Related Articles
sciPy stats.tvar() function | Python
• Last Updated : 07 Feb, 2019

scipy.stats.tvar(array, limits=None, inclusive=(1, 1)) function calculates the trimmed variance of the array elements along with ignoring the values lying outside the specified limits.

It’s formula – Parameters :
array: Input array or object having the elements to calculate the trimmed variance.
limits: Lower and upper bound of the array to consider, values less than the lower limit or greater than the upper limit will be ignored. If limits is None [default], then all values are used.
inclusive: Decide whether to include the values equal to lower or upper bound, or to exclude them while calculation.

Returns : Trimmed variance of the array elements based on the set parameters.

Code #1:

 `# Trimmed Variance `` ` `from` `scipy ``import` `stats``import` `numpy as np `` ` `# array elements ranging from 0 to 19``x ``=` `np.arange(``20``)``  ` `print``(``"Trimmed Variance :"``, stats.tvar(x)) `` ` ` ` `print``(``"\nTrimmed Variance by setting limit : "``, ``      ``stats.tvar(x, (``2``, ``10``)))`
Output:
```Trimmed Variance : 35.0

Trimmed Variance by setting limit :  7.5
```

Code #2: Checking “inclusive” flags

 `# Trimmed Variance `` ` `from` `scipy ``import` `stats``import` `numpy as np `` ` `# array elements ranging from 0 to 19``x ``=` `np.arange(``20``)`` ` `# Setting limits``print``(``"\nTrimmed Variance by setting limit : "``, ``      ``stats.tvar(x, (``2``, ``10``))) `` ` `# using flag``print``(``"\nTrimmed Variance by setting limit : "``, ``      ``stats.tvar(x, (``2``, ``10``), (``False``, ``True``))) `` ` `print``(``"\nTrimmed Variance by setting limit : "``, ``      ``stats.tvar(x, (``2``, ``12``), (``True``, ``False``))) `
Output:
```Trimmed Variance by setting limit :  7.5

Trimmed Variance by setting limit :  6.0

Trimmed Variance by setting limit :  9.16666666667
```

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up