| Name | zaps JSON |
| Version |
1.1
JSON |
| download |
| home_page | None |
| Summary | Low-code Python wrapper for Exploratory Data Analysis |
| upload_time | 2024-12-05 20:20:25 |
| maintainer | None |
| docs_url | None |
| author | Amr Muhammad YT/@AmMoPy |
| requires_python | >=3.9 |
| license | MIT License (MIT) Copyright (c) 2024 Amr Muhammad YT/@AmMoPy Zaps: Pythonic Exploratory Data Analysis Framework Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
data analysis
data manipulation
eda
exploratory
insights
outliers
visualizations
wrapper
pipeline
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
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No coveralls.
|
<picture align="center">
<img alt="zaps Logo" src="https://drive.google.com/uc?id=1QxZ0ZEadn1_1HNItsOTN6kqILWQbsLP0">
</picture>
-----------------
# ZAPS: Pythonic Exploratory Data Analysis Framework
ZAPS is a lightweight, low-code Python wrapper designed to simplify and accelerate the exploratory data analysis (EDA) process. Built on top of industry-standard libraries, it provides an intuitive and efficient framework for data inspection, visualization, and preparation.
With ZAPS, you can quickly and easily perform a wide range of EDA tasks, without the need for complex code or extensive programming expertise; allowing you to focus on insights and decision-making rather than tedious data manipulation, all for unlocking deeper understanding and actionable insights from your data.
[](https://pypi.org/project/zaps/)
[](https://pypi.org/project/zaps/)
[](https://github.com/AmMoPy/zaps/blob/main/LICENSE.txt)
[](https://zaps.readthedocs.io/)
[](https://youtu.be/XIAk670WAWM)
## Table of Contents
- [Main Features](#main-features)
- [Installation](#installation)
- [Dependencies](#dependencies)
- [Quickstart](#quickstart)
- [Tutorials](#tutorials)
- [License](#license)
- [API Documentation](#api-documentation)
- [Contributing to ZAPS](#contributing-to-zaps)
## Main Features
- Combined capabilities of multiple modules in a concise single interface
- Highlighting potential underlying problems and summarizing input data
- Rich tabular reports and highly customizable interactive visualizations
- Statistical modeling with commonly used algorithms and metrics
- Flexible alternation between numeric and categorical data
- User friendly error handling and informative workflow
- Easy integration with Scikit-learn Pipeline
## Installation
ZAPS is tested and supported on 64-bit systems with:
- Python 3.9 or latter
- Debian 12
- Windows 8.1 or later
You can install ZAPS with Python's pip package manager:
```python
# install zaps
pip install zaps
```
## Dependencies
- [Numpy](https://numpy.org)
- [Pandas](https://pandas.pydata.org)
- [Seaborn](https://seaborn.pydata.org)
- [Matplotlib](https://matplotlib.org)
- [Plotly](https://plotly.com)
- [Statsmodels](https://www.statsmodels.org)
- [Scipy](https://scipy.org)
- [Scikit-learn](https://scikit-learn.org)
- [Distfit](https://erdogant.github.io/distfit)
## Quickstart
#### Analysing and highlighting data problems
```python
import pandas as pd
from zaps.eda import UniStat, Dist, Olrs, NumAna, CatAna
# loading sample dataset
df = pd.read_csv('...')
# univariate stats - highlighting skewed numeric features and rare categories and
u_s = UniStat(df)
num_cols, cat_cols, dup_df = u_s.peek()
# visualizing data problems
u_s.stats_plot()
# skewed features goodness of fit: Normal distribution
u_s.skew_plot()
```
#### Visualizing distributions
```python
# plotting numeric distributions with no user input
dsts = Dist(df = df, cols = num_cols, silent = True)
# Histograms iterative plotting
dsts.hs()
# analysing best fitting distribution
dsts.best_fit(
cols = num_cols,
distr = ['norm', 'expon', 'lognorm', 'uniform']
)
# visualize best fitting distribution
dsts.best_vis()
```
#### Identifying and handling outliers
```python
# outliers: identifying and capping using different methods
lrs = Olrs(num_cols,
mapping = {'column_name': ('iqr', 1.5)},
method = 'q',
hide_p_bar = True
)
trans_df = lrs.fit_transform(df)
```
#### Numeric features analysis
```python
# numeric analysis - fitting regression models and displaying results
n_a = NumAna(df, num_cols, 'numeric_target_column_name',
hide_p_bar = True).fit_models()
# target and feature correlation - with filtered display
corr_df, feat_corr = n_a.corr(plot = True, thresh = .5)
# visualizing trend lines and overlaying outliers on a selected subset
n_a.vis_fit(olrs_mapping = lrs.z_olrs_)
# assess fit results visually - OLS
n_a.vis_ols_fit()
# check fit results
n_a.z_fit_results_['column_name'].summary()
# interactive multivariate analysis
n_a.vis_multi(col = 'column_name1', color = 'column_name2', symbol = 'column_name3',
trendline = 'ols', olrs_idx = lrs.z_olrs_idx_)
```
#### Categorical features analysis
```python
# categorical analysis - cats vs num
c_a = CatAna(df, cat_cols, 'numeric_target_column_name', hide_p_bar = True)
# ANOVA with assumptions and mutual info scores
anova = c_a.ana_owva()
# Post-hoc displaying groups that could be merged
post_hoc = c_a.ana_post(multi_tst_corrc = 'bonf')
```
```python
# categorical analysis - cats vs cat
c_a = CatAna(df, cat_cols, 'categorical_target_column_name', hide_p_bar = True)
# chi2 test of independence
chi2 = c_a.ana_chi2()
```
#### Preprocessing Pipeline
```python
# sklearn pipline integration
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
# setup
feats = ['column_name1', 'column_name2']
lrs = Olrs(cols = feats, hide_p_bar = True)
poly = PolynomialFeatures(interaction_only = True, include_bias = False).set_output(transform = "pandas")
# pipline
pl = Pipeline([
('pf', poly),
('olrs', lrs),
])
pl.fit_transform(df[feats])
```
## Tutorials
[ZAPS in Colab](https://colab.research.google.com/drive/1TWAz1kTRLatXOr1MWVf_oMEdnMXJFNoM?usp=sharing)
## License
[MIT](LICENSE.txt)
## API Documentation
The official documentation is hosted at [Read The Docs](https://zaps.readthedocs.io/).
## Contributing to ZAPS
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
-----------------
[TOC](#table-of-contents)
Raw data
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"description": "<picture align=\"center\">\n<img alt=\"zaps Logo\" src=\"https://drive.google.com/uc?id=1QxZ0ZEadn1_1HNItsOTN6kqILWQbsLP0\">\n</picture>\n\n-----------------\n\n# ZAPS: Pythonic Exploratory Data Analysis Framework\n\nZAPS is a lightweight, low-code Python wrapper designed to simplify and accelerate the exploratory data analysis (EDA) process. Built on top of industry-standard libraries, it provides an intuitive and efficient framework for data inspection, visualization, and preparation. \n\nWith ZAPS, you can quickly and easily perform a wide range of EDA tasks, without the need for complex code or extensive programming expertise; allowing you to focus on insights and decision-making rather than tedious data manipulation, all for unlocking deeper understanding and actionable insights from your data.\n\n[](https://pypi.org/project/zaps/)\n[](https://pypi.org/project/zaps/)\n[](https://github.com/AmMoPy/zaps/blob/main/LICENSE.txt)\n[](https://zaps.readthedocs.io/)\n\n[](https://youtu.be/XIAk670WAWM)\n\n## Table of Contents\n\n- [Main Features](#main-features)\n- [Installation](#installation)\n- [Dependencies](#dependencies)\n- [Quickstart](#quickstart)\n- [Tutorials](#tutorials)\n- [License](#license)\n- [API Documentation](#api-documentation)\n- [Contributing to ZAPS](#contributing-to-zaps)\n\n## Main Features\n\n- Combined capabilities of multiple modules in a concise single interface\n- Highlighting potential underlying problems and summarizing input data\n- Rich tabular reports and highly customizable interactive visualizations\n- Statistical modeling with commonly used algorithms and metrics\n- Flexible alternation between numeric and categorical data\n- User friendly error handling and informative workflow \n- Easy integration with Scikit-learn Pipeline\n\n## Installation\n\nZAPS is tested and supported on 64-bit systems with:\n\n- Python 3.9 or latter\n- Debian 12\n- Windows 8.1 or later\n\nYou can install ZAPS with Python's pip package manager:\n\n```python\n# install zaps\npip install zaps\n```\n\n## Dependencies\n\n- [Numpy](https://numpy.org)\n- [Pandas](https://pandas.pydata.org)\n- [Seaborn](https://seaborn.pydata.org)\n- [Matplotlib](https://matplotlib.org)\n- [Plotly](https://plotly.com)\n- [Statsmodels](https://www.statsmodels.org)\n- [Scipy](https://scipy.org)\n- [Scikit-learn](https://scikit-learn.org)\n- [Distfit](https://erdogant.github.io/distfit)\n\n## Quickstart\n\n#### Analysing and highlighting data problems\n\n```python\nimport pandas as pd\nfrom zaps.eda import UniStat, Dist, Olrs, NumAna, CatAna\n\n# loading sample dataset\ndf = pd.read_csv('...')\n\n# univariate stats - highlighting skewed numeric features and rare categories and \nu_s = UniStat(df)\n\nnum_cols, cat_cols, dup_df = u_s.peek()\n\n# visualizing data problems\nu_s.stats_plot()\n\n# skewed features goodness of fit: Normal distribution\nu_s.skew_plot()\n```\n\n#### Visualizing distributions\n\n```python\n# plotting numeric distributions with no user input\ndsts = Dist(df = df, cols = num_cols, silent = True)\n\n# Histograms iterative plotting \ndsts.hs()\n\n# analysing best fitting distribution\ndsts.best_fit(\n cols = num_cols,\n distr = ['norm', 'expon', 'lognorm', 'uniform']\n )\n\n# visualize best fitting distribution\ndsts.best_vis()\n```\n\n#### Identifying and handling outliers\n\n```python\n# outliers: identifying and capping using different methods\nlrs = Olrs(num_cols,\n\t\t mapping = {'column_name': ('iqr', 1.5)},\n\t\t method = 'q',\n hide_p_bar = True\n )\n\ntrans_df = lrs.fit_transform(df)\n```\n\n#### Numeric features analysis\n\n```python\n# numeric analysis - fitting regression models and displaying results\nn_a = NumAna(df, num_cols, 'numeric_target_column_name', \n\t\t\t hide_p_bar = True).fit_models()\n\n# target and feature correlation - with filtered display\ncorr_df, feat_corr = n_a.corr(plot = True, thresh = .5)\n\n# visualizing trend lines and overlaying outliers on a selected subset\nn_a.vis_fit(olrs_mapping = lrs.z_olrs_)\n\n# assess fit results visually - OLS\nn_a.vis_ols_fit()\n\n# check fit results\nn_a.z_fit_results_['column_name'].summary()\n\n# interactive multivariate analysis\nn_a.vis_multi(col = 'column_name1', color = 'column_name2', symbol = 'column_name3', \n trendline = 'ols', olrs_idx = lrs.z_olrs_idx_)\n```\n\n#### Categorical features analysis\n\n```python\n# categorical analysis - cats vs num\nc_a = CatAna(df, cat_cols, 'numeric_target_column_name', hide_p_bar = True)\n\n# ANOVA with assumptions and mutual info scores\nanova = c_a.ana_owva()\n\n# Post-hoc displaying groups that could be merged\npost_hoc = c_a.ana_post(multi_tst_corrc = 'bonf')\n```\n\n```python\n# categorical analysis - cats vs cat\nc_a = CatAna(df, cat_cols, 'categorical_target_column_name', hide_p_bar = True)\n\n# chi2 test of independence\nchi2 = c_a.ana_chi2()\n```\n\n#### Preprocessing Pipeline\n\n```python\n# sklearn pipline integration\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.pipeline import Pipeline\n\n# setup\nfeats = ['column_name1', 'column_name2']\n\nlrs = Olrs(cols = feats, hide_p_bar = True)\npoly = PolynomialFeatures(interaction_only = True, include_bias = False).set_output(transform = \"pandas\")\n\n# pipline\npl = Pipeline([\n ('pf', poly),\n ('olrs', lrs),\n ])\n \npl.fit_transform(df[feats])\n```\n\n## Tutorials\n\n[ZAPS in Colab](https://colab.research.google.com/drive/1TWAz1kTRLatXOr1MWVf_oMEdnMXJFNoM?usp=sharing)\n\n## License\n\n[MIT](LICENSE.txt)\n\n## API Documentation\n\nThe official documentation is hosted at [Read The Docs](https://zaps.readthedocs.io/).\n\n## Contributing to ZAPS\n\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.\n\n-----------------\n\n[TOC](#table-of-contents)\n",
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