How to Efficiently Add Multiple Columns to a Pandas DataFrame Simultaneously?

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:

<code class="python">df[['column_new_1', 'column_new_2', 'column_new_3']] = [np.nan, 'dogs', 3]</code>

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

<code class="python">df['column_new_1'], df['column_new_2'], df['column_new_3'] = np.nan, 'dogs', 3</code>

Method 2: Expand Single Row to Match Index

<code class="python">df[['column_new_1', 'column_new_2', 'column_new_3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)</code>

Method 3: Combine with Temporary DataFrame Using pd.concat

<code class="python">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
)</code>

Method 4: Combine with Temporary DataFrame Using .join

<code class="python">df = df.join(pd.DataFrame(
    [[np.nan, 'dogs', 3]], 
    index=df.index, 
    columns=['column_new_1', 'column_new_2', 'column_new_3']
))</code>

Method 5: Use Dictionary for Temporary DataFrame

<code class="python">df = df.join(pd.DataFrame(
    {
        'column_new_1': np.nan,
        'column_new_2': 'dogs',
        'column_new_3': 3
    }, index=df.index
))</code>

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

<code class="python">df = df.assign(column_new_1=np.nan, column_new_2='dogs', column_new_3=3)</code>

Method 7: Create Columns, Then Assign Values

<code class="python">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</code>

Method 8: Multiple Sequential Assignments

<code class="python">df['column_new_1'] = np.nan
df['column_new_2'] = 'dogs'
df['column_new_3'] = 3</code>

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.

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