How to Efficiently Add Multiple Columns to a Pandas DataFrame?

Assigning Multiple Columns to Pandas DataFrame Simultaneously

In the context of working with dataframes in Pandas, the question arises on how to efficiently add multiple columns in one assignment.

Initial Attempt and Its Shortcoming

Many users naturally attempt the following syntax to accomplish this task:

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

However, this approach fails because Pandas requires the right-hand side to be a DataFrame when creating new columns with the column-list syntax.

Alternative Approaches

Multiple viable solutions exist to achieve the desired result. Here are some of the recommended approaches:

1. Single-Column Assignments with Iterator Unpacking

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

2. DataFrame Expansion with Pandas.DataFrame()

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

3. Concatenation with Pandas.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
)

4. Join with Pandas.join

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

5. Dictionary Expansion with Pandas.join

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

6. Multiple Column Arguments with .assign()

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

7. Column Creation and Assignment

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

8. Separate Assignments

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

The choice of approach depends on the specific requirements of the user. For simplicity and efficiency, separate assignments may often be the preferred solution. However, if adding multiple columns with the same type or value is desired, the other approaches provide flexibility and conciseness.

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