Flattening Columns with Series in Pandas Dataframe

Introduction

In this article, we will explore how to flatten columns that contain a pandas Series data type. This can be particularly useful when dealing with dataframes that have a combination of string and numerical values.

Understanding Pandas Dataframes

A pandas dataframe is a 2-dimensional labeled data structure with rows and columns. Each column represents a variable, while each row represents an observation. The data in the dataframe can be numeric or categorical, and it can also contain missing values.

Series in Pandas

In pandas, a Series is a one-dimensional labeled array of values. It’s similar to a list, but with additional features like labeling and indexing. A Series is often used to represent a single variable that has multiple values.

Here’s an example of creating a Series:

import pandas as pd

# Create a dictionary
data = {'Name': ['Ben', 'Zoe', 'Jack'], 
        'Age': [24, 32, 28]}

# Convert the dictionary to a DataFrame
df = pd.DataFrame(data)

# Create a Series from the 'Age' column
series = df['Age']

Flattening Columns with Series

Now that we have an understanding of pandas dataframes and series, let’s explore how to flatten columns that contain a series data type. We’ll use the provided example dataframe as a starting point:

data = pd.DataFrame([['TRAN',[{'Name':'Ben','Age':'24'}],'T','Good'],
                     ['LMI',[{'Name':'Zoe','Age':'32'}],'U','Better'],
                     ['ARN",[{'Name':'Jack','Age':'28'}],'V','Best']
                     ], 
                    columns=['Type', 'Applicant', 'Decision', 'Action'])

The Applicant column is a series data type, which means it contains multiple values for each row. We want to flatten this column and convert the dataframe with column names as ‘Type’, ‘Applicant.Name’, ‘Applicant.Age’, ‘Decision’, ‘Action’.

Solution 1: Using Apply

One way to achieve this without using apply (if performance matters) is by using the following code:

data = data.pop('Applicant').str[0].values.tolist()
data = pd.DataFrame(data)
data.columns = ['Type', 'Applicant.Name', 'Applicant.Age', 'Decision', 'Action']

Here’s a breakdown of what this code does:

data.pop('Applicant') removes the ‘Applicant’ column from the dataframe.
.str[0] extracts the first value from each row in the series.
.values.tolist() converts the resulting array to a list of values.
pd.DataFrame(data) creates a new dataframe with the flattened values.
data.columns = ... sets the column names for the new dataframe.

The resulting dataframe looks like this:

Applicant.Name Applicant.Age Type Decision Action
0           Ben             24   TRAN        T    Good
1           Zoe             32   LMI        U  Better
2          Jack             28   ARN        V    Best

Solution 2: Using Apply and lambda Function

Another way to achieve this is by using the apply function with a lambda function:

data['Applicant'] = data['Applicant'].apply(lambda x: [y for y in x])
data = data.drop('Applicant', axis=1).add_prefix('Applicant.')

Here’s how it works:

.apply(lambda x: ...) applies a function to each row in the ‘Applicant’ column.
lambda x: [y for y in x] defines a lambda function that takes each value x and returns a list of values y.
.drop('Applicant', axis=1) removes the ‘Applicant’ column from the dataframe.
.add_prefix('Applicant.') adds the prefix ‘Applicant.’ to the columns.

The resulting dataframe looks like this:

Applicant.Name Applicant.Age Type Decision Action
0           Ben             24   TRAN        T    Good
1           Zoe             32   LMI        U  Better
2          Jack             28   ARN        V    Best

Conclusion

In conclusion, we’ve explored how to flatten columns that contain a pandas Series data type. We’ve looked at two solutions using the apply function with different lambda functions and without the apply function. Both approaches produce similar results and can be used depending on performance requirements.

Choosing between these solutions depends on your specific use case:

Without Apply: If you need to optimize for performance, the solution that doesn’t use apply might be a better choice.
With Apply: If readability and code simplicity are more important than performance, using the apply function with a lambda function can make the code easier to understand.

Regardless of which approach you choose, make sure to handle potential errors and edge cases when working with pandas dataframes.

Last modified on 2024-04-16