Filter and Drop Rows Based on a Condition for a List of List Column in DataFrame

As data analysts and scientists, we often work with complex data structures that involve multiple lists within a single column. In this article, we will explore how to filter and drop rows from a Pandas DataFrame based on a condition applied to a list of list column.

Introduction

Pandas is an excellent library for data manipulation in Python. One of its key features is the ability to work with DataFrames, which are two-dimensional data structures that can handle missing data, perform operations on multiple columns, and more. In this article, we will dive into how to apply conditions to a list of list column and then filter or drop rows based on those conditions.

Sample DataFrame

We start with the provided sample DataFrame:

import pandas as pd

data = {"Trial": ['Trial_1', 'Trial_2', 'Trial 3', 'Trial 4'], 
        "Results" : [[['a', 11.0, 1, 1.0], ['b', 12.0, 0, 6.0], ['c', 2.6, 0, 3.0]], 
                    [['d', 7.3, 1, 8.0], ['e', 13.0, 0, 5.0], ['f', 8.6, 0, 3.0]],
                    [[ 'g', 9.1, 1, 1.0], ['h', 10.0, 0, 7.0], ['i', 95.6, 0, 5.0]], 
                    [['j', 6.6, 1, 1.0], ['k', 14.0, 0, 3.0], ['l', 8.1, 0, 9.0]]]}

df = pd.DataFrame(data)

Query 1: Filter Rows Based on a Condition

The first query asks us to filter the DataFrame to display only rows where within the Results column list of lists, there is an item at index 2 equal to 1 and an item at index 3 not equal to 1. This condition would show only Trial 2 as it contains [’d’, 7.3, 1, 8.0] with an index 2 item equal to 1.0 but an index 3 item equal to 8.0.

We can achieve this using the loc function:

df_filtered = df.loc[df['Results'].apply(lambda x: len([y for y in x if y[2] == 1 and y[3] != 1]) == 1)]

This code applies a lambda function to each row in the ‘Results’ column, which checks if there is exactly one item at index 2 equal to 1 and an item at index 3 not equal to 1. If this condition is met for any row, that row will be included in df_filtered.

Query 2: Drop Rows Where a Condition is True

The second query asks us to drop the rows where the condition stated in Query 1 is true. This means we want to remove rows like Trial_2 from our DataFrame because it meets the filtering criteria.

We can achieve this using the drop function:

df_unmatched = df.loc[~df['Results'].apply(lambda x: len([y for y in x if y[2] == 1 and y[3] != 1]) == 1)]

Here, we use a negation operator (~) to exclude rows that match the filtering criteria.

Alternative Approach Using apply with List Comprehension

The provided answer uses an alternative approach involving the apply function on each row of the DataFrame and iterating through lists. It collects indices where the condition is met and then filters or drops rows based on these indices.

idxs = []  # for collecting indices
def loop_results(x):
    for res in x['Results']:
        if res[2] == 1 and res[3] != 1:
            idxs.append(x.name)  # here, .name is the index value

df_temp = df.apply(loop_results, axis=1)  # apply the function to each row
idxs = list(set(idxs))  # if there are duplicates, set() will remove them

df_match = df.loc[idxs]  # matched criteria
df_unmatched = df.drop(idxs, axis=0)  # drops rows matching criteria

This approach uses a custom function loop_results to iterate through each element in the ‘Results’ column list of lists and check if it meets the condition. It then collects the indices where this condition is true and filters or drops rows accordingly.

Conclusion

In this article, we explored how to filter and drop rows from a Pandas DataFrame based on a condition applied to a list of list column. We discussed two approaches: using loc with a lambda function to filter rows directly and using apply with a custom function to iterate through lists and collect indices for filtering or dropping rows.

Last modified on 2023-07-26