Unlock the Power of Percentiles: A Statistical Measure to Analyze Data Distribution

What is a Percentile?

A percentile is a statistical measure that represents the value below which a specific percentage of data falls. It’s a powerful tool to analyze the distribution of a dataset, helping you understand the underlying patterns and trends.

Computing Percentiles with NumPy

In NumPy, the percentile() function computes the q-th percentile of data along a specified axis. This function takes in an input array, the q-th percentile to find, and optional arguments such as axis, out, keepdims, override_input, and method.

Understanding the Syntax

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

The arguments are:

• array: The input array, which can be array_like.
• q: The q-th percentile to find, which can be array_like of float.
• axis: The axis or axes along which the means are computed, optional.
• out: The output array in which to place the result, optional.
• keepdims: A boolean value specifying whether to preserve the shape of the original array, optional.
• override_input: A boolean value determining if intermediate calculations can modify an array, optional.
• method: The interpolation method to use, optional.

Default Values and Output Data Type

By default, axis is set to None, meaning the percentile of the entire array is taken. keepdims and override_input are set to False. The interpolation method is 'linear'. If the input contains integers or floats smaller than float64, the output data type is float64. Otherwise, the output data type is the same as that of the input.

Examples in Action

Let’s dive into some examples to see how percentile() works:

Example 1: Find the Percentile of a ndArray

import numpy as np

data = np.array([1, 2, 3, 4, 5])
q = 50
result = np.percentile(data, q)
print(result)  # Output: 3.0

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

import numpy as np

data = np.array([1, 2, 3, 4, 5])
q = 50
out_array = np.empty(())
result = np.percentile(data, q, out=out_array)
print(out_array)  # Output: [3.]

Example 3: Using Optional keepdims Argument

import numpy as np

data = np.array([[1, 2], [3, 4]])
q = 50
result = np.percentile(data, q, keepdims=True)
print(result)  # Output: [[3.]]

By mastering the percentile() function, you’ll be able to unlock new insights into your data and make more informed decisions.