5 Best Ways to Filter Rows from a Pandas DataFrame Based on Sum

💡 Problem Formulation: The task is to filter rows from a Pandas DataFrame based on a condition applied to the sum of certain values within these rows. For instance, imagine we have a DataFrame with various numerical columns, and we want only those rows where the sum of specific columns exceeds a certain threshold value. Our desired output would be a subset DataFrame that fulfills this sum condition.

Method 1: Using Boolean Indexing with sum(axis=1)

Boolean indexing combined with the sum(axis=1) function in Pandas allows for filtering based on the sum across a row. This method is perfect for scenarios where we want to apply a condition to the row-wise sum of certain columns. By specifying axis=1, the sum is calculated horizontally across the row.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Filter rows where the sum of 'A' and 'B' is greater than 5
filtered_df = df[df[['A', 'B']].sum(axis=1) > 5]

print(filtered_df)

Output:

   A  B  C
1  2  5  8
2  3  6  9

This code snippet creates a DataFrame and filters for rows where the sum of columns ‘A’ and ‘B’ exceeds 5. The df[['A', 'B']].sum(axis=1) computes the sum along the rows for the specified columns and returns a boolean Series. Rows corresponding to a ‘True’ value in this Series are selected into the new DataFrame called filtered_df.

Method 2: Using query() Method

The query() method in Pandas offers an SQL-like querying of DataFrame rows. By using string expressions, it can easily be used to filter rows based on the sum of specific columns being above a certain value. The query() method is more readable compared to complex boolean indexing.

Here’s an example:

# Using the same sample DataFrame as in Method 1.

# Filter rows with the query method
filtered_df = df.query('A + B > 5')

print(filtered_df)

Output:

   A  B  C
1  2  5  8
2  3  6  9

The query() method is used to filter the DataFrame. The condition ‘A + B > 5’ is passed as a string, which feels similar to an SQL query. This method returns a DataFrame where only the rows meeting the condition are returned.

Method 3: Using a Custom Function with apply()

In cases where you have more complex requirements or want to reuse logic, you can define a customized function and use apply() with axis=1 to filter rows based on the sum. This method enhances reusability and encapsulates logic within a function.

Here’s an example:

# Using the same sample DataFrame as in Method 1.

# Define custom filter function
def custom_filter(row, threshold):
    return row.sum() > threshold

# Apply custom filter
filtered_df = df[df.apply(custom_filter, axis=1, threshold=9)]

print(filtered_df)

Output:

   A  B  C
2  3  6  9

The custom function custom_filter takes a row of the DataFrame and a threshold as arguments, then applies a condition to the row’s sum. The apply() method uses this function row-wise on the DataFrame, and the resulting boolean Series is used to filter out the rows.

Method 4: Filtering with a Lambda Function

For a concise and often one-off row filtering based on a sum, a lambda function can be employed directly within the DataFrame indexing. Lambda functions are anonymous functions defined in line, offering both brevity and expressiveness for simple operations.

Here’s an example:

# Using the same sample DataFrame as in Method 1.

# Filter using a lambda function
filtered_df = df[df.apply(lambda x: sum(x[['A', 'B']]) > 5, axis=1)]

print(filtered_df)

Output:

   A  B  C
1  2  5  8
2  3  6  9

The lambda function within the apply() method here is used to calculate the sum of columns ‘A’ and ‘B’ for each row. The anonymous function makes this code compact and effectively filters the DataFrame.

Bonus One-Liner Method 5: List Comprehension

Python’s list comprehension can be utilized for filtering DataFrame rows in a single line of code when combined with Pandas indexing. It’s a Pythonic and efficient way to execute operations and filter data.

Here’s an example:

# Using the same sample DataFrame as in Method 1.

# Filter using list comprehension
filtered_df = df[[sum(row) > 9 for _, row in df.iterrows()]]

print(filtered_df)

Output:

   A  B  C
2  3  6  9

In this compact one-liner, we use a list comprehension to iterate through the rows of the DataFrame and calculate the sum of each row. True or False values are then used to filter rows directly through Pandas indexing.

Summary/Discussion

Method 1: Boolean Indexing with sum(axis=1). Strengths: Easy to read and straightforward. Weaknesses: Limited to simple sum operations across specified columns.Method 2: query() Method. Strengths: Readable and neat, SQL-like querying. Weaknesses: Can become less efficient with large datasets and complex queries.Method 3: Custom Function with apply(). Strengths: Offers reusability and encapsulation of complex conditions. Weaknesses: Slightly more verbose and potentially less performant for simple conditions.Method 4: Lambda Function. Strengths: Quick and flexible for simple filtering conditions. Weaknesses: Can be less readable and difficult to debug for complex operations.Method 5: List Comprehension. Strengths: Pythonic and very efficient for simple conditions. Weaknesses: Not as readable and can get complicated with more complex conditions.