Merging Dataframes with Pandas in Python

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In this article, we’ll explore how to add a new column to a dataframe based on the values of another dataframe. We’ll use the pandas library in Python to accomplish this task.

Introduction to DataFrames and Merge Operations

A DataFrame is a two-dimensional data structure consisting of rows and columns, similar to an Excel spreadsheet or a table in a relational database. In pandas, DataFrames are used to store and manipulate data.

The merge function in pandas allows us to combine two DataFrames based on common columns between them. However, when using tuples as values for certain columns, the merge operation can be tricky.

Creating the Initial Dataframes

Let’s start by creating our initial DataFrames. We have two DataFrames: df1 and df2.

import pandas as pd

data1 = [[1,2,2], [1,2,5], [3,4,5], [1,2,7], [3,4,3]]
data2 = [[1,2,0], [2,1,3], [3,4,10]]

df1 = pd.DataFrame(data1, columns=['A', 'B', 'D'])
df2 = pd.DataFrame(data2, columns=['A', 'B', 'C'])

The Problem with Tuples

When we create the DataFrames using tuples as values for certain columns, pandas stores them as a single column instead of separate columns. This is why our initial merge operation fails.

result = pd.merge(df1, df2, on=['A','B'])

The Solution: Using List Comprehension to Create the New Column

To fix this issue, we can use list comprehension to create a new column that combines values from both DataFrames. Here’s an example:

df['tuple'] = list(zip(df.A, df.B, df.C, df.D))

This line of code uses the zip function to combine values from columns ‘A’, ‘B’, and ‘C’ from DataFrame df2 with columns ‘A’, ‘B’, and ‘D’ from DataFrame df1. The resulting tuples are then stored in a new column called ’tuple’.

Understanding the Code

Let’s break down what happens in this code:

• We use zip(df.A, df.B, df.C, df.D) to combine values from each pair of columns.
• We store these combined values in a tuple using list().
• The resulting tuples are stored in the new ’tuple’ column.

Example Output

Here’s what our final DataFrame looks like:

    A   B   D   C   tuple
0   1   2   2   0   (1, 2, 0, 2)
1   1   2   5   0   (1, 2, 0, 5)
2   1   2   7   0   (1, 2, 0, 7)
3   3   4   5   10  (3, 4, 10, 5)
4   3   4   3   10  (3, 4, 10, 3)

Conclusion

In this article, we explored how to add a new column to a DataFrame based on the values of another DataFrame using pandas in Python. We discussed the issue with tuples and demonstrated a solution that uses list comprehension to create the new column.

By understanding how data is stored and manipulated within DataFrames, you can effectively merge DataFrames while handling complex operations like adding new columns.

Last modified on 2025-03-05