Metadata-Version: 2.4
Name: dssnippets
Version: 0.1.0
Summary: Print ready-to-copy Data Science code snippets (preprocessing, models, plotting) from Python.
Author: Mahadevan
License: MIT
Keywords: data science,snippets,pandas,scikit-learn,cheatsheet
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Education
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: pretty
Requires-Dist: rich; extra == "pretty"
Provides-Extra: run
Requires-Dist: numpy; extra == "run"
Requires-Dist: pandas; extra == "run"
Requires-Dist: matplotlib; extra == "run"
Requires-Dist: seaborn; extra == "run"
Requires-Dist: scipy; extra == "run"
Requires-Dist: scikit-learn; extra == "run"
Dynamic: license-file

# dssnippets

Print ready-to-copy Data Science code snippets straight from Python:
loading, preprocessing, ANOVA/correlation, regression, logistic regression,
decision tree, random forest, K-means, Apriori, evaluation and plotting.
Every line is commented.

```bash
pip install dssnippets
```

```python
import dssnippets as ds

ds.topics()                       # list sections and snippet names
ds.show("preprocessing")          # print a whole section
ds.show("handle_missing_values")  # print one snippet
ds.show("all")                    # print everything
ds.search("outlier")              # find snippets by keyword
ds.save("models", "models.py")    # save to a file
```

Command line:

```bash
dssnippets topics
dssnippets show train_kmeans
dssnippets search anova
```

Viewing snippets needs no other libraries. To actually run the code:
`pip install dssnippets[run]`. For coloured output: `pip install dssnippets[pretty]`.
