keyerror in Pandas merge

Handling KeyError in Pandas Merge

The KeyError in Pandas occurs when trying to merge two DataFrames on a column that does not exist in one of the DataFrames. It typically happens when there is a mismatch between the column names used in the on parameter of the merge() function and the actual columns in the DataFrames. In this article, we’ll explain how to handle the KeyError and avoid common issues when merging DataFrames.

What is a KeyError in Pandas Merge?

A KeyError during a merge operation happens when Pandas cannot find the column(s) you’re trying to merge on in either of the DataFrames. This can occur if you misspell the column name, the column doesn’t exist in one of the DataFrames, or you’re trying to merge on a column that has a different name in each DataFrame.

For example, if you try to merge two DataFrames on a column that doesn’t exist, Pandas will raise a KeyError.

Example of KeyError During Merge

Consider the following example where the column ‘Age’ is missing in one of the DataFrames, resulting in a KeyError:

import pandas as pd

# Sample DataFrames
df1 = pd.DataFrame({'Name': ['John', 'Alice', 'Bob'], 'Age': [25, 30, 22]})
df2 = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Salary': [55000, 40000, 48000]})

# Attempting to merge on a column that exists in only one DataFrame
merged_df = pd.merge(df1, df2, on='Age')

Output:

KeyError: 'Age'

In this case, the KeyError occurs because the ‘Age’ column is not present in df2, and Pandas cannot find the column to merge on in df2.

How to Fix KeyError in Pandas Merge

To fix the KeyError, ensure the column you’re merging on exists in both DataFrames. You can also specify different columns to merge on using the left_on and right_on parameters if the column names differ between the DataFrames.

Fixing the Error by Merging on Existing Columns

In this example, we merge on the ‘Name’ column, which exists in both DataFrames:

# Correctly merging on 'Name'
merged_df = pd.merge(df1, df2, on='Name')
print(merged_df)

Output:

     Name  Age  Salary
0   Alice   30   55000
1     Bob   22   40000

Using Different Columns with left_on and right_on

If the columns have different names in each DataFrame, you can specify which column to use in each DataFrame for the merge using the left_on and right_on parameters.

df1 = pd.DataFrame({'Name': ['John', 'Alice', 'Bob'], 'Age': [25, 30, 22]})
df2 = pd.DataFrame({'Employee': ['Alice', 'Bob', 'Charlie'], 'Salary': [55000, 40000, 48000]})

# Merging with different column names using left_on and right_on
merged_df = pd.merge(df1, df2, left_on='Name', right_on='Employee')
print(merged_df)

Output:

     Name  Age  Employee  Salary
0   Alice   30    Alice   55000
1     Bob   22      Bob   40000

Summary

A KeyError in Pandas merge operation occurs when the specified column for merging does not exist in one or both DataFrames. To resolve this, make sure the column exists in both DataFrames or use the left_on and right_on parameters to merge on different columns. Always verify column names before merging to prevent the KeyError.