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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
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')
- data: pandas DataFrame object, containing the statistical dataset (population)
- size: Sample size
- 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)
- data: pandas DataFrame object, containing the statistical dataset (population)
- col: column whose mean is to be calculated
- weight: Default value is
None
. To calculate weighted mean, setweight
as the column name which contains the weight
Returns : Float/integer
Root mean square
stats.RMS(data, col)
- data: pandas DataFrame object, containing the statistical dataset (population)
- col: column whose mean is to be calculated
Returns : Float/integer
Variance
stats.variance(data, col)
- data: pandas DataFrame object, containing the statistical dataset (population)
- col: column whose mean is to be calculated
Returns : Float/integer
Standard Deviation
stats.SD(data, col)
- data: pandas DataFrame object, containing the statistical dataset (population)
- 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
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