Pandas DataFrame pow() method with examples

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The pow() method in pandas calculates the exponential power of dataframe and other, element-wise (binary operator pow). This function is essentially same as the dataframe ** other but with a support to fill the missing values in one of the input data.

Syntax –

DataFrame.pow(other, axis='columns', level=None, fill_value=None)

other – Any single or multiple element data structure, or list-like object.

axis – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

level – Broadcast across a level, matching Index values on the passed MultiIndex level.

fill_value – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Examples –

Let’s create a dataframe to work with.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A':[1, 2, 3, 4, 5, np.nan],
                 'B':[6, 7, 8, 9, 10, np.nan]})

1 . Raise to the power –

Let’s say we want to raise each value in the dataframe to the power of 2. One way of doing this is

df ** 2

Another way to do this is using the pow() method in pandas.


2 . Fill missing values then raise to the power –

If you look at the dataframe, you can see that it contains some missing values. We can first fill these missing values using the fill_value parameter and then raise to the power.

df.pow(2, fill_value=5)

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