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

pip install statmanager-kr

Update

pip install statmanager-kr --upgrade

Import

import pandas as pd
from statmanager import Stat_Manager

# use your data file instead of 'testdf.csv'
df = pd.read_csv('testdf.csv', index_col = 'id') 
sm = Stat_Manager(df, language = 'eng')

Independent Samples T-test

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

Dependent Samples T-test

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

Pearson's Correlation

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

One-way ANOVA with Post-hoc test

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

One-way Repeated Measure ANOVA with Post-hoc test

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

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