semvec/pypi-packages huggingface dataset:
nb-probability 0.1.0
A package for Gaussian and Binomial distributions
Requires Python: >=3.6

# nb_probability Package

A Python package for working with Gaussian and Binomial distributions. This package provides classes and methods to calculate and visualize Gaussian and Binomial distributions.

## Features

- Calculate mean and standard deviation for Gaussian and Binomial distributions
- Compute probability density functions
- Plot distributions using Matplotlib
- Read data from files and update distribution statistics

## Installation

You can install the package using pip:

pip install nb_probability


Usage
Here are some examples of how to use the package:

Gaussian Distribution
The Gaussian class provides methods to calculate and visualize Gaussian distributions.

from probability_distributions import Gaussian

# Create a Gaussian distribution
gaussian = Gaussian(mean=0, stdev=1)

# Calculate mean and standard deviation
mean = gaussian.calculate_mean()
stdev = gaussian.calculate_stdev()

# Print mean and standard deviation
print(f'Mean: {mean}, Standard Deviation: {stdev}')

# Plot the Gaussian distribution
gaussian.plot_histogram_pdf()

Methods
calculate_mean(): Calculates the mean of the data set.
calculate_stdev(sample=True): Calculates the standard deviation of the data set.
read_data_file(file_name, sample=True): Reads data from a file and updates the mean and standard deviation.
plot_histogram(): Plots a histogram of the data.
pdf(x): Calculates the probability density function for a given point.
plot_histogram_pdf(n_spaces=50): Plots the normalized histogram and probability density function.
__add__(self, other): Adds two Gaussian distributions.
__repr__(): Returns the string representation of the Gaussian distribution.
Binomial Distribution
The Binomial class provides methods to calculate and visualize Binomial distributions.

Example

from probability_distributions import Binomial

# Create a Binomial distribution
binomial = Binomial(prob=0.5, size=20)

# Calculate mean and standard deviation
mean = binomial.calculate_mean()
stdev = binomial.calculate_stdev()

# Print mean and standard deviation
print(f'Mean: {mean}, Standard Deviation: {stdev}')

# Plot the Binomial distribution
binomial.plot_bar_pdf()

Methods
calculate_mean(): Calculates the mean of the Binomial distribution.
calculate_stdev(): Calculates the standard deviation of the Binomial distribution.
replace_stats_with_data(file_name): Reads data from a file and updates n, p, mean, and stdev.
plot_bar(): Plots a bar chart of the data.
pdf(k): Calculates the probability density function for a given number of positive outcomes.
plot_bar_pdf(): Plots the probability density function.
__add__(self, other): Adds two Binomial distributions with equal p values.
__repr__(): Returns the string representation of the Binomial distribution.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.

License
This project is licensed under the MIT License - see the LICENSE.txt file for details.
minz 0.0.1
Very simple cli framework for the very lazy
Requires Python: >=3.10

# WIP!! This is incomplete/theoretical examples!

# minz - very simple cli framework (for the very lazy)

Got No time to learn and download a huge framework just
to make some simple clis? Yeah me neither I got a board game
meetup to get to after this!

Why not just write a bunch of _vanilla python functions_ and
let minz deal with the weird linux stuff?!

```python

# this can already be a cli command
def say_hello():
    # docstring adds a description to --help options
    """
    Prints hello to the screen
    """
    print("hello lazy!")

if __name__ == '__main__':
    minz = Minz()
    minz.register(say_hello)
    minz.run()
```

```
$ python cli.py say-hello
hello lazy!
```

But generally you would add an entrypoint to your cli
that discovers all functions

```python
from minz import Minz

minz.autorun(dir="./commands")
```

_All non-private defined functions in commands/ will be registerd in the cli_
Prefix with a `_` to be ignored. That's the only way.

## philosophies

### Be a strong, independent package who don't need no pypi

Dependencies means vulnerabilities, which means frequent patching
and updating. WHO GOT TIME FOR DAT?

Write it all in-house.

### Update like it's moving apartments

Would you move to a new studio bedroom every month?

Command line syntax hasn't changed for decades, what's there to update?

Make it feature complete and reliable.

The only exceptions are if there's a bug. Just like in your apartment, you
would make the effort to get rid of bugs.

### There's no zen here, bub!

We don't follow zen of python. Zen is nice but we live in the real
world, ya dig? No bad-ass mf got time for explicitly stating everything.
So just learn the conventions here once and save big on keystrokes!
geometryLibPy 1.0.0
This is the simplest module for calculating the area of a geometric figures.
Requires Python: >=3.8
Keywords: math geometry figures area

# Simple GeometryLibpy #

## What is this? ##
Python lib that allows to calculate area of geometric figures

----------

## Using ##

Using the library is as simple and convenient as possible:

Let's import it first:
First, import everything from the library (use the `from `...` import *` construct).

Examples of all operations:

### Triangle ###

Create a triangle and get it area:

    triangle = Triangle([15, 9, 7])
    area = triangle.get_area()
    print(area)


Resize the triangle and get updated area:

    triangle.update_sides([5, 6, 7])
    area = triangle.get_area()
    print(area)

### Circumference ###
Create a circumference and get area:

    circle = Circumference(5)
    area = circle.get_area()
    print(area)


Update radius of the circumference and get new area:

    circle.update_radius(4)
    area = circle.get_area()
    print(area)
----------


## UnitTest ##

Run in terminal in root directory:
    
    cd GeometryLibPy
    python -m unittest -v testLib 
    

----------

## Developer ##
My GitHub: [**_Link_**](https://github.com/mav735)