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Statmanager-kr

Statmanager-kr: A User-friendly Statistical Package for Python in Pandas - Published in JOSS (2024)

https://github.com/ckdckd145/statmanager-kr

Science Score: 93.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 3 DOI reference(s) in README and JOSS metadata
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    Academic publication links
    Links to: joss.theoj.org
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    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

clinical-trials correlation-analysis easy-to-use null-hypothesis pandas pandas-python posthoc-comparisons research social-science statistical-analysis statistical-methods statistical-tests statistics ttest user-friendly visualize-differences visualizes-data

Scientific Fields

Economics Social Sciences - 85% confidence
Last synced: 4 months ago · JSON representation

Repository

Open-source statistical package in Python based on Pandas

Basic Info
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 4
  • Open Issues: 0
  • Releases: 19
Topics
clinical-trials correlation-analysis easy-to-use null-hypothesis pandas pandas-python posthoc-comparisons research social-science statistical-analysis statistical-methods statistical-tests statistics ttest user-friendly visualize-differences visualizes-data
Created about 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

PyPI version license status

logo

Available Operating Systems

Availabe Python Versions


Statmanager-kr is open-source statistical package for researchers, data scientists, psychologist, studends, and anyone who need statistical analysis. Statmanager-kr aims to be a user-friendly statistical package that can be easily used by people who unfamiliar with programming language.

Currently, KOREAN and ENGLISH are supported.

Documentaion

Official documentation - Korean
Official Documentation - English

Source Code & Dependency

Source codes are available in the Github respository

Dependency

  • pandas
  • statsmodels
  • scipy
  • numpy
  • matplotlib
  • seaborn
  • XlsxWriter

It is recommended to use the latest versions of these libraries and packages to avoid unexpected errors.

Contribution Guidelines

Please check the guidelines in official documentation.

Please use Github Discussion to let me know the questions, bugs, suggestions or anything.

Quick Start

If you want to start with sample file, click this Read manual in documentation |

Installation

python pip install statmanager-kr

Update

python pip install statmanager-kr --upgrade

Import

```Python import pandas as pd from statmanager import Stat_Manager

use your data file instead of 'testdf.csv'

df = pd.readcsv('testdf.csv', indexcol = 'id') sm = Stat_Manager(df, language = 'eng') ```

Independent Samples T-test

python sm.progress(method = 'ttest_ind', vars = 'age', group_vars = 'sex').figure()

Output (Click to See) | | female | male | | --- | --- | --- | | n | 15.00 | 15.00 | | mean | 27.33 | 28.00 | | median | 26.00 | 26.00 | | sd | 4.88 | 6.94 | | min | 21.00 | 20.00 | | max | 39.00 | 39.00 | | dependent variable | t-value | degree of freedom | p-value | 95% CI | Cohen'd | | --- | --- | --- | --- | --- | --- | | height | -0.304 | 28 | 0.763 | [-5.153, 3.820] | -0.111 | ![figure](./doc/output_ttest_ind.png)

Dependent Samples T-test

python sm.progress(method = 'ttest_rel', vars = ['prescore', 'postscore']).figure()

Output (Click to See) | | prescore | postscore | | --- | --- | --- | | n | … | … | | mean | 5.13 | 4.23 | | median | 5.50 | 4.00 | | sd | 2.85 | 2.91 | | min | … | … | | max | … | … | | variables | t-value | degree of freedom | p-value | 95% CI | Cohen's d | | --- | --- | --- | --- | --- | --- | | ['prescore', 'postscore'] | 1.198 | 29 | 0.24 | [-0.636, 2.436] | 0.313 | ![figure](./doc/output_ttest_rel.png)

Pearson's Correlation

python sm.progress(method = 'pearsonr', vars = ['income', 'prescore', 'age']).figure()

Output (Click to See) | | n | Pearson's r | p-value | 95%_confidence_interval | | --- | --- | --- | --- | --- | | income & prescore | 30 | -0.103 | 0.588 | [-0.447, 0.267] | | income & age | 30 | -0.051 | 0.789 | [-0.404, 0.315] | | prescore & age | 30 | -0.044 | 0.816 | [-0.398, 0.321] | | | income | prescore | age | | --- | --- | --- | --- | | income | 1.000 | -0.103 | -0.051 | | prescore | -0.103 | 1.000 | -0.044 | | age | -0.051 | -0.044 | 1.000 | ![figure](./doc/output_pearsonr.png)

One-way ANOVA with Post-hoc test

python sm.progress(method = 'f_oneway', vars = 'age', group_vars = 'condition', posthoc = True).figure()

Output (Click to See) | | test_group | sham_group | control_group | | --- | --- | --- | --- | | n | 10 | 10 | 10 | | mean | 28.5 | 28.3 | 26.2 | | median | 27 | 29 | 25.5 | | sd | 6.57 | 5.56 | 5.88 | | min | … | … | … | | max | … | … | … | | | sum_sq | df | F | p-value | partial eta squared | | --- | --- | --- | --- | --- | --- | | Intercept | 6864.4 | 1 | 189.469 | 0 | 0.872 | | C(condition) | 32.467 | 2 | 0.448 | 0.644 | 0.004 | | Residual | 978.2 | 27 | NaN | NaN | 0.124 | |Test Multiple Comparison ttest_ind FWER=0.05 method=bonf alphacSidak=0.02, alphacBonf=0. | | | | | | | --- | --- | --- | --- | --- | --- | | group1 | group2 | stat | pval | pval_corr | reject | | --- | --- | --- | --- | --- | --- | | control_group | sham_group | -0.8204 | 0.4227 | 1 | FALSE | | control_group | test_group | -0.8246 | 0.4204 | 1 | FALSE | | sham_group | test_group | -0.0735 | 0.9422 | 1 | FALSE | ![figure](./doc/output_f_oneway.png)

One-way Repeated Measure ANOVA with Post-hoc test

python sm.progress(method = 'f_oneway_rm', vars = ['prescore','postscore','fupscore'], posthoc = True).figure()

Output (Click to See) | | prescore | postscore | fupscore | | --- | --- | --- | --- | | n | 30.00 | 30.00 | 30.00 | | mean | 5.13 | 4.23 | 4.37 | | median | 5.50 | 4.00 | 4.00 | | sd | 2.85 | 2.91 | 2.62 | | min | … | … | … | | max | … | … | … | | | F Value | Num DF | Den DF | p-value | partial etq squared | | --- | --- | --- | --- | --- | --- | | variable | 1.079 | 2 | 58 | 0.347 | 0.02 | |Test Multiple Comparison ttest_ind FWER=0.05 method=bonf alphacSidak=0.02, alphacBonf=0. | | | | | | | --- | --- | --- | --- | --- | --- | | group1 | group2 | stat | pval | pval_corr | reject | | --- | --- | --- | --- | --- | --- | | fupscore | postscore | 0.1866 | 0.8526 | 1 | FALSE | | fupscore | prescore | -1.0849 | 0.2824 | 0.8473 | FALSE | | postscore | prescore | -1.2106 | 0.231 | 0.6929 | FALSE | ![figure](./doc/output_f_oneway_rm.png)


Related Software

As mentioned earlier, Statmanager-kr was developed to provide a user-friendly way to perform statistical analysis methods to test hypotheses, even if the researcher is not familiar with programming languages such as Python. As such, a related software that provides similar user-friendly features is Pingouin.

The main difference is that Statmanager-kr was developed with the goal of being a package that can be used by researchers who lack programming knowledge or experience. To this end, rather than implementing independent methods for each analysis, Statmanager-kr is designed to allow users to enter code in the same way at any time to perform statistical analysis and obtain the results. Of course, Pingouin also has user-friendly characteristics, but it is a package that is better suited for users with more programming experience and knowledge than Statmanager-kr. Due to this difference in characteristics, Statmanager-kr does not support the ability to fine-tune analysis methods by adjusting parameters, whereas Pingouin is useful for adjusting parameters to obtain more careful and suitable results.

In conclusion, Statmanager-kr is a good package for researchers who lack programming experience and knowledge and want to see results quickly. Pingouin, on the other hand, is a more suitable package for researchers with more programming experience and knowledge, who need a fine-tuned approach to each analysis method.

How to cite?

For inserting the citations, please use this: * Lee, C., (2024). Statmanager-kr: A User-friendly Statistical Package for Python in Pandas. Journal of Open Source Software, 9(102), 6642, https://doi.org/10.21105/joss.06642

Development: Changseok Lee

Copyright (C) 2023 Changseok Lee

Owner

  • Name: Changseok Lee
  • Login: ckdckd145
  • Kind: user

JOSS Publication

Statmanager-kr: A User-friendly Statistical Package for Python in Pandas
Published
October 10, 2024
DOI
Volume 9, Issue 102, Page 6642
Authors
Changseok Lee ORCID
DYPHI Research Institute, DYPHI Inc., Daejeon, Republic of Korea
Editor
Teon Brooks ORCID
Tags
statistical analysis social science null-hypothesis user-friendly
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 731 last-month
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  • Total versions: 37
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pypi.org: statmanager-kr

Open-source statistical package in Python based on the Pandas

  • Versions: 37
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 731 Last month
Rankings
Dependent packages count: 7.5%
Average: 38.7%
Dependent repos count: 69.8%
Maintainers (1)
ckdckd145
Last synced: 12 months ago

Dependencies

statmanager/setup.py pypi
  • matplotlib *
  • pandas *
  • scipy *
  • seaborn *
  • statsmodels *