The mean() function in pandas is used to obtain the mean of the values over a specified axis in a given DataFrame.
Mathematically, the mean can be defined as the sum of all values in a dataset divided by the number of values.
The mean() function has the following syntax:
DataFrame.mean(axis=NoDefault.no_default, skipna=True, numeric_only=None, **kwargs)
The mean() function takes the following optional parameter values:
axis: This represents the name of the row ( designated as 0 or 'index') or the column (designated as 1 or columns) axis from which to take the mean.skipna: This takes a boolean value. It determines whether null values are to be excluded or not in the calculation of the mean.numeric_only: This takes a boolean value. It determines whether only float, int, or boolean columns are included in the calculation.**kwargs: This is an additional keyword argument that can be passed to the function.# A code to illustrate the mean() function in Pandas# importing the pandas libraryimport pandas as pd# creating a dataframedf = pd.DataFrame([[1,2,3,4,5],[1,7,5,9,0.5],[3,11,13,14,12]],columns=list('ABCDE'))# printing the dataframeprint(df)# obtaining the mean value vertically across rowsprint("Mean across rows: ", df.mean())# obtaining the mean value horizontally over columnsprint("Mean across columns: ", df.mean(axis="columns"))
pandas library.df DataFrame.df DataFrame.mean() function, we obtain the mean of the values running downwards across the rows (axis 0). We print the result to the console.mean() function, we obtain the mean of the values running horizontally across columns (axis 1). We print the result to the console.