To use this module, you must import statistics at the top of your code.
This module is primarily used to do calculations regarding statistics of numerical data.

Here are example functions within this module,
- statistics.mean() returns the mean of the data
- statistics.median() returns the median of the data
- statistics.mode() returns the mode of the data
- statistics.pstdev() returns standard deviation from entire population
- statistics.stdev() returns standard deviation from sample of data
- statistics.pvariance() returns variance from entire population
- statistics.variance() returns variance from sample of data


University of Michigan - Ann Arbor

Here are examples of modules within the Python Standard Library:
- random module
- request module
- statistics module
- math module
- cmath module

Python Standard Library Modules

https://docs.python.org/3/library/index.html

Python Standard Library Documentation

W3Schools offers online tutorials about different parts of programming in Python: https://www.w3schools.com/python/default.asp

W3Schools Python Tutorial

To use this module, you must import random at the top of your code.

This module is primarily used to generate random numbers.

Here are example functions from this module,
- random.randrange() returns a random number within the given range
- random.randint() returns a random number within the given range
- random.random() returns a float between 0 and 1
- random.uniform() returns a float between two given parameters

Random Module

Statistics Module

Python's `math` module provides access to the mathematical functions defined by the C standard. 
```
import math
```

These functions cannot be used with complex numbers; use the functions of the same name from the `cmath` module if complex number support is required.

Examples of math functions:
- math.ceil(x)
- math.copysign(x, y)
- math.fabs(x)
- math.factorial(x)
- math.floor(x)
- math.gcd(*integers)
- math.isfinite(x)
- math.isinf(x)
- math.isnan(x)
- math.isqrt(n)
- math.lcm(*integers)
- math.prod(iterable, *, start=1)
- math.exp(x)
- math.log(x[, base])

Learn Before

Related