Metadata-Version: 2.1
Name: nycdb
Version: 0.2.1
Summary: database of nyc housing data
Home-page: https://github.com/nycdb/nycdb
Author: nycdb
Author-email: nycdb@riseup.net
License: AGPL-3.0-or-later
Description: # NYCDB
        
        **a tool for building a database of NYC housing data**
        
        This is a Python library and cli tool for installing, updating and managing NYCDB, a postgres database of NYC Housing Data.
        
        For more background information on this project and links to download copies of full database dump visit: https://github.com/nycdb/nycdb. We use the term **nycdb** to refer to both the python software and the running copy of the postgres database.
        
        ## Using the cli tool
        
        You will need python 3.6+ and Postgres. The latest version can be installed from pypi with pip:  `python3 -m pip install nycdb`
        
        If the installation is successful, you can view a summary of the tool's options by running `nycdb --help`
        
        To print a list of datasets: ` nycdb --list-datasets`
        
        `nycdb`'s main job is to download datasets and import them into postgres. It does not manage the database for you. You can use the flags `-U/--user`, `-D/--database`, `-P/--password`, and `-H/--host` to instruct nycdb to connect to the correct database. See `nycdb --help` for the defaults.
        
        Example: downloading, loading, and verifying the dataset **hpd_violations**:
        
        ``` sh
        nycdb --download hpd_violations
        nycdb --load hpd_violations
        nycdb --verify hpd_violations
        ```
        
        You can also verify all datasets: ` nycdb --verify-all `
        
        By default the downloaded data files are is stored in `./data`. Use `--root-dir` to change the location of the data directory.
        
        You can export a `.sql` file for any dataset by using the `--dump` command
        
        ## Development
        
        There are two development workflows: one using python virtual environments and one using docker.
        
        ### Using docker and docker-compose
        
        To get started all you have to do is run `docker-compose up`.
        
        On the first run Docker will take longer to downloads and build the images. It
        will start a Postgres server on port 5432 of your local machineYou can also press
        <kbd>CTRL</kbd>-<kbd>C</kbd> at any point to stop the server.
        
        In a separate terminal, you will be able to now use the nycdb cli: `docker-compose run nycdb --help`
        
        You can also open a python3 shell: `docker-compose run --entrypoint=python3 nycdb` or run the test suit `docker-compose run --entrypoint=pytest nycdb`
        
        You may also develop on nycdb itself:
        
        * Any changes you make to the tool's source code will automatically be reflected
          in future invocations of `nycdb` and the test suite.
        * The postgres database server is forwarded to localhost:5432 which you can connect to via a desktop client if you like.
        * If you don't have a desktop Postgres client, you can always run
          `nycdb --dbshell` to interactively inspect the database with [psql](http://postgresguide.com/utilities/psql.html).
        
        To stop the database run `docker-compose down`. The downloaded files and database data are stored in docker volumes and are not automatically removed.
        
        however, if you ever want to wipe the database, run `docker-compose down -v`.
        
        ### Python3 virtual environments
        
        If you have postgres installed separately, you can use this alternative method without docker:
        
        Setup and active a virtual environment:
        
        ``` sh
        python3 -m venv venv
        source venv/bin/activate
        ```
        
        Install nycdb: ` pip install -e ./src`
        
        As long as the virtual environment is activated, you can use `nycdb` directly in your shell.
        
        ###  Adding New Datasets
        
        See the [guide here](ADDING_NEW_DATASETS.md) for the steps to add a new dataset
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3
Description-Content-Type: text/markdown
