# Pandas ML Utils
Pandas Machine Learning Utilities is part of a bigger set of libraries for a convenient experience. Usually exploring
statistical models start with a pandas `DataFrame`.
But soon enough you will find yourself converting your data frames to numpy, splitting arrays, applying min
max scalers, lagging and concatenating columns etc. As a result your notebook looks messy and became and
unreadable beast. Yet the mess becomes only worse once you start to deploy your research into a productive
application. The untested hard coded data pipelines need be be maintained at two places.
The aim of this library is to conveniently operate with data frames without and abstract away the ugly unreproducible
data pipelines. The only thing you need is the original unprocessed data frame where you started.
The data pipeline becomes a part of your model and gets saved that way. Going into production is as easy as
this:
```python
import pandas as pd
import pandas_ml_utils # monkey patch the `DataFrame`
from pandas_ml_utils import Model
# alternatively as a one liner `from pandas_ml_utils import pd, Model`
model = Model.load('your_saved.model')
df = pd.read_csv('your_raw_data.csv')
df_prediction = df.model.predict(model)
# do something with your prediction
df_prediction.plot()
```
is intended to help you through your journey of statistical or machine learning models,
while you never need to leave the world of pandas.
## Installation
The basic implementation supports [scikit learn][e1] classifiers and regressors.
```shell script
pip install pandas-ml-utils
```
Additional machine learning libraries are available as an add on:
```shell script
pip install pandas-ml-utils-torch # pytorch implementation
pip install pandas-ml-utils-keras # keras + tensorflow 1.x implementation
```
Note that the keras/tensorflow version is currently stalled as I focus on pytorch recently. This might change
with PyMC4 and tensorflow probability
## Example
You will find some demo projects in the [examples][ghl1] directory. But It might also be worth it to check
the unit tests and the [integration tests][ghl2]. Here is how classification challenge
might look like:
![Classification Example][ghi1]
[e1]: https://scikit-learn.org/stable/
[ghl1]: https://github.com/KIC/pandas-ml-quant/tree/0.2.5/pandas-ml-utils/./examples/
[ghl2]: https://github.com/KIC/pandas-ml-quant/tree/0.2.5/pandas-ml-utils/../pandas-ml-1ntegration-test
[ghi1]: https://github.com/KIC/pandas-ml-quant/raw/0.2.5/pandas-ml-utils/../.readme/images/classification.png
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Python库是一组预先编写的代码模块,旨在帮助开发者实现特定的编程任务,无需从零开始编写代码。这些库可以包括各种功能,如数学运算、文件操作、数据分析和网络编程等。Python社区提供了大量的第三方库,如NumPy、Pandas和Requests,极大地丰富了Python的应用领域,从数据科学到Web开发。Python库的丰富性是Python成为最受欢迎的编程语言之一的关键原因之一。这些库不仅为初学者提供了快速入门的途径,而且为经验丰富的开发者提供了强大的工具,以高效率、高质量地完成复杂任务。例如,Matplotlib和Seaborn库在数据可视化领域内非常受欢迎,它们提供了广泛的工具和技术,可以创建高度定制化的图表和图形,帮助数据科学家和分析师在数据探索和结果展示中更有效地传达信息。
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pandas-ml-utils-0.2.5.tar.gz (208个子文件)
setup.cfg 79B
SPY.csv 484KB
banknote_authentication.csv 45KB
multi_index_row_summary.df 1KB
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test__model_context.py.ipynb 4KB
Readme.md 3KB
test_ctx.model 22KB
test__model_context.py.outnb 5KB
PKG-INFO 3KB
PKG-INFO 3KB
features_and_labels_definition.py 14KB
test_abstract_model.py 12KB
base_model.py 11KB
figures.py 11KB
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scikit_learn_model.py 9KB
test__features_and_labels_extract.py 5KB
test_skmodel.py 5KB
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features_and_labels_extractor.py 4KB
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fit.py 3KB
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confidence_interval.py 2KB
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features_and_labels_definition.cpython-38.pyc 13KB
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features_and_labels_definition.cpython-37.pyc 12KB
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figures.cpython-38.pyc 11KB
figures.cpython-37.pyc 10KB
test_abstract_model.cpython-38.pyc 9KB
scikit_learn_model.cpython-37.pyc 8KB
scikit_learn_model.cpython-38.pyc 8KB
model_patch.cpython-38.pyc 8KB
model_patch.cpython-37.pyc 8KB
test__features_and_labels_extract.cpython-38.pyc 7KB
test_abstract_model.cpython-37-pytest-6.1.0.pyc 7KB
test__features_and_labels_extract.cpython-37.pyc 6KB
base_summary.cpython-38.pyc 5KB
base_summary.cpython-37.pyc 5KB
test__features_and_Labels.cpython-37-pytest-6.1.0.pyc 5KB
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