numpy.nanpercentile() in Python

numpy.nanpercentile() function compute the nth percentile of the given data (array elements) along the specified axis and ignores nan values. Example:

import numpy as np
a = np.array([10, 20, np.nan, 40])
res = np.nanpercentile(a, 50)
print(res)

20.0

Explanation: The 50th percentile (median) is calculated over [10, 20, 40], ignoring the NaN.

Syntax

numpy.nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=<no value>, method='linear')

Parameters:

Returns: A scalar or ndarray of percentiles, depending on q and axis.

Examples

Example 1: Computing multiple percentiles

import numpy as np
a = np.array([5, 15, np.nan, 25])
res = np.nanpercentile(a, [25, 50, 75])
print(res)

[10. 15. 20.]

Explanation: Calculates the 25th, 50th, and 75th percentiles of the non-NaN values.

Example 2: Applying along a specific axis

import numpy as np
a = np.array([[10, 20, np.nan], [30, np.nan, 50]])
res = np.nanpercentile(a, 50, axis=0)
print(res)

[20. 20. 50.]

Explanation: The median of each column is computed by ignoring NaN values.

• Column 0: median(10, 30) = 20
• Column 1: median(20) = 20
• Column 2: median(50) = 50.

Example 3: Using different interpolation method

import numpy as np
a = np.array([1, 2, 3, 4, 5, np.nan])
res = np.nanpercentile(a, 40, method='nearest')
print(res)

3.0

Explanation: With method='nearest', the function picks the value nearest to the 40th percentile position.