SQL to Python: Max Sales Calculation

Translation of SQL Query to Python

The following Python code snippet uses the pandas library to replicate the SQL query logic. The SQL query's functionality is to find the maximum sales amount (Max_Sales_By_Product) for each product (ProductID).

Assumptions

• The data from the Sales and Products tables are loaded into pandas DataFrames.

Python Code

import pandas as pd

# Sample data to mimic the Sales and Products tables
sales_data = {
    'ProductID': [1, 2, 1, 3, 2, 3],
    'Amount': [150, 200, 300, 400, 250, 100]
}

products_data = {
    'ProductID': [1, 2, 3],
    'ProductName': ['ProductA', 'ProductB', 'ProductC']
}

# Creating pandas DataFrames
sales_df = pd.DataFrame(sales_data)
products_df = pd.DataFrame(products_data)

# Merge Sales and Products DataFrame on 'ProductID'
merged_df = pd.merge(sales_df, products_df, on='ProductID')

# Group by 'ProductID' and calculate the max sales amount for each product
result_df = merged_df.groupby('ProductID')['Amount'].max().reset_index()
result_df.rename(columns={'Amount': 'Max_Sales_By_Product'}, inplace=True)

print(result_df)

Explanation

1. Importing pandas: Import the pandas library which is commonly used for data manipulation in Python.
2. Creating DataFrames: We create two sample DataFrames, sales_df and products_df, that mimic the structure of your Sales and Products tables.
3. Merging DataFrames: Using pd.merge() to join the sales_df and products_df DataFrames on the ProductID column.
4. Grouping and Aggregating: Grouping by ProductID and applying the max aggregation function to the Amount column to find the maximum sales amount for each product.
5. Renaming Columns: Renaming the resulting column to Max_Sales_By_Product to match the output of the SQL query.

Additional Notes

• Ensure that pandas is installed in your Python environment. You can install it using pip install pandas.
• This example assumes that the data from the Sales and Products tables will be loaded into pandas DataFrames; adjust this part according to your data source.

This code preserves the logic and structure of the original SQL query while adhering to Python conventions.