Pandas DataFrame Filtering || Keeping only Consecutive Elements of a Column

As a data analyst or scientist working with Pandas DataFrames, you often encounter situations where you need to filter your data based on specific conditions. One such scenario is when you want to keep only the consecutive elements of a column for each element in another column. In this article, we’ll explore how to achieve this using Pandas filtering techniques.

Introduction

In this article, we will focus on Pandas DataFrame filtering and specifically on keeping only consecutive elements of a column. We will cover two main approaches: using Series.explode followed by merge, and using DataFrame.explode directly. Additionally, we’ll discuss how to avoid resetting the default index values when merging DataFrames.

Setting Up

To begin with, let’s set up our DataFrame. We have two DataFrames:

import pandas as pd


df = pd.DataFrame({'a': [201, 201, 201, 201, 202, 202, 202, 203, 203, 203],
                   'b': [1, 2, 3, 5, 1, 2, 6, 1, 3, 4]})

df_filter = pd.DataFrame({'a': [      201,    202, 203],
                          'b': [[1, 2, 3], [1, 2], [1]]}).set_index('a')

These DataFrames contain the a and b columns. We want to filter df using df_filter, keeping only the consecutive elements of b for each element in a.

Approach 1: Using Series.explode

One way to achieve this is by converting lists in df_filter['b'] to rows using Series.explode. Then, we can merge df with the resulting DataFrame.

# Convert lists to rows using Series.explode
df_exploded = df_filter['b'].explode()

# Merge df with the exploded DataFrame and set default inner join
df_filtered = df_exploded.reset_index().merge(df)

print (df_filtered)

This approach works by converting each list in df_filter['b'] to a separate row, allowing us to merge it with df. The resulting DataFrame will have consecutive elements of b for each element in a.

Approach 2: Using DataFrame.explode

Another way to achieve this is by using DataFrame.explode directly on the input DataFrame.

# Convert lists to rows using DataFrame.explode
df_exploded = df_filter.explode('b')

# Merge df with the exploded DataFrame and set default inner join
df_filtered = df_exploded.merge(df)

print (df_filtered)

This approach is more concise and directly achieves our goal. We can merge df with the resulting DataFrame to get the desired result.

Avoiding Reset Default Index Values

When using this method, it’s essential to avoid resetting the default index values in df. This can be done by removing the reset_index step:

# Convert lists to rows using DataFrame.explode
df_exploded = df_filter.explode('b')

# Merge df with the exploded DataFrame and set default inner join
df_filtered = df_exploded.set_index(df_exploded.index).merge(df)

print (df_filtered)

Alternatively, you can use set_index to set the index of the resulting DataFrame:

# Convert lists to rows using DataFrame.explode
df_exploded = df_filter.explode('b')

# Merge df with the exploded DataFrame and set default inner join
df_filtered = df_exploded.set_index(df_exploded.index).merge(df)

print (df_filtered)

Conclusion

In this article, we explored how to filter a Pandas DataFrame to keep only consecutive elements of a column. We covered two approaches using Series.explode followed by merge, and DataFrame.explode. Additionally, we discussed how to avoid resetting default index values when merging DataFrames.

By mastering these techniques, you’ll be able to efficiently clean and preprocess your data in Pandas.

Last modified on 2024-01-05