NumPy - percentile() function

The NumPy percentile() function returns the q-th percentile of the array elements or q-th percentile the data along the specified axis.

Syntax

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

Parameters

`a`	`Required.` Specify the input array (array_like).
`q`	`Required.` Specify percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive (array_like of float).
`axis`	`Optional.` Specify axis or axes along which to operate. The default, axis=None, operation is performed on flattened array.
`out`	`Optional.` Specify the output array in which to place the result. It must have the same shape as the expected output.
`interpolation`	`Optional.` Specify the interpolation method to use when the desired percentile lies between two data points. It can take value from {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
`keepdims`	`Optional.` If this is set to True, the reduced axes are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return Value

Returns percentile points of a. If q is a single percentile and axis=None, then the result is a scalar. In other cases, the result is an array.

Example: percentile() of flattened array

In the example below, percentile() function is used to return the maximum of all values present in the array.

import numpy as np
Arr = np.array([[10,20, 30],[40, 50, 60]])

print("Array is:")
print(Arr)

print()
#calculating 50th percentile point
print("50th percentile:", np.percentile(Arr, 50))

print()
#calculating (25, 50, 75) percentile points
print("[25, 50, 75] percentile:\n", 
       np.percentile(Arr, (25, 50, 75)))

The output of the above code will be:

Array is:
[[10 20 30]
 [40 50 60]]

50th percentile: 35.0

[25, 50, 75] percentile:
 [22.5 35.  47.5]

Example: using axis parameter

When axis parameter is provided, the percentile points are calculated over the specified axes. Consider the following example.

import numpy as np
Arr = np.array([[10,20, 30],[40, 50, 60]])

print("Array is:")
print(Arr)

print()
#calculating 50th percentile point along axis=0
print("50th percentile (axis=0):", 
       np.percentile(Arr, 50, axis=0))

#calculating 50th percentile point along axis=1
print("50th percentile (axis=1):", 
       np.percentile(Arr, 50, axis=1))

The output of the above code will be:

Array is:
[[10 20 30]
 [40 50 60]]

50th percentile (axis=0): [25. 35. 45.]
50th percentile (axis=1): [20. 50.]

Example: using interpolation parameter

The interpolation parameter can be used to specify the interpolation method to be used while calculating percentile points. Consider the example below:

import numpy as np
Arr = np.array([[10,20, 30],[40, 50, 60]])

print("Array is:")
print(Arr)

print()
#calculating 50th percentile point
print("50th percentile:", 
      np.percentile(Arr, 50, interpolation='lower'))

print()
#calculating (25, 50, 75) percentile points
print("[25, 50, 75] percentile:\n", 
       np.percentile(Arr, (25, 50, 75), interpolation='lower'))

The output of the above code will be:

Array is:
[[10 20 30]
 [40 50 60]]

50th percentile: 30

[25, 50, 75] percentile:
 [20 30 40]

Example: visualize interpolation method

The different types of interpolation can be visualized graphically:

import numpy as np
import matplotlib.pyplot as plt

x = [0, 1, 2, 3]
p = np.linspace(0, 100, 5000)

fig, ax = plt.subplots()

lines = [
    ('linear', None),
    ('higher', '--'),
    ('lower', '--'),
    ('nearest', '-.'),
    ('midpoint', '-.'),
]

for method, style in lines:
  ax.plot(p, np.percentile(x, p, interpolation=method),
        label=method, linestyle=style)

ax.set(title='Interpolation methods for list: ' + str(x),
       xlabel='Percentile',
       ylabel='List item returned',
       yticks=x)
ax.legend()
plt.show()

The output of the above code will be:

❮ NumPy - Functions