Understanding List Elements in Pandas DataFrames

In this blog post, we will explore how to determine if the elements of a list from a DataFrame column exist in another list. This is a common problem when working with data that contains lists as values.

Background

Pandas DataFrames are a powerful data structure for storing and manipulating tabular data. They provide an efficient way to perform various operations on data, such as filtering, grouping, and merging. However, when dealing with lists within DataFrame columns, things can get more complex.

In this post, we will dive into the world of list elements in Pandas DataFrames and explore how to determine if the elements of a list from one column exist in another list.

The Problem

Let’s consider an example where we have a DataFrame df with a column Tlf that contains lists of telephone numbers. We also have a list tlf_r that contains some repeated telephone numbers. Our goal is to extract the indices of rows from the DataFrame where at least one value in the Tlf list matches an element in tlf_r.

|Id | Name | Tlf                      |
|:- |:----:| -------------------------:|
| 0 |  A   | ['xxxxxxxxx','zzzzzzzzz'] |
| 1 |  B   | ['yyyyyyyyy']             |
| 2 |  C   | ['zzzzzzzzz','ooooooooo'] |
| 3 |  D   | None                      |
| 4 |  E   | ['xxxxxxxxx']             |
| 5 |  F   | None                      |

tlf_r = [None, 'xxxxxxxxx', 'zzzzzzzzz']

The Solution

The solution to this problem lies in using the explode function to break down the lists into individual rows. We can then use the isin function to filter out the rows where no value matches an element in tlf_r.

# Explode the list of telephone numbers
df_exploded = df.explode('Tlf')

# Filter out rows where no value matches an element in tlf_r
filtered_df = df_exploded.loc[df_exploded['Tlf'].isin(tlf_r)]

# Sort the values by the original list order (Id)
sorted_df = filtered_df.sort_values('Id')

Alternatively, if we want to keep rows where at least one value matches an element in tlf_r, we can use the following approach:

# Explode the list of telephone numbers and group by row
grouped_df = df_exploded['Tlf'].explode().groupby(level=0).max()

# Filter out rows where no value matches an element in tlf_r
filtered_grouped_df = grouped_df.loc[grouped_df.isin(tlf_r)]

# Reset the index to get back to the original row structure
reset_index_df = filtered_grouped_df.reset_index()

How explode Works

The explode function is a powerful tool for breaking down lists into individual rows. It works by iterating over each element in the list and creating a new row for each value.

Here’s an example of how explode transforms the original DataFrame:

|Id | Name | Tlf                      |
|:- |:----:| -------------------------:|
| 0 |  A   | ['xxxxxxxxx','zzzzzzzzz'] |

# Before explode

| Id | Tlf                  |
|--- | ----------------- |
| 0 | xxxxxxxxx          |
| 0 | zzzzzzzzz          |
| 1 | yyyyyyyyy         |

How isin Works

The isin function is used to check if a list of values exists within another list. It returns a boolean Series where each element indicates whether the value at that index exists in the original list.

Here’s an example of how isin works:

tlf_r = [None, 'xxxxxxxxx', 'zzzzzzzzz']

# Before isin

| Tlf        |
|-----------|
| None       |
| xxxxxxxxx |
| zzzzzzzzz |

# After isin

| Id | Name | Tlf         |
|--- |-----|------------|
| 0 |   A | xxxxxxxxx  |
| 4 |   E | xxxxxxxxx  |
| 0 |   A | zzzzzzzzz  |
| 2 |   C | zzzzzzzzz  |

# After isin

Conclusion

In this blog post, we explored how to determine if the elements of a list from a DataFrame column exist in another list. We covered the basics of explode and isin, two essential functions for working with lists within DataFrames.

By using these functions and techniques, you can efficiently filter out rows where no value matches an element in tlf_r.

Last modified on 2025-02-24