| Name | skmiscpy JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | Contains a few functions useful for data-analysis, causal inference etc. |
| upload_time | 2024-08-16 11:04:21 |
| maintainer | None |
| docs_url | None |
| author | Shafayet Khan Shafee |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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# skmiscpy
[](https://pypi.org/project/skmiscpy/)   [](https://github.com/shafayetShafee/skmiscpy/actions/workflows/ci-cd.yml) [](https://codecov.io/github/shafayetShafee/skmiscpy)
Contains a few functions useful for data-analysis, causal inference etc.
## Installation
```bash
pip install skmiscpy
```
## Usage
So far, `skmiscpy` can be used to do a basic causal analysis. Here very simple examples are shown for demonstration purposes.
Check [Causal Analysis Workflow & Estimating ATE Using `skmiscpy`](https://skmiscpy.readthedocs.io/en/latest/example.html) for
better understanding.
``` python
import pandas as pd
from skmiscpy import compute_smd, plot_smd
from skmiscpy import plot_mirror_histogram
```
### Draw a mirror histogram
``` python
data = pd.DataFrame({
'treatment': [1, 1, 0, 0, 1, 0],
'propensity_score': [2.0, 3.5, 3.0, 2.2, 2.2, 3.3]
})
plot_mirror_histogram(data=data, var='propensity_score', group='treatment')
# Draw a weighted mirror histogram
data_with_weights = pd.DataFrame({
'treatment': [1, 1, 0, 0, 1, 0],
'propensity_score': [2.0, 3.5, 3.0, 2.2, 2.2, 3.3],
'weights': [1.0, 1.5, 2.0, 1.2, 1.1, 0.8]
})
plot_mirror_histogram(
data=data_with_weights, var='propensity_score', group='treatment', weights='weights',
xlabel='Propensity Score', ylabel='Weighted Count', title='Weighted Mirror Histogram'
)
```
### Compute Standardized Mean Difference (SMD)
```python
sample_df = pd.DataFrame({
'age': np.random.randint(18, 66, size=100),
'weight': np.round(np.random.uniform(120, 200, size=100), 1),
'gender': np.random.choice(['male', 'female'], size=100),
'race': np.random.choice(
['white', 'black', 'hispanic'],
size=100, p=[0.4, 0.3, 0.3]
),
'educ_level': np.random.choice(
['bachelor', 'master', 'doctorate'],
size=100, p=[0.3, 0.4, 0.3]
),
'ps_wts': np.round(np.random.uniform(0.1, 1.0, size=100), 2),
'group': np.random.choice(['treated', 'control'], size=100),
'date': pd.date_range(start='2024-01-01', periods=100, freq='D')
})
# 1. Basic usage with unadjusted SMD only:
compute_smd(sample_df, vars=['age', 'weight', 'gender'], group='group', estimand='ATE')
# 2. Including weights for adjusted SMD:
compute_smd(
sample_df,
vars=['age', 'weight', 'gender'],
group='group', wt_var='ps_wts',
estimand='ATE'
)
# 3. Including categorical variables for adjusted SMD:
compute_smd(
sample_df,
vars=['age', 'weight', 'gender'],
group='group',
wt_var='ps_wts',
cat_vars=['race', 'educ_level'],
estimand='ATE'
)
```
### Create a love plot (point plot of SMD)
``` python
data = pd.DataFrame({
'variables': ['age', 'bmi', 'blood_pressure'],
'unadjusted_smd': [0.25, 0.4, 0.1],
'adjusted_smd': [0.05, 0.2, 0.08]
})
plot_smd(data)
## Adding a reference line at 0.1
plot_smd(data, add_ref_line=True, ref_line_value=0.1)
## Customizing the Seaborn plot with additional keyword arguments
plot_smd(data, add_ref_line=True, ref_line_value=0.1, palette='coolwarm', markers=['o', 's'])
```
## Contributing
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
## License
`skmiscpy` was created by Shafayet Khan Shafee. It is licensed under the terms of the MIT license.
## Credits
`skmiscpy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
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"description": "# skmiscpy\n\n[](https://pypi.org/project/skmiscpy/)   [](https://github.com/shafayetShafee/skmiscpy/actions/workflows/ci-cd.yml) [](https://codecov.io/github/shafayetShafee/skmiscpy)\n\nContains a few functions useful for data-analysis, causal inference etc.\n\n## Installation\n\n```bash\npip install skmiscpy\n```\n\n## Usage\n\nSo far, `skmiscpy` can be used to do a basic causal analysis. Here very simple examples are shown for demonstration purposes.\nCheck [Causal Analysis Workflow & Estimating ATE Using `skmiscpy`](https://skmiscpy.readthedocs.io/en/latest/example.html) for\nbetter understanding.\n\n``` python\nimport pandas as pd\nfrom skmiscpy import compute_smd, plot_smd\nfrom skmiscpy import plot_mirror_histogram\n```\n\n### Draw a mirror histogram\n\n``` python\ndata = pd.DataFrame({\n 'treatment': [1, 1, 0, 0, 1, 0],\n 'propensity_score': [2.0, 3.5, 3.0, 2.2, 2.2, 3.3]\n})\n\nplot_mirror_histogram(data=data, var='propensity_score', group='treatment')\n\n# Draw a weighted mirror histogram\ndata_with_weights = pd.DataFrame({\n 'treatment': [1, 1, 0, 0, 1, 0],\n 'propensity_score': [2.0, 3.5, 3.0, 2.2, 2.2, 3.3],\n 'weights': [1.0, 1.5, 2.0, 1.2, 1.1, 0.8]\n})\n\nplot_mirror_histogram(\n data=data_with_weights, var='propensity_score', group='treatment', weights='weights',\n xlabel='Propensity Score', ylabel='Weighted Count', title='Weighted Mirror Histogram'\n)\n```\n### Compute Standardized Mean Difference (SMD)\n\n```python\nsample_df = pd.DataFrame({\n 'age': np.random.randint(18, 66, size=100),\n 'weight': np.round(np.random.uniform(120, 200, size=100), 1),\n 'gender': np.random.choice(['male', 'female'], size=100),\n 'race': np.random.choice(\n ['white', 'black', 'hispanic'],\n size=100, p=[0.4, 0.3, 0.3]\n ),\n 'educ_level': np.random.choice(\n ['bachelor', 'master', 'doctorate'],\n size=100, p=[0.3, 0.4, 0.3]\n ),\n 'ps_wts': np.round(np.random.uniform(0.1, 1.0, size=100), 2),\n 'group': np.random.choice(['treated', 'control'], size=100),\n 'date': pd.date_range(start='2024-01-01', periods=100, freq='D')\n})\n\n# 1. Basic usage with unadjusted SMD only:\ncompute_smd(sample_df, vars=['age', 'weight', 'gender'], group='group', estimand='ATE')\n\n# 2. Including weights for adjusted SMD:\ncompute_smd(\n sample_df, \n vars=['age', 'weight', 'gender'], \n group='group', wt_var='ps_wts',\n estimand='ATE'\n)\n\n# 3. Including categorical variables for adjusted SMD:\ncompute_smd(\n sample_df,\n vars=['age', 'weight', 'gender'],\n group='group',\n wt_var='ps_wts',\n cat_vars=['race', 'educ_level'],\n estimand='ATE'\n)\n```\n\n### Create a love plot (point plot of SMD)\n\n``` python\ndata = pd.DataFrame({\n 'variables': ['age', 'bmi', 'blood_pressure'],\n 'unadjusted_smd': [0.25, 0.4, 0.1],\n 'adjusted_smd': [0.05, 0.2, 0.08]\n})\n\nplot_smd(data)\n\n## Adding a reference line at 0.1\nplot_smd(data, add_ref_line=True, ref_line_value=0.1)\n\n## Customizing the Seaborn plot with additional keyword arguments\nplot_smd(data, add_ref_line=True, ref_line_value=0.1, palette='coolwarm', markers=['o', 's'])\n\n```\n\n## Contributing\n\nInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.\n\n## License\n\n`skmiscpy` was created by Shafayet Khan Shafee. It is licensed under the terms of the MIT license.\n\n## Credits\n\n`skmiscpy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).\n\n",
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