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

Adding a New Column to an Existing DataFrame

In data manipulation tasks, it's often necessary to augment existing DataFrames with additional columns. Here, we address the question of how to achieve this in Python using Pandas.

Problem Statement

Consider the following DataFrame with indexed columns and rows with non-continuous numbers:

          a         b         c         d
2  0.671399  0.101208 -0.181532  0.241273
3  0.446172 -0.243316  0.051767  1.577318
5  0.614758  0.075793 -0.451460 -0.012493

Our goal is to add a new column, 'e', to this DataFrame without altering the existing data. The new column should have the same length as the DataFrame.

Solution

Method 1 (assign):

The most efficient method to add a Series of values as a new column to a DataFrame is using the assign function:

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

where:

• df1 is the original DataFrame
• sLength is the length of the original DataFrame
• np.random.randn(sLength) generates a Series of random values

Method 2 (loc):

Another method is to use the loc accessor to set the values of the new column:

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

where:

• df1 is the original DataFrame
• 'f' is the name of the new column
• pd.Series(np.random.randn(sLength)) generates a Series of random values
• df1.index sets the index of the new column to match the original DataFrame

Both methods effectively add the desired new column 'e' to the DataFrame, preserving the existing data.

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