#Json query github python install

Run pip install pythonql to install pythonql for Python2.7, or pip install pythonql3 for Python 3.x. # Basic SQL query, but with some data cleaning res = [ select ( name, sum( price) as sum)įor o in ords let price = try float( o. # Basically, some data might be dirty, but you still want to be able to write a simple query from collections import namedtuple ord = namedtuple( 'Order', )Ĭust = namedtuple( 'Cust', )Ĭusts =

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You can download a single binary, scp it to a far away machine of the same type, and expect it to work.

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jq is written in portable C, and it has zero runtime dependencies. Just throw this little guy up at the top of your file: import json A Little Vocabulary The process of encoding JSON is usually called serialization. #coding: pythonql # This example illustrates the try-catch business in PythonQL. jq is like sed for JSON data - you can use it to slice and filter and map and transform structured data with the same ease that sed, awk, grep and friends let you play with text. Python Supports JSON Natively Python comes with a built-in package called json for encoding and decoding JSON data. Python has pretty advanced comprehensions, that cover a big chunk of SQL, to the point where PonyORM was able to build a whole ORM system based on comprehensions. So we can write path expression in the query language (and elsewhere in Python expressions) like this: PythonQL is an extension to Python that allows language-integrated queries against relational, XML and JSON data, as well an Python's collections. expr where expr must evaluate to string expr where expr must evaluate to stringĭescendants step: for x in data. We have modeled our path expression on XPath, however we use a much simplified verison:Ĭhild step: for x in data. When working with nested data that has varied structure, path expressions are extremely useful. We propose the following extensions to Python( that are implemeneted in this demo preprocessor and query executor): However, group by mechanisms, outerjoins and support for semi-structured data are not handled well at all. PythonQL is an extension to Python that allows language-integrated queries against relational, XML and JSON data, as well an Python's collections Here is a quick example: avg_temp : float = jq. Once you created your query object you can then query over it usingĪ variety of methods. String ( ''' ''' ) # OR jq : JsonQuery = JsonQuery (). TOMLFile / toml_file, TOMLString / toml_string or It is a rewrite of the Go package 'GoJSONQ' which you can find here: GoJSONQ Github Repository I decided to rewrite this package in Python because I love it's simple API and is just a beauty to work with. MongoSQL: A JSON query engine with SqlAlchemy as a back-end. PyJsonQuery (pyjsonq) PyJsonQ is an easy to use and package to query json data.

Using either File / file, String / string, JSON (de)serialization, GraphQL and JSON schema generation using Python typing. Next create a new JsonQuery instance and load the data into the query

Operators for the Where method: holds, notHolds Installation pip install pyjsonqueryįirst import the JsonQuery class into your project from pyjsonq import JsonQuery Useful stuff to it, but for now the only thing I added are two

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Over time I will potentially update this package and add more fun and Simple API and is just a beauty to work with. I decided to rewrite this package in Python because I love it's It is a rewrite of the Go package 'GoJSONQ' which you can find here: PyJsonQ is an easy to use and package to query json data.