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
