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sciPy stats.tsem() function | Python
• Last Updated : 10 Feb, 2019

`scipy.stats.tsem(array, limits=None, inclusive=(True, True))` calculates the trimmed standard error of the mean of array elements along the specified axis of the array.

Its formula :- Parameters :
array: Input array or object having the elements to calculate the trimmed standard error of the mean.
axis: Axis along which the trimmed standard error of the mean is to be computed. By default axis = 0.
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.

Returns : Trimmed standard error of the mean of array elements based on the set parameters.

Code #1:

 `# Trimmed Standard error ``  ` `from` `scipy ``import` `stats``import` `numpy as np ``  ` `# array elements ranging from 0 to 19``x ``=` `np.arange(``20``)``   ` `print``(``"Trimmed Standard error :"``, stats.tsem(x)) ``  ` `  ` `print``(``"\nTrimmed Standard error by setting limit : "``, ``      ``stats.tsem(x, (``2``, ``10``)))`
Output:
```Trimmed Standard error : 1.32287565553

Trimmed Standard error by setting limit :  0.912870929175
```

Code #2: With multi-dimensional data, axis() working

 `# Trimmed Standard error ``  ` `from` `scipy ``import` `stats``import` `numpy as np `` ` `arr1 ``=` `[[``1``, ``3``, ``27``], ``        ``[``5``, ``3``, ``18``], ``        ``[``17``, ``16``, ``333``], ``        ``[``3``, ``6``, ``82``]] ``  ` ` ` `# using axis = 0``print``(``"\nTrimmed Standard error is with default axis = 0 : \n"``, ``      ``stats.tsem(arr1, axis ``=` `1``))`
Output:
```Trimmed Standard error is with default axis = 0 :
27.1476974115
```

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