[](https://badge.fury.io/py/statmanager-kr)
[](https://github.com/ckdckd145/statmanager-kr/blob/main/LICENSE)
[](https://joss.theoj.org/papers/d88c1a10e30fbfc39104534970afcd23)
<img src="https://img.shields.io/badge/Python-3776AB?style=flat&logo=Python&logoColor=white">
<img src="https://img.shields.io/badge/Pandas-150458?style=flat&logo=Pandas&logoColor=white">
<img src="https://img.shields.io/badge/Jupyter-F37626?style=flat&logo=Jupyter&logoColor=white">
<img src="https://img.shields.io/badge/Scipy-8CAAE6?style=flat&logo=Scipy&logoColor=white">
### Available Operating Systems
<img src="https://img.shields.io/badge/Windows_10-00adef">
<img src="https://img.shields.io/badge/Windows_11-0078d4">
<img src="https://img.shields.io/badge/Mac_OS-000000">
<img src="https://img.shields.io/badge/Linux-FCC624">
### Availabe Python Versions
<img src="https://img.shields.io/badge/Python_3.10-3776AB">
<img src="https://img.shields.io/badge/Python_3.11-3776AB">
<img src="https://img.shields.io/badge/Python_3.12-3776AB">
<br>
<b>Statmanager-kr</b> is open-source statistical package for researchers, data scientists, psychologist, studends, and anyone who need statistical analysis. <b>Statmanager-kr</b> aims to be a user-friendly statistical package that can be easily used by people who unfamiliar with programming language.
Currently, <b><u>KOREAN</u></b> and <b><u>ENGLISH</u></b> are supported.
## Documentaion
[Official documentation - Korean](https://cslee145.notion.site/fd776d4f9a4f4c9db2cf1bbe60726971?v=3b2b237555fc4cd3a41a8da337d80c01)
[Official Documentation - English](https://cslee145.notion.site/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0&pvs=4)
## Source Code & Dependency
Source codes are available in the [Github respository](https://github.com/ckdckd145/statmanager-kr)
#### Dependency
* pandas
* statsmodels
* scipy
* numpy
* matplotlib
* seaborn
* XlsxWriter
It is recommended to use the <b>latest versions</b> of these libraries and packages to avoid unexpected errors.
## Contribution Guidelines
Please check the [guidelines](https://www.notion.so/cslee145/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0&pvs=4#96a4e9547ae54a41928ff4114729f6c2) in official documentation.
Please use [Github Discussion](https://github.com/ckdckd145/statmanager-kr/discussions) to let me know the questions, bugs, suggestions or anything.
# Quick Start
[If you want to start with sample file, click this](https://github.com/ckdckd145/statmanager-kr/blob/main/test.ipynb)
[Read manual in documentation](https://www.notion.so/cslee145/Documentation-74a610c12881402d96dc5d1654f97433?pvs=4#be93db7f4159419fa73eb324d6567793) |
### 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.read_csv('testdf.csv', index_col = 'id')
sm = Stat_Manager(df, language = 'eng')
```
### Independent Samples T-test
```python
sm.progress(method = 'ttest_ind', vars = 'age', group_vars = 'sex').figure()
```
<details markdown="1">
<summary><b>Output (Click to See)</b></summary>
| | 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 |
</details>
### Dependent Samples T-test
```python
sm.progress(method = 'ttest_rel', vars = ['prescore', 'postscore']).figure()
```
<details markdown="1">
<summary><b>Output (Click to See)</b></summary>
| | 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 |
</details>
### Pearson's Correlation
```python
sm.progress(method = 'pearsonr', vars = ['income', 'prescore', 'age']).figure()
```
<details markdown="1">
<summary><b>Output (Click to See)</b></summary>
| | 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 |
</details>
### One-way ANOVA with Post-hoc test
```python
sm.progress(method = 'f_oneway', vars = 'age', group_vars = 'condition', posthoc = True).figure()
```
<details markdown="1">
<summary><b>Output (Click to See)</b></summary>
| | 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 |
</details>
### One-way Repeated Measure ANOVA with Post-hoc test
```python
sm.progress(method = 'f_oneway_rm', vars = ['prescore','postscore','fupscore'], posthoc = True).figure()
```
<details markdown="1">
<summary><b>Output (Click to See)</b></summary>
| | 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 |
</details>
<br>
# 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`](https://pingouin-stats.org/build/html/index.html).
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`](https://pingouin-stats.org/build/html/index.html) 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`](https://pingouin-stats.org/build/html/index.html) 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`](https://pingouin-stats.org/build/html/index.html), 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
<a href="https://www.github.com/ckdckd145" target="_blanck">
<img src="https://img.shields.io/badge/Github-github?style=flat&logo=Github&color=black"
</a>
<a href="https://www.linkedin.com/in/cslee0052" target="_blank">
<img src="https://img.shields.io/badge/LinkedIn%20Profile-kakao?style=flat&logo=LinkedIn&logoColor=white&color=black">
</a>
</a>
<a href="mailto:ckdckd145@gmail.com" target="_blank">
<img src="https://img.shields.io/badge/Gmail-gmail?style=flat&logo=Gmail&logoColor=white&color=black">
</a>
</center>
#
### Copyright (C) 2023 Changseok Lee
Raw data
{
"_id": null,
"home_page": "https://cslee145.notion.site/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0&pvs=4",
"name": "statmanager-kr",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "statistic, socialscience, stats, analysis, data, research, science",
"author": "ckdckd145",
"author_email": "ckdckd145@gmail.com",
"download_url": "https://github.com/ckdckd145/statmanager-kr",
"platform": null,
"description": " \r\n[](https://badge.fury.io/py/statmanager-kr)\r\n[](https://github.com/ckdckd145/statmanager-kr/blob/main/LICENSE)\r\n[](https://joss.theoj.org/papers/d88c1a10e30fbfc39104534970afcd23)\r\n<img src=\"https://img.shields.io/badge/Python-3776AB?style=flat&logo=Python&logoColor=white\">\r\n<img src=\"https://img.shields.io/badge/Pandas-150458?style=flat&logo=Pandas&logoColor=white\">\r\n<img src=\"https://img.shields.io/badge/Jupyter-F37626?style=flat&logo=Jupyter&logoColor=white\">\r\n<img src=\"https://img.shields.io/badge/Scipy-8CAAE6?style=flat&logo=Scipy&logoColor=white\">\r\n\r\n\r\n\r\n\r\n### Available Operating Systems\r\n\r\n<img src=\"https://img.shields.io/badge/Windows_10-00adef\">\r\n<img src=\"https://img.shields.io/badge/Windows_11-0078d4\">\r\n<img src=\"https://img.shields.io/badge/Mac_OS-000000\">\r\n<img src=\"https://img.shields.io/badge/Linux-FCC624\">\r\n\r\n### Availabe Python Versions\r\n\r\n<img src=\"https://img.shields.io/badge/Python_3.10-3776AB\">\r\n<img src=\"https://img.shields.io/badge/Python_3.11-3776AB\">\r\n<img src=\"https://img.shields.io/badge/Python_3.12-3776AB\">\r\n\r\n<br>\r\n\r\n<b>Statmanager-kr</b> is open-source statistical package for researchers, data scientists, psychologist, studends, and anyone who need statistical analysis. <b>Statmanager-kr</b> aims to be a user-friendly statistical package that can be easily used by people who unfamiliar with programming language.\r\n\r\nCurrently, <b><u>KOREAN</u></b> and <b><u>ENGLISH</u></b> are supported. \r\n\r\n\r\n## Documentaion\r\n\r\n[Official documentation - Korean](https://cslee145.notion.site/fd776d4f9a4f4c9db2cf1bbe60726971?v=3b2b237555fc4cd3a41a8da337d80c01) \r\n[Official Documentation - English](https://cslee145.notion.site/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0&pvs=4)\r\n\r\n## Source Code & Dependency\r\nSource codes are available in the [Github respository](https://github.com/ckdckd145/statmanager-kr)\r\n\r\n#### Dependency\r\n* pandas\r\n* statsmodels\r\n* scipy\r\n* numpy\r\n* matplotlib\r\n* seaborn\r\n* XlsxWriter\r\n\r\nIt is recommended to use the <b>latest versions</b> of these libraries and packages to avoid unexpected errors.\r\n\r\n## Contribution Guidelines\r\n\r\nPlease check the [guidelines](https://www.notion.so/cslee145/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0&pvs=4#96a4e9547ae54a41928ff4114729f6c2) in official documentation. \r\n\r\nPlease use [Github Discussion](https://github.com/ckdckd145/statmanager-kr/discussions) to let me know the questions, bugs, suggestions or anything. \r\n\r\n\r\n# Quick Start\r\n\r\n[If you want to start with sample file, click this](https://github.com/ckdckd145/statmanager-kr/blob/main/test.ipynb)\r\n[Read manual in documentation](https://www.notion.so/cslee145/Documentation-74a610c12881402d96dc5d1654f97433?pvs=4#be93db7f4159419fa73eb324d6567793) | \r\n\r\n\r\n### Installation\r\n```python\r\npip install statmanager-kr\r\n```\r\n\r\n### Update\r\n```python\r\npip install statmanager-kr --upgrade\r\n```\r\n\r\n### Import \r\n\r\n```Python\r\nimport pandas as pd\r\nfrom statmanager import Stat_Manager\r\n\r\n# use your data file instead of 'testdf.csv'\r\ndf = pd.read_csv('testdf.csv', index_col = 'id') \r\nsm = Stat_Manager(df, language = 'eng')\r\n```\r\n\r\n### Independent Samples T-test\r\n\r\n```python\r\nsm.progress(method = 'ttest_ind', vars = 'age', group_vars = 'sex').figure()\r\n```\r\n\r\n<details markdown=\"1\">\r\n <summary><b>Output (Click to See)</b></summary>\r\n\r\n| | female | male |\r\n| --- | --- | --- |\r\n| n | 15.00 | 15.00 |\r\n| mean | 27.33 | 28.00 |\r\n| median | 26.00 | 26.00 |\r\n| sd | 4.88 | 6.94 |\r\n| min | 21.00 | 20.00 |\r\n| max | 39.00 | 39.00 |\r\n\r\n| dependent variable | t-value | degree of freedom | p-value | 95% CI | Cohen'd |\r\n| --- | --- | --- | --- | --- | --- |\r\n| height | -0.304 | 28 | 0.763 | [-5.153, 3.820] | -0.111 |\r\n\r\n\r\n\r\n</details>\r\n\r\n### Dependent Samples T-test\r\n\r\n```python\r\nsm.progress(method = 'ttest_rel', vars = ['prescore', 'postscore']).figure()\r\n```\r\n\r\n<details markdown=\"1\">\r\n <summary><b>Output (Click to See)</b></summary>\r\n\r\n| | prescore | postscore |\r\n| --- | --- | --- |\r\n| n | \u2026 | \u2026 |\r\n| mean | 5.13 | 4.23 |\r\n| median | 5.50 | 4.00 |\r\n| sd | 2.85 | 2.91 |\r\n| min | \u2026 | \u2026 |\r\n| max | \u2026 | \u2026 |\r\n\r\n| variables | t-value | degree of freedom | p-value | 95% CI | Cohen's d |\r\n| --- | --- | --- | --- | --- | --- |\r\n| ['prescore', 'postscore'] | 1.198 | 29 | 0.24 | [-0.636, 2.436] | 0.313 |\r\n\r\n\r\n\r\n</details>\r\n\r\n### Pearson's Correlation\r\n\r\n```python\r\nsm.progress(method = 'pearsonr', vars = ['income', 'prescore', 'age']).figure()\r\n```\r\n\r\n<details markdown=\"1\">\r\n <summary><b>Output (Click to See)</b></summary>\r\n\r\n| | n | Pearson's r | p-value | 95%_confidence_interval |\r\n| --- | --- | --- | --- | --- |\r\n| income & prescore | 30 | -0.103 | 0.588 | [-0.447, 0.267] |\r\n| income & age | 30 | -0.051 | 0.789 | [-0.404, 0.315] |\r\n| prescore & age | 30 | -0.044 | 0.816 | [-0.398, 0.321] |\r\n\r\n| | income | prescore | age |\r\n| --- | --- | --- | --- |\r\n| income | 1.000 | -0.103 | -0.051 |\r\n| prescore | -0.103 | 1.000 | -0.044 |\r\n| age | -0.051 | -0.044 | 1.000 |\r\n\r\n\r\n\r\n</details>\r\n\r\n### One-way ANOVA with Post-hoc test\r\n\r\n```python\r\nsm.progress(method = 'f_oneway', vars = 'age', group_vars = 'condition', posthoc = True).figure()\r\n```\r\n\r\n<details markdown=\"1\">\r\n <summary><b>Output (Click to See)</b></summary>\r\n\r\n| | test_group | sham_group | control_group |\r\n| --- | --- | --- | --- |\r\n| n | 10 | 10 | 10 |\r\n| mean | 28.5 | 28.3 | 26.2 |\r\n| median | 27 | 29 | 25.5 |\r\n| sd | 6.57 | 5.56 | 5.88 |\r\n| min | \u2026 | \u2026 | \u2026 |\r\n| max | \u2026 | \u2026 | \u2026 |\r\n\r\n| | sum_sq | df | F | p-value | partial eta squared |\r\n| --- | --- | --- | --- | --- | --- |\r\n| Intercept | 6864.4 | 1 | 189.469 | 0 | 0.872 |\r\n| C(condition) | 32.467 | 2 | 0.448 | 0.644 | 0.004 |\r\n| Residual | 978.2 | 27 | NaN | NaN | 0.124 |\r\n\r\n|Test Multiple Comparison ttest_ind FWER=0.05 method=bonf alphacSidak=0.02, alphacBonf=0. | | | | | | \r\n| --- | --- | --- | --- | --- | --- |\r\n\r\n| group1 | group2 | stat | pval | pval_corr | reject |\r\n| --- | --- | --- | --- | --- | --- |\r\n| control_group | sham_group | -0.8204 | 0.4227 | 1 | FALSE |\r\n| control_group | test_group | -0.8246 | 0.4204 | 1 | FALSE |\r\n| sham_group | test_group | -0.0735 | 0.9422 | 1 | FALSE |\r\n\r\n\r\n\r\n\r\n</details>\r\n\r\n### One-way Repeated Measure ANOVA with Post-hoc test\r\n\r\n```python\r\nsm.progress(method = 'f_oneway_rm', vars = ['prescore','postscore','fupscore'], posthoc = True).figure()\r\n```\r\n\r\n<details markdown=\"1\">\r\n <summary><b>Output (Click to See)</b></summary>\r\n\r\n| | prescore | postscore | fupscore |\r\n| --- | --- | --- | --- |\r\n| n | 30.00 | 30.00 | 30.00 |\r\n| mean | 5.13 | 4.23 | 4.37 |\r\n| median | 5.50 | 4.00 | 4.00 |\r\n| sd | 2.85 | 2.91 | 2.62 |\r\n| min | \u2026 | \u2026 | \u2026 |\r\n| max | \u2026 | \u2026 | \u2026 |\r\n\r\n| | F Value | Num DF | Den DF | p-value | partial etq squared |\r\n| --- | --- | --- | --- | --- | --- |\r\n| variable | 1.079 | 2 | 58 | 0.347 | 0.02 |\r\n\r\n\r\n|Test Multiple Comparison ttest_ind FWER=0.05 method=bonf alphacSidak=0.02, alphacBonf=0. | | | | | | \r\n| --- | --- | --- | --- | --- | --- |\r\n\r\n| group1 | group2 | stat | pval | pval_corr | reject |\r\n| --- | --- | --- | --- | --- | --- |\r\n| fupscore | postscore | 0.1866 | 0.8526 | 1 | FALSE |\r\n| fupscore | prescore | -1.0849 | 0.2824 | 0.8473 | FALSE |\r\n| postscore | prescore | -1.2106 | 0.231 | 0.6929 | FALSE |\r\n\r\n\r\n\r\n\r\n</details>\r\n\r\n<br>\r\n\r\n# Related Software\r\n\r\nAs 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`](https://pingouin-stats.org/build/html/index.html). \r\n\r\nThe 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`](https://pingouin-stats.org/build/html/index.html) 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`](https://pingouin-stats.org/build/html/index.html) is useful for adjusting parameters to obtain more careful and suitable results. \r\n\r\nIn conclusion, `Statmanager-kr` is a good package for researchers who lack programming experience and knowledge and want to see results quickly. [`Pingouin`](https://pingouin-stats.org/build/html/index.html), 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. \r\n\r\n\r\n## How to cite? \r\n\r\nFor inserting the citations, please use this:\r\n* 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\r\n\r\n\r\n## Development: Changseok Lee\r\n\r\n<a href=\"https://www.github.com/ckdckd145\" target=\"_blanck\">\r\n <img src=\"https://img.shields.io/badge/Github-github?style=flat&logo=Github&color=black\"\r\n</a>\r\n<a href=\"https://www.linkedin.com/in/cslee0052\" target=\"_blank\">\r\n <img src=\"https://img.shields.io/badge/LinkedIn%20Profile-kakao?style=flat&logo=LinkedIn&logoColor=white&color=black\">\r\n</a>\r\n</a>\r\n<a href=\"mailto:ckdckd145@gmail.com\" target=\"_blank\">\r\n <img src=\"https://img.shields.io/badge/Gmail-gmail?style=flat&logo=Gmail&logoColor=white&color=black\">\r\n</a>\r\n</center>\r\n\r\n\r\n\r\n#\r\n### Copyright (C) 2023 Changseok Lee\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Open-source statistical package in Python based on the Pandas",
"version": "1.8.1.15",
"project_urls": {
"Download": "https://github.com/ckdckd145/statmanager-kr",
"Homepage": "https://cslee145.notion.site/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0&pvs=4"
},
"split_keywords": [
"statistic",
" socialscience",
" stats",
" analysis",
" data",
" research",
" science"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "93b86e49c3f2643695a463d13b98487c4a07fb3ca369cefcb09c01e7dc9d847d",
"md5": "3bccaec09e7a437da698697c58a8b086",
"sha256": "9fb18b3e4e9abaa88186ee1433ec4f05ec00455a93e74ebeed5bce80b061e474"
},
"downloads": -1,
"filename": "statmanager_kr-1.8.1.15-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3bccaec09e7a437da698697c58a8b086",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 328373,
"upload_time": "2024-10-18T06:24:24",
"upload_time_iso_8601": "2024-10-18T06:24:24.730251Z",
"url": "https://files.pythonhosted.org/packages/93/b8/6e49c3f2643695a463d13b98487c4a07fb3ca369cefcb09c01e7dc9d847d/statmanager_kr-1.8.1.15-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-18 06:24:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ckdckd145",
"github_project": "statmanager-kr",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "statmanager-kr"
}
JSON
