Merging Pandas DataFrames with Common Columns

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Merging pandas dataframes can be a challenging task, especially when dealing with multiple dataframes that share common columns. In this article, we will explore the different ways to merge two or more pandas dataframes which have 4 columns in common.

Problem Statement

Suppose we have three datasets, A, B, and C, which are sub-datasets of a larger dataset (df_A). The datasets are of different lengths, with each dataset having 5 columns: a, b, c, d, and e. Columns a, b, c, and d do not have any repetitions, while column e is different for each dataset. Each dataset has a different index from the others.

Our goal is to merge these dataframes together without losing any row and pairing them correctly without using the index.

Solution Overview

To solve this problem, we will use the pandas library’s built-in merge function. We will explicitly state the joining columns and use the suffix parameter to rename the overlapping columns.

Example Data

Let’s create example dataframes A, B, and C using pandas:

A = pd.DataFrame({'a': ['x', 'x', 'x', 'y', 'x'],
                  'b': ['y', 'y', 'z', 'z', 'z'],
                  'c': [0, 1, 0, 0, 0],
                  'd': [1, 1, 0, 1, 1],
                  'e': [0.99, 0.43, 0.9, 0.11, 0.78]})

B = pd.DataFrame({'a': ['x', 'x', 'y'],
                  'b': ['y', 'z', 'z'],
                  'c': [0, 0, 0],
                  'd': [1, 0, 1],
                  'e': [0.12, 0.01, 0.45]})

C = pd.DataFrame({'a': ['x', 'x', 'x'],
                  'b': ['y', 'z', 'z'],
                  'c': [1, 0, 0],
                  'd': [1, 0, 1],
                  'e': [0.06, 0.65, 0.2]})

Merging Dataframes

We will merge dataframes A and B using the merge function:

e = a.merge(b, how='left', left_on=['a', 'b', 'c', 'd'], right_on=['a','b','c', 'd'], suffixes=['_A', '_B'])

This will create a new dataframe e that contains the merged data from A and B.

Next, we will merge the result with dataframe C:

e = e.merge(c, how='left', left_on=['a', 'b', 'c', 'd'], right_on=['a','b','c', 'd'])

Renaming Overlapping Columns

To avoid overlapping column names, we will rename the e column of dataframe C using the rename function:

e = e.rename(columns={'e': 'e_C'})

Final Result

The final result is a merged dataframe that contains all columns and rows from the original dataframes. The code for this solution is as follows:

import pandas as pd

a = pd.read_csv('a.csv')
b = pd.read_csv('b.csv')
c = pd.read_csv('c.csv')

e = a.merge(b, how='left', left_on=['a', 'b', 'c', 'd'], right_on=['a','b','c', 'd'], suffixes=['_A', '_B'])
e = e.merge(c, how='left', left_on=['a', 'b', 'c', 'd'], right_on=['a','b','c', 'd'])

e = e.rename(columns={'e': 'e_C'})

print(e.head())

Conclusion

Merging pandas dataframes can be a challenging task, but using the built-in merge function and specifying joining columns and suffixes can make it easier. By explicitly merging the dataframes together and renaming overlapping column names, we can create a final merged dataframe that contains all columns and rows from the original dataframes.

Last modified on 2024-02-24