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Quick Example ​

To use a distribution, follow the steps below.

Example: This demonstration utilizes the Normal distribution.

Creating a Distribution ​

A distribution can be instantiated by providing the required parameters to the respective function:

python
distribution = phitter.continuous.Normal({"mu": 5, "sigma": 2})

💡 Replace Normal with the desired distribution and adjust the parameters accordingly.

Retrieving Distribution Metrics ​

Cumulative Distribution Function (CDF) ​

Computes the probability that a random variable is less than or equal to a specified value:

python
distribution.cdf(3.56446)

Probability Density Function (PDF) ​

Evaluates the likelihood of the variable taking a specific value:

python
distribution.pdf(3.56446)

Percent Point Function (PPF) ​

Determines the quantile function, which is the inverse of the CDF:

python
distribution.ppf(0.6344)

Sampling from the Distribution ​

Generates a sample dataset of the specified size:

python
data = distribution.sample(1000)

Statistical Properties ​

Mean ​

python
distribution.mean

Variance ​

python
distribution.variance

Skewness ​

python
distribution.skewness

Kurtosis ​

python
distribution.kurtosis

Median ​

python
distribution.median

Mode ​

python
distribution.mode

This example provides a fundamental workflow for utilizing distributions in Phitter. Additional functionalities and customizations can be explored in the full documentation.