NumPy percentile()

import numpy as np

# create an array
array1 = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# calculate the 25th, 50th and 75th percentile of the array
p25 = np.percentile(array1, 25)
p50 = np.percentile(array1, 50)
p75 = np.percentile(array1, 75)

print(p25, p50, p75)

# Output: 1.75 3.5 5.25

percentile() Syntax

The syntax of percentile() is:

numpy.percentile(array, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, interpolation=None)

percentile() Arguments

The percentile() method takes the following arguments:

• array - input array(can be array_like)
• q- qth percentile to find(can be array_like of float)
• axis(optional)- axis or axes along which the means are computed(int or tuple of int)
• out(optional)- output array in which to place the result(ndarray)
• keepdims(optional)- specifies whether to preserve the shape of the original array(bool)
• override_input(optional)- bool value that determines if intermediate calculations can modify an array
• method(optional)- interpolation method to use
• interpolation(optional)- the deprecated name for the method keyword argument

percentile() Return Value

The percentile() method returns the q-th percentile(s) of the input array along the specified axis.

Percentile

The percentile is a statistical measure that represents the value below which a specific percentage of data falls. It helps analyze the distribution of a dataset.

In NumPy, the percentile() function computes the q-th percentile of data along the specified axis.

The q-th percentile represents the value below which q percent of the data falls. For example, the 50th percentile (also known as the median) divides the data into two equal halves, with 50% of the values below it.

Example 1: Find the Percentile of a ndArray

import numpy as np

# create an array
array1 = np.array([[[0, 1],[2, 3]],
   [[4, 5],[6, 7]]])

# find the 50th percentile of entire array
percentile1 = np.percentile(array1, q=50)

# find the 50th percentile across axis 0
percentile2 = np.percentile(array1, q=50, axis=0)

# find the 50th percentile across axis 0 and 1
percentile3 = np.percentile(array1, q=50, axis=(0, 1))

print('\n50th Percentile of the entire array:', percentile1)
print('\n50th Percentile across axis 0:\n', percentile2)
print('\n50th Percentile across axis 0 and 1:', percentile3)

Output

50th Percentile of the entire array: 3.5

50th Percentile across axis 0:
[[2. 3.]
 [4. 5.]]

50th Percentile across axis 0 and 1:[3. 4.]

Example 2: Use out to Store the Result in Desired Location

The out parameter allows to specify an output array where the result will be stored.

import numpy as np

arr = np.array([[1, 2, 3],
                [4, 5, 6]])

# create an output array
output = np.zeros(3)

# compute 25th percentile and store the result in the output array
np.percentile(arr, 25, out = output, axis = 0)

print('25th Percentile:', output)

Output

25th Percentile: [1.75 2.75 3.75]

Example 3: Using Optional keepdims Argument

If keepdims is set to True, the resultant mean array is of the same number of dimensions as the original array.

import numpy as np

arr = np.array([[1, 2, 3],
                [4, 5, 6]])

# keepdims defaults to False
result1 = np.percentile(arr, 50 , axis = 0)

# pass keepdims as True
result2 = np.percentile(arr, 50, axis = 0, keepdims = True)

print('Dimensions in original array:', arr.ndim)
print('Without keepdims:', result1, 'with dimensions', result1.ndim)
print('With keepdims:', result2, 'with dimensions', result2.ndim)

Output

Dimensions in original array: 2
Without keepdims: [2.5 3.5 4.5] with dimensions 1
With keepdims: [[2.5 3.5 4.5]] with dimensions 2