numpy.nanquantile(arr, q, axis = None) : Compute the qth quantile of the given data (array elements) along the specified axis, ignoring the nan values.
Quantile plays a very important role in Statistics when one deals with the Normal Distribution.
In the figure given above,
Q2 is the
median of the normally distributed data.
Q3 - Q2 represents the Interquantile Range of the given dataset.
arr : [array_like]input array.
q : quantile value.
axis : [int or tuples of int]axis along which we want to calculate the quantile value. Otherwise, it will consider arr to be flattened(works on all the axis). axis = 0 means along the column and axis = 1 means working along the row.
out : [ndarray, optional]Different array in which we want to place the result. The array must have same dimensions as expected output.
Results : qth quantile of the array (a scalar value if axis is none) or array with quantile values along specified axis, ignoring nan values.
Code #1 :
arr : [20, 2, 7, nan, 34] Q1 - quantile of arr : nan Q2 - quantile of arr : nan Q3 - quantile of arr : nan Q1 - nanquantile of arr : 13.5 Q2 - nanquantile of arr : 5.75 Q3 - nanquantile of arr : 23.5
arr : [[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]] Q2 quantile of arr, axis = None : nan Q2 quantile of arr, axis = None : 14.5 0th quantile of arr, axis = None : 1.0
arr : [[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]] Q2 quantile of arr, axis = 0 : [15. 2. 19.5 8. 19. ] 0th quantile of arr, axis = 0 : [14. 2. 12. 1. 4.] Q2 quantile of arr, axis = 1 : [23.5 17. 3. ] 0th quantile of arr, axis = 1 : [12. 8. 1.] Q2 quantile of arr, axis = 1 : [[23.5] [17. ] [ 3. ]] 0th quantile of arr, axis = 1 : [[12.] [ 8.] [ 1.]]
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