How to Add Multiple Columns to a Pandas DataFrame Simultaneously?

Adding Multiple Columns to Pandas Dataframes Simultaneously: A Step-by-Step Guide

In the endeavor of data analysis, it is often necessary to augment existing Pandas dataframes with additional columns. To simplify this process, we seek a streamlined approach to adding multiple columns at once.

Initial Misconception: Assigning Values to Multiple Columns

Intuitively, one might expect the following syntax to accomplish the task:

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

However, this approach encounters a hurdle due to Pandas' requirement for the right-hand side of column-list assignments (df[[new1, new2]] = ...) to be a DataFrame.

Working Solutions: Assigning Multiple Columns

Undeterred, we navigate various techniques to achieve our goal:

1. Iterator Unpacking for Simultaneous Assignments

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

2. Expanding a Single Row with DataFrame()

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

3. Concatenation with Temporary DataFrames

<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>

4. Joining with Temporary DataFrames

<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>

5. Dictionary-Based Temporary DataFrames

<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>

6. .assign() for Multiple Column Arguments (Python 3.6 )

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

7. Create Columns, Assign Values Separately

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

8. Separate Assignments

While it lacks the elegance of other solutions, this approach remains straightforward:

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

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