Adding Multiple Columns to a Pandas DataFrame Simultaneously

In Pandas data manipulation, efficiently adding multiple new columns to a DataFrame can be a task that requires an elegant solution. While the intuitive approach of using the column-list syntax with an equal sign may seem straightforward, it can lead to unexpected results.

The Challenge

As illustrated in the provided example, the following syntax fails to create the new columns as intended:

df[['column_new_1', 'column_new_2', 'column_new_3']] = [np.nan, 'dogs', 3]

This is because Pandas requires the right-hand side of the assignment to be a DataFrame when using the column-list syntax. Scalar values or lists are not compatible with this approach.

Solutions

Several alternative methods offer viable solutions for adding multiple columns simultaneously:

Method 1: Individual Assignments Using Iterator Unpacking

df['column_new_1'], df['column_new_2'], df['column_new_3'] = np.nan, 'dogs', 3

Method 2: Expand Single Row to Match Index

df[['column_new_1', 'column_new_2', 'column_new_3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)

Method 3: Combine with Temporary DataFrame Using pd.concat

df = pd.concat(
    [
        df,
        pd.DataFrame(
            [[np.nan, 'dogs', 3]], 
            index=df.index, 
            columns=['column_new_1', 'column_new_2', 'column_new_3']
        )
    ], axis=1
)

Method 4: Combine with Temporary DataFrame Using .join

df = df.join(pd.DataFrame(
    [[np.nan, 'dogs', 3]], 
    index=df.index, 
    columns=['column_new_1', 'column_new_2', 'column_new_3']
))

Method 5: Use Dictionary for Temporary DataFrame

df = df.join(pd.DataFrame(
    {
        'column_new_1': np.nan,
        'column_new_2': 'dogs',
        'column_new_3': 3
    }, index=df.index
))

Method 6: Use .assign() with Multiple Column Arguments

df = df.assign(column_new_1=np.nan, column_new_2='dogs', column_new_3=3)

Method 7: Create Columns, Then Assign Values

new_cols = ['column_new_1', 'column_new_2', 'column_new_3']
new_vals = [np.nan, 'dogs', 3]
df = df.reindex(columns=df.columns.tolist()   new_cols)    # add empty cols
df[new_cols] = new_vals        # multi-column assignment works for existing cols

Method 8: Multiple Sequential Assignments

df['column_new_1'] = np.nan
df['column_new_2'] = 'dogs'
df['column_new_3'] = 3

Choosing the most appropriate method will depend on factors such as the DataFrame's size, the number of new columns to be added, and the performance requirements of the task. Nonetheless, these techniques empower Pandas users with diverse options for efficiently adding multiple columns to their DataFrames.