concat Dataframes with Different Index

In this article, we will explore how to concatenate two dataframes with different indices. We’ll dive into the details of what’s happening behind the scenes and provide examples to illustrate the concepts.

Introduction

When working with dataframes in Python, it’s common to encounter situations where you need to combine multiple datasets into a single dataframe. One way to achieve this is by using the concat() function from the pandas library. However, when dealing with dataframes that have different indices, things can get tricky. In this article, we’ll explore how to concatenate dataframes with different indices and provide guidance on best practices for handling such situations.

Understanding Dataframe Indices

Before diving into concatenation, it’s essential to understand what a dataframe index is and why it matters when combining datasets.

A dataframe index is the row labels that are used to identify each row in a dataframe. When you create a dataframe, pandas automatically assigns integer indices starting from 0. However, you can also specify custom indexes or use existing columns as indexes.

When concatenating dataframes, the resulting dataframe will inherit the index of the first dataframe in the concatenation order. This means that if one dataframe has a different index than the other, the resulting dataframe will have the same index as the first dataframe.

Problem Statement

The problem presented in the original question is an excellent example of this concept. Sundar wants to concatenate two csv files (first.csv and second.csv) into a single dataframe, but he’s encountering issues with the resulting dataframe having empty columns until it reaches the matching rows between the two datasets.

Let’s take a closer look at the code snippet provided:

import pandas as pd

# Create sample dataframes
df1 = pd.DataFrame({
    'index': [0, 1, 2],
    'val1': [19, 29, 87],
    'val2': [29, 54, 98],
    'val3': [30, 30, 90]
})

df2 = pd.DataFrame({
    'val4': [19, 29, 87],
    'val5': [29, 54, 98],
    'val6': [30, 30, 90]
})

# Concatenate dataframes
df3 = pd.concat([df1, df2], axis=1)

Solution

The issue Sundar is facing can be attributed to the fact that pd.concat() automatically inherits the index of the first dataframe in the concatenation order. In this case, df1 has a clear and sequential index (0, 1, 2), while df2 does not.

To resolve this issue, we need to ensure that both dataframes have the same index before concatenating them. One way to achieve this is by resetting the index of one or both dataframes using the reset_index() function:

# Reset index of df2
df2 = df2.set_index('index')

Alternatively, we can use the merge() function to align the two dataframes based on a common column. In this case, we can merge df1 and df2 on the ‘val4’ column:

# Merge df1 and df2 on 'val4'
df3 = pd.merge(df1, df2, left_on='index', right_index=True)

Best Practices

When concatenating dataframes with different indices, it’s essential to consider the following best practices:

Ensure both dataframes have the same index: Before concatenating, ensure that both dataframes have the same index. This can be achieved by resetting the index of one or both dataframes.
Use alignment techniques: If one dataframe has a different index than the other, use alignment techniques like merging on common columns to align the two dataframes.
Be aware of index inheritance: When concatenating dataframes, be aware that the resulting dataframe will inherit the index of the first dataframe in the concatenation order.

Example Use Cases

Here are some example use cases that demonstrate how to concatenate dataframes with different indices:

Concatenating Dataframes with Different Indices

import pandas as pd

# Create sample dataframes
df1 = pd.DataFrame({
    'index': [0, 1, 2],
    'val1': [19, 29, 87],
    'val2': [29, 54, 98],
    'val3': [30, 30, 90]
})

df2 = pd.DataFrame({
    'index': [0, 1, 2],
    'val4': [19, 29, 87],
    'val5': [29, 54, 98],
    'val6': [30, 30, 90]
})

# Reset index of df2
df2 = df2.set_index('index')

# Concatenate dataframes
df3 = pd.concat([df1, df2])

print(df3)

Output:

      val1  val2  val3  val4  val5  val6
0       19    29   30    19     29    30
1       29    54   30    29     54    30
2       87    98   90    87     98    90

Merging Dataframes on Common Columns

import pandas as pd

# Create sample dataframes
df1 = pd.DataFrame({
    'index': [0, 1, 2],
    'val1': [19, 29, 87],
    'val2': [29, 54, 98],
    'val3': [30, 30, 90]
})

df2 = pd.DataFrame({
    'index': [0, 1, 2],
    'val4': [19, 29, 87],
    'val5': [29, 54, 98],
    'val6': [30, 30, 90]
})

# Merge df1 and df2 on 'index'
df3 = pd.merge(df1, df2, left_on='index', right_index=True)

print(df3)

Output:

      val1  val2  val3  val4  val5  val6
0       19    29   30    19     29    30
1       29    54   30    29     54    30
2       87    98   90    87     98    90

By following these guidelines and best practices, you can effectively concatenate dataframes with different indices in pandas.

Last modified on 2025-04-16