ProbStatipy is a package of Python modules equipped with functions that are used in Statistics.

While the functions are extensively documented, you can check out my Jupyter Notebooks in the Numerical Summary repo to fully understand my thought process while coming up with these functions.

As of now, this package contains three modules in the src folder (src > ProbStatipy):

1. central.py: Contains functions to measure central tendency such as Mean, Median and Mode.
2. spread.py: Contains functions to measure dispersion/spread such as Variance (Mean Squared Deviation), Standard Deviation and Mean Absolute Deviation (MAD).
3. probability.py: Contains a function to compute probability and classes outlining the properties and methods of Sample Spaces and Events.

To install the package, run:

pip install ProbStatipy

To upgrade it, run:

pip install --upgrade ProbStatipy

To use the modules in your Python Code, ensure to include the following import statements:

from ProbStatipy import central
from ProbStatipy import spread
from ProbStatipy import probability

Now you can access the functions to conduct your statistical analysis:

print(central.mean([3,4,5]))
print(spread.variance([3,4,5]))
print(probability.probability(3, 10))

>>> 4.0
>>> 0.6666666666666
>>> 0.3

You can also import the modules using an alias as observed below:

from ProbStatipy import central as ctr
from ProbStatipy import spread as spr
from ProbStatipy import probability as prb

print(ctr.mean([3,4,5]))
print(spr.variance([3,4,5]))
print(prb.probability(3, 10))

>>> 4.0
>>> 0.6666666666666
>>> 0.3

Below is a catalogue of functions available in each module

central.py

mean() Calculates the population arithmetic mean

median() Calculates the median value of the population

mode() Calculates the mode

spread.py

variance() calculates the population variance

stdeviation() computes the population standard deviation

mad() Computes the population mean absolute deviation

get_range gets the range of the dataset

iqr() gets the interquartile range of the dataset

probability.py

probability() Derives the probability of a successful occurrence given the number of occurrences and successful observations.

probability.py

SampleSpace SampleSpace is a class that represents the sample space of a random experiment.

Event Event is a Class designed to mimick a subset of a sample space.