The Pandas Series - mean() function returns the mean of the values over the specified axis. The syntax for using this function is mentioned below:

### Syntax

Series.mean(axis=None, skipna=None, level=None, numeric_only=None)

### Parameters

 axis Optional. Specify {0 or 'index'}. Specify axis for the function to be applied on. skipna Optional. Specify True to exclude NA/null values when computing the result. Default is True. level Optional. Specify level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. A str specifies the level name. numeric_only Optional. Specify True to include only float, int or boolean data. Default: False

### Return Value

Returns scalar or Series if a level is specified.

### Example: Using mean() on a Series

In the example below, the mean() function is used to get the mean of values of a given series.

import pandas as pd
import numpy as np

idx = pd.MultiIndex.from_arrays([
['even', 'even', 'even', 'odd', 'odd', 'odd']],
names=['DataType'])

x = pd.Series([10, 20, 30, 5, 7, 9], name='Numbers', index=idx)

print("The Series contains:")
print(x)

#mean of all values in the series
print("\nx.mean() returns:")
print(x.mean())

#mean of all values within given level
print("\nx.mean(level='DataType') returns:")
print(x.mean(level='DataType'))
print("\nx.mean(level=0) returns:")
print(x.mean(level=0))

The output of the above code will be:

The Series contains:
DataType
even    10
even    20
even    30
odd      5
odd      7
odd      9
Name: Numbers, dtype: int64

x.mean() returns:
13.5

x.mean(level='DataType') returns:
DataType
even    20
odd      7
Name: Numbers, dtype: int64

x.mean(level=0) returns:
DataType
even    20
odd      7
Name: Numbers, dtype: int64

### Example: Using mean() on selected series in a DataFrame

Similarly, the mean() function can be applied on selected series/column of a given DataFrame. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
"Bonus": [5, 3, 2, 4],
"Last Salary": [58, 60, 63, 57],
"Salary": [60, 62, 65, 59]},
index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#mean of all values of 'Salary' series
print("\ndf['Salary'].mean() returns:")
print(df["Salary"].mean())

The output of the above code will be:

The DataFrame is:
Bonus  Last Salary  Salary
John       5           58      60
Marry      3           60      62
Sam        2           63      65
Jo         4           57      59

df['Salary'].mean() returns:
61.5

❮ Pandas Series - Functions

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