Pandas DataFrame.mean()
The mean() function is used to return the mean of the values for the requested axis. If we apply this method on a Series object , then it returns a scalar value , which is the mean value of all the observations in the dataframe.
If we apply this method on a DataFrame object, then it returns a Series object which contains mean of values over the specified axis.
Syntax
DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters
- axis: {index (0), columns (1)}.
This refers to the axis for a function that is to be applied. - skipna: It excludes all the null values when computing result.
- level: It counts along with a particular level and collapsing into a Series if the axis is a MultiIndex (hierarchical),
- numeric_only: It includes only int, float, boolean columns. If None, it will attempt to use everything, then use only numeric data. Not implemented for Series.
Returns
It returns the mean of the Series or DataFrame if the level is specified.
Example
# importing pandas as pd import pandas as pd # Creating the dataframe info = pd.DataFrame({"A":[8, 2, 7, 12, 6], "B":[26, 19, 7, 5, 9], "C":[10, 11, 15, 4, 3], "D":[16, 24, 14, 22, 1]}) # Print the dataframe info # If axis = 0 is not specified, then # by default method return the mean over # the index axis info.mean(axis = 0)
Output
A 7.0
B 13.2
C 8.6
D 15.4
dtype: float64
Example2
# importing pandas as pd import pandas as pd # Creating the dataframe info = pd.DataFrame({"A":[5, 2, 6, 4, None], "B":[12, 19, None, 8, 21], "C":[15, 26, 11, None, 3], "D":[14, 17, 29, 16, 23]}) # while finding mean, it skip null values info.mean(axis = 1, skipna = True)
Output
0 11.500000
1 16.000000
2 15.333333
3 9.333333
4 15.666667
dtype: float64