Python | Pandas Series.rmul()

Python | Pandas Series.sem()

Last Updated : 05 Feb, 2019

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.sem() function return unbiased standard error of the mean over requested axis. The result is normalized by N-1 by default. This can be changed using the ddof argument.

Syntax: Series.sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Parameter :
axis : {index (0)}
skipna : Exclude NA/null values.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
ddof : Delta Degrees of Freedom.
numeric_only : Include only float, int, boolean columns.

Returns : scalar or Series (if level specified)

Example #1 : Use Series.sem() function to find the standard error of the mean of the underlying data in the given Series object.

# importing pandas as pd 
import pandas as pd 
  
# Creating the Series 
sr = pd.Series([100, 25, 32, 118, 24, 65]) 
  
# Print the series 
print(sr) 

Output :

Now we will use Series.sem() function to find the standard error of the mean of the underlying data.

# find standard error of the mean 
sr.sem() 

Output :

As we can see in the output, Series.sem() function has successfully calculated the standard error the mean of the underlying data in the given Series object.

Example #2 : Use Series.sem() function to find the standard error of the mean of the underlying data in the given Series object. The given Series object also contains some missing values.

# importing pandas as pd 
import pandas as pd 
  
# Creating the Series 
sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None]) 
  
# Print the series 
print(sr) 

Output :

Now we will use Series.sem() function to find the standard error of the mean of the underlying data.

# find standard error of the mean 
# Skip all the missing values 
sr.sem(skipna = True) 

Output :

As we can see in the output, Series.sem() function has successfully calculated the standard error the mean of the underlying data in the given Series object.

Python | Pandas Series.rmul()

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