# sciPy stats.tmean() | Python

• Last Updated : 10 Feb, 2019

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

It’s formula – Parameters :
array: Input array or object having the elements to calculate the trimmed mean.
axis: Axis along which the trimmed 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 mean of the array elements based on the set parameters.

Code #1:

 `# Trimmed Mean ``  ` `from` `scipy ``import` `stats``import` `numpy as np ``  ` `# array elements ranging from 0 to 19``x ``=` `np.arange(``20``)``   ` `print``(``"Trimmed Mean :"``, stats.tmean(x)) ``  ` `  ` `print``(``"\nTrimmed Mean by setting limit : "``, ``      ``stats.tmean(x, (``2``, ``10``)))`
Output:
```Trimmed Mean : 9.5

Trimmed Mean by setting limit :  6.0
```

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

 `# Trimmed Mean ``  ` `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 Mean is with default axis = 0 : \n"``, ``      ``stats.tmean(arr1, axis ``=` `1``))`
Output:
```Trimmed Mean is with default axis = 0 :
42.8333333333
```

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