Statispy

Satisfy your cravings for statistics using statispy. It's a python toolset what will help you in your journey of statistical analysis through a plug and play manner. All you need to do is import the tools, feed the data, and collect the result, all through one single line of code. Our mission: Make statistics easy for non-coding statisticians.

Table of contents

• Prerequisites
• Installating
• Documentation
  • sample
  • stats
  • correlation

Prerequisites

As of now, the statispy toolset supports only the Pandas Dataframe format for data. The dependencies that you need to use this toolset are

Python >=3.6
pandas==1.0.1

Installing

Installing the package is a no brainer.

pip install statispy

Documentation

Please read the documentation to understand how to use the toolset.

sample

Sample is a sub-package that deals with sampling data. Currently the tools support only basic sampling techniques, which includes : Random sampling and Systematic sampling.

To import the sampling tools use

from statispy import sample

Basic sampling

sample.basic_sample(data, size, method = 'random')

1. data: pandas DataFrame object, containing the statistical dataset (population)
2. size: Sample size
3. method: sampling technique. method = 'random' creates a random sample. method = 'systematic' creates a systematic sample. Returns : Dataframe

stats

The stats sub-package offers basic statistical tools which includes : Mean, Root mean square,Standard deviation and variance

To import the sampling tools use

from statispy import stats

Mean

stats.mean(data, col, weight = None)

1. data: pandas DataFrame object, containing the statistical dataset (population)
2. col: column whose mean is to be calculated
3. weight: Default value is None. To calculate weighted mean, set weight as the column name which contains the weight

Returns : Float/integer

Root mean square

stats.RMS(data, col)

1. data: pandas DataFrame object, containing the statistical dataset (population)
2. col: column whose mean is to be calculated

Returns : Float/integer

Variance

stats.variance(data, col)

1. data: pandas DataFrame object, containing the statistical dataset (population)
2. col: column whose mean is to be calculated

Returns : Float/integer

Standard Deviation

stats.SD(data, col)

1. data: pandas DataFrame object, containing the statistical dataset (population)
2. col: column whose mean is to be calculated

Returns : Float/integer

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

• Shankhanil Ghosh - Initial work

See also the list of contributors who participated in this project.

License

This project is licensed under the Apache-2.0 License - see the LICENSE.md file for details