Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.rpow() function return the exponential power of series and other, element-wise (binary operator rsub). It is equivalent to other ** series, but with support to substitute a fill_value for missing data in one of the inputs.
Syntax: Series.rpow(other, level=None, fill_value=None, axis=0)
Parameter :
other : Series or scalar value
fill_value : Fill existing missing (NaN) values
level : Broadcast across a level, matching Index values on the passed MultiIndex levelReturns : result : Series
Example #1: Use Series.rpow() function to raise a scalar value to the power of each element in the given Series object.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([10, 25, 3, 11, 24, 6])   # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']   # set the index sr.index = index_   # Print the series print(sr) |
Output :
Now we will use Series.rpow() function to raise the scalar to the power of each element of the given series object.
# raise 2 to the power of each element in # the sr object selected_items = sr.rpow(other = 2) Â Â # Print the returned Series object print(selected_items) |
Output :
As we can see in the output, the Series.rpow() function has successfully returned a series object which is the result of exponentiation operation.
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Example #2 : Use Series.rpow() function to raise a scalar value to the power of each element in the given Series object.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None])   # Print the series print(sr) |
Output :
Now we will use Series.rpow() function to raise the scalar to the power of each element of the given series object. We will substitute 100 at the place of all the missing values.
# raise 2 to the power of each element in # the sr object selected_items = sr.rpow(other = 2, fill_value = 100) Â Â # Print the returned Series object print(selected_items) |
Output :
As we can see in the output, the Series.rpow() function has successfully returned a series object which is the result of exponentiation operation.
