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[lab-sql-9] - Anna javashvili #33

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388 changes: 388 additions & 0 deletions .ipynb_checkpoints/lab-sql-9-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "716cf0f1",
"metadata": {},
"source": [
"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",
"\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."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2e6f857e",
"metadata": {},
"outputs": [],
"source": [
"import pymysql\n",
"from sqlalchemy import create_engine\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "283c46dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"········\n"
]
}
],
"source": [
"import getpass # To get the password without showing the input\n",
"password = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "84e25554",
"metadata": {},
"outputs": [],
"source": [
"connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
"engine = create_engine(connection_string)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e6ab611b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\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>RENTAL_COUNT</th>\n",
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"<p>520 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" CUSTOMER_ID RENTAL_COUNT\n",
"0 197 8\n",
"1 506 7\n",
"2 109 7\n",
"3 269 6\n",
"4 239 6\n",
".. ... ...\n",
"515 431 1\n",
"516 351 1\n",
"517 10 1\n",
"518 136 1\n",
"519 61 1\n",
"\n",
"[520 rows x 2 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_may = \"SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT FROM SAKILA.RENTALS_MAY GROUP BY CUSTOMER_ID ORDER BY RENTAL_COUNT DESC\"\n",
"df_may = pd.read_sql_query(query_may, engine)\n",
"\n",
"# Execute the second query and store the result in a dataframe\n",
"query_june = \"SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT FROM SAKILA.RENTALS_JUNE GROUP BY CUSTOMER_ID ORDER BY RENTAL_COUNT DESC\"\n",
"df_june = pd.read_sql_query(query_june, engine)\n",
"\n",
"\n",
"\n",
"df_may"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "13da5007",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>RENTAL_COUNT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>197</td>\n",
" <td>8</td>\n",
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" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>61</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>520 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" CUSTOMER_ID RENTAL_COUNT\n",
"0 197 8\n",
"1 506 7\n",
"2 109 7\n",
"3 269 6\n",
"4 239 6\n",
".. ... ...\n",
"515 431 1\n",
"516 351 1\n",
"517 10 1\n",
"518 136 1\n",
"519 61 1\n",
"\n",
"[520 rows x 2 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_may"
]
},
{
"cell_type": "markdown",
"id": "16474d15",
"metadata": {},
"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."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b17fd5a4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Customer borrowed more films in June compared to May\n"
]
}
],
"source": [
"def compare_rentals(df_may, df_june, customer_id):\n",
"\n",
" may_rentals = df_may[df_may['CUSTOMER_ID'] == customer_id]['RENTAL_COUNT'].values\n",
" june_rentals = df_june[df_june['CUSTOMER_ID'] == customer_id]['RENTAL_COUNT'].values\n",
"\n",
" if len(may_rentals) > 0 and len(june_rentals) > 0:\n",
" if may_rentals[0] < june_rentals[0]:\n",
" return \"Customer borrowed more films in June compared to May\"\n",
" elif may_rentals[0] > june_rentals[0]:\n",
" return \"Customer borrowed fewer films in June compared to May\"\n",
" else:\n",
" return \"Customer borrowed the same number of films in May and June\"\n",
" else:\n",
" return \"Customer ID not found in one or both datasets\"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "09ad1f81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Customer ID not found in one or both datasets'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compare_rentals(df_may, df_june, 34)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
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"nbformat_minor": 5
}
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