# numpy.percentile() in python

## numpy.percentile() in python

numpy.percentile()function used to compute the nth percentile of the given data (array elements) along the specified axis.

Syntax : numpy.percentile(arr, n, axis=None, out=None)
Parameters :
arr :input array.
n : 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 :nth Percentile of the array (a scalar value if axis is none)or array with percentile values along specified axis.

Code #1 : Working

 `# Python Program illustrating` `# numpy.percentile() method` `  ` `import` `numpy as np` `  ` `# 1D array` `arr ``=` `[``20``, ``2``, ``7``, ``1``, ``34``]` `print``(``"arr : "``, arr)` `print``(``"50th 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``))`

Output :

```arr :  [20, 2, 7, 1, 34]
50th percentile of arr :  7.0
25th percentile of arr :  2.0
75th percentile of arr :  20.0

```

Code #2 :

 `# Python Program illustrating` `# numpy.percentile() method ` `import` `numpy as np` `# 2D array` `arr ``=` `[[``14``, ``17``, ``12``, ``33``, ``44``], ` `       ``[``15``, ``6``, ``27``, ``8``, ``19``],` `       ``[``23``, ``2``, ``54``, ``1``, ``4``,]]` `print``(``"\narr : \n"``, arr)` `   ` `# Percentile of the flattened array` `print``(``"\n50th Percentile of arr, axis = None : "``,` `      ``np.percentile(arr, ``50``))` `print``(``"0th Percentile of arr, axis = None : "``,` `      ``np.percentile(arr, ``0``))` `   ` `# Percentile along the axis = 0` `print``(``"\n50th Percentile of arr, axis = 0 : "``,` `      ``np.percentile(arr, ``50``, axis ``=``0``))` `print``(``"0th Percentile of arr, axis = 0 : "``,` `      ``np.percentile(arr, ``0``, axis ``=``0``))`

Output :

```arr :
[[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]]

50th Percentile of arr, axis = None :  15.0
0th Percentile of arr, axis = None :  1.0

50th Percentile of arr, axis = 0 :  [15.  6. 27.  8. 19.]
0th Percentile of arr, axis = 0 :  [14.  2. 12.  1.  4.]

50th Percentile of arr, axis = 1 :  [17. 15.  4.]
0th Percentile of arr, axis = 1 :  [12.  6.  1.]

```

Code #3 :

 `# Python Program illustrating` `# numpy.percentile() method` `import` `numpy as np` `# 2D array` `arr ``=` `[[``14``, ``17``, ``12``, ``33``, ``44``], ` `       ``[``15``, ``6``, ``27``, ``8``, ``19``],` `       ``[``23``, ``2``, ``54``, ``1``, ``4``,]]` `print``(``"\narr : \n"``, arr)` `# Percentile along the axis = 1` `print``(``"\n50th Percentile of arr, axis = 1 : "``,` `      ``np.percentile(arr, ``50``, axis ``=``1``))` `print``(``"0th Percentile of arr, axis = 1 : "``,` `      ``np.percentile(arr, ``0``, axis ``=``1``))` ` ` `print``(``"\n0th Percentile of arr, axis = 1 : \n"``,` `      ``np.percentile(arr, ``50``, axis ``=``1``, keepdims``=``True``))` `print``(``"\n0th Percentile of arr, axis = 1 : \n"``,` `      ``np.percentile(arr, ``0``, axis ``=``1``, keepdims``=``True``))`

Output :

```arr :
[[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]]

0th Percentile of arr, axis = 1 :
[[17.]
[15.]
[ 4.]]

0th Percentile of arr, axis = 1 :
[[12.]
[ 6.]
[ 1.]]```

Last Updated on November 13, 2021 by admin

## Python – math.cos() functionPython – math.cos() function

Python | math.cos() function In Python, math module contains a number of mathematical operations, which

## numpy.where() in Pythonnumpy.where() in Python

numpy.where() in Python The numpy.where() function returns the indices of elements in an input array where the

## Change data type of given numpy arrayChange data type of given numpy array

Change data type of given numpy array In this post, we are going to see

## numpy.sum() in Pythonnumpy.sum() in Python

numpy.sum() in Python numpy.sum(arr, axis, dtype, out) : This function returns the sum of array elements over

## How to inverse a matrix using NumPyHow to inverse a matrix using NumPy

How to inverse a matrix using NumPy The inverse of a matrix is just a

## numpy.linspace() in Pythonnumpy.linspace() in Python

numpy.linspace() in Python The numpy.linspace() function returns number spaces evenly w.r.t interval. Similar to numpy.arrange() function but instead of

## numpy.mean() in Pythonnumpy.mean() in Python

numpy.mean() in Python numpy.mean(arr, axis = None) : Compute the arithmetic mean (average) of the given data

## numpy.multiply() in Pythonnumpy.multiply() in Python

numpy.multiply() in Python numpy.multiply() function is used when we want to compute the multiplication of two