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.
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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.
