Filter pandas DataFrame Based on Values in Multiple Columns

In this article, we will explore a common problem when working with pandas DataFrames: filtering rows based on values in multiple columns. Specifically, we’ll examine how to filter out rows where the values in certain columns are either ‘7’ or ‘N’ (or NaN). We’ll discuss various approaches and provide code examples to illustrate each solution.

Problem Description

You have a large DataFrame with 472 columns, but only 99 of them are relevant for filtering. The other columns contain noise data that you want to exclude from your analysis. However, some of the ‘dxpoa’ columns can have values like ‘7’, ‘N’, or NaN, which you want to filter out.

Solution Overview

To solve this problem, we’ll use a combination of pandas functions and logical operations. The approach involves:

Identifying the relevant ‘dxpoa’ columns.
Filtering rows based on the presence of ‘7’ or ‘N’ in these columns.
Excluding rows that contain both ‘7’ and ‘N’ (or NaN) to ensure strict filtering.

Step 1: Import Necessary Libraries

import pandas as pd
import numpy as np

Step 2: Generate Sample Data

We’ll create a sample DataFrame with 15 rows and 7 columns, including the relevant ‘dxpoa’ columns. The other columns will contain random data.

rows = 15

s = [''] + list('YWE17N0AZX')
df = pd.DataFrame(np.random.choice(s, size=(rows, 7)), columns=list('abc') + ['dxpoa1', 'dxpoa2', 'dxpoa3', 'dxpoa4'])

cols = df.filter(like='dxpoa').columns

Step 3: Filter Rows Based on Presence of ‘7’ or ‘N’

df_filtered_7_or_n = df.loc[df[cols].isin(['7','N']).any(axis=1)]
print(df_filtered_7_or_n)

This step filters rows where any of the ‘dxpoa’ columns contain ‘7’ or ‘N’.

Step 4: Exclude Rows with Both ‘7’ and ‘N’

To ensure strict filtering, we’ll exclude rows that contain both ‘7’ and ‘N’. We can do this by using the all function instead of any.

df_filtered_strict = df.loc[~df[cols].isin(['7','N']).all(axis=1)]
print(df_filtered_strict)

Step 5: Example Use Cases

This approach can be applied to various use cases, such as:

Removing rows with missing values in specific columns.
Excluding rows that contain outliers or anomalies in certain columns.

By understanding how to filter DataFrames based on multiple columns, you’ll become more proficient in working with pandas and handling complex data analysis tasks.

Conclusion

In this article, we’ve explored a common problem when working with pandas DataFrames: filtering rows based on values in multiple columns. We’ve discussed various approaches and provided code examples to illustrate each solution. By applying these techniques, you’ll be better equipped to handle complex data analysis tasks and extract meaningful insights from your datasets.

Last modified on 2025-01-21