The prob_distributions package is used to calculate and plot Gaussian/ Normal and Binomial distributions.
Import prob_distributions and it contains following classes:
pip install on windows using: pip install probabi-distributions
1. Gaussian class
Gaussian distribution class for calculating and visualizing a Gaussian distribution.
*Attributes:*
mean (float) representing the mean value of the distribution
stdev (float) representing the standard deviation of the distribution
data_list (list of floats) a list of floats extracted from the data file
Syntax: Gaussian(mean, stdev)
*Methods available:*
1. calculate_mean() : it calculates mean
2. calculate_stdev(sample=True) : calculates standard deviation (if passing population then sample=False)
3. plot_histogram() : plots histogram
4. pdf(x) : probabilty density function
5. plot_histogram_pdf(n_spaces = 50) : plots graph for probability density function
6. add(gaussian1, gaussian2) : it adds two gaussians
7. repr() : output the characteristics of the Gaussian instance
2. Binomial class
<p>Binomial distribution class for calculating and visualizing a Binomial distribution.</p>
*Attributes:*
mean (float) representing the mean value of the distribution
stdev (float) representing the standard deviation of the distribution
data_list (list of floats) a list of floats to be extracted from the data file
p (float) representing the probability of an event occurring
n (int) number of trials
Syntax: Binomial(mean, stdev, prob, size)
*Methods available:*
1. calculate_mean() : it calculates mean
2. calculate_stdev(sample=True) : calculates standard deviation (if passing population then sample=False)
3. replace_stats_with_data() : Function to calculate p and n from the data set
3. plot_bar() : plots bar graph
4. pdf(k) : probabilty density function
5. plot_bar_pdf() : plots graph for probability density function
6. add(binomial1, binomial2) : it adds two binomial distributions
7. repr() : output the characteristics of the binomial instance