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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We hope this article has helped you to resolve the problem. Apart from numpy.nanpercentile () in Python, check other array Python module-related topics.

Want to excel in Python? See our review of the best Python online courses 2023. If you are interested in Data Science, check also how to learn programming in R.

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Ken Danburry

San Francisco | 2023-03-21

I was preparing for my coding interview, thanks for clarifying this - numpy.nanpercentile () in Python in Python is not the simplest one. Checked yesterday, it works!

Walter Innsbruck

Moscow | 2023-03-21

Simply put and clear. Thank you for sharing. numpy.nanpercentile () in Python and other issues with Expect PHP module was always my weak point 😁. Will get back tomorrow with feedback

Xu Richtgofen

Rome | 2023-03-21

I was preparing for my coding interview, thanks for clarifying this - numpy.nanpercentile () in Python in Python is not the simplest one. I just hope that will not emerge anymore

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