How Do I Select Multiple Columns from a Pandas DataFrame?

Selecting Multiple Columns in a Pandas DataFrame

When working with dataframes, the need to select specific columns is often encountered. In Pandas, there are multiple ways to achieve this.

One common misconception is attempting to use slicing to select columns:

df1 = df['a':'b']

This approach will not work as column names cannot be sliced directly. Instead, there are two viable options:

1. Selective Column Retrieval by Name:

This method involves passing a list of column names to the [] operator:

import pandas as pd

df = pd.DataFrame({
    'a': [2, 3],
    'b': [3, 4],
    'c': [4, 5],
})

df1 = df[['a', 'b']]

2. Indexing by Column Position:

If the column positions are known in advance, you can use iloc to select columns by index:

df1 = df.iloc[:, 0:2]  # Remember that slicing is exclusive of the ending index

Additional Tips:

• To obtain column indices using the get_loc function:

{df.columns.get_loc(c): c for idx, c in enumerate(df.columns)}

• To ensure that the selected columns are a copy instead of a view, use the copy() method:

df1 = df.iloc[:, 0:2].copy()

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