diff --git a/.ipynb_checkpoints/[lab-sql-9-python_connection]leonardo-checkpoint.ipynb b/.ipynb_checkpoints/[lab-sql-9-python_connection]leonardo-checkpoint.ipynb
new file mode 100644
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--- /dev/null
+++ b/.ipynb_checkpoints/[lab-sql-9-python_connection]leonardo-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab SQL | 9.sql b/Lab SQL | 9.sql
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+++ b/Lab SQL | 9.sql
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+-- Create a table `rentals_may` to store the data from rental table with information for the month of May.
+-- Insert values in the table `rentals_may` using the table rental, filtering values only for the month of May.
+
+-- Create a table `rentals_may` to store the data from rental table with information for the month of May.
+CREATE TABLE rentals_may AS
+SELECT
+ rental_id,
+ rental_date,
+ inventory_id,
+ customer_id,
+ staff_id
+FROM
+ sakila.rental
+WHERE
+ MONTH(rental_date) = 5;
+
+-- Create a table `rentals_june` to store the data from rental table with information for the month of June.
+CREATE TABLE rentals_june AS
+SELECT
+ rental_id,
+ rental_date,
+ inventory_id,
+ customer_id,
+ staff_id
+FROM
+ sakila.rental
+WHERE
+ MONTH(rental_date) = 6;
+
+
+select * from rentals_may;
+select * from rentals_june;
+
+-- Check the number of rentals for each customer for May.
+-- Check the number of rentals for each customer for June.
+
+select
+ customer_id,
+ count(rental_id) as count_rentals
+from rentals_may
+group by customer_id;
+
+select
+ customer_id,
+ count(rental_id) as count_june_rentals
+from rentals_june
+group by customer_id;
+
+
+
+
+
+
diff --git a/[lab-sql-9-python_connection]leonardo.ipynb b/[lab-sql-9-python_connection]leonardo.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "784328a2",
+ "metadata": {},
+ "source": [
+ "# Lab | SQL Queries 9\n",
+ "\n",
+ "In this lab, you will be using the [Sakila](https://dev.mysql.com/doc/sakila/en/) database of movie rentals. You have been using this database for a couple labs already, but if you need to get the data again, refer to the official [installation link](https://dev.mysql.com/doc/sakila/en/sakila-installation.html).\n",
+ "\n",
+ "The database is structured as follows:\n",
+ "\n",
+ "\n",
+ "### Instructions\n",
+ "\n",
+ "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.\n",
+ "\n",
+ "- Create a table `rentals_may` to store the data from rental table with information for the month of May.\n",
+ "- Insert values in the table `rentals_may` using the table rental, filtering values only for the month of May.\n",
+ "- Create a table `rentals_june` to store the data from rental table with information for the month of June.\n",
+ "- Insert values in the table `rentals_june` using the table rental, filtering values only for the month of June.\n",
+ "- Check the number of rentals for each customer for May.\n",
+ "- Check the number of rentals for each customer for June.\n",
+ "- Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:\n",
+ "\n",
+ " - Check the number of rentals for each customer for May\n",
+ " - Check the number of rentals for each customer for June\n",
+ "\n",
+ " **Hint**: You can store the results from the two queries in two separate dataframes.\n",
+ "\n",
+ "- 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](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "a0053186",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import pymysql\n",
+ "from sqlalchemy import create_engine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "adc8a371",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Create a Python connection with SQL database\n",
+ "\n",
+ "#import getpass\n",
+ "#password = getpass.getpass()\n",
+ "\n",
+ "\n",
+ "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+ "engine = create_engine(connection_string)\n",
+ "may_rent = pd.read_sql_query('select customer_id, count(rental_id) as count_rentals from sakila.rentals_may group by customer_id', engine)\n",
+ "june_rent = pd.read_sql_query('select customer_id, count(rental_id) as count_rentals from sakila.rentals_june group by customer_id', engine)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "6c391170",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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