5 Best Ways to Split a Pandas DataFrame Row Into Multiple Rows

When working with Pandas DataFrames, a common challenge is to split a single row into multiple rows based on a column’s values. This scenario often arises when a row contains list-like data or multiple entries in a single cell. For example, you might encounter a DataFrame with a ‘Names’ column where each cell contains multiple names separated by commas, and the goal is to create a new row for each name.

Method 1: Using explode()

This method utilizes the Pandas explode() function, which transforms each element of a list-like to a row, replicating the index values. It is particularly suited for columns containing lists or arrays and simplifies the process of row expansion.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Name': [['Alice', 'Bob'], ['Charlie'], ['David', 'Eve', 'Frank']]
})

# Exploding the 'Name' column
df_exploded = df.explode('Name')

print(df_exploded)

Output:

      Name
0    Alice
0      Bob
1  Charlie
2    David
2      Eve
2    Frank

The code snippet creates a DataFrame with a ‘Name’ column containing lists of names. It then uses explode('Name') to create individual rows for each name, expanding the DataFrame.

Method 2: Using apply() with pd.Series()

The apply() method along with pd.Series() can be used when you need to split a DataFrame’s rows based on a column containing strings that need to be separated into multiple elements. This combination is useful for customized splitting operations.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Name': ['Alice,Bob', 'Charlie', 'David,Eve,Frank']
})

# Expanding the 'Name' column into multiple rows
df_expanded = df['Name'].apply(lambda x: pd.Series(x.split(','))).stack().reset_index(level=1, drop=True).to_frame('Name')

print(df_expanded)

Output:

       Name
0     Alice
0       Bob
1   Charlie
2     David
2       Eve
2     Frank

This code uses the apply() function to apply a lambda function that splits each string at the comma, converts the result to a Pandas Series, stacks the series to convert it into a DataFrame, and then resets the index.

Method 3: Iterative Row Expansion

An iterative approach can be taken using a for-loop to manually split the rows and append them to a new DataFrame. This method offers high flexibility and control over the splitting process, which can be advantageous for complex scenarios.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Name': ['Alice,Bob', 'Charlie', 'David,Eve,Frank']
})

# Placeholder for new DataFrame
expanded_rows = []

# Iterating and expanding rows
for index, row in df.iterrows():
    names = row['Name'].split(',')
    for name in names:
        expanded_rows.append({'Name': name})

df_expanded = pd.DataFrame(expanded_rows)

print(df_expanded)

Output:

      Name
0    Alice
1      Bob
2  Charlie
3    David
4      Eve
5    Frank

The for-loop iterates over the original DataFrame, splits each ‘Name’ cell into a list of names, and then generates a dict for each name. These dicts are appended to a list which is then converted to a new DataFrame.

Method 4: Using melt()

The melt() function in Pandas is generally used to transform data from wide format to long format. If a row has multiple columns that each need to become a separate row, melt() can facilitate this transformation.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'ID': [1, 2, 3],
    'Name1': ['Alice', 'Charlie', 'David'],
    'Name2': ['Bob', None, 'Eve'],
    'Name3': [None, None, 'Frank']
})

# Melting the DataFrame
df_melted = df.melt(id_vars=['ID'], value_vars=['Name1', 'Name2', 'Name3'], var_name='NameType', value_name='Name').dropna(subset=['Name'])

print(df_melted[['ID', 'Name']])

Output:

   ID     Name
0   1    Alice
1   2  Charlie
2   3    David
3   1      Bob
5   3      Eve
8   3    Frank

This snippet converts the DataFrame from wide to long format, resulting in each entry from ‘Name1’, ‘Name2’, and ‘Name3’ columns becoming individual rows. It then drops rows with NaN values in the ‘Name’ column.

Bonus One-Liner Method 5: Using List Comprehension and pandas.concat()

When you want to avoid verbosity and aim for a concise one-liner solution, list comprehension coupled with pandas.concat() can unfold a row into multiple rows in a very compact form.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Name': ['Alice,Bob', 'Charlie', 'David,Eve,Frank']
})

# One-liner expansion using list comprehension and concat
df_expanded = pd.concat([pd.Series(row['Name'].split(','), name='Name') for _, row in df.iterrows()], ignore_index=True)

print(df_expanded)

Output:

      Name
0    Alice
1      Bob
2  Charlie
3    David
4      Eve
5    Frank

This powerful one-liner iterates through the DataFrame rows, splits the ‘Name’ column, and uses pd.concat() to combine the resulting series into one DataFrame.

Summary/Discussion

• Method 1: Explode: Straightforward. Ideal for list-like data. Limited to columns with array-like entries.
• Method 2: Apply with Series: Versatile. Good for complex string operations. More verbose and potentially less performant than explode.
• Method 3: Iterative Row Expansion: Very flexible. Useful for complex row transformation. Less efficient due to for-loop usage.
• Method 4: Melt: Best for formatting multiple columns into rows. Not suitable for list-like data within cells.
• Bonus Method 5: List Comprehension with Concat: Compact and elegant. Can be less readable and potentially improper for very complex transformations.