In Python, the pandas DataFrame.pow() function is used to calculate the exponential power of DataFrame and other types of objects. The other type of objects can be sequence, series, constant, and more. It is equivalent to the DataFrame ** other operation.
Note: The**operator can also be used, but it does not provide the facility to fill out empty fields, broadcast values across a level, and so on.
The syntax of the function is given below:
DataFrame.pow(other, axis='columns', level=None, fill_value=None)
The DataFrame.pow() function takes the following argument values:
other: This can be DataFrame, sequence, series, or scalar.axis: defaut='columns', this can be 1, 'columns', 0, or 'index'.level: default=None, this broadcasts value across a level and can either be int or None.fill_value: default=None, this can either be a float value or None, and it helps to fill empty (NaN) fields.This method returns a DataFrame after performing certain
We can use this function in multiple ways. Some of the scenarios where we use this function are listed below:
DataFramesDataFrame and a constant DataFrame and seriesDataFramesLet's say we have two objects that are both DataFrames:
# importing pandas as pdimport pandas as pd# Creating a the DataFramedf1= pd.DataFrame({"A":[5, 9, 6, 4],"B":[1, None, 4, 3],"C":[4, 3, 8, None],"D":[5, 4, 2, 7]})# Creating another DataFramedf2= pd.DataFrame({"A":[None, 4, 5, 9],"B":[1, None, 4, 3],"C":[14, 3, -1, None],"D":[2, 4, 8, 8]})# Evaluating floor division between df1 and df2floorDiv= df1.pow(df2, fill_value = 3)# print resultsprint(floorDiv)
DataFrame named df1, which has four rows and four columns.DataFrame named df2, which has the same dimensions as df1.DataFrame.pow() function from the pandas module to calculate, element-wise, the exponential power between the DataFrames that we created.DataFrame and constantLet's say we have two objects, where one is a DataFrame and another is a constant:
# importing pandas as alias pdimport pandas as pd# Creating a DataFramedf = pd.DataFrame({"Alpha":[15, 3, 16, 8],"Beta":[1, None, 4, 13],"Charli":[4, 3, 8, None],"Delta":[5, 1, 12, 8]})# Invoking method for floor divisionresult= df.pow(4, fill_value = 5)# print results on consoleprint(result)
DataFrame named df.df.pow() function to calculate, element-wise, the power 4 of the created DataFrame.DataFrame on the console.DataFrame and seriesLet's say we have two objects, where one is a DataFrame and another is a series:
# importing pandas as pdimport pandas as pdimport numpy as np# Creating the DataFramedf = pd.DataFrame([[15, 3, None, 4],[11, None, 4, 3],[4, 0, 8, None],[5, 1, 2, 4]])# Creating Numpy arraydata = np.array([2, 1, 7, 9])# Converting to Python seriesseries = pd.Series(data)# Invoking floor division methodresult= df.pow(series)# printing resultsprint(result)
DataFrame named df, which consists of 16 numerical values that are distributed in a 4x4 frame.series that consists of four integer values.df.pow(series) function with series as an argument.