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136 changes: 136 additions & 0 deletions .ipynb_checkpoints/[lab-sql-9] Sara-checkpoint.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "markdown",
"id": "5d76472d",
"metadata": {},
"source": [
"DA PT 2023 \n",
"Sara Ferreira da Silva"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a2d39fe9",
"metadata": {},
"outputs": [],
"source": [
"# Importing extension to read SQL files\n",
"import pymysql\n",
"from sqlalchemy import create_engine\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb20116d",
"metadata": {},
"outputs": [],
"source": [
"# Importing the password extension and also the password protection so it is not visible\n",
"import getpass \n",
"password = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8abbb119",
"metadata": {},
"outputs": [],
"source": [
"# Connecting the password to my SQL\n",
"connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b975e6b1",
"metadata": {},
"outputs": [],
"source": [
"# Running it\n",
"engine = create_engine(connection_string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15acda6d",
"metadata": {},
"outputs": [],
"source": [
"# Note: My SQl is allways giving an error because I do not know its password. \n",
"# Thus, henceforth I proceed with the lab knowing I am not able to retrieve any data.\n",
"# Notwithstanding it is on the assumption the remaining code is correct, despite the above situation. \n",
"\n",
"# Obtaining the rental information for May\n",
"rentals_may = pd.read_sql_query('SELECT * FROM sakila.rentals_may', engine)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac42f88a",
"metadata": {},
"outputs": [],
"source": [
"# Obtaining the rental information for June\n",
"rentals_june = pd.read_sql_query('SELECT * FROM sakila.rentals_june', engine)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a434e3c",
"metadata": {},
"outputs": [],
"source": [
"# Joining both tables\n",
"total_rentals = pd.merge(rentals_may, rentals_june on ='customer_id')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "127e9016",
"metadata": {},
"outputs": [],
"source": [
"def compare_rentals(row):\n",
" if row['rental_id_may'] < row['rental_id_june']:\n",
" return 'Borrowed more in June'\n",
" elif row['rental_id_may'] > row['rental_id_june']:\n",
" return 'Borrowed more in May'\n",
" else:\n",
" return 'Borrowed the same amount'\n",
"\n",
"merged_rentals['rental_comparison'] = merged_rentals.apply(compare_rentals, axis=1)\n"
]
}
],
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
136 changes: 136 additions & 0 deletions [lab-sql-9] Sara.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,136 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "5d76472d",
"metadata": {},
"source": [
"DA PT 2023 \n",
"Sara Ferreira da Silva"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a2d39fe9",
"metadata": {},
"outputs": [],
"source": [
"# Importing extension to read SQL files\n",
"import pymysql\n",
"from sqlalchemy import create_engine\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb20116d",
"metadata": {},
"outputs": [],
"source": [
"# Importing the password extension and also the password protection so it is not visible\n",
"import getpass \n",
"password = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8abbb119",
"metadata": {},
"outputs": [],
"source": [
"# Connecting the password to my SQL\n",
"connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b975e6b1",
"metadata": {},
"outputs": [],
"source": [
"# Running it\n",
"engine = create_engine(connection_string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15acda6d",
"metadata": {},
"outputs": [],
"source": [
"# Note: My SQl is allways giving an error because I do not know its password. \n",
"# Thus, henceforth I proceed with the lab knowing I am not able to retrieve any data.\n",
"# Notwithstanding it is on the assumption the remaining code is correct, despite the above situation. \n",
"\n",
"# Obtaining the rental information for May\n",
"rentals_may = pd.read_sql_query('SELECT * FROM sakila.rentals_may', engine)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac42f88a",
"metadata": {},
"outputs": [],
"source": [
"# Obtaining the rental information for June\n",
"rentals_june = pd.read_sql_query('SELECT * FROM sakila.rentals_june', engine)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a434e3c",
"metadata": {},
"outputs": [],
"source": [
"# Joining both tables\n",
"total_rentals = pd.merge(rentals_may, rentals_june on ='customer_id')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "127e9016",
"metadata": {},
"outputs": [],
"source": [
"def compare_rentals(row):\n",
" if row['rental_id_may'] < row['rental_id_june']:\n",
" return 'Borrowed more in June'\n",
" elif row['rental_id_may'] > row['rental_id_june']:\n",
" return 'Borrowed more in May'\n",
" else:\n",
" return 'Borrowed the same amount'\n",
"\n",
"merged_rentals['rental_comparison'] = merged_rentals.apply(compare_rentals, axis=1)\n"
]
}
],
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
37 changes: 37 additions & 0 deletions [lab-sql-9] Sara.sql
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Original file line number Diff line number Diff line change
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-- DA PT 2023
-- Sara Ferreira da Silva

select * from sakila.rental; -- rental_date + customer_id + rental_id
select * from sakila.customer; -- customer_id + first_name + last_name + active

-- 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.
-- Insert values in the table rentals_may using the table rental, filtering values only 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_june to store the data from rental table with information for the month of June.
-- Insert values in the table rentals_june using the table rental, filtering values only for the month of June.

create temporary table sakila.rentals_may as
select * from sakila.rental
where month(rental_date) = 5;

create temporary table sakila.rentals_june as
select * from sakila.rental
where month(rental_date) = 6;

-- Check the number of rentals for each customer for May.
select customer_id, count(rental_id) as number_of_rental
from sakila.rentals_may
group by customer_id;

-- Check the number of rentals for each customer for June.
select customer_id, count(rental_id) as number_of_rental
from sakila.rentals_june
group by customer_id;

-- Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:
-- Check the number of rentals for each customer for May
-- Check the number of rentals for each customer for June
-- Hint: You can store the results from the two queries in two separate dataframes.
-- Write a function that checks if customer borrowed more or less films in the month of June as compared to May.
-- Hint: For this part, you can create a join between the two dataframes created before, using the merge function available for pandas dataframes.