How to Efficiently Split Comma-Separated Strings in Pandas DataFrames?

Splitting Comma-Separated String Entries in Pandas DataFrame

Input data is often structured with values separated by characters such as commas. When working with Pandas dataframes, it becomes necessary to split these string entries and create separate rows for each value. In this article, we will delve into the methods available for achieving this goal efficiently.

Utilizing Pandas' .explode() Method

Introduced in Pandas versions 0.25.0 and 1.3.0, the .explode() method offers a straightforward and efficient solution for exploding columns containing lists or arrays. It operates on both single and multiple columns, providing flexibility in handling complex datasets.

Syntax:

dataframe.explode(column_name)

Example:

import pandas as pd

# Dataframe with a column containing comma-separated values
df = pd.DataFrame({'var1': ['a,b,c', 'd,e,f'], 'var2': [1, 2]})

# Exploding the 'var1' column
df = df.explode('var1')

# Resulting dataframe with separate rows for each value
print(df)

Custom Vectorized Function for Exploding Multiple Columns

For more complex scenarios where exploding multiple columns is required, a custom vectorized function can provide a versatile solution:

Function Definition:

def explode(df, lst_cols, fill_value='', preserve_index=False):
    # Calculate lengths of lists
    lens = df[lst_cols[0]].str.len()

    # Repeat values for non-empty lists
    res = (pd.DataFrame({
                col:np.repeat(df[col].values, lens)
                for col in df.columns.difference(lst_cols)},
                index=np.repeat(df.index.values, lens))
             .assign(**{col:np.concatenate(df.loc[lens>0, col].values)
                            for col in lst_cols}))

    # Append rows with empty lists
    if (lens == 0).any():
        res = (res.append(df.loc[lens==0, df.columns.difference(lst_cols)], sort=False)
                  .fillna(fill_value))

    # Revert index order and reset index if requested
    res = res.sort_index()
    if not preserve_index:
        res = res.reset_index(drop=True)
    return res

Example:

# Dataframe with multiple columns containing lists
df = pd.DataFrame({
    'var1': [['a', 'b'], ['c', 'd']],
    'var2': [['x', 'y'], ['z', 'w']]
})

# Exploding 'var1' and 'var2' columns
df = explode(df, ['var1', 'var2'])

# Resulting dataframe with separate rows for each list item
print(df)

Transforming by Grouping

Another approach involves using .transform() to apply a custom function that splits the string entries and creates new rows:

Custom Function:

def split_fun(row):
    return [row['var1'].split(',')]

Example:

# Dataframe with a column containing comma-separated values
df = pd.DataFrame({'var1': ['a,b,c', 'd,e,f'], 'var2': [1, 2]})

# Creating a new column with split values using transform
df['var1_split'] = df.transform(split_fun)

# Unnest the newly created column to separate rows
df = df.unnest('var1_split')

# Resulting dataframe with separate rows for each value
print(df)

Conclusion

Depending on the specific requirements and complexity of the dataset, different methods can be employed to split comma-separated string entries in Pandas dataframes. Utilizing the .explode() method offers a straightforward and efficient approach, while custom vectorized functions provide flexibility for handling more complex scenarios.

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