Adding a New Column to an Existing DataFrame

When working with pandas DataFrames, it often becomes necessary to add new columns to existing dataframes. There are multiple approaches to achieve this, each with its own advantages and drawbacks.

1. Using assign (Recommended for Pandas 0.17 and above):

import pandas as pd
import numpy as np

# Generate a sample DataFrame
df1 = pd.DataFrame({
    'a': [0.671399, 0.446172, 0.614758],
    'b': [0.101208, -0.243316, 0.075793],
    'c': [-0.181532, 0.051767, -0.451460],
    'd': [0.241273, 1.577318, -0.012493]
})

# Add a new column 'e' with random values
sLength = len(df1['a'])
df1 = df1.assign(e=pd.Series(np.random.randn(sLength)).values)

2. Using loc[row_index, col_indexer] = value:

# Add a new column 'f' using loc
df1.loc[:, 'f'] = pd.Series(np.random.randn(sLength), index=df1.index)

3. Using df[new_column_name] = pd.Series(values, index=df.index):

# Add a new column 'g' using the old method
df1['g'] = pd.Series(np.random.randn(sLength), index=df1.index)

Remember that the latter method may trigger the SettingWithCopyWarning in newer versions of pandas. Using assign or loc is generally recommended for efficiency and clarity.