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Python | Pandas Series.var

  • Last Updated : 28 Jan, 2019

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.

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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.




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