{
    "info": {
        "author": "Robert Doiel, Thomas E Morrell",
        "author_email": "rsdoiel@caltech.edu, tmorrell@caltech.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows :: Windows 10",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Other",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
        ],
        "description": "\n\n# py_dataset   [![DOI](https://data.caltech.edu/badge/175684474.svg)](https://data.caltech.edu/badge/latestdoi/175684474)\n\npy_dataset is a Python wrapper for the [dataset](https://github.com/caltechlibrary/dataset) \ncommand line tool, Go package, and  C shared library for working with \n[JSON](https://en.wikipedia.org/wiki/JSON) objects as collections. \nCollections can be stored on disc or in \nCloud Storage.  JSON objects are stored in collections as \nplain UTF-8 text. This means the objects can be accessed with common \nUnix text processing tools as well as most programming languages.\n\nThis package wraps all [dataset](docs/dataset.html) operations such \nas initialization of collections, creation, \nreading, updating and deleting JSON objects in the collection. Some of \nits enhanced features include the ability to generate data \n[frames](docs/frame.html) as well as the ability to \nimport and export JSON objects to and from CSV files and Google Sheets.\n\n## Install\n\nAvailable via pip `pip install py_dataset` or by downloading this repo and\ntyping `python setup.py install`. This repo includes dataset shared C libraries\ncompiled for Windows, Mac, and Linux and the appripriate library will be used\nautomatically.\n\n## Features\n\n[dataset](docs/dataset) supports \n\n- Basic storage actions ([create](docs/create.html), [read](docs/read.html), [update](docs/update.html) and [delete](docs/delete.html))\n- listing of collection [keys](docs/keys.html) (including filtering and sorting)\n- import/export  of [CSV](how-to/working-with-csv.html) files and [Google Sheets](how-to/working-with-gsheets.html)\n- An experimental full text [search](how-to/indexing-and-search.html) interface based on [Blevesearch](https://blevesearch.com)\n- The ability to reshape data by performing simple object [joins](docs/join.html)\n- The ability to create data [grids](docs/grid.html) and [frames](docs/frame.html) from collections based \n  on keys lists and [dot paths](docs/dotpath.html) into the JSON objects stored\n\n### Limitations of _dataset_\n\n_dataset_ has many limitations, some are listed below\n\n- it is not a multi-process, multi-user data store (it's files on \"disc\" without locking)\n- it is not a replacement for a repository management system\n- it is not a general purpose database system\n- it does not supply version control on collections or objects\n\n## Tutorial\n\nThis module provides the functionality of the _dataset_ command line tool as a Python 3.6 module.\nOnce installed try out the following commands to see if everything is in order (or to get familier with\n_dataset_).\n\nThe \"#\" comments don't have to be typed in, they are there to explain the commands as your type them.\nStart the tour by launching Python3 in interactive mode.\n\n```shell\n    python3\n```\n\nThen run the following Python commands.\n\n```python\n    from py_dataset import dataset\n    # Almost all the commands require the collection_name as first paramter, we're storing that name in c_name for convience.\n    c_name = \"a_tour_of_dataset.ds\"\n\n    # Let's create our a dataset collection. We use the method called 'init' it returns True or False\n    dataset.init(c_name)\n\n    # Let's check our collection to see if it is OK\n    dataset.status(c_name)\n\n    # Let's count the records in our collection (should be zero)\n    cnt = dataset.count(c_name)\n    print(cnt)\n\n    # Let's read all the keys in the collection (should be an empty list)\n    keys = dataset.keys(c_name)\n    print(keys)\n\n    # Now let's add a record to our collection. To create a record we need to know\n    # this collection name (e.g. c_name), the key (most be string) and have a record (i.e. a dict literal or variable)\n    key = \"one\"\n    record = {\"one\": 1}\n    ok = dataset.create(c_name, key, record)\n    # If ok is False we can check the last error message with the 'error_message' method\n    if ok == False:\n        print(dataset.error_message())\n\n    # Let's count and list the keys in our collection, we should see a count of '1' and a key of 'one'\n    dataset.count(c_name)\n    keys = dataset.keys(c_name)\n    print(keys)\n\n    # We can read the record we stored using the 'read' method.\n    new_record = dataset.read(c_name, key)\n    print(new_record)\n\n    # Let's modify new_record and update the record in our collection\n    new_record[\"two\"] = 2\n    ok = dataset.update(c_name, key, new_record)\n    if ok == False:\n        print(dataset.error_message())\n\n    # Let's print out the record we stored using read method\n    print(dataset.read(c_name, key)\n\n    # Finally we can remove (delete) a record from our collection\n    ok = dataset.delete(c_name, key)\n    if ok == False:\n        print(dataset.error_message())\n\n    # We should not have a count of Zero records\n    cnt = dataset.count(c_name)\n    print(cnt)\n```\n\n\n",
        "description_content_type": "text/markdown",
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        "download_url": "https://github.com/caltechlibrary/py_dataset/releases",
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        "home_page": "https://github.com/caltechlibrary/py_dataset",
        "keywords": "GitHub,metadata,data,software,json",
        "license": "https://data.caltech.edu/license",
        "maintainer": "",
        "maintainer_email": "",
        "name": "py-dataset",
        "package_url": "https://pypi.org/project/py-dataset/",
        "platform": "",
        "project_url": "https://pypi.org/project/py-dataset/",
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        "summary": "A command line tool for working with JSON documents on local disc, in an S3 bucket or on Google Sheets/Cloud Storage",
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