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How to Merge Pandas DataFrames with Different Column Names and Avoid Duplicates

In this blog, we will learn about a common challenge faced by data scientists and software engineers: merging data from various sources. While Pandas is a robust tool for data manipulation and analysis, merging DataFrames becomes challenging, particularly when dealing with columns having distinct names. The focus of this article is to delve into techniques for merging Pandas DataFrames with disparate column names, while also addressing the issue of avoiding duplicates.
By Saturn Cloud | | Miscellaneous | Updated:

As a data scientist or software engineer, one of the most common tasks you’ll encounter is merging data from multiple sources. Pandas is a powerful tool for data manipulation and analysis, but merging DataFrames can be tricky, especially when the columns have different names. In this article, we will explore how to merge Pandas DataFrames with different column names and avoid duplicates.

Table of Contents

  1. What is Pandas Merge?
  2. Merging DataFrames with Different Column Names
  3. Avoiding Duplicates in Merged DataFrames
  4. Common Errors and Solutions
  5. Conclusion

What is Pandas Merge?

Pandas merge is a function that combines two or more DataFrames into a single DataFrame. The merge function compares the values in the specified columns of the two DataFrames and returns a new DataFrame with the matched values. The resulting DataFrame has all the columns from both DataFrames.

Merging DataFrames with Different Column Names

When merging DataFrames, it’s essential to ensure that the columns used for merging have the same name in both DataFrames. However, in some cases, the columns may have different names. In such cases, we can use the left_on and right_on parameters to specify the columns' names in each DataFrame.

Suppose we have two DataFrames, df1 and df2, with columns id and name in df1 and columns customer_id and amount in df2.

import pandas as pd

# Sample DataFrames
data1 = {'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie']}
data2 = {'customer_id': [2, 3, 4], 'amount': [50, 30, 20]}

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

To merge these DataFrames, we can use the following code:

merged_df = pd.merge(df1, df2, left_on='id', right_on='customer_id', how='inner')
print(merged_df)

Output:


   id     name  customer_id  amount
0   2      Bob            2      50
1   3  Charlie            3      30

In the above code, we use the left_on parameter to specify the column name in df1, and the right_on parameter to specify the column name in df2. The resulting merged_df DataFrame will have columns id, name, customer_id, and amount.

Avoiding Duplicates in Merged DataFrames

One common issue when merging DataFrames is the presence of duplicate values. Duplicate values can skew the results and lead to inaccurate analysis. To avoid duplicates, we can use the drop_duplicates function to drop any duplicate rows in the merged DataFrame.

merged_df.drop_duplicates(subset=['id'], keep='first', inplace=True)

In the above code, we use the subset parameter to specify the column(s) to consider when dropping duplicates. In this case, we use the id column. The keep parameter specifies which duplicate(s) to keep. In this case, we keep the first occurrence of each duplicate value. The inplace parameter specifies whether to modify the original DataFrame or return a new DataFrame.

Common Errors and Solutions

Error: Column name not found.

Solution: Double-check column names and ensure they exist in both DataFrames.

Error: Unexpected duplicate columns.

Solution: Use the suffixes parameter to append custom suffixes and disambiguate the columns.

Conclusion

Merging Pandas DataFrames with different column names is a common task in data analysis and manipulation. In this article, we have explored how to merge DataFrames with different column names and avoid duplicates. By using the left_on and right_on parameters, we can merge DataFrames with different column names. Additionally, by using the drop_duplicates function, we can avoid duplicates in the merged DataFrame. Pandas is a powerful tool for data manipulation and analysis, and mastering its merge function is essential for any data scientist or software engineer. With the tips and techniques outlined in this article, you should be able to merge DataFrames with different column names and avoid duplicates with ease.

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