NumPy Statistics Functions

What Are NumPy Statistics Functions?

NumPy statistics functions are built-in functions used to analyze numerical data stored in arrays. They help summarize data by calculating central tendency, spread, and distribution values. These functions are fast, accurate, and commonly used in data analysis, scientific computing, and machine learning.

Why Use NumPy for Statistics?

NumPy statistical functions:

• Work efficiently on large datasets

• Support multi-dimensional arrays

• Allow axis-wise calculations

• Are faster than manual Python calculations

• Integrate easily with data science workflows

Measures of Central Tendency

Mean

Calculates the average value.

import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.mean(data)) # Output: # 30.0

Median

Returns the middle value when data is sorted.

 import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.median(data)) # Output: # 30.0

Sum

Adds all elements.

 import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.sum(data)) # Output: # 150

Measures of Dispersion

Minimum and Maximum

 import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.min(data)) print(np.max(data)) # Output: # 10 # 50

Range (Max − Min)

import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.ptp(data)) # Output: # 40

Variance

Measures how far values are spread from the mean.

import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.var(data)) # Output: # 200.0

Standard Deviation

Square root of variance.

import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.std(data)) # Output: # 14.142135623730951

Percentiles & Quantiles

Percentile

import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.percentile(data)) # Output: # 20.0

Returns the value below which 25% of the data lies.

Quantile

import numpy as np data = np.array([10, 20, 30, 40, 50]) print(np.quantile(data)) # Output: # 30.0

Quantiles are similar to percentiles but expressed between 0 and 1.

Other Useful Statistical Functions