scipy.stats.tstd(array, limits=None, inclusive=(True, True)) calculates the trimmed standard deviation 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 standard deviation.
axis: Axis along which the trimmed standard deviation 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 deviation of the array elements based on the set parameters.
Code #1:
# Trimmed Standard Deviation from scipy import stats import numpy as np # array elements ranging from 0 to 19 x = np.arange(20) print("Trimmed Standard Deviation :", stats.tstd(x)) print("\nTrimmed Standard Deviation by setting limit : ", stats.tstd(x, (2, 10))) |
Output:
Trimmed Standard Deviation : 5.9160797831 Trimmed Standard Deviation by setting limit : 2.73861278753
Code #2: With multi-dimensional data, axis() working
# Trimmed Standard Deviation 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("Trimmed Standard Deviation is with default axis = 0 : \n", stats.tstd(arr1, axis = 1)) |
Output:
Trimmed Standard Deviation is with default axis = 0 : 94.0423824505
