How to efficiently add multiple columns to a Pandas DataFrame in a single assignment?

Adding Multiple Columns to Pandas DataFrames in a Single Assignment

In Pandas, adding multiple columns simultaneously can be achieved in various ways. One approach is to assign values to each column individually, but this can become tedious for multiple columns. A more efficient method is to add the columns in one step.

At first glance, assigning a list or array to multiple new columns using the column-list syntax (e.g., df[['new1', 'new2]] = [scalar, scalar]) may seem intuitive. However, this assignment only works for existing columns.

To add new columns and assign values in a single operation, you can use several approaches:

1. Iterator Unpacking:

<code class="python">df['new1'], df['new2'], df['new3'] = np.nan, 'dogs', 3</code>

This approach assigns values iteratively to each new column.

2. DataFrame Expansion:

<code class="python">df[['new1', 'new2', 'new3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)</code>

This method creates a DataFrame with a single row that matches the index of the original DataFrame, then uses Pandas' concat function to merge the new columns into the original.

3. Temporary DataFrame Join:

<code class="python">df = pd.concat([df, pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index, columns=['new1', 'new2', 'new3'])], axis=1)</code>

This approach creates a temporary DataFrame with the new columns and values, then joins it with the original DataFrame.

4. Dictionary Assignment:

<code class="python">df = df.join(pd.DataFrame({'new1': np.nan, 'new2': 'dogs', 'new3': 3}, index=df.index))</code>

This method uses a dictionary to create a temporary DataFrame that is then joined with the original DataFrame.

5. .assign() Method:

<code class="python">df = df.assign(new1=np.nan, new2='dogs', new3=3)</code>

The .assign() method allows for assignment of multiple columns at once.

6. Create Columns and Assign Values:

<code class="python">new_cols = ['new1', 'new2', 'new3']
new_vals = [np.nan, 'dogs', 3]
df = df.reindex(columns=df.columns.tolist() + new_cols)
df[new_cols] = new_vals</code>

This technique creates empty columns and assigns values separately.

Multiple Individual Assignments:

<code class="python">df['new1'] = np.nan
df['new2'] = 'dogs'
df['new3'] = 3</code>

While not as efficient as the other methods, individual assignments are straightforward and can be used for a small number of new columns.

The best choice depends on the specific requirements and performance considerations. For adding multiple columns simultaneously, the DataFrame expansion or temporary DataFrame join approaches provide a concise and efficient solution.

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