How Can I Split a Pandas DataFrame String Column into Multiple Columns?

How to split a dataframe string column into two columns?

In the realm of data analysis, encountering dataframes with string columns that need to be split is a common occurrence. To achieve this feat, two essential questions arise:

• How to perform the split?
• How to create the new columns from the split?

The solution lies in the versatile str attribute of a pandas Series, specifically its indexing interface:

df['AB'].str[0]  # accesses the first element of each string
df['AB'].str[1]  # accesses the second element of each string

By leveraging this indexing interface and tuple unpacking, we can create new columns with the split elements:

df['A'], df['B'] = df['AB'].str.split('-').str

Alternatively, Pandas provides a convenient built-in method, str.split(), for splitting strings and automatically returning a Series of lists:

df['AB_split'] = df['AB'].str.split('-')

To expand this list into separate columns, we employ the expand=True parameter:

df[['A', 'B']] = df['AB'].str.split(' ', n=1, expand=True)

For cases with varied split lengths, expand=True handles it gracefully, ensuring consistent column lengths:

df.join(df['AB'].str.split('-', expand=True).rename(columns={0:'A', 1:'B', 2:'C'}))

Through these techniques, Pandas empower you to efficiently split string columns and restructure your dataframe to meet your specific analysis needs.

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