Filling NaN Values in a DataFrame Based on Grouped Data Using Python Pandas

Understanding the Problem: Filling NaN Values in a DataFrame based on Grouped Data

As data analysts and scientists, we often encounter situations where we need to fill missing values (NaN) in a dataset based on specific conditions. In this article, we will explore how to achieve this using Python Pandas.

Background and Context

Python Pandas is a powerful library used for data manipulation and analysis. It provides an efficient way to handle structured data, including tabular data such as spreadsheets and SQL tables. One of the key features of Pandas is its ability to group data by specific columns and perform operations on each group.

In this article, we will focus on how to use Pandas’ grouping capabilities to fill NaN values in a DataFrame based on a group of values from two other columns.

The Problem Statement

We have a DataFrame with four columns: id, col1, col2, and col3. Some of the values in these columns are missing, represented as NaN. We need to group the data by the id and col_date columns and fill the NaN values in the other three columns using the maximum value from each group.

The Solution

To solve this problem, we will use a combination of Pandas’ grouping and aggregation functions. Specifically, we will use the groupby_transform function to broadcast the maximum value to all rows of the group and then use the fillna function to fill missing values.

Here’s how you can do it:

import pandas as pd

# Create a sample DataFrame with NaN values
data = [
    (1, None, None, None, '2023-01-10'),
    (1, None, None, None, '2023-01-10'),
    (1, 9, 0, 0.55, '2023-01-10'),
    (2, None, None, None, '2023-11-22'),
    (2, 88, 1, 0.68, '2023-11-22')
]

df = pd.DataFrame(data, columns=['id', 'col1', 'col2', 'col3', 'col_date'])

# Convert the 'col_date' column to datetime format
df['col_date'] = pd.to_datetime(df['col_date'])

Grouping and Filling NaN Values

Next, we will use the groupby_transform function to broadcast the maximum value to all rows of each group. We will then use the fillna function to fill missing values.

# Group by 'id' and 'col_date', and fill NaN values in other columns with the maximum value from each group
df.fillna(df.groupby(['id', 'col_date']).transform(lambda x: x.max()), inplace=True)

print(df)

The Result

When we run this code, we get the following output:

   id  col1  col2  col3   col_date
0   1   9.0   0.0  0.55 2023-01-10
1   1   9.0   0.0  0.55 2023-01-10
2   1   9.0   0.0  0.55 2023-01-10
3   2  88.0   1.0  0.68 2023-11-22
4   2  88.0   1.0  0.68 2023-11-22

Conclusion

In this article, we explored how to fill NaN values in a DataFrame based on a group of values from two other columns using Python Pandas. We used the groupby_transform function to broadcast the maximum value to all rows of each group and then used the fillna function to fill missing values.

This technique is useful when working with data that has missing values and requires imputation based on specific conditions.

Last modified on 2025-01-27