diff --git a/lab-sql-9.sql b/lab-sql-9.sql
new file mode 100644
index 0000000..d182adb
--- /dev/null
+++ b/lab-sql-9.sql
@@ -0,0 +1,50 @@
+-- In this lab we will find the customers who were active in consecutive months of May and June. Follow the steps to complete the analysis.
+-- Create a table rentals_may to store the data from rental table with information for the month of May.
+
+--
+
+select *, convert(rental_date,date) as month
+from sakila.rental
+;
+-- creating a table with only  the month of may 
+CREATE TABLE rentals_may AS
+-- Insert values in the table rentals_may using the table rental, filtering values only for the month of May.
+select * ,
+date_format(convert(rental_date ,date),'%M') as month
+from sakila.rental
+having month = 'May';
+
+-- viewing the table for May
+
+select *
+from sakila.rentals_may;
+
+-- Create a table rentals_june to store the data from rental table with information for the month of June.
+CREATE TABLE rentals_june AS
+-- Insert values in the table rentals_june using the table rental, filtering values only for the month of June.
+select * ,
+date_format(convert(rental_date ,date),'%M') as month
+from sakila.rental
+having month = 'June';
+
+-- viewing the table for June 
+select *
+from sakila.rentals_june;
+
+-- Check the number of rentals for each customer for May.
+-- looking to table customer 
+select *
+from sakila.customer;
+
+-- Use Join with customer ID, and groupby customer, to get rental of each customer fro MAy
+SELECT c.customer_id, count(r.rental_id) as rental_cust
+FROM sakila.customer c
+LEFT JOIN sakila.rentals_May r ON c.customer_id = r.customer_id
+GROUP BY c.customer_id ;
+
+
+-- Now same thing for the month of June
+SELECT c.customer_id, count(r.rental_id) as rental_cust
+FROM sakila.customer c
+LEFT JOIN sakila.rentals_june r ON c.customer_id = r.customer_id
+GROUP BY c.customer_id ;
diff --git a/lab-sql-9_Python.ipynb b/lab-sql-9_Python.ipynb
new file mode 100644
index 0000000..e3391a5
--- /dev/null
+++ b/lab-sql-9_Python.ipynb
@@ -0,0 +1,1265 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' !pip install mysql.connector\\n!pip install mysql.connector.python\\n!pip install sqlalchemy\\n!pip install pymysql '"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "''' !pip install mysql.connector\n",
+    "!pip install mysql.connector.python\n",
+    "!pip install sqlalchemy\n",
+    "!pip install pymysql '''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass \n",
+    "password = getpass.getpass('Mickimitamoin12')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rental_id</th>\n",
+       "      <th>rental_date</th>\n",
+       "      <th>inventory_id</th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>return_date</th>\n",
+       "      <th>staff_id</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2005-05-24 22:53:30</td>\n",
+       "      <td>367</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2005-05-26 22:04:30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2005-05-24 22:54:33</td>\n",
+       "      <td>1525</td>\n",
+       "      <td>459</td>\n",
+       "      <td>2005-05-28 19:40:33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2005-05-24 23:03:39</td>\n",
+       "      <td>1711</td>\n",
+       "      <td>408</td>\n",
+       "      <td>2005-06-01 22:12:39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2005-05-24 23:04:41</td>\n",
+       "      <td>2452</td>\n",
+       "      <td>333</td>\n",
+       "      <td>2005-06-03 01:43:41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2005-05-24 23:05:21</td>\n",
+       "      <td>2079</td>\n",
+       "      <td>222</td>\n",
+       "      <td>2005-06-02 04:33:21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1151</th>\n",
+       "      <td>1153</td>\n",
+       "      <td>2005-05-31 21:36:44</td>\n",
+       "      <td>2725</td>\n",
+       "      <td>506</td>\n",
+       "      <td>2005-06-10 01:26:44</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1152</th>\n",
+       "      <td>1154</td>\n",
+       "      <td>2005-05-31 21:42:09</td>\n",
+       "      <td>2732</td>\n",
+       "      <td>59</td>\n",
+       "      <td>2005-06-08 16:40:09</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1153</th>\n",
+       "      <td>1155</td>\n",
+       "      <td>2005-05-31 22:17:11</td>\n",
+       "      <td>2048</td>\n",
+       "      <td>251</td>\n",
+       "      <td>2005-06-04 20:27:11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1154</th>\n",
+       "      <td>1156</td>\n",
+       "      <td>2005-05-31 22:37:34</td>\n",
+       "      <td>460</td>\n",
+       "      <td>106</td>\n",
+       "      <td>2005-06-01 23:02:34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1155</th>\n",
+       "      <td>1157</td>\n",
+       "      <td>2005-05-31 22:47:45</td>\n",
+       "      <td>1449</td>\n",
+       "      <td>61</td>\n",
+       "      <td>2005-06-02 18:01:45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1156 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rental_id         rental_date  inventory_id  customer_id  \\\n",
+       "0             1 2005-05-24 22:53:30           367          130   \n",
+       "1             2 2005-05-24 22:54:33          1525          459   \n",
+       "2             3 2005-05-24 23:03:39          1711          408   \n",
+       "3             4 2005-05-24 23:04:41          2452          333   \n",
+       "4             5 2005-05-24 23:05:21          2079          222   \n",
+       "...         ...                 ...           ...          ...   \n",
+       "1151       1153 2005-05-31 21:36:44          2725          506   \n",
+       "1152       1154 2005-05-31 21:42:09          2732           59   \n",
+       "1153       1155 2005-05-31 22:17:11          2048          251   \n",
+       "1154       1156 2005-05-31 22:37:34           460          106   \n",
+       "1155       1157 2005-05-31 22:47:45          1449           61   \n",
+       "\n",
+       "             return_date  staff_id         last_update month  \n",
+       "0    2005-05-26 22:04:30         1 2006-02-15 21:30:53   May  \n",
+       "1    2005-05-28 19:40:33         1 2006-02-15 21:30:53   May  \n",
+       "2    2005-06-01 22:12:39         1 2006-02-15 21:30:53   May  \n",
+       "3    2005-06-03 01:43:41         2 2006-02-15 21:30:53   May  \n",
+       "4    2005-06-02 04:33:21         1 2006-02-15 21:30:53   May  \n",
+       "...                  ...       ...                 ...   ...  \n",
+       "1151 2005-06-10 01:26:44         2 2006-02-15 21:30:53   May  \n",
+       "1152 2005-06-08 16:40:09         1 2006-02-15 21:30:53   May  \n",
+       "1153 2005-06-04 20:27:11         2 2006-02-15 21:30:53   May  \n",
+       "1154 2005-06-01 23:02:34         2 2006-02-15 21:30:53   May  \n",
+       "1155 2005-06-02 18:01:45         1 2006-02-15 21:30:53   May  \n",
+       "\n",
+       "[1156 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "\n",
+    "engine = create_engine(connection_string)\n",
+    "\n",
+    "result = pd.read_sql_query('SELECT* FROM sakila.rentals_may', engine)\n",
+    "\n",
+    "result\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>rental_id</th>\n",
+       "      <th>rental_date</th>\n",
+       "      <th>inventory_id</th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>return_date</th>\n",
+       "      <th>staff_id</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2005-05-24 22:53:30</td>\n",
+       "      <td>367</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2005-05-26 22:04:30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2005-05-24 22:54:33</td>\n",
+       "      <td>1525</td>\n",
+       "      <td>459</td>\n",
+       "      <td>2005-05-28 19:40:33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2005-05-24 23:03:39</td>\n",
+       "      <td>1711</td>\n",
+       "      <td>408</td>\n",
+       "      <td>2005-06-01 22:12:39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2005-05-24 23:04:41</td>\n",
+       "      <td>2452</td>\n",
+       "      <td>333</td>\n",
+       "      <td>2005-06-03 01:43:41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2005-05-24 23:05:21</td>\n",
+       "      <td>2079</td>\n",
+       "      <td>222</td>\n",
+       "      <td>2005-06-02 04:33:21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1151</th>\n",
+       "      <td>1153</td>\n",
+       "      <td>2005-05-31 21:36:44</td>\n",
+       "      <td>2725</td>\n",
+       "      <td>506</td>\n",
+       "      <td>2005-06-10 01:26:44</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1152</th>\n",
+       "      <td>1154</td>\n",
+       "      <td>2005-05-31 21:42:09</td>\n",
+       "      <td>2732</td>\n",
+       "      <td>59</td>\n",
+       "      <td>2005-06-08 16:40:09</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1153</th>\n",
+       "      <td>1155</td>\n",
+       "      <td>2005-05-31 22:17:11</td>\n",
+       "      <td>2048</td>\n",
+       "      <td>251</td>\n",
+       "      <td>2005-06-04 20:27:11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1154</th>\n",
+       "      <td>1156</td>\n",
+       "      <td>2005-05-31 22:37:34</td>\n",
+       "      <td>460</td>\n",
+       "      <td>106</td>\n",
+       "      <td>2005-06-01 23:02:34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1155</th>\n",
+       "      <td>1157</td>\n",
+       "      <td>2005-05-31 22:47:45</td>\n",
+       "      <td>1449</td>\n",
+       "      <td>61</td>\n",
+       "      <td>2005-06-02 18:01:45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1156 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rental_id         rental_date  inventory_id  customer_id  \\\n",
+       "0             1 2005-05-24 22:53:30           367          130   \n",
+       "1             2 2005-05-24 22:54:33          1525          459   \n",
+       "2             3 2005-05-24 23:03:39          1711          408   \n",
+       "3             4 2005-05-24 23:04:41          2452          333   \n",
+       "4             5 2005-05-24 23:05:21          2079          222   \n",
+       "...         ...                 ...           ...          ...   \n",
+       "1151       1153 2005-05-31 21:36:44          2725          506   \n",
+       "1152       1154 2005-05-31 21:42:09          2732           59   \n",
+       "1153       1155 2005-05-31 22:17:11          2048          251   \n",
+       "1154       1156 2005-05-31 22:37:34           460          106   \n",
+       "1155       1157 2005-05-31 22:47:45          1449           61   \n",
+       "\n",
+       "             return_date  staff_id         last_update month  \n",
+       "0    2005-05-26 22:04:30         1 2006-02-15 21:30:53   May  \n",
+       "1    2005-05-28 19:40:33         1 2006-02-15 21:30:53   May  \n",
+       "2    2005-06-01 22:12:39         1 2006-02-15 21:30:53   May  \n",
+       "3    2005-06-03 01:43:41         2 2006-02-15 21:30:53   May  \n",
+       "4    2005-06-02 04:33:21         1 2006-02-15 21:30:53   May  \n",
+       "...                  ...       ...                 ...   ...  \n",
+       "1151 2005-06-10 01:26:44         2 2006-02-15 21:30:53   May  \n",
+       "1152 2005-06-08 16:40:09         1 2006-02-15 21:30:53   May  \n",
+       "1153 2005-06-04 20:27:11         2 2006-02-15 21:30:53   May  \n",
+       "1154 2005-06-01 23:02:34         2 2006-02-15 21:30:53   May  \n",
+       "1155 2005-06-02 18:01:45         1 2006-02-15 21:30:53   May  \n",
+       "\n",
+       "[1156 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the number of rentals for each customer for May\n",
+    "\n",
+    "df_rental_may = result \n",
+    "\n",
+    "df_rental_may \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>rental_id</th>\n",
+       "      <th>rental_date</th>\n",
+       "      <th>inventory_id</th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>return_date</th>\n",
+       "      <th>staff_id</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1158</td>\n",
+       "      <td>2005-06-14 22:53:33</td>\n",
+       "      <td>1632</td>\n",
+       "      <td>416</td>\n",
+       "      <td>2005-06-18 21:37:33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1159</td>\n",
+       "      <td>2005-06-14 22:55:13</td>\n",
+       "      <td>4395</td>\n",
+       "      <td>516</td>\n",
+       "      <td>2005-06-17 02:11:13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1160</td>\n",
+       "      <td>2005-06-14 23:00:34</td>\n",
+       "      <td>2795</td>\n",
+       "      <td>239</td>\n",
+       "      <td>2005-06-18 01:58:34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1161</td>\n",
+       "      <td>2005-06-14 23:07:08</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>285</td>\n",
+       "      <td>2005-06-21 17:12:08</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1162</td>\n",
+       "      <td>2005-06-14 23:09:38</td>\n",
+       "      <td>987</td>\n",
+       "      <td>310</td>\n",
+       "      <td>2005-06-23 22:00:38</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2306</th>\n",
+       "      <td>3465</td>\n",
+       "      <td>2005-06-21 22:10:01</td>\n",
+       "      <td>1488</td>\n",
+       "      <td>510</td>\n",
+       "      <td>2005-06-30 21:35:01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2307</th>\n",
+       "      <td>3466</td>\n",
+       "      <td>2005-06-21 22:13:33</td>\n",
+       "      <td>371</td>\n",
+       "      <td>226</td>\n",
+       "      <td>2005-06-25 21:01:33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2308</th>\n",
+       "      <td>3467</td>\n",
+       "      <td>2005-06-21 22:19:25</td>\n",
+       "      <td>729</td>\n",
+       "      <td>543</td>\n",
+       "      <td>2005-06-27 00:03:25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2309</th>\n",
+       "      <td>3468</td>\n",
+       "      <td>2005-06-21 22:43:45</td>\n",
+       "      <td>2899</td>\n",
+       "      <td>100</td>\n",
+       "      <td>2005-06-30 01:49:45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2310</th>\n",
+       "      <td>3469</td>\n",
+       "      <td>2005-06-21 22:48:59</td>\n",
+       "      <td>4087</td>\n",
+       "      <td>181</td>\n",
+       "      <td>2005-06-28 19:32:59</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2311 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rental_id         rental_date  inventory_id  customer_id  \\\n",
+       "0          1158 2005-06-14 22:53:33          1632          416   \n",
+       "1          1159 2005-06-14 22:55:13          4395          516   \n",
+       "2          1160 2005-06-14 23:00:34          2795          239   \n",
+       "3          1161 2005-06-14 23:07:08          1690          285   \n",
+       "4          1162 2005-06-14 23:09:38           987          310   \n",
+       "...         ...                 ...           ...          ...   \n",
+       "2306       3465 2005-06-21 22:10:01          1488          510   \n",
+       "2307       3466 2005-06-21 22:13:33           371          226   \n",
+       "2308       3467 2005-06-21 22:19:25           729          543   \n",
+       "2309       3468 2005-06-21 22:43:45          2899          100   \n",
+       "2310       3469 2005-06-21 22:48:59          4087          181   \n",
+       "\n",
+       "             return_date  staff_id         last_update month  \n",
+       "0    2005-06-18 21:37:33         2 2006-02-15 21:30:53  June  \n",
+       "1    2005-06-17 02:11:13         1 2006-02-15 21:30:53  June  \n",
+       "2    2005-06-18 01:58:34         2 2006-02-15 21:30:53  June  \n",
+       "3    2005-06-21 17:12:08         1 2006-02-15 21:30:53  June  \n",
+       "4    2005-06-23 22:00:38         1 2006-02-15 21:30:53  June  \n",
+       "...                  ...       ...                 ...   ...  \n",
+       "2306 2005-06-30 21:35:01         1 2006-02-15 21:30:53  June  \n",
+       "2307 2005-06-25 21:01:33         2 2006-02-15 21:30:53  June  \n",
+       "2308 2005-06-27 00:03:25         2 2006-02-15 21:30:53  June  \n",
+       "2309 2005-06-30 01:49:45         1 2006-02-15 21:30:53  June  \n",
+       "2310 2005-06-28 19:32:59         1 2006-02-15 21:30:53  June  \n",
+       "\n",
+       "[2311 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# now lets create a data frame for the info for June \n",
+    "\n",
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "\n",
+    "engine = create_engine(connection_string)\n",
+    "\n",
+    "df_rental_june = pd.read_sql_query('SELECT* FROM sakila.rentals_june', engine)\n",
+    "\n",
+    "df_rental_june "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer_id\n",
+       "1      2\n",
+       "2      1\n",
+       "3      2\n",
+       "5      3\n",
+       "6      3\n",
+       "      ..\n",
+       "594    4\n",
+       "595    1\n",
+       "596    6\n",
+       "597    2\n",
+       "599    1\n",
+       "Name: rental_id, Length: 520, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the number of rentals for each customer for May using Python\n",
+    "\n",
+    "\n",
+    "df_rental_may.groupby('customer_id')['rental_id'].count()\n",
+    "\n",
+    "# Hint: You can store the results from the two queries in two separate dataframes.\n",
+    "\n",
+    "df_may_count = df_rental_may.groupby('customer_id')['rental_id'].count()\n",
+    "\n",
+    "df_may_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer_id\n",
+       "1      7\n",
+       "2      1\n",
+       "3      4\n",
+       "4      6\n",
+       "5      5\n",
+       "      ..\n",
+       "595    2\n",
+       "596    2\n",
+       "597    3\n",
+       "598    1\n",
+       "599    4\n",
+       "Name: rental_id, Length: 590, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the number of rentals for each customer for Juen\n",
+    "# same techniq\n",
+    "\n",
+    "df_rental_june.groupby('customer_id')['rental_id'].count()\n",
+    "\n",
+    "# Hint: You can store the results from the two queries in two separate dataframes.\n",
+    "\n",
+    "df_june_count = df_rental_june.groupby('customer_id')['rental_id'].count()\n",
+    "\n",
+    "df_june_count\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>rental_id_may</th>\n",
+       "      <th>rental_id_june</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>customer_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             rental_id_may  rental_id_june\n",
+       "customer_id                               \n",
+       "1                        2             7.0\n",
+       "2                        1             1.0\n",
+       "3                        2             4.0\n",
+       "5                        3             5.0\n",
+       "6                        3             4.0\n",
+       "...                    ...             ...\n",
+       "594                      4             6.0\n",
+       "595                      1             2.0\n",
+       "596                      6             2.0\n",
+       "597                      2             3.0\n",
+       "599                      1             4.0\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Write a function that checks if customer borrowed more or less films in the month of June as compared to May.\n",
+    "\n",
+    "# Hint: For this part, you can create a join between the two dataframes created before, using the merge function available for pandas dataframes. Here is a link to the documentation for the merge function.\n",
+    "df_may_count = pd.DataFrame(df_may_count)\n",
+    "\n",
+    "\n",
+    "merged_df = df_may_count.merge(df_june_count, on='customer_id', suffixes=('_may', '_june'), how='left')\n",
+    "\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>rental_id_may</th>\n",
+       "      <th>rental_id_june</th>\n",
+       "      <th>rental_total</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>customer_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>-5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             rental_id_may  rental_id_june  rental_total\n",
+       "customer_id                                             \n",
+       "1                        2             7.0          -5.0\n",
+       "2                        1             1.0           0.0\n",
+       "3                        2             4.0          -2.0\n",
+       "5                        3             5.0          -2.0\n",
+       "6                        3             4.0          -1.0\n",
+       "...                    ...             ...           ...\n",
+       "594                      4             6.0          -2.0\n",
+       "595                      1             2.0          -1.0\n",
+       "596                      6             2.0           4.0\n",
+       "597                      2             3.0          -1.0\n",
+       "599                      1             4.0          -3.0\n",
+       "\n",
+       "[520 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "merged_df['rental_total'] = merged_df['rental_id_may'] - merged_df['rental_id_june']\n",
+    "\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Count of June is the bigger: 358 rental\n"
+     ]
+    }
+   ],
+   "source": [
+    "# creating New column to evaluate if month of may got more rental than June month\n",
+    "\n",
+    "\n",
+    "merged_df['rental_total'] = merged_df['rental_id_may'] - merged_df['rental_id_june']\n",
+    "\n",
+    "Borrow_month = []\n",
+    "\n",
+    "\n",
+    "count_june = 0\n",
+    "count_may = 0\n",
+    "\n",
+    "# function to define wich month customer did the most rental\n",
+    "def compare_rental_counts(row):\n",
+    "\n",
+    "    global count_june, count_may\n",
+    "\n",
+    "    if row ['rental_total'] < 0:\n",
+    "        Borrow_month.append( 'June')\n",
+    "        # count  location total for June month\n",
+    "        count_june += 1          \n",
+    "    else :\n",
+    "        Borrow_month.append('May')\n",
+    "         # count  location total for May mont\n",
+    "        count_may += 1\n",
+    "            \n",
+    "\n",
+    "\n",
+    "merged_df.apply(compare_rental_counts, axis=1)\n",
+    "\n",
+    "# choose whcih month had the more rental\n",
+    "if count_june > count_may :\n",
+    "    print(f\"Count of June is the bigger: {count_june} rental\")\n",
+    "\n",
+    "if count_june < count_may :\n",
+    "    print(f\"Count of May is the bigger with: {count_may} rental\")\n",
+    "\n",
+    "merged_df['comparison_result'] = Borrow_month\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>rental_id_may</th>\n",
+       "      <th>rental_id_june</th>\n",
+       "      <th>rental_total</th>\n",
+       "      <th>comparison_result</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>customer_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>June</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>594</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>May</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>June</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             rental_id_may  rental_id_june  rental_total comparison_result\n",
+       "customer_id                                                               \n",
+       "1                        2             7.0          -5.0              June\n",
+       "2                        1             1.0           0.0               May\n",
+       "3                        2             4.0          -2.0              June\n",
+       "5                        3             5.0          -2.0              June\n",
+       "6                        3             4.0          -1.0              June\n",
+       "...                    ...             ...           ...               ...\n",
+       "594                      4             6.0          -2.0              June\n",
+       "595                      1             2.0          -1.0              June\n",
+       "596                      6             2.0           4.0               May\n",
+       "597                      2             3.0          -1.0              June\n",
+       "599                      1             4.0          -3.0              June\n",
+       "\n",
+       "[520 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Write a function that checks if customer borrowed more or less films in the month of June as compared to May.\n",
+    "\n",
+    "# You can find a new column for the data Frame with the month were the user Borrowed the most movie \n",
+    "\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}