Pandas apply condition on a column that contains list

Introduction

In data analysis, it’s common to work with columns that contain lists of values. However, when we want to perform operations on these lists, we often need to convert them into separate rows or columns. In this article, we’ll explore how to apply conditions on such columns using Pandas.

Background

When working with Pandas DataFrames, each column can be a list-like object (e.g., np.array, list) containing multiple values. To perform operations on these lists, we need to convert them into separate rows or columns. This is where the explode method comes in handy.

Explode Method

The explode method transforms each element of a list-like to a row, returning an exploded DataFrame with the same index and column names as the original Series.

# Create a sample Series
import pandas as pd

series = pd.Series([1, 2, 3], index=['a', 'b', 'c'])

# Apply explode method
explode_df = series.explode()

print(explode_df)

Output:

a    1
b    2
c    3
dtype: int64

As we can see, the explode method has transformed each element of the original Series into a separate row.

Applying Conditions on List-like Columns

Now that we’ve explored the explode method, let’s dive deeper into applying conditions on list-like columns. We’ll use two different approaches: one using the explode method and another using set operations.

Option 1: Using Explode Method

Seems to be a bit faster on a larger set than Option 2 below:

# Create a sample DataFrame
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "ID": [1, 2, 3, 4, 5],
    "BRAND": [[], ["LVH"], ["FER", "MER", "POR"], ["WDC", "AUD", "LVH"], ["AST"]]
})

# Define target list
target_list = ["LVH", "WDC"]

# Apply explode method and condition
df['FLAG'] = df.BRAND.explode().isin(target_list).groupby(level=0, sort=False)\
    .any().map({True:'Y',False:'N'})

print(df)

Output:

   ID            BRAND FLAG
0   1               []    N
1   2            [LVH]    Y
2   3  [FER, MER, POR]    N
3   4  [WDC, AUD, LVH]    Y
4   5            [AST]    N

As we can see, the explode method has transformed each element of the BRAND column into a separate row, and then applied the condition using the isin method.

Option 2: Using Set Operations

Basically same performance as the answer by @AnoushiravanR:

# Create a sample DataFrame
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "ID": [1, 2, 3, 4, 5],
    "BRAND": [[], ["LVH"], ["FER", "MER", "POR"], ["WDC", "AUD", "LVH"], ["AST"]]
})

# Define target list
target_list = ["LVH", "WDC"]

# Apply set operations and condition
df['FLAG'] = df.BRAND.apply(lambda x: 'Y' if len(set(x) & set(target_list)) 
                            else 'N')

print(df)

Output:

   ID            BRAND FLAG
0   1               []    N
1   2            [LVH]    Y
2   3  [FER, MER, POR]    N
3   4  [WDC, AUD, LVH]    Y
4   5            [AST]    N

In this example, we’re using set operations to check if the elements of the BRAND column are present in the target_list. The len(set(x) & set(target_list)) == 0 condition checks if there’s any intersection between the two sets, which means no common elements.

Conclusion

In this article, we’ve explored how to apply conditions on columns that contain lists using Pandas. We’ve used both the explode method and set operations to achieve our goal. While both approaches have their own strengths and weaknesses, they can be useful in different scenarios. By understanding how to work with list-like columns and applying conditions accordingly, you’ll become more proficient in data analysis and manipulation with Pandas.

Last modified on 2023-07-10