Pandas DataFrame mean() Method
Pandas DataFrame mean()
Pandas dataframe.mean() function returns the mean of the values for the requested axis. If the method is applied on a pandas series object, then the method returns a scalar value which is the mean value of all the observations in the Pandas Dataframe. If the method is applied on a Pandas Dataframe object, then the method returns a Pandas series object which contains the mean of the values over the specified axis.
Syntax: DataFrame.mean(axis=0, skipna=True, level=None, numeric_only=False, **kwargs)
Parameters :
- axis : {index (0), columns (1)}
- skipna : Exclude NA/null values when computing the result
- level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
- numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Returns : mean : Series or DataFrame (if level specified)
Pandas DataFrame.mean() Examples
Example 1:
Use mean() function to find the mean of all the observations over the index axis.
# importing pandas as pd
import pandas as pd
# Creating the dataframe
df = pd.DataFrame({"A":[12, 4, 5, 44, 1],
"B":[5, 2, 54, 3, 2],
"C":[20, 16, 7, 3, 8],
"D":[14, 3, 17, 2, 6]})
# Print the dataframe
df
Let’s use the Dataframe.mean() function to find the mean over the index axis.
# Even if we do not specify axis = 0,
# the method will return the mean over
# the index axis by default
df.mean(axis = 0)
Output:
Example 2:
Use mean() function on a Dataframe that has None values. Also, find the mean over the column axis.
# importing pandas as pd
import pandas as pd
# Creating the dataframe
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
"B":[7, 2, 54, 3, None],
"C":[20, 16, 11, 3, 8],
"D":[14, 3, None, 2, 6]})
# skip the Na values while finding the mean
df.mean(axis = 1, skipna = True)
Output: