jsontable 0.1.1

Convert a JSON to a table

JSON Table

A little package to convert a JSON into a table! This project was born out of a need to transform many JSONs mined from APIs to something that Pandas or a relational database could understand. The difference between this package and json path packages is that its designed to create tables, not just extract single values.

How to install

The package is available through pypi. So simply go to your command line and:

pip install jsontable

You're also welcome to download the code from github and modify to suit your needs. And if you have time let me know what cool functionality you added and we can improve the project!

How it works

It works in a similar manner to JSON parsers

1. Create a converter object
2. Give the converter a list of paths you want to explore and how you want to name each column
3. Give the converter a decoded JSON object you want to read, and it returns a table

Usage

Here is a quick example to get you going

import jsontable as jsontable

#Create a list of paths you want to extract
paths = [{"$.id":"id"},	{"$.name":"name"}, {"$.address.city":"city"}]
#The JSON object you want to explore
sample = {"id":"1","name":"Foo","address":{"city":"Bar"}}

#Create an instance of a converter
converter = jsontable.converter()
#Set the paths you want to extract
converter.set_paths(paths)
#Input a JSON to be interpreted
converter.convert_json(sample)

In this case, you will get a table with two columns and two rows (header and first row of data) like these:

[['id', 'name', 'city'], ['1', 'Foo', 'Bar']]

For more examples, refer to the tests folder

How it works

JSON Paths

Each path you specify is a column in your final table. Each path that is setup is expanded according to the standard JSON Path functionality. This is, for each path, the converter starts at the root of the JSON object and navigates each step (a.k.a node) of the path in order. When it reaches the final step in the path (a.k.a leaf), it outputs the resulting element of the JSON into the cell.

The final cell value is converted based on the standard JSON values as follows:

JSON Value	Conversion	Sample Output
object	stringified object	'{"city":"Bar"}'
array	stringified array	'[1,2,3]'
string	string	'Foo'
number	number	4.7
boolean	stringidied boolean	'False'
null	None	None
missing value (i.e. the path did not find an element)	None	None

The intention behind stringifying the object, array and boolean is to be able to pass the output to other data libraries (e.g Pandas) or to a relational database.

Array Expansion

With the exception of the final node, array elements are automatically expanded into rows. So for example a path '$.a.b' applied to a JSON {"a":[{"b":1},{"b":2}]} would result into two rows [[1],[2]]. The array expansion functionality can be applied to the final node by explicitly using the * operator as a final step (e.g. $.a.*)

Example:

paths = [{"$.name":"Name"},{"$.telephones.type":"Telephone Type"},{"$.telephones.number":"Telephone Number"}]
sample = {
			"name":"Foo",
			"telephones":[
				{"type":"mobile", "number":"0000"},
				{"type":"home", "number":"1111"}
			]
		}
converter = jsontable.converter()
converter.set_paths(paths)
converter.convert_json(sample)

Result:

[['Name', 'Telephone Number', 'Telephone Type'], ['Foo', '0000', 'mobile'], ['Foo', '1111', 'home']]

The reverse of this functionality (not expand arrays if they are encountered before the end) is not implemented only due to the lack of need.

Joining Columns

Since a path may result in multiple rows, there is the need to be able to combine the result of each column into the same table. The joining mechanism is similar to an SQL join, where each cell (row-cell combination) is "matched" to a row in the result using a "matching value". The matching value in this case is the last common element of the paths.

This is best illustrated with an example, the following table shows the transformations applied to the sample JSON.

sample = {
	"contacts":[
		{
			"name":"Foo",
			"telephones":[
				{"type":"mobile", "number":"0000"},
				{"type":"home", "number":"1111"}
			],
			"emails":[
				{"type":"work", "email":"[e-mail]"},
				{"type":"personal", "email":"[e-mail]"}
			]
		},
		{
			"name":"Bar",
			"telephones":[
				{"type":"mobile", "number":"2222"},
				{"type":"home", "number":"3333"}
			],
			"emails":[
				{"type":"work", "email":"[e-mail]"},
				{"type":"personal", "email":"[e-mail]"}
			]
		}
	]
}

Paths	Result
[ {"$.contacts.name":"Name"}, {"$.contacts.telephones.type":"Type"}, {"$.contacts.telephones.number":"Number"} ]	[ ['Name', 'Type', 'Number'], ['Foo', 'mobile', '0000'], ['Foo', 'home', '1111'], ['Bar', 'mobile', '2222'], ['Bar', 'home', '3333'] ]
[ {"$.contacts.name":"Name"}, {"$.contacts.telephones.number":"Number"}, {"$.contacts.emails.email":"Email"} ]	[ ['Name', 'Number', 'Email'], ['Foo', '0000', '[e-mail]'], ['Foo', '1111', '[e-mail]'], ['Foo', '0000', '[e-mail]'], ['Foo', '1111', '[e-mail]'], ['Bar', '0000', '[e-mail]'], ['Bar', '1111', '[e-mail]'], ['Bar', '0000', '[e-mail]'], ['Bar', '1111', '[e-mail]'], ]

In the first case, the type and number have a common path telephone and therefore the columns are combined for the same telephone element. If we then look at the name path it has a common path contacts with the rest of the columns, and therefore, the value is repeated across the rows.

In the second case the email and number only have a common path contacts and since each path results in two rows, the only possible way to match these is to combine all the values, resulting in 4 rows per contact (total 8 rows since there are 2 contacts).

Operators

Currently there are two operators supported: * and ~

Syntax	Description
*	Returns all values of the current element. If its an array, it will return one row per array value. If its an object (dictionary in Python) it will return one row per value. If its a value (string, number, boolean, null), it returns the same value
~	Return all indices of the current element. If its an array, it returns an ascending numbered sequence starting with 0 (e.g. [1,2] would return [[0],[1]]) . If its an object, it will return the keys (e.g. {"a":1,"b":2} would return [['a'],['b']]). If its a value it returns 0

More operators will be implemented in later releases.

New in this version

• A bug that was preventing list expansions at different depths (e.g. $.a as well as $.b.c) has been fixed.
• Implementation of the * and ~ operators

Both these changes were made possible by changing the search method from depth first to breadth first, as well as recursing through a tree rather than iterating through one column at a time.

Coming up

In the wishlist we have:

• Filtering
• List indexing
• More functions (basic arithmetics, string concatenation and expansion)
• Square bracket notation ($[a][b] for $.a.b)
• Stringify objects as an option
• Option to output pandas style named array
• Method to set paths and convert at the same time
• CSV Output/Input

References

I want to mention that whilst I inted to expand the functionality of this package, at the moment it can only take a simple sequence of keys to navigate a path. This is, the full functionality proposed by Stefan Gossner in his jsonpath is not yet implemented.... but we will get there.

If you are looking for a package that simply extracts a single value from a JSON by using more complex paths (and its functions), I recommend you look at jsonpath-rw by Kenn Knowles jsonpath-ng by Tomas Aparicio or jsonpath2 by Mark Borkum.

Final disclaimer

I will continue to look for improvements in the package and hopefully add some useful functionality. Given the current popularity of the package, the maintenance is in a best effort manner. However if you have issues or bugs to report let me know here and I will try my best to help.

You can use this package as you wish, but unfortunatelly, I cannot take responsibility of how this code is used, or the results it provides. It is up to you to test this does what you want it to!