "4               Watches present  1429216.68\n",
       "..                          ...         ...\n",
       "69                     PC Gamer     2174.43\n",
       "70              House Comfort 2     1710.54\n",
       "71               cds music dvds     1199.43\n",
       "72  Fashion Children's Clothing      785.67\n",
       "73       insurance and services      324.51\n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"select P.product_category, \n",
    "Round(sum(Q.payment_value),2) as total_sales\n",
    "from products P\n",
    "join order_items O \n",
    "on P.product_id = O.product_id\n",
    "Join payments Q\n",
    "on Q.order_id = O.order_id\n",
    "Group by P.product_category\n",
    "order by total_sales DESC\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data =cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['Category','Sales'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902b494c",
   "metadata": {},
   "source": [
    "## Calculate the percentage of orders that were paid in installments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49b52dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(Decimal('99.9981'),)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"SELECT \n",
    "    ((SUM(CASE\n",
    "        WHEN payment_installments >= 1 THEN 1\n",
    "        ELSE 0\n",
    "    END)) / COUNT(*)) * 100 AS order_percentage\n",
    "FROM\n",
    "    payments\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5240d061",
   "metadata": {},
   "source": [
    "## Count the number of customers from each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "28b15201",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"select customer_state, count(customer_id)\n",
    "    from customers\n",
    "    Group By customer_state\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns=['State','Number of Customer'])\n",
    "df = df.sort_values(by='Number of Customer', ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1fcc30da",
   "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>State</th>\n",
       "      <th>Number of Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP</td>\n",
       "      <td>41746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RJ</td>\n",
       "      <td>12852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MG</td>\n",
       "      <td>11635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RS</td>\n",
       "      <td>5466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PR</td>\n",
       "      <td>5045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SC</td>\n",
       "      <td>3637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BA</td>\n",
       "      <td>3380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DF</td>\n",
       "      <td>2140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ES</td>\n",
       "      <td>2033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GO</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PE</td>\n",
       "      <td>1652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CE</td>\n",
       "      <td>1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PA</td>\n",
       "      <td>975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MT</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MA</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MS</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PB</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PI</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RN</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AL</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SE</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TO</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RO</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AM</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AC</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AP</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RR</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Number of Customer\n",
       "0     SP               41746\n",
       "4     RJ               12852\n",
       "2     MG               11635\n",
       "5     RS                5466\n",
       "3     PR                5045\n",
       "1     SC                3637\n",
       "9     BA                3380\n",
       "13    DF                2140\n",
       "8     ES                2033\n",
       "7     GO                2020\n",
       "15    PE                1652\n",
       "12    CE                1336\n",
       "6     PA                 975\n",
       "16    MT                 907\n",
       "10    MA                 747\n",
       "11    MS                 715\n",
       "21    PB                 536\n",
       "23    PI                 495\n",
       "14    RN                 485\n",
       "19    AL                 413\n",
       "25    SE                 350\n",
       "22    TO                 280\n",
       "20    RO                 253\n",
       "17    AM                 148\n",
       "24    AC                  81\n",
       "18    AP                  68\n",
       "26    RR                  46"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d158ffdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.bar(df['State'],df['Number of Customer'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Number of Customer')\n",
    "plt.title('Count of Customer by State')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4f69f0",
   "metadata": {},
   "source": [
    "## Calculate the number of orders per month in 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d060bdd9",
   "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 \n",
    "    MONTHNAME(order_purchase_timestamp) AS month,\n",
    "    COUNT(order_id)\n",
    "FROM\n",
    "    orders\n",
    "WHERE\n",
    "    YEAR(order_purchase_timestamp) = 2018\n",
    "GROUP BY month\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df  = pd.DataFrame(data, columns=['Month','Number of orders'])\n",
    "\n",
    "o = [\"January\", \"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\"]\n",
    "\n",
    "\n",
    "ax = sns.barplot(x = df['Month'],y = df['Number of orders'],data = df, order = o, color = 'green')\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.title('Count of orders by Months in 2018')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86905443",
   "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>City</th>\n",
       "      <th>Average order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>padre carvalho</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>celso ramos</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>datas</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>candido godoi</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>matias olimpio</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4105</th>\n",
       "      <td>sao mamede</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4106</th>\n",
       "      <td>sambaiba</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4107</th>\n",
       "      <td>japaratuba</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4108</th>\n",
       "      <td>tuiuti</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4109</th>\n",
       "      <td>tibau do sul</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4110 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City Average order\n",
       "0     padre carvalho         28.00\n",
       "1        celso ramos         26.00\n",
       "2              datas         24.00\n",
       "3      candido godoi         24.00\n",
       "4     matias olimpio         20.00\n",
       "...              ...           ...\n",
       "4105      sao mamede          4.00\n",
       "4106        sambaiba          4.00\n",
       "4107      japaratuba          4.00\n",
       "4108          tuiuti          4.00\n",
       "4109    tibau do sul          4.00\n",
       "\n",
       "[4110 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"With count_order as(\n",
    "select orders.order_id, orders.customer_id, count(order_items.order_id) as order_count\n",
    "from orders\n",
    "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_order.order_count),2) as avg_order\n",
    "from count_order\n",
    "join customers\n",
    "on count_order.customer_id = customers.customer_id\n",
    "group by  customers.customer_city\n",
    "order by avg_order DESC\n",
    "\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['City','Average order'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf3897d",
   "metadata": {},
   "source": [
    "## Calculate the percentage of total revenue contributed by each product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4454bc0b",
   "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 Distribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bed table bath</td>\n",
       "      <td>42.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HEALTH BEAUTY</td>\n",
       "      <td>41.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>computer accessories</td>\n",
       "      <td>39.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Furniture Decoration</td>\n",
       "      <td>35.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Watches present</td>\n",
       "      <td>35.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>PC Gamer</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>House Comfort 2</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>cds music dvds</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Fashion Children's Clothing</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>insurance and services</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Category  Sales Distribution\n",
       "0                bed table bath               42.79\n",
       "1                 HEALTH BEAUTY               41.41\n",
       "2          computer accessories               39.61\n",
       "3          Furniture Decoration               35.73\n",
       "4               Watches present               35.71\n",
       "..                          ...                 ...\n",
       "69                     PC Gamer                0.05\n",
       "70              House Comfort 2                0.04\n",
       "71               cds music dvds                0.03\n",
       "72  Fashion Children's Clothing                0.02\n",
       "73       insurance and services                0.01\n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"SELECT \n",
    "    products.product_category,\n",
    "    ROUND((SUM(payments.payment_value) / (SELECT \n",
    "                    SUM(payment_value)\n",
    "                FROM\n",
    "                    payments)) * 100,\n",
    "            2) AS percent_cont\n",
    "FROM\n",
    "    products\n",
    "        JOIN\n",
    "    order_items ON products.product_id = order_items.product_id\n",
    "        JOIN\n",
    "    payments ON order_items.order_id = payments.order_id\n",
    "GROUP BY products.product_category\n",
    "ORDER BY percent_cont DESC\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['Category','Sales Distribution'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b2751b",
   "metadata": {},
   "source": [
    "## Identify the correlation between product price and the number of times a product has been purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67ac2c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation betwwen product price and number of times a product has been purchased is  -0.10631514167157562\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"SELECT \n",
    "    products.product_category,\n",
    "    COUNT(order_items.product_id) order_count,\n",
    "    Round(AVG(order_items.price),2) price\n",
    "FROM\n",
    "    products\n",
    "        JOIN\n",
    "    order_items ON products.product_id = order_items.product_id\n",
    "GROUP BY products.product_category\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\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",
    "\n",
    "print('The correlation betwwen product price and number of times a product has been purchased is ',a[0][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2727a42",
   "metadata": {},
   "source": [
    "##  Calculate the total revenue generated by each seller, and rank them by revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b3977b71",
   "metadata": {
    "scrolled": true
   },
   "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>Sellers</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>2028667.63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1025f0e2d44d7041d6cf58b6550e0bfa</td>\n",
       "      <td>1232888.16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4a3ca9315b744ce9f8e9374361493884</td>\n",
       "      <td>1204981.08</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1f50f920176fa81dab994f9023523100</td>\n",
       "      <td>1161013.68</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53243585a1d6dc2643021fd1853d8905</td>\n",
       "      <td>1139612.32</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>da8622b14eb17ae2831f4ac5b9dab84a</td>\n",
       "      <td>1088877.28</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4869f7a5dfa277a7dca6462dcf3b52b2</td>\n",
       "      <td>1056664.48</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>955fee9216a65b617aa5c0531780ce60</td>\n",
       "      <td>945289.20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Sellers     Revenue  Rank\n",
       "0  7c67e1448b00f6e969d365cea6b010ab  2028667.63     1\n",
       "1  1025f0e2d44d7041d6cf58b6550e0bfa  1232888.16     2\n",
       "2  4a3ca9315b744ce9f8e9374361493884  1204981.08     3\n",
       "3  1f50f920176fa81dab994f9023523100  1161013.68     4\n",
       "4  53243585a1d6dc2643021fd1853d8905  1139612.32     5\n",
       "5  da8622b14eb17ae2831f4ac5b9dab84a  1088877.28     6\n",
       "6  4869f7a5dfa277a7dca6462dcf3b52b2  1056664.48     7\n",
       "7  955fee9216a65b617aa5c0531780ce60   945289.20     8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"With  top_seller As\n",
    "(\n",
    "SELECT \n",
    "    order_items.seller_id,\n",
    "    ROUND(SUM(payments.payment_value), 2) AS total_revenue\n",
    "FROM\n",
    "    order_items\n",
    "        JOIN\n",
    "    payments ON order_items.order_id = payments.order_id\n",
    "GROUP BY order_items.seller_id\n",
    "\n",
    ")\n",
    "Select *, Rank() over(order by total_revenue DESC) as seller_rank\n",
    "from top_seller\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['Sellers','Revenue','Rank'])\n",
    "\n",
    "df.head(8)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066d69d5",
   "metadata": {},
   "source": [
    "###  Calculate the moving average of order values for each customer over their order history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "33a934c0",
   "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</th>\n",
       "      <th>moving average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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>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>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>415539</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>415540</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>415541</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>415542</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>415543</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>415544 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id order_purchase_timestamp  payment  \\\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       00012a2ce6f8dcda20d059ce98491703      2017-11-14 16:08:26   114.74   \n",
       "4       000161a058600d5901f007fab4c27140      2017-07-16 09:40:32    67.41   \n",
       "...                                  ...                      ...      ...   \n",
       "415539  ffffa3172527f765de70084a7e53aae8      2017-09-02 11:53:32    45.50   \n",
       "415540  ffffe8b65bbe3087b653a978c870db99      2017-09-29 14:07:03    18.37   \n",
       "415541  ffffe8b65bbe3087b653a978c870db99      2017-09-29 14:07:03    18.37   \n",
       "415542  ffffe8b65bbe3087b653a978c870db99      2017-09-29 14:07:03    18.37   \n",
       "415543  ffffe8b65bbe3087b653a978c870db99      2017-09-29 14:07:03    18.37   \n",
       "\n",
       "        moving average  \n",
       "0           114.739998  \n",
       "1           114.739998  \n",
       "2           114.739998  \n",
       "3           114.739998  \n",
       "4            67.410004  \n",
       "...                ...  \n",
       "415539       45.500000  \n",
       "415540       18.370001  \n",
       "415541       18.370001  \n",
       "415542       18.370001  \n",
       "415543       18.370001  \n",
       "\n",
       "[415544 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "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 moving_avg\n",
    "from\n",
    "(\n",
    "SELECT \n",
    "    orders.customer_id,\n",
    "    orders.order_purchase_timestamp,\n",
    "    payments.payment_value AS payment\n",
    "FROM\n",
    "    orders\n",
    "        JOIN\n",
    "    payments ON orders.order_id = payments.order_id) as a\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['customer_id','order_purchase_timestamp','payment','moving average'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60af40c",
   "metadata": {},
   "source": [
    "##  Calculate the cumulative sales per month for each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc429f69",
   "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>sales</th>\n",
       "      <th>cumulative sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>1008.96</td>\n",
       "      <td>1008.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>236361.92</td>\n",
       "      <td>237370.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>78.48</td>\n",
       "      <td>237449.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>553952.16</td>\n",
       "      <td>791401.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>1167632.04</td>\n",
       "      <td>1959033.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>1799454.40</td>\n",
       "      <td>3758487.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1671152.12</td>\n",
       "      <td>5429640.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>2371675.28</td>\n",
       "      <td>7801315.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>2045105.52</td>\n",
       "      <td>9846420.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>2369531.68</td>\n",
       "      <td>12215952.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2697585.28</td>\n",
       "      <td>14913537.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>2911049.80</td>\n",
       "      <td>17824587.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>3118711.52</td>\n",
       "      <td>20943299.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>4779531.20</td>\n",
       "      <td>25722830.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>3513605.92</td>\n",
       "      <td>29236436.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>4460016.72</td>\n",
       "      <td>33696453.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>3969853.36</td>\n",
       "      <td>37666306.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>4638608.48</td>\n",
       "      <td>42304914.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>4643141.92</td>\n",
       "      <td>46948056.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>4615928.60</td>\n",
       "      <td>51563985.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>4095522.00</td>\n",
       "      <td>55659507.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>4266163.00</td>\n",
       "      <td>59925670.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>4089701.29</td>\n",
       "      <td>64015371.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>17758.16</td>\n",
       "      <td>64033129.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>2358.68</td>\n",
       "      <td>64035488.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month       sales  cumulative sales\n",
       "0   2016      9     1008.96           1008.96\n",
       "1   2016     10   236361.92         237370.88\n",
       "2   2016     12       78.48         237449.36\n",
       "3   2017      1   553952.16         791401.52\n",
       "4   2017      2  1167632.04        1959033.56\n",
       "5   2017      3  1799454.40        3758487.96\n",
       "6   2017      4  1671152.12        5429640.08\n",
       "7   2017      5  2371675.28        7801315.36\n",
       "8   2017      6  2045105.52        9846420.88\n",
       "9   2017      7  2369531.68       12215952.56\n",
       "10  2017      8  2697585.28       14913537.84\n",
       "11  2017      9  2911049.80       17824587.64\n",
       "12  2017     10  3118711.52       20943299.16\n",
       "13  2017     11  4779531.20       25722830.36\n",
       "14  2017     12  3513605.92       29236436.28\n",
       "15  2018      1  4460016.72       33696453.00\n",
       "16  2018      2  3969853.36       37666306.36\n",
       "17  2018      3  4638608.48       42304914.84\n",
       "18  2018      4  4643141.92       46948056.76\n",
       "19  2018      5  4615928.60       51563985.36\n",
       "20  2018      6  4095522.00       55659507.36\n",
       "21  2018      7  4266163.00       59925670.36\n",
       "22  2018      8  4089701.29       64015371.65\n",
       "23  2018      9    17758.16       64033129.81\n",
       "24  2018     10     2358.68       64035488.49"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"select year, month, value,\n",
    "sum(value) over(order by year, month) as cumulative_sales\n",
    "from\n",
    "(select year(orders.order_purchase_timestamp) as year,\n",
    "month(orders.order_purchase_timestamp) as month,\n",
    "Round(sum(payments.payment_value),2) as value\n",
    "from orders\n",
    "join payments \n",
    "on orders.order_id = payments.order_id\n",
    "group by year, month \n",
    "order by year, month) as a\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['year','month','sales','cumulative sales'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec1b6cc",
   "metadata": {},
   "source": [
    "## Calculate the year-over-year growth rate of total sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "02176fca",
   "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>sales</th>\n",
       "      <th>YOY % growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>237449.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>28998986.91</td>\n",
       "      <td>12112.703757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>34799052.21</td>\n",
       "      <td>20.000924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year        sales  YOY % growth\n",
       "0  2016    237449.36           NaN\n",
       "1  2017  28998986.91  12112.703757\n",
       "2  2018  34799052.21     20.000924"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"with sales_rate as(\n",
    "SELECT \n",
    "    YEAR(orders.order_purchase_timestamp) AS year,\n",
    "    ROUND(SUM(payments.payment_value), 2) AS sales\n",
    "FROM\n",
    "    orders\n",
    "        JOIN\n",
    "    payments ON orders.order_id = payments.order_id\n",
    "GROUP BY year\n",
    ")\n",
    "select year, sales, ((sales - lag(sales,1) over (order by year)) / lag(sales,1) over(order by year))*100 as year_growth_rate\n",
    "from sales_rate\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['year','sales','YOY % growth'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c46e28d",
   "metadata": {},
   "source": [
    "## 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": 25,
   "id": "fc5ae2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(None,)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"With a as(\n",
    "SELECT \n",
    "    customers.customer_id,\n",
    "    MIN(orders.order_purchase_timestamp) AS first_order\n",
    "FROM\n",
    "    customers\n",
    "        JOIN\n",
    "    orders 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\n",
    "join orders\n",
    "on a.customer_id = orders.customer_id\n",
    "and orders.order_purchase_timestamp > first_order\n",
    "and  orders.order_purchase_timestamp < 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)) as retention_rate\n",
    "from a left join b \n",
    "on a.customer_id = b.customer_id\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d7b996",
   "metadata": {},
   "source": [
    "## Identify the top 3 customers who spent the most money in each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "561e4dbe",
   "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>customer</th>\n",
       "      <th>payment</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>a9dc96b027d1252bbac0a9b72d837fc6</td>\n",
       "      <td>5694.200195</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1d34ed25963d5aae4cf3d7f3a4cda173</td>\n",
       "      <td>5602.959961</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>4a06381959b6670756de02e07b83815f</td>\n",
       "      <td>4911.120117</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1617b1357756262bfa56ab541c47bc16</td>\n",
       "      <td>54656.320312</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>c6e2731c5b391845f6800c97401a43a9</td>\n",
       "      <td>27717.240234</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>3fd6777bbce08a352fddd04e4a7cc8f6</td>\n",
       "      <td>26906.640625</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>ec5b2ba62e574342386871631fafd3fc</td>\n",
       "      <td>29099.519531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>f48d464a0baaea338cb25f816991ab1f</td>\n",
       "      <td>27688.839844</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>e0a2412720e9ea4f26c1ac985f6a7358</td>\n",
       "      <td>19237.759766</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year                          customer       payment  rank\n",
       "0  2016  a9dc96b027d1252bbac0a9b72d837fc6   5694.200195     1\n",
       "1  2016  1d34ed25963d5aae4cf3d7f3a4cda173   5602.959961     2\n",
       "2  2016  4a06381959b6670756de02e07b83815f   4911.120117     3\n",
       "3  2017  1617b1357756262bfa56ab541c47bc16  54656.320312     1\n",
       "4  2017  c6e2731c5b391845f6800c97401a43a9  27717.240234     2\n",
       "5  2017  3fd6777bbce08a352fddd04e4a7cc8f6  26906.640625     3\n",
       "6  2018  ec5b2ba62e574342386871631fafd3fc  29099.519531     1\n",
       "7  2018  f48d464a0baaea338cb25f816991ab1f  27688.839844     2\n",
       "8  2018  e0a2412720e9ea4f26c1ac985f6a7358  19237.759766     3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"With top_customers as(\n",
    "select  *, \n",
    "rank() over(partition by year order by year, indi_purchase DESC) as cust_rank\n",
    "from(\n",
    "select year(orders.order_purchase_timestamp) year,\n",
    "orders.customer_id, sum(payments.payment_value) as indi_purchase\n",
    "from orders\n",
    "join payments\n",
    "on orders.order_id = payments.order_id\n",
    "group by year, orders.customer_id) as a\n",
    ")\n",
    "select *\n",
    "from top_customers\n",
    "where cust_rank<=3\"\"\"\n",
    "\n",
    "cur.execute(query)\n",
    "\n",
    "data = cur.fetchall()\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['year','customer','payment','rank'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aee8d77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.barplot(x='customer',y='payment',data=df, hue='year')\n",
    "plt.title('Top 3 Customers in each year')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7601b39c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}