Fill NaN Information with Value in Same DataFrame

As data scientists, we often encounter missing values in our datasets, which can be a challenge to handle. In this article, we will explore different methods for filling NaN information in the same dataframe.

Introduction

Missing values in a dataset can lead to biased results and incorrect conclusions. There are several methods to fill missing values, including mean, median, mode, and imputation using machine learning algorithms. In this article, we will focus on the GroupBy function and custom functions to fill NaN information in the same dataframe.

Problem Statement

The input dataframe has NaN values in some columns. We want to fill these NaN values with a specific value while keeping the rest of the data intact. The code provided shows an example of how to use the GroupBy function to achieve this.

import pandas as pd

# Create a sample dataframe with NaN values
df = pd.DataFrame({
    '0': ['x', 'x', 'y', 'y', 'x', 'x'],
    '1': ['x', 'y', 'y', 'z', 'x', 'y'],
    '2': [10, 20, 4, 5, 1, 1],
    '3': [5, 9, 4, 2, np.nan, 9],
    '4': [7, 4, 4, 7, 7, 4],
    '5': [4, 5, 4, 4, 4, 5],
    '6': [9, 10, 4, 0, 9, 10]
})

# Print the original dataframe
print(df)

Solution Using GroupBy

One way to fill NaN values in the same dataframe is by using the GroupBy function. This approach involves grouping the data by unique combinations of columns and filling NaN values with the first non-NaN value in each group.

# Use GroupBy to fill NaN values
df1 = df.groupby(['0', '1']).apply(lambda x: x.dropna().iloc[0]).reset_index()

# Print the result
print(df1)

However, this approach can lead to data loss if there are multiple rows with missing values in a group.

Custom Function

Another way to fill NaN values is by using a custom function. This approach allows us to specify the value that should be filled and handle the NaN values accordingly.

import pandas as pd
import numpy as np

# Create a sample dataframe with NaN values
df = pd.DataFrame({
    '0': ['x', 'x', 'y', 'y', 'x', 'x'],
    '1': ['x', 'y', 'y', 'z', 'x', 'y'],
    '2': [10, 20, 4, 5, 1, 1],
    '3': [5, 9, 4, 2, np.nan, 9],
    '4': [7, 4, 4, 7, 7, 4],
    '5': [4, 5, 4, 4, 4, 5],
    '6': [9, 10, 4, 0, 9, 10]
})

# Define a custom function to fill NaN values
def f(x):
    df1 = pd.DataFrame({y: pd.Series(x[y].dropna().values) for y in x})
    return (df1)

# Use the custom function to fill NaN values
df2 = df.set_index(['0', '1']).groupby(['0', '1']).apply(f).reset_index(level=2, drop=True).reset_index()

# Print the result
print(df2)

In this example, the custom function f(x) creates a new dataframe with only non-NaN values in each group. The rest of the data is dropped.

Conclusion

Filling NaN information in the same dataframe can be achieved using various methods, including GroupBy and custom functions. While GroupBy provides an efficient way to handle missing values, it may lead to data loss if there are multiple rows with missing values in a group. Custom functions offer more flexibility but require manual implementation of the logic for handling NaN values. By understanding the strengths and weaknesses of each approach, we can choose the best method for our specific use case.

Last modified on 2024-01-16