How to Add a New Column to a Pandas DataFrame Efficiently?

How to Incorporate a New Column into an Existing DataFrame

Existing DataFrames often require the addition of new columns to accommodate expanding datasets. The DataFrame's non-sequential row numbering and named columns pose a challenge to this operation. To seamlessly add a new column, 'e', without altering the DataFrame's structure, follow these steps:

Method 1: Direct Assignment

Using direct assignment, you can assign a series to the new column while preserving the existing indexes:

df1['e'] = pd.Series(np.random.randn(sLength), index=df1.index)

However, this method may trigger a SettingWithCopyWarning in recent pandas versions, indicating a potential performance inefficiency.

Method 2: DataFrame.assign()

For optimal efficiency, the DataFrame.assign() method is recommended:

df1 = df1.assign(e=pd.Series(np.random.randn(sLength)).values)

Both methods effectively add the new column 'e' to the DataFrame, providing convenient ways to incorporate additional data.

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