Python | How to slice columns in python

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Slicing columns

Slicing columns in a Pandas DataFrame is a common operation when working with data. The basic syntax for slicing columns in a Pandas DataFrame is as follows:

df[start_col:end_col:step]

where start_col and end_col are the starting and ending column indices, and step is the step size between columns.

For example, let's say we have a DataFrame df with columns 'A', 'B', 'C', 'D', and 'E'. To slice the columns from 'B' to 'D' with a step size of 1, we would use the following code:

df.loc[:, 'B':'D':1]

This returns a DataFrame that includes all rows, but only the columns 'B', 'C', and 'D'.

If we want to include every other column between 'B' and 'D', we would use a step size of 2, like this:

df.loc[:, 'B':'D':2]

This returns a DataFrame that includes all rows, but only the columns 'B' and 'D'.

Alternatively, we can use the column indices to slice columns. For example, to select columns 1 to 3, we can use the following code:

df.iloc[:, 1:4]

This returns a DataFrame that includes all rows, but only the columns with indices 1, 2, and 3.

Overall, slicing columns in a Pandas DataFrame is a straightforward operation, and can be done using either column labels or column indices.