Merging Two Dataframes with Different Number of Rows Using Pandas

Merging two dataframes with different number of rows is a common task in data analysis and manipulation. In this article, we will explore ways to achieve this using the popular Python library pandas.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types). The DataFrame is the primary data structure used in pandas, and it offers various methods for filtering, sorting, grouping, merging, reshaping, and pivoting data.

In this article, we will focus on merging two DataFrames with different number of rows. We will explore different approaches to achieve this using various pandas functions and methods.

Setting the Stage

To demonstrate the concepts discussed in this article, let’s start by creating a sample dataset. We have two DataFrames: df and df1.

import pandas as pd

# Create the first DataFrame (primary)
data = {
    'period': ['2000-01-01', '2000-04-01', '2000-07-01', '2000-10-01'],
    'value': [100, 200, 300, 400]
}
df = pd.DataFrame(data)

# Create the second DataFrame (update)
data1 = {
    'period': ['2000-07-01', '2000-10-01', '2001-01-01'],
    'value': [350, 450, 550]
}
df1 = pd.DataFrame(data1)

Approach 1: Using combine_first

One way to merge the two DataFrames is by using the combine_first method. This method merges the two DataFrames based on the index and fills missing values with the values from the first DataFrame.

# Set the index of both DataFrames
df.set_index('period', inplace=True)
df1.set_index('period', inplace=True)

# Combine the two DataFrames using combine_first
df = df1.combine_first(df)

This approach works well when the dtype of some index is object. In this case, we need to convert both indexes to datetime objects using the to_datetime method.

# Convert the indexes to datetime objects
df.index = pd.to_datetime(df.index)
df1.index = pd.to_datetime(df1.index)

# Combine the two DataFrames using combine_first
df = df1.combine_first(df)

Approach 2: Using intersection and combine_first

Another way to merge the two DataFrames is by filtering the second DataFrame to only include rows that exist in both DataFrames, and then combining them.

# Set the index of both DataFrames
df.set_index('period', inplace=True)
df1.set_index('period', inplace=True)

# Filter the second DataFrame using intersection
df = df1.loc[df1.index.intersection(df.index)].combine_first(df)

This approach works well when one or both indexes have a non-numeric dtype.

Approach 3: Using numpy.setdiff1d and concat

Another way to merge the two DataFrames is by using np.setdiff1d to get the unique rows in each DataFrame, concatenating them, and then combining the results.

# Set the index of both DataFrames
df.set_index('period', inplace=True)
df1.set_index('period', inplace=True)

# Get the unique rows in each DataFrame using np.setdiff1d
df_unique = df.loc[np.setdiff1d(df.index, df1.index)]
df1_unique = df1.loc[np.setdiff1d(df1.index, df.index)]

# Concatenate the DataFrames and combine them
df = pd.concat([df_unique, df1_unique]).combine_first()

Conclusion

Merging two DataFrames with different number of rows is a common task in data analysis and manipulation. In this article, we explored three approaches to achieve this using pandas functions and methods.

We demonstrated how to use combine_first, intersection and combine_first, and np.setdiff1d and concat. These approaches can be used depending on the specific requirements of your project.

By understanding these different approaches, you can effectively merge two DataFrames with different number of rows in pandas.

Last modified on 2023-08-14