Replacing Missing Values in Pandas DataFrames: A Step-by-Step Guide

Data Manipulation with Pandas: Replacing Missing Values in One DataFrame with Entries from Another

Python’s pandas library provides an efficient way to manipulate and analyze data, including handling missing values. In this article, we will explore how to replace missing entries of a column in one DataFrame with entries from another DataFrame using pandas.

Background and Context

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).

In this article, we will use the pd.merge function to perform a left merge on two DataFrames, df1 and df2, based on their common column A. We will then use the Series.fillna method to replace missing values in the merged DataFrame’s column b.

Understanding DataFrames and Series

Before we dive into replacing missing values, let’s understand the basics of DataFrames and Series:

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It consists of rows and columns.
A Series is a 1-dimensional labeled array. It can be thought of as a column in a DataFrame.

Here is an example of creating two simple DataFrames using pandas:

import pandas as pd

# Create the first DataFrame
df1 = pd.DataFrame({
    'A': [1,2,3,4,5,6,7,8],
    'b': [101,123,np.nan,678,np.nan,672,np.nan,786],
    'C': ['ABC', 'DER', 'ERC','DFE','HJI','JKL','SDH',np.Nan]
})

# Create the second DataFrame
df2 = pd.DataFrame({
    'A': [3,7],
    'B': [563,785]
})

Performing a Left Merge

To perform a left merge on df1 and df2, we use the pd.merge function with the how='left' parameter. This type of merge returns all rows from df1 that are also present in df2.

Here is how you can perform the left merge:

# Perform a left merge on df1 and df2 based on column 'A'
merged_df = pd.merge(df1, df2, on='A', how='left')

Replacing Missing Values

After performing the left merge, we want to replace missing values in df1’s column b with entries from df2’s column B. We can do this using the Series.fillna method.

Here is how you can replace missing values:

# Replace missing values in 'b' of merged_df with 'B' of df2
merged_df['b'] = merged_df['b'].fillna(merged_df.pop('B'))

Example Usage

Let’s take a look at the entire code snippet that performs this operation:

import pandas as pd
import numpy as np

# Create the first DataFrame
df1 = pd.DataFrame({
    'A': [1,2,3,4,5,6,7,8],
    'b': [101,123,np.nan,678,np.nan,672,np.nan,786],
    'C': ['ABC', 'DER', 'ERC','DFE','HJI','JKL','SDH',np.Nan]
})

# Create the second DataFrame
df2 = pd.DataFrame({
    'A': [3,7],
    'B': [563,785]
})

# Perform a left merge on df1 and df2 based on column 'A'
merged_df = pd.merge(df1, df2, on='A', how='left')

# Replace missing values in 'b' of merged_df with 'B' of df2
merged_df['b'] = merged_df['b'].fillna(merged_df.pop('B'))

print(merged_df)

Conclusion

In this article, we explored how to replace missing entries of a column in one DataFrame with entries from another DataFrame using pandas. We learned about the pd.merge function and the Series.fillna method.

By following these steps, you can efficiently manipulate your data in Python.

Please let me know if there’s anything else I can help you with!

Last modified on 2024-12-14