pandas Power Up: Effortlessly Combine DataFrames Using the merge() Function

Merge (Join) Operation in pandas

In pandas, merging (or joining) DataFrames is a fundamental operation for combining data from different sources. It allows you to create a new DataFrame that includes columns from both original DataFrames based on a specified relationship between them. This relationship is typically defined by one or more common columns in both DataFrames.

Using merge() Function

The primary method for merging DataFrames in pandas is the merge() function. Here's the basic syntax:

merged_df = df1.merge(df2, on=merge_columns, how=join_type)

• df1 and df2: The two DataFrames you want to merge.
• on=merge_columns: (Optional) A list containing the names of the columns in both DataFrames that will be used for the merge. If not specified, pandas will attempt to merge on columns with the same names in both DataFrames.
• how=join_type: (Optional) A string specifying the type of join to perform. Valid options include:
  • 'inner': (Default) Only rows with matching values in all merge columns from both DataFrames are included in the result.
  • 'left': All rows from the left DataFrame (df1) are kept, and matching rows from the right DataFrame (df2) are included. Missing values are filled with NaNs (Not a Number) in the right DataFrame for non-matching columns.
  • 'right': Similar to 'left', but keeps all rows from the right DataFrame (df2).
  • 'outer': Includes all rows from both DataFrames, with NaNs for missing values in non-matching columns.

Merging on Multiple Columns

To merge DataFrames on multiple columns, you provide a list of column names to the on parameter in the merge() function. For example:

customer_df = pd.DataFrame({'CustomerID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie']})
order_df = pd.DataFrame({'OrderID': [100, 101, 102], 'CustomerID': [1, 1, 2], 'Product': ['A', 'B', 'C']})

merged_df = customer_df.merge(order_df, on=['CustomerID'])
print(merged_df)

This code will create a new DataFrame merged_df that combines data from customer_df and order_df based on the matching values in the CustomerID column.

Key Points

• The merge() function provides a powerful way to combine data from multiple DataFrames in pandas.
• When merging on multiple columns, ensure that the data types in the corresponding columns are compatible for merging.
• Explore different join types ('inner', 'left', 'right', 'outer') to select the appropriate outcome based on your data analysis needs.

I hope this explanation is helpful!

Example 1: Merging on Multiple Columns (Inner Join)

import pandas as pd

# Create sample DataFrames
customer_df = pd.DataFrame({'CustomerID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie'], 'City': ['New York', 'Los Angeles', 'Chicago']})
order_df = pd.DataFrame({'OrderID': [100, 101, 102, 103], 'CustomerID': [1, 1, 2, 4], 'Product': ['A', 'B', 'C', 'D'], 'Status': ['Shipped', 'Pending', 'Shipped', 'Cancelled']})

# Inner join on 'CustomerID' and 'City' (only matching rows are included)
merged_df = customer_df.merge(order_df, on=['CustomerID', 'City'], how='inner')
print(merged_df)

This code will output a DataFrame containing only rows where both CustomerID and City values match in both DataFrames.

# Modify order_df slightly
order_df = pd.DataFrame({'Order ID': [100, 101, 102, 103], 'Customer ID': [1, 1, 2, 4], 'Product': ['A', 'B', 'C', 'D'], 'Status': ['Shipped', 'Pending', 'Shipped', 'Cancelled']})

# Left join on 'CustomerID' from customer_df and 'Customer ID' from order_df (all rows from customer_df kept)
merged_df = customer_df.merge(order_df, left_on='CustomerID', right_on='Customer ID', how='left')
print(merged_df)

This example demonstrates merging on columns with different names in each DataFrame using left_on and right_on parameters. Rows from customer_df are kept even if there's no matching order in order_df (filled with NaNs).

Example 3: Merging with Outer Join

# Merge all rows from both DataFrames (including mismatched rows with NaNs)
merged_df = customer_df.merge(order_df, on='CustomerID', how='outer')
print(merged_df)

This code performs an outer join, keeping all rows from both DataFrames and filling missing values with NaNs.

Concatenation (Stacking DataFrames):

• Use pd.concat() to stack DataFrames vertically (along the row axis) or horizontally (along the column axis).
• This is useful when DataFrames have compatible column structures but don't require merging based on specific columns.

Example (Vertical Concatenation):

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})

combined_df = pd.concat([df1, df2])
print(combined_df)

Dictionary Comprehension (Manual Merging):

• If you have a small number of DataFrames and a clear understanding of how to combine them, you can use dictionary comprehension to iterate through rows and create a new dictionary containing combined data.
• This approach is less efficient for large DataFrames.

Example:

df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [1, 3], 'Age': [25, 30]})

combined_dict = {row['ID']: {'Name': row['Name'], 'Age': df2.loc[df2['ID'] == row['ID'], 'Age'].values[0] if len(df2.loc[df2['ID'] == row['ID']]) > 0 else None} for _, row in df1.iterrows()}
combined_df = pd.DataFrame(combined_dict).transpose()
print(combined_df)

join() Function (Less Common):

• The join() function can be used for merging based on index labels.
• It performs a left join by default, but you can specify other join types using the how parameter.
• merge() is generally preferred due to its flexibility in handling different merge criteria.

Consider using these alternatives when:

• Concatenation: You simply want to stack DataFrames without merging based on specific columns.
• Dictionary Comprehension: You have a small number of DataFrames and a clear understanding of how to combine them (less efficient for large datasets).
• join() Function: You need to merge based on index labels (less common use case).

Remember that merge() is the most versatile and efficient option for most DataFrame merging scenarios in pandas.