Understanding Data Manipulation in Pandas: Duplicate Rows Based on Delimiters

Overview of Pandas and its Data Manipulation Features

Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types). Pandas offers various methods to manipulate and transform data, including filtering, sorting, grouping, merging, reshaping, and pivoting.

In this article, we will explore the explode function in pandas, which is used to split each row into separate rows based on a specified delimiter. We will also discuss how to use the assign method to create new columns and the str.split method to manipulate string data.

Introduction to the explode Function

The explode function is a powerful tool for splitting rows in pandas DataFrames. It takes an iterable (such as a Series or DataFrame) and repeats each row, creating new rows with the same values from the original row, but with an additional value.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Cars': ['Alto'],
    'Country': ['Australia'],
    'Trans': ['Automatic & Manual']
})

# Apply the explode function to split the Trans column
df_exploded = df.assign(new_Trans=df['Trans'].str.split(' & ')).explode(['new_Trans'])
print(df_exploded)

Output:

As shown, the explode function has repeated each row in the original DataFrame and created a new row for each value in the new_Trans column.

Understanding the assign Method

The assign method is used to create new columns or modify existing ones in a pandas DataFrame. It takes a dictionary where keys are the column names and values are the column values.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Cars': ['Alto'],
    'Country': ['Australia']
})

# Apply the assign method to create a new column
df_assigned = df.assign(new_Cars=df['Cars'])
print(df_assigned)

Output:

As shown, the assign method has created a new column called New_Cars with the values from the original ‘Cars’ column.

Using the str.split Method

The str.split method is used to split strings into multiple values. It takes a string and an optional delimiter as input.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Trans': ['Automatic & Manual']
})

# Apply the str.split method to split the Trans column
df_split = df.assign(new_Trans=df['Trans'].str.split(' & '))
print(df_split)

Output:

As shown, the str.split method has split the string in the ‘Trans’ column into two values.

Using the explode Function with Multiple Columns

The explode function can be used to split multiple columns in a pandas DataFrame. It takes an iterable (such as a Series or DataFrame) and repeats each row, creating new rows with the same values from the original row, but with additional values.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Cars': ['Alto'],
    'Country': ['Australia'],
    'Trans': ['Automatic & Manual']
})

# Apply the explode function to split the Trans column
df_exploded = df.assign(new_Trans=df['Trans'].str.split(' & '), new_trans_id=df['trans_id'].str.split(' & '))
df_exploded = df_exploded.explode(['new_Trans','new_trans_id'])
print(df_exploded)

Output:

As shown, the explode function has repeated each row in the original DataFrame and created new rows for each value in both the new_Trans and new_trans_id columns.

Best Practices

When using the explode function, it’s essential to understand its behavior and limitations. Here are some best practices:

• Use the explode function when you need to split a single row into multiple rows.
• Be aware of the performance implications of using explode, especially when working with large DataFrames.

Conclusion

In this article, we have explored how to use the explode function in pandas to split rows based on a delimiter. We have also discussed the importance of understanding data manipulation and analysis concepts in pandas. By mastering these concepts, you can efficiently manipulate and analyze your data using pandas.

Last modified on 2025-03-11