Pandas core window rolling Rolling function sem() in Python

The Pandas core window rolling Rolling function sem() used to calculate the rolling standard error of mean. It returns either Series or DataFrame as the original object with np.float64 dtype.

Syntax

 1 Rolling.sem(ddof = 1, numeric_only = False)

ddof : It is an integer that specifies the Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. If not specified the default value will be 1.

numeric_only : It can be boolean value that include only float, int, or boolean data. If not specified the default value will be False.

sem() function

 1 import pandas as pd
 2 
 3 ser = pd.Series([1, 2, 3, 2, 5])
 4 rolling = ser.rolling(2, min_periods = 1)
 5 res = rolling.sem()
 6 print('The unbiased standard error of the mean :')
 7 print(res)

In the above example, an Series object is created by passing an array. A rolling sem() function is called that compute the unbiased standard error of mean and assign result to the variable that will be printed on console.

Output

 1 The unbiased standard error of the mean :
 2 0         NaN
 3 1    0.707107
 4 2    0.707107
 5 3    0.707107
 6 4    2.121320
 7 dtype: float64

Example 2

 1 import pandas as pd
 2 
 3 ser = pd.Series([1, 2, 3, 4])
 4 print('The series object :')
 5 print(ser)
 6 
 7 rolling = ser.rolling(2, min_periods = 1)
 8 res = rolling.sem()
 9 print('The unbiased standard error of the mean :')
 10 print(res)

In the above example, an Series object is created by passing an array. A rolling sem() function is called that compute the unbiased standard error of mean and assign result to the variable.

Output

 1 The series object :
 2 0    1
 3 1    2
 4 2    3
 5 3    4
 6 dtype: int64
 7 The unbiased standard error of the mean :
 8 0         NaN
 9 1    0.707107
 10 2    0.707107
 11 3    0.707107
 12 dtype: float64