discrete_pmf 0.1.4

Compute and plot PMFs for Binomial, Geometric, Negative Binomial and Poisson distributions.

Discrete PMF — Python Module for Plotting PMFs of Discrete Distributions

Discrete PMF is a lightweight and easy-to-use Python module for computing and visualizing Probability Mass Functions (PMFs) of common discrete probability distributions. It is ideal for educators, data scientists, statisticians, and learners who want to visualize and interact with probability distributions in Python.

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Supported Distributions

• Binomial Distribution
• Geometric Distribution
• Negative Binomial Distribution
• Poisson Distribution

Each function:

• Computes the PMF for a given distribution
• Optionally generates a matplotlib stem plot
• Returns the PMF values as a dictionary for further processing

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Requirements

• Python ≥ 3.6
• matplotlib (for plotting)
• Standard Library: math

Install dependencies with:

pip install matplotlib

Or if you're using Poetry:

poetry add matplotlib

Function Overview

🔹 binom_pmf(x_values, n, p_list, plot=True)

Compute and (optionally) plot the Binomial PMF.

Parameters:

• x_values (List[int]): Values of the random variable X (e.g., [0, 1, 2, ..., n])
• n (int): Number of trials
• p_list (List[float]): List of success probabilities (e.g., [0.3, 0.7])
• plot (bool): If True, displays a matplotlib stem plot (default: True)

Returns:

• Dict[float, List[float]]: Mapping from each p to its list of PMF values.

Example:

from discrete_pmf import binom_pmf

x = list(range(0, 6))
n = 5
p_values = [0.3, 0.7]

binom_pmf(x_values=x, n=n, p_list=p_values, plot=True)

📚 Documentation

Each function includes Python docstrings explaining:

• Function purpose
• Parameters and types
• Return values
• Example usage

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📝 License

MIT License. See LICENSE file for details.

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🤝 Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change or add.