numpy.nanpercentile () in Python

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numpy.nanpercentile() used to calculate the nth percentile of data (array elements) along the specified axis, ang ignores nan values.

Syntax: numpy.nanpercentile (arr, q, axis = None, out = None)
Parameters:
arr: input array.
q: percentile value.
axis: axis along which we want to calculate the percentile 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: Different array in which we want to place the result. The array must have same dimensions as expected output.

Return: Percentile of the array (a scalar value if axis is none) or array with percentiles of values ‚Äã‚Äãalong specified axis.

Code # 1: Work

# Python program illustrating
# numpy.nanpercentile () method

import numpy as np


# 1D array

arr = [ 20 , 2 , 7 , np.nan, 34 ]

print ( " arr: " , arr)

print ( " 30th percentile of arr: " ,

np.percentile (arr, 50 ))

print ( "25th percentile of arr:" ,

np.percentile (arr, 25 ))

print ( "75th percentile of arr:" ,

np.percentile (arr, 75 ))

print ( "30th percentile of arr:" ,

np.nanpercentile (arr, 50 ))

print ( "25th percentile of arr:" ,

np.nanpercentile (arr, 25 ))

print ( "75th percentile of arr:" ,

n p.nanpercentile (arr, 75 ))

Output:

 arr: [20, 2, 7, nan, 34] 30th percentile of arr: nan 25th percentile of arr: nan 75th percentile of arr: nan 30th percentile of arr: 13.5 25th percentile of arr: 5.75 75th percentile of arr: 23.5 

Code # 2:

# Python program illustrating
# numpy.nanpercentile () 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 ( "arr:" , arr)


# Flattened array percentile

print ( "50th Percentile of arr, axis = None:" ,

np.percentile (arr, 50 ))

print ( " 50th Percentile of arr, axis = None: " ,

np.nanpercentile (arr, 50 ))

print ( "0th Percentile of arr, axis = None:" ,

np.nanpercentile (arr, 0 ))


# Axis percentile = 0

print ( "50th Percentile of arr, axis = 0:" ,

np.nanpercentile (arr, 50 , axis = 0 ))

print ( "0th Percentile of arr, axis = 0:" ,

np.nanpercentile (arr, 0 , axis = 0 ))


# Axis percentile = 1

print ( "50th Percentile of arr, axis = 1:" ,

np.nanpercentile (arr, 50 , axis = 1 ) )

print ( "0th Percentile of arr, axis = 1: " ,

np.nanpercentile (arr, 0 , axis = 1 ))

print ( "0th Percentile of arr, axis = 1:" ,

np.nanpercentile (arr, 50 , axis = 1 , keepdims = True ))

print ( "0th Percentile of arr, axis = 1:" ,

np.nanpercentile (arr, 0 , axis = 1 , keepdims = True ))

Output:

 arr: [[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]] 50th Percentile of arr , axis = None: nan 50th Percentile of arr, axis = None: 14.5 0th Percentile of arr, axis = None: 1.0 50th Percentile of arr, axis = 0: [15. 2. 19.5 8. 19.] 0th Percentile of arr, axis = 0: [14. 2. 12. 1. 4.] 50th Percentile of arr, axis = 1: [23.5 17. 3.] 0th Percentile of arr, axis = 1: [12. 8. 1.] 0th Percentile of arr, axis = 1: [[23.5] [17. ] [3.]] 0th Percentile of arr, axis = 1: [[12.] [8.] [1.]] 

Code # 3:

# Python program illustrating
# numpy.nanpercentile () method

import numpy as np

# 2D array

arr = [[ 14 , np.nan, 12 , 33 , 44 ],

[ 15 , np.nan, 27 , 8 , 19 ],

[ 23 , np.nan, np.nan, 1 , 4 ,]]

print ( "arr:" , arr )

# Axis percentile = 1

print ( "50th Percentile of arr, axis = 1:" ,

np.nanpercentile (arr, 50 , axis = 1 ))

print ( " 50th Percentile of arr, axis = 0: " ,

np.nanpercentile (arr, 50 , axis = 0 ))

Output:

 arr: [[14, nan, 12, 33, 44] , [15, nan, 27, 8, 19], [23, nan, nan, 1, 4]] 50th Percentile of arr, axis = 1: [23.5 17. 4.] 50th Percentile of arr, axis = 0: [fifteen. nan 19.5 8. 19.] RuntimeWarning: All-NaN slice encountered overwrite_input, interpolation) 

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