Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
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.var() function return unbiased variance over requested axis. The variance is normalized by N-1 by default. This can be changed using the ddof argument.
Syntax: Series.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)
Parameter :
axis : {index (0)}
skipna : Exclude NA/null values. If an entire row/column is NA, the result will be NA
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar
ddof : Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N represents the number of elements.
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 : var : scalar or Series (if level specified)
Example #1: Use Series.var() function to find the variance of the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([19.5, 16.8, 22.78, 20.124, 18.1002]) # Print the series print(sr) |
Output :
Now we will use Series.var() function to find the variance of the given series object.
# find the variance sr.var() |
Output :
As we can see in the output, the Series.var() function has returned the variance of the given Series object.
Example #2: Use Series.var() function to find the variance of the given Series object. The given Series object contains some missing values.
Note : We can skip the missing values by setting the skipna parameter to True.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([100, 214, 325, 88, None, 325, None, 68]) # Print the series print(sr) |
Output :
Now we will use Series.var() function to find the variance of the given series object.
# find the variance sr.var(skipna = True) |
Output :
As we can see in the output, the Series.var() function has returned the variance of the given Series object.
