{ "info": { "author": "Shankar Rao Pandala", "author_email": "", "bugtrack_url": null, "classifiers": [], "description": "lazyeda\n==============================\n\nLazy EDA is a python library to help shorten the coding time for basic understanding of data, EDA techniques and repetitive tasks with less lines of code\n\nProject Organization\n------------\n\n \u251c\u2500\u2500 LICENSE\n \u251c\u2500\u2500 Makefile <- Makefile with commands like `make data` or `make train`\n \u251c\u2500\u2500 README.md <- The top-level README for developers using this project.\n \u251c\u2500\u2500 data\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 external <- Data from third party sources.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 interim <- Intermediate data that has been transformed.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 processed <- The final, canonical data sets for modeling.\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 raw <- The original, immutable data dump.\n \u2502\n \u251c\u2500\u2500 docs <- A default Sphinx project; see sphinx-doc.org for details\n \u2502\n \u251c\u2500\u2500 models <- Trained and serialized models, model predictions, or model summaries\n \u2502\n \u251c\u2500\u2500 notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),\n \u2502 the creator's initials, and a short `-` delimited description, e.g.\n \u2502 `1.0-jqp-initial-data-exploration`.\n \u2502\n \u251c\u2500\u2500 references <- Data dictionaries, manuals, and all other explanatory materials.\n \u2502\n \u251c\u2500\u2500 reports <- Generated analysis as HTML, PDF, LaTeX, etc.\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 figures <- Generated graphics and figures to be used in reporting\n \u2502\n \u251c\u2500\u2500 requirements.txt <- The requirements file for reproducing the analysis environment, e.g.\n \u2502 generated with `pip freeze > requirements.txt`\n \u2502\n \u251c\u2500\u2500 setup.py <- makes project pip installable (pip install -e .) so src can be imported\n \u251c\u2500\u2500 src <- Source code for use in this project.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 __init__.py <- Makes src a Python module\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 data <- Scripts to download or generate data\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 make_dataset.py\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 features <- Scripts to turn raw data into features for modeling\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 build_features.py\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 models <- Scripts to train models and then use trained models to make\n \u2502 \u2502 \u2502 predictions\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 predict_model.py\n \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 train_model.py\n \u2502 \u2502\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 visualization <- Scripts to create exploratory and results oriented visualizations\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 visualize.py\n \u2502\n \u2514\u2500\u2500 tox.ini <- tox file with settings for running tox; see tox.testrun.org\n\n\n--------\n\n

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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