Metadata-Version: 2.1
Name: petpy
Version: 2.2.1
Summary: Wrapper for the Petfinder API
Home-page: https://github.com/aschleg/petpy
Author: Aaron Schlegel
Author-email: aaron@aaronschlegel.me
License: MIT
Description: # Petpy - Python Wrapper for the Petfinder API
        
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        :cat2: :dog2: :rooster: :rabbit2: :racehorse:
        
        ## Installation
        
        `petpy` is easily installed through `pip`.
        
        ~~~ python
        pip install petpy
        ~~~
        
        The library can also be cloned or downloaded into a location of your choosing and then installed using the `setup.py` 
        file per the following:
        
        ~~~ python
        git clone git@github.com:aschleg/petpy.git
        cd petpy
        python setup.py install
        ~~~
        
        ## Examples and usage
        
        An account must first be created with [Petfinder](https://www.petfinder.com/developers/) to receive an API and secret 
        key. The API and secret key will be used to grant access to the Petfinder API, which lasts for 3600 seconds, or one 
        hour. After the authentication period ends, you must re-authenticate with the Petfinder API. The following are some 
        quick examples for using `petpy` to get started. More in-depth tutorials for `petpy` and some examples of what 
        can be done with the library, please see the More Examples and Tutorials section below.
        
        ### Authenticating with the Petfinder API
        
        Authenticating the connection with the Petfinder API is done at the same time the `Petfinder` class is initialized.
        
        ~~~ python
        pf = Petfinder(key=key, secret=secret)
        ~~~
        
        The following are some quick examples for getting started with `petpy` and the Petfinder API.
        ### Finding animal types
        
        ~~~ python
        # All animal types and their relevant data.
        all_types = pf.animal_types()
        
        # Returning data for a single animal type
        dogs = pf.animal_types('dog')
        
        # Getting multiple animal types at once
        cat_dog_rabbit_types = pf.animal_types(['cat', 'dog', 'rabbit'])
        ~~~
        
        ### Getting animal breeds for available animal types
        
        ~~~ python
        cat_breeds = pf.breeds('cat')
        dog_breeds = pf.breeds('dog')
        
        # All available breeds or multiple breeds can also be returned.
        
        all_breeds = pf.breeds()
        cat_dog_rabbit = pf.breeds(types=['cat', 'dog', 'rabbit'])
        ~~~ 
        
        The `breeds` method can also be set to coerce the returned JSON results into a pandas DataFrame by setting 
        the parameter `return_df = True`.
        
        ~~~ python
        cat_breeds_df = pf.breeds('cat', return_df = True)
        all_breeds_df = pf.breeds(return_df = True)
        ~~~
        
        ### Finding available animals on Petfinder
        
        The `animals()` method returns animals based on specified criteria that are listed in the Petfinder database. Specific 
        animals can be searched using the `animal_id` parameter, or a search of the database can be performed by entering 
        the desired search criteria.
        
        ~~~ python
        # Getting first 20 results without any search criteria
        animals = pf.animals()
        
        # Extracting data on specific animals with animal_ids
        
        animal_ids = []
        for i in animals['animals'][0:3]:
            animal_ids.append(i['id'])
            
        animal_data = pf.animals(animal_id=animal_ids)
        
        # Returning a pandas DataFrame of the first 150 animal results
        animals = pf.animals(results_per_page=50, pages=3, return_df=True)
        ~~~
        
        ### Getting animal welfare organizations in the Petfinder database 
        
        Similar to the `animals()` method described above, the `organizations()` method returns data on animal welfare 
        organizations listed in the Petfinder database based on specific criteria, if any. In addition to a general search 
        of animal welfare organizations, specific organizational data can be extracted by supplying the `organizations()` 
        method with organization IDs.
        
        ~~~ python
        # Return the first 1,000 animal welfare organizations as a pandas DataFrame
        
        organizations = pf.organizations(results_per_page=100, pages=10, return_df=True)
        
        # Get organizations in the state of Washington
        
        wa_organizations = pf.organizations(state='WA')
        ~~~
        
        ## More Examples and Tutorials
        
        [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/aschleg/petpy/master?filepath=notebooks)
        
        A series of IPython notebooks that introduce and explore some of the functionality and possible uses of the 
        `petpy` library. The notebooks can also be launched interactively with [binder](https://mybinder.org/) by clicking the 
        "launch binder" badge.
        
        * [01 -Introduction to petpy](https://github.com/aschleg/petpy/blob/master/notebooks/01-Introduction%20to%20petpy.ipynb)
        * [02 - Download 45,000 Adoptable Cat Images using petpy and multiprocessing](https://github.com/aschleg/petpy/blob/master/notebooks/02-Download%2045%2C000%20Adoptable%20Cat%20Images%20with%20petpy%20and%20multiprocessing.ipynb)
          - Please note the following notebook is still based on the legacy version of Petfinder and thus are not fully 
            representative of the functionality and methods of the most recent version of `petpy` and the Petfinder API. These 
            are currently being updated to reflect the new version of `petpy`.
        * [03 - Download Pure Breeds Cat Images with petpy for Deep Neural Network training](https://github.com/aschleg/petpy/blob/master/notebooks/03-Download%20Pure%20Breeds%20Cat%20Images%20with%20petpy%20for%20Deep%20Neural%20Network%20training%20-%20multiprocessing.ipynb)
          - Provided by contributor [ma755](https://github.com/ma7555)
        
        ### Other
        
        The following are longer usage examples and tutorials that have been posted to external media websites such as 
        [Medium.com](medium.com):
        
        * [Analyze Petfinder Adoptable Pet Descriptions with the IBM Watson Tone Analyzer — Part One](https://medium.com/@AaronSchlegel/analyze-petfinder-adoptable-pet-descriptions-with-the-ibm-watson-tone-analyzer-part-one-4efabaa1164b)
        
        ## Documentation
        
        * [Petpy documentation](http://petpy.readthedocs.io/en/latest/)
        * [Petpy changelog](https://github.com/aschleg/petpy/blob/master/CHANGELOG.md)
        * [Petfinder API v2.0 documentation](https://www.petfinder.com/developers/v2/docs/)
        
        ## Requirements
        
        * Python >= 3.6
        * [requests](http://docs.python-requests.org/en/master/) >= 2.18.4
        * Although not strictly required to use `petpy`, the [pandas](https://pandas.pydata.org/) library is needed 
          for returning the results as a DataFrame.
        
        ## About [Petfinder.com](https://www.petfinder.com)
        
        Petfinder.com is one of the largest online, searchable databases for finding a new pet online. The database contains 
        information on over 14,000 animal shelters and adoption organizations across North America with nearly 300,000 animals 
        available for adoption. Not only does this make it a great resource for those looking to adopt their new best friend, 
        but the data and information provided in Petfinder's database makes it ideal for analysis. 
        
        ## Contributors
        
        * [ma755](https://github.com/ma7555) - Fixed several functions that use an `animal` parameter and 
          implementing checks for exceeding the Petfinder API limit.
        * [ljlevins](https://github.com/ljlevins) - Found and fixed an error with the `distance` parameter used in the 
          `organizations` API endpoint.  
        
        ## License
        
        MIT
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: Win32 (MS Windows)
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
