![]() In this article, we read data from the Users entity. Use SQL to create a statement for querying GitHub. Use the connect function for the CData GitHub Connector to create a connection for working with GitHub data.Ĭnxn = mod.connect("OAuthClientId=MyOAuthClientId OAuthClientSecret=MyOAuthClientSecret CallbackURL= Create a SQL Statement to Query GitHub ![]() You can now connect with a connection string. Code snippets follow, but the full source code is available at the end of the article.įirst, be sure to import the modules (including the CData Connector) with the following: Once the required modules and frameworks are installed, we are ready to build our ETL app. Pip install pandas Build an ETL App for GitHub Data in Python Use the pip utility to install the required modules and frameworks: pip install petl See the Getting Started chapter of the CData help documentation for an authentication guide.Īfter installing the CData GitHub Connector, follow the procedure below to install the other required modules and start accessing GitHub through Python objects. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. GitHub uses the OAuth 2 authentication standard. For this article, you will pass the connection string as a parameter to the create_engine function. ![]() Create a connection string using the required connection properties. When you issue complex SQL queries from GitHub, the driver pushes supported SQL operations, like filters and aggregations, directly to GitHub and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).Ĭonnecting to GitHub data looks just like connecting to any relational data source. With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live GitHub data in Python. This article shows how to connect to GitHub with the CData Python Connector and use petl and pandas to extract, transform, and load GitHub data. With the CData Python Connector for GitHub and the petl framework, you can build GitHub-connected applications and pipelines for extracting, transforming, and loading GitHub data. The path represents every level in the Json on a single line, separated trough the slash ('/') symbol.The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. The sytanx used for the query have got 3 parts: from jsonquery import jsonquery jsonquery ( 'archivo.json', 1001 '] Syntax The module have got a function called jsonquery and you must declare the correct import. The second way to use it is like a function. load ( input ) jsonquery = JsonQuery ( dataset = data ) retval = jsonquery. You can use a dict objet to if you don't want to use a file.Įxample: from json_query import JsonQuery import json data = None with open ( 'test.json', 'r' ) as input : data = json. query: Query applied over the data (see syntax).execute ( '/' ) #the result it's an dict or list with the result Once you has initialized the object, you can make the query over the JSON data loaded from the file (that is made when you init the object) using the execute method:Įxample: query = JsonQuery ( 'path/to/file.json' ) query. This class takes 1 parameters: file path that contains the json The library have two ways to use it, one way is usgin the class JsonQuery. Once the repository is cloned we can use the library adding the following import: from jsonquery import JsonQuery Install aplication from : pip install arkho-jsonquery Install application from GitHub using git client: git clone ![]() JSON Query State: PROTOTYPE AUTHOR: Marcelo Silva Language: Python Setup
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