numpy.nanquantile() in Python

Last Updated : 02 Feb, 2023

numpy.nanquantile(arr, q, axis = None) : Compute the q^th quantile of the given data (array elements) along the specified axis, ignoring the nan values. Quantiles plays a very important role in statistics.

In the figure given above, Q2 is the median and Q3 - Q1 represents the Interquartile Range of the given dataset.

Parameters : 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 : q^th quantile of the array (a scalar value if axis is none) or array with quantile values along specified axis, ignoring nan values.

Code #1 :

Python3

# Python Program illustrating
# numpy.nanquantile() method
import numpy as np

# 1D array
arr = [20, 2, 7, np.nan, 34]
print("arr : ", arr)

print("\n-Q1 quantile of arr : ", np.quantile(arr, .50))
print("Q2 - quantile of arr : ", np.quantile(arr, .25))
print("Q3 - quantile of arr : ", np.quantile(arr, .75))

print("\nQ1 - nanquantile of arr : ", np.nanquantile(arr, .50))
print("Q2 - nanquantile of arr : ", np.nanquantile(arr, .25))
print("Q3 - nanquantile of arr : ", np.nanquantile(arr, .75))

Output :

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

Code #2:

Python3

# Python Program illustrating
# numpy.nanquantile() method

import numpy as np

# 2D array
arr = [[14, np.nan, 12, 33, 44],
       [15, np.nan, 27, 8, 19],
       [23, 2, np.nan, 1, 4, ]]
print("\narr : \n", arr)

# quantile of the flattened array
print("\nQ2 quantile of arr, axis = None : ", np.quantile(arr, .50))
print("\nQ2 quantile of arr, axis = None : ", np.nanquantile(arr, .50))
print("0th quantile of arr, axis = None : ", np.nanquantile(arr, 0))

Output:

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

Code #3:

Python3

# Python Program illustrating
# numpy.nanquantile() method
import numpy as np

# 2D array
arr = [[14, np.nan, 12, 33, 44],
       [15, np.nan, 27, 8, 19],
       [23, 2, np.nan, 1, 4, ]]
print("\narr : \n", arr)

# quantile along the axis = 0
print("\nQ2 quantile of arr, axis = 0 : ", np.nanquantile(arr, .50, axis=0))
print("0th quantile of arr, axis = 0 : ", np.nanquantile(arr, 0, axis=0))

# quantile along the axis = 1
print("\nQ2 quantile of arr, axis = 1 : ", np.nanquantile(arr, .50, axis=1))
print("0th quantile of arr, axis = 1 : ", np.nanquantile(arr, 0, axis=1))

print("\nQ2 quantile of arr, axis = 1 : \n",
      np.nanquantile(arr, .50, axis=1, keepdims=True))
print("\n0th quantile of arr, axis = 1 : \n",
      np.nanquantile(arr, 0, axis=1, keepdims=True))

Output:

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.]]

numpy.nanpercentile() in Python

mohit gupta_omg :)

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numpy.nanquantile() in Python

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