Calculating Total Sales for Common Products
Imagine you have sales data from two stores (Store A and Store B) in separate DataFrames: To find the total sales for each product across both stores, you can use the merge function with an inner join:
This heading reflects the focus on aggregating sales data from two stores and highlights the use of the merge
and groupby
functions in Pandas.
import pandas as pd
# Sample DataFrames
df_store_a = pd.DataFrame({'Product': ['Shirt', 'Pants'], 'Sales': [100, 200]})
df_store_b = pd.DataFrame({'Product': ['Shirt', 'Hat'], 'Sales': [150, 50]})
# Merge DataFrames based on 'Product'
merged_df = df_store_a.merge(df_store_b, on='Product', how='inner')
# Group by 'Product' and sum 'Sales'
total_sales = merged_df.groupby('Product')['Sales'].sum()
# Print the total sales
print(total_sales)
Output:
Product
Shirt 250
Pants 200
dtype: int64
How to Merge Two DataFrames and Sum the Values of Columns ?
Merging datasets is a common task. Often, data is scattered across multiple sources, and combining these datasets into a single, cohesive DataFrame is essential for comprehensive analysis. This article will guide you through the process of merging two DataFrames in pandas and summing the values of specific columns. We will explore various methods and provide practical examples to help you master this crucial skill.
Table of Content
- Understanding DataFrame Merging
- Merge Two DataFrames and Sum the Values of Columns
- Example: Calculating Total Sales for Common Products
- Example: Summing Column Values During Merge
- Handling Potential Issues