Saravanan666/pypi-org huggingface dataset:
Package: jsonflattifier
jsonflattifierConverts a JSON Document with nested objects and their parameters to the JSON Document with Flat Denormalised Data.Installation>pipinstalljsonflattifierInput{"name":"John","has":["apple","peach"]}Get flat JSON>python3-mjsonflattifierflattify'{"name":"John","has":["apple","peach"]}'--json--jsonpath-keys--no-table[{"$['name']":"John","$['has'][0]":"apple"},{"$['name']":"John","$['has'][1]":"peach"}]Get CSV>python3-mjsonflattifierflattify'{"name":"John","has":["apple","peach"]}'--csv--no-table['name'],['has']
John,apple
John,peachPrint Table>python3-mjsonflattifierflattify'{"name":"John","has":["apple","peach"]}'['name']['has']JohnappleJohnpeach2 rows in setMore Exampleshttps://gitlab.com/v.grigoryevskiy/json-flattifier/-/tree/master/tests/data
Package: jsonforge
JSON ForgeUse CSS, XPath and JMESPath to make nice JSON files out of ugly (X|HT)ML, CSV or JSON blobs found on the Internet.Free software: GNU General Public License v3Documentation: tbdFeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-12-17)First release on PyPI.
Package: jsonfy
Jsonfy: Lightweight two-way binding of JSON and object without third-party dependency, suppoert nesting,complex obj[Jsonfy: 一款轻量级的无第三方依赖的json和对象的双向绑定的工具，支持复杂类型，嵌套类型，自定义某个字段是否序列化等,欢迎使用]
Package: jsong
jsong streamlines the process of copying data into json filesValid FILETYPEs: JSON ('.json') and Excel ('.xlsx')For discographies:
Pick a link of your choice (e.g.,https://www.allmusic.com/artist/taylor-swift-mn0000472102/discography,https://www.allmusic.com/artist/elton-john-mn0000796734/discography, etc.) and pass the url to the function getDiscography(URL_HERE, FILE_TYPE_HERE)For albums:
Pick a link of your choice (https://www.allmusic.com/album/innervisions-mw0000192406,https://www.allmusic.com/album/a-night-at-the-opera-mw0000391519, etc.) and pass the url to the function getAlbum(URL_HERE, FILE_TYPE_HERE)Data is collected in part with the use of a Selenium webdriver. Web pages will be periodically opened and closed in order to webscrape information; please do not close these pages -- they will be automatically closed when the code has been executed. Thank you :)NOTE: Jsong version 0.8 takes significantly longer to process full discographies, but is more user-friendly. For a quicker process, please download Jsong version 0.7.
Package: json-get
Get values from JSON objects usings a path expression. Optional type
checking is possible:>>> from jsonget import json_get, json_get_default, JList
>>> j = {
...     "foo": {"num": 3.4, "s": "Text"},
...     "arr": [10, 20, 30],
... }
>>> json_get(j, "/foo/num")
3.4
>>> json_get(j, "/arr[1]")
20
>>> json_get(j, "/foo/unknown")
Traceback (most recent call last):
    ...
ValueError: JSON path '/foo/unknown' not foundValues are optionally checked against one of the following types:str,int,float,bool,list, anddict.
Checking for null values is not supported:>>> json_get(j, "/foo/num", str)
Traceback (most recent call last):
    ...
TypeError: wrong JSON type str != floatfloatwill match any number,intwill only match numbers without
a fractional part:>>> json_get(j, "/foo/num", float)
3.4
>>> json_get(j, "/foo/num", int)
Traceback (most recent call last):
    ...
TypeError: wrong JSON type int != floatAdditionally, the type of list values can be checked:>>> json_get(j, "/arr", JList(int))
[10, 20, 30]json_get_default()can be used to return a default value if a given
path does not exist:>>> json_get_default(j, "/bar", "default value")
'default value'
Package: jsongit
git your dictUse Git as a key-value store that can track and merge arbitrary data in Python:>>> r = jsongit.init('repo')
>>> r.commit('foo', {})
>>> r.checkout('foo', 'bar')
>>> r.commit('foo', {'roses': 'red'})
>>> r.commit('bar', {'violets': 'blue'})
>>> r.merge('foo', 'bar').message
Auto-merge of be92d3dcb6 and dbde44bada from shared parent 5d55214e4f
>>> r.show('foo')
{u'roses': u'red', u'violets': u'blue'}
>>> for commit in r.log('foo'):
...     print(commit)
'foo'='{u'roses': u'red', u'violets': u'blue'}'@fc9e0f3106
'bar'='{u'violets': u'blue'}'@be92d3dcb6
'bar'='{}'@5bb29ad7dc
'foo'='{u'roses': u'red'}'@dbde44bada
'foo'='{}'@5d55214e4fInstallationLibgit2is used to build and modify the git repository. You can find
instructions for installing ithere.Pip handles the rest:$ pip install jsongitYou can find full installation instructions in thedocumentation.DocumentationDocumentation is onRead the Docs.
Package: json-graph-lite
Lightweight library for JSON graphLibrary, similar to:https://github.com/jsongraph/jsongraph.pyNB:json-graph-litelibrary uses list for nodes, not object, so it's not compatible with JSON Graph Format.JSON Graph Format compatible classes (Graph, Graphs) can be found fromjson_graph_lite.jgf.Incompatible change: list is used for nodes, not object, because JSON object can't represent integer keys.The intention of json-graph-lite is to provide lightweight graph library, which serializes
and deserializes graphs a la JSON graph format.Take a look attests/test_graphs.pyfor usage examples.
Package: jsongraphtree
jsongraphtreejsongraph usage instructions
jsongraph用法说明Purpose: Visualize json as a tree to facilitate json visualization of content form
用途：将json可视化为一棵树，便于json可视化内容形式jsongraphtree xxx (fill in xxx as path
用法：jsongraphtree xxx （xxx填写为路径）Example: jsongraphtree data.json
例子：jsongraphtree data.jsonThe result: A json visual view is generated with the name json_tree_test.png
结果：生成一张json可视化视图，图的名字为json_tree_test.png
Package: jsongrep
jsongrep is a shell tool for extracting values from JSON documents. It supports shell-like globbing for property names, and emits the matched values separated by newlines.ExamplesLet’s start with a real-world example.Let’s grab the even tweets from the last 10 pulled from Twitter’s JSON feedhttp://apiwiki.twitter.com/Twitter-REST-API-Method:-statuses-public_timeline$ curl -s 'http://twitter.com/statuses/public_timeline.json' | jsongrep '[02468].text'
ARGHHHHHH. facebook is being gay
5-5 in the darts between Barney and Whitlock. Amazing. #darts
I wonder if I'm still located on 5th ave?
Estou de volta  a internet .... Essa chuva n para !   Estou de boa com a minha familia .Yeah, that’s just about what I expected.Now suppose you have a JSON document like the one intests/ongz.jsonwhich looks like this:{
    "bah" : {
        "feh" : true,
        "foo" : 3,
        "lah" : "songz"
    },
    "blah" : {
        "lol" : "gongz"
    },
    "arr" : [
        "a", "b", "c", "d", "e",
        "f", "g", "h", "i", "j",
        "k", "l", "m", "n", "o",
        "p", "q", "r", "s", "t",
        "u", "v", "w", "x", "y",
        "z"
    ]
}jsongrep will let you match structural patterns, where.(dot) separates nested properties.Let’s glob on property names:$ jsongrep 'b*.l*' tests/ongz.json
gongz
songzWorks on arrays, too:$ jsongrep 'arr.?' tests/ongz.json
a
b
c
d
e
f
g
h
i
jNote that we’re still globbing, even though arrays have numeric indices:$ jsongrep 'arr.2?' tests/ongz.json
u
v
w
x
y
zIf you specify a JSON subtree, that’s what you get back:$ jsongrep 'bah' tests/ongz.json
{"foo": 3, "lah": "songz", "feh": true}Syntaxjsongrep currently supports normal shell glob patterns within property names:?       Matches any one character
*       Matches any number of characters within a field
[seq]   Matches any of the characters in seq
[!seq]  Matches any of the characters not in seqDot is the field separator.UsageUsage: jsongrep [options] [PATTERN | -e PATTERN [-e PATTERN ...]] [FILE]

Parses JSON data structurally to select a subset of data.

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -e PATTERNS, --pattern=PATTERNS
                        Additional patterns to match.
  -D, --detect-encoding
                        Attempts to detect the character encoding of input if
                        LC_TYPE and file.encoding provide insufficient hints.
                        (Slow) [default: False]TODOSupport star-star (**) non-greedy matches of spanning subgraphsSupport unicode, escapes in patternsSupport extended regexpsSupport no-pattern-matching lookupOptions:Property separator in patterns (. by default)Output separator (newline by default)Quote string values in output?1/0 vs true/false for bool values in output?FeedbackOpen a ticket athttp://github.com/dsc/jsongrep, or send me an email atdsc@less.ly.
Package: json-grep
json-grepDescriptionSimple tool for filtering JSON dict keys from STDOUTUsagejson-grep --help
usage: json-grep [-h] [-e EXCLUDE_KEYS] [-m] [-v] [-l HIGHLIGHT] [-i IHIGHLIGHT] [-s] [-f INPUT_FILE] filter_keys [filter_keys ...]

JSON GREP v1.3.2 is utility for filtering selected keys from json string piped from STDOUT

positional arguments:
  filter_keys           List of keys which you want to filter from json dict. If key is in deeper level of tree structure use '.' separator to specify how deep is key in dict tree structure. You can also use '*' at the end of key name to filter keys as 'beginning with'. You can also specify value of item which you want to pass only by operator '=' or
                        '~'. '~' means that value is somewhere in string.

options:
  -h, --help            show this help message and exit
  -e EXCLUDE_KEYS, --exclude EXCLUDE_KEYS
                        Exclude lines contains key or key=value combination
  -m, --multiline-output
                        Use multiline output for filtered result
  -v, --values_only     Show only values without keys description
  -l HIGHLIGHT, --hl HIGHLIGHT
                        Highlight case sensitively words in filtered results
  -i IHIGHLIGHT, --ihl IHIGHLIGHT
                        Highlight case insensitively words in filtered results
  -s, --show-errors     Show errors caused by json decode
  -p, --pass-non-json   Pass non json lines into stdout
  -f INPUT_FILE, --file INPUT_FILE
                        Input file instead of PIPEInstallationwgethttps://gitlab.com/alda78/json-grep/-/archive/master/json-grep-master.tar&&\tar-xfjson-grep-master.tar&&\cdjson-grep-master/&&\sudopython3setup.pyinstall&&\cd../&&\sudorm-rfjson-grep-masteror simplypip3installjson-grep
Package: jsongui
JSON CRUD Operations in PythonThis Python script demonstrates how to perform basic CRUD (Create, Read, Update, Delete) operations on a JSON file. It uses a simple JSON file to store data and provides functions to add, remove, update, and display all elements in the file.Step-by-Step Explanation1. Importing the JSON ModuleimportjsonWe start by importing thejsonmodule, which provides functions to encode and decode JSON data.2. Defining Constants# Ruta del archivo JSON donde se almacenarÃ¡n los datosARCHIVO_JSON='datos.json'We define a constantARCHIVO_JSONto specify the path to the JSON file where we'll store our data.3. Loading Data from the JSON File# FunciÃ³n para cargar los datos desde el archivo JSONdefcargar_datos():try:withopen(ARCHIVO_JSON,'r')asf:datos=json.load(f)exceptFileNotFoundError:datos=[]returndatosThecargar_datos()function is used to load data from the JSON file. It opens the file in read mode ('r') and uses thejson.load()function to parse the JSON data into a Python dictionary. If the file doesn't exist, it creates an empty list to avoid errors.4. Saving Data to the JSON File# FunciÃ³n para guardar los datos en el archivo JSONdefguardar_datos(datos):withopen(ARCHIVO_JSON,'w')asf:json.dump(datos,f,indent=4)Theguardar_datos()function is used to save data to the JSON file. It opens the file in write mode ('w') and uses thejson.dump()function to convert the Python dictionary back into JSON format. Theindent=4argument is used to make the JSON output more readable.5. Adding a New Element# FunciÃ³n para agregar un nuevo elementodefagregar_elemento(nombre,imagen,enlace):datos=cargar_datos()nuevo_elemento={'Nombre':nombre,'Imagen':imagen,'Enlace':enlace}Generatedby[BlackboxAI](https://www.blackbox.ai)
Package: json-handler
JSON HandlerIt's easy-to-use library which helps you to work with data in JSON files and Python dictionaries like if they were Python objects.Installationpip install json_handlerNo dependencies!Getting StartedExample 1.You can easily read existing JSON file and modify it. Here is JSON file (example.json):{"field_1":"Hi","field_2":"Hello world!","field_3":{"sub_field":123}}We can modify the content of that by using following code:fromjson_handlerimportJsonHandlerhandler=JsonHandler('example.json')handler.field_1=123handler['field_2']="What's up?"handler.field_3={}handler.field_3.sub_field=[1,2,3]handler.save()The result of modifications will be (example.json):{"field_1":123,"field_2":"What's up?","field_3":{"sub_field":[1,2,3]}}Example 2.If file does not exist, it will be automatically
created after usingsave()method.fromjson_handlerimportJsonHandlerhandler=JsonHandler('example2.json')handler.a=5handler.b='Hi there'handler.save()In the same directory file(example2.json)will be created with following content:{"a":5,"b":"Hi there"}Example 3.There is way to automatically save all changes happening with data.
To do that you should just set parameter'auto_save'toTrue.fromjson_handlerimportJsonHandlerhandler=JsonHandler('example3.json',auto_save=True)handler.hi='Hello'handler.five=5In the same directory file(example3.json)will be created with following content :{"hi":"Hello","five":5}Example 4.You can usePython built-in dictmethods with JsonHandler object. For example:fromjson_handlerimportJsonHandlerhandler=JsonHandler('example4.json',auto_save=True)handler.hi='Hello'handler.five=5handler.sub_dict={}handler.sub_dict.fine='ok'print(handler.keys())print(handler.values())print(handler.items())The output will be:dict_keys(['hi', 'five', 'sub_dict'])
dict_values(['Hello', 5, {'fine': 'ok'}])
dict_items([('hi', 'Hello'), ('five', 5), ('sub_dict', {'fine': 'ok'})])Also you can clear data by usingdict.clear()method:handler.clear()print(handler)The output will be:{}Yeah, you can actually printJsonHandlerobject and it will be printed
like usualPython dictExample 5.You can pretty print yourJsonHandlerobject like anydictobject by using
built-in python modulepprint.frompprintimportpprintfromjson_handlerimportJsonHandlerhandler=JsonHandler()handler.well=[{'hi':'hello'}for_inrange(5)]pprint(handler)The output will be:{'well': [{'hi': 'hello'},
          {'hi': 'hello'},
          {'hi': 'hello'},
          {'hi': 'hello'},
          {'hi': 'hello'}]}
Package: jsonhome
Helpers for handling jsonhome documentsFree software: Apache licenseDocumentation:https://readthedocs.org/projects/jsonhomeSource:https://github.com/jamielennox/jsonhomeBugs:http://bugs.launchpad.net/python-jsonhomeIntroductionThe jsonhome library provides a simple way to build and consume compliant json-home documents.BuildingTo build a jsonhome document you create a document and then add resources:>>> import jsonhome

>>> doc = jsonhome.Document()

>>> doc.add_resource('http://mysite.com/rel/widgets',
...                  uri='/widgets{/widget_id}'
...                  uri_vars={'widget_id': 'http://mysite.com/param/widget'},
...                  allow_get=True,
...                  accept_post=['application/json'])

>>> print(doc.to_json())
{
    "resources": {
        "http://mysite.com/rel/widgets": {
            "href-template": "/widgets{/widget_id}",
            "href-vars": {
                "widget_id": "http://mysite.com/param/widget"
            },
            "hints": {
                "accept-post": [
                    "application/json"
                ],
                "allow": [
                    "GET",
                    "POST"
                ]
            }
        }
    }
}Additional parameters to creating resources can be found on the module documentation.ConsumingTo consume a json-home document you load it and then fetch the URIs you need:>>> doc = jsonhome.Document.from_json(data)

>>> print(doc.get_uri('http://mysite.com/rel/widgets', widget_id='1234')
'/widgets/1234'Or for specific information you can find helpers on the resource:>>> print(doc['http://mysite.com/rel/widgets'].href_vars)
{"widget_id": "http://mysite.com/param/widget"}
Package: json_hyper_schema
.. image:: https://drone.io/bitbucket.org/eodolphi/json-hyper-schema/status.png:target: https://drone.io/bitbucket.org/eodolphi/json-hyper-schema/latestJSON Hyper Schema=============Implementation of json schema draft 03http://tools.ietf.org/html/draft-zyp-json-schema-03Installation------------$ pip install json_hyper_schemaUsage------------JSON Hyper Schema makes it possible to validate arbitrary json objects against a json schema.>>> form json_hyper_schema import Schema>>> schema = Schema({'properties': {'a': {'type': 'string', 'enum': ['a', 'b']},'b': {'type': 'array','items': {'properties': {'b': {'type': 'string', 'enum': ['c', 'd']},'c': {'uuid': True}}}},'c': {'default': 'blabla'}},'links': [{'href': '/id/{a}','rel': 'self'}]})>>> schema.validate({'a': 'b', 'b': [{'b': 'c'}, {'b': 'd'}]})If the data is not valid against the schema a `json_schema.ValidationError` will be raised. This will contain a dictionary of errors. The keys of the dictionary are `json_pointer.Pointer` pointers to the invalid field
Package: jsonifable
JSONIFABLEA very small library, consisting of one decorator, which addsto_jsonmethod, that converts your class to JSON.Installation:pip install jsonifableExample:fromjsonifableimportJsonifable# it is not required to use dataclasses# using them will just make this example shorterfromdataclassesimportdataclass@Jsonifable@dataclassclassPerson:name:strsurname:strperson=Person("Avery","Oliwa")jsonified=person.to_json()print(jsonified)Will result in:{"name": "Avery", "surname": "Oliwa"}You can also use it with nested classes!fromjsonifableimportJsonifablefromdataclassesimportdataclass@Jsonifable@dataclassclassAnimal:name:strspecies:str# Notice how you're not required to add @Jsonifable decorator if you don't need the class instance to be manually converted using to_json@dataclassclassAddress:street_no:intstreet_name:strcity:str@Jsonifable@dataclassclassPerson:name:strsurname:straddress:Addressanimal:Animalperson=Person("Avery","Oliwa",Address(20,"STREET","London"),Animal("Guido","dog"))jsonified=person.to_json()print(jsonified)Will result in:{"name": "Avery", "surname": "Oliwa", "address": {"street_no": 20, "street_name": "STREET", "city": "London"}, "animal": {"name": "Guido", "species": "dog"}}It's because Jsonifable forces nested classes to be converted to JSON too.CAUTION:Versions0.0.1and0.0.2do not work, do not install them, only install the newest version.
Package: jsonify-opening-hours
Python Opening Hours parserTHIS PROJECT HAS MOVED TOparse-opening-hours. PLEASE USE THAT PACKAGE IF YOU WANT UPDATESThis library parses opening hours from various human-readable strings such as "Mon- Fri 9:00am - 5:30pm" into a more standard JSON format that can be processed more easily.The formatopening_hours=[{"day":"monday","opens":"9:00","closes":"17:00"},//..]Installationpip install jsonify-opening-hoursUsageThe simplest example is just printing the JSON for an opening hours string:fromopening_hoursimportOpeningHoursprint(OpeningHours.parse("Mon- Fri 9:00am - 5:30pm").json())This should give you the below output:[
	{'day': 'monday', 'opens': '9:00', 'closes': '17:30'},
	{'day': 'tuesday', 'opens': '9:00', 'closes': '17:30'},
	{'day': 'wednesday', 'opens': '9:00', 'closes': '17:30'},
	{'day': 'thursday', 'opens': '9:00', 'closes': '17:30'},
	{'day': 'friday', 'opens': '9:00', 'closes': '17:30'}
]This has been tested using Python 3.8.5DocumentationIn addition to this README, there is some documentation generated from inline documentation comments. This is available at htttps://moralcode.github.io/jsonify-opening-hours/Environment variablesSetting the environment variableOH_DEBUGto a value ofYwill set the root logging level to debug and will cause log entries to appear in stdout for debugging purposesTroubleshootingAssumptionsWhen specifying a time without AM or PM indicators, you may get an error that readsTypeError: Cannot convert a time of unknown type (AM, PM or 24H) without assuming its type.. To resolve this, passassume_type=TimeType.AMwhen calling theparse()function. This will use AM in place of an unknown AM or PM designation. In cases like the string "9-5", if the second value in the range (in this case the5is smaller than the first (i.e. the9) then it will be converted to PM automaticallyTests and Coveragerun pytet and generate coverage databasepipenv run pytest --cov=./show coverage report:pipenv run coverage report
Package: jsonify-resume
jsonify-resumejsonify-resumeis a command-line tool that converts resumes into JSON Resume schema. Use this tool to save time on the conversion process.InstallationTo get started, install Jsonify-Resume using the following command:pipxinstalljsonify-resumeUsageConfigure Access Token: Before using Jsonify-Resume, set yourOpenAI access tokenas an environment variable:exportOPENAI_ACCESS_TOKEN=<YOUR_ACCESS_TOKEN>Conversion Process:Use the following command to convert your resume (in PDF format) to JSON:jsonify-resumeresume.pdf>resume.jsonContributingFound a bug or have a feature in mind?Open an issue. To contribute, fork the repo, make changes, and submit a PR.LicenseThis project is licensed under theMIT License.AcknowledgementsThis project relies on therevchatgpt. Thanks to the developers of revchatgpt.jsonify-resumeis maintained by@ashishbinu. Connect with me onTwitter.
Package: json-include
json-include is an extended way for JSON to support file inclusion, it has two parts:A syntax definition calledinclude syntax.A Python implementation to parse and build JSON file that containsinclude syntax.Syntaxjson-include supports an extended syntax calledinclude syntaxin JSON,
formatted as:{"...":"<json file name>"}The include syntax means that this object (the whole{"...":"<json filename>"}) in JSON
is a reference to the JSON file named in<json file name>notation,
and should be included into its place.The included JSON should always be an object (dict) rather than an array (list),
to prevent implicit meaning and make sure we can get a clear view of the structure
without looking into the included JSON files.In a normal JSON when we want to include another JSON on an attribute, it should
be written as follows:{"username":"alice","profile":{"...":"<profile_model.json>"}}In this JSON aprofile_model.jsonis included to presentprofileattribute,
if the content ofprofile_model.jsonis like:{"age":18,"gender":"female"}then what we mean by the include syntax is that, when this JSON is being used
as a normal JSON, it should be seen as:{"username":"alice","profile":{"age":18,"gender":"female"}}To ture JSON with include syntax into a normal JSON, a build process is needed,
that’s what the implementation does.Python ImplementationImplementation could be of any language as long as it can understand the include syntax
and output as expected, this repo contains a Python implementation for use.Installationpip install json-includeUsageBy installation a command calledjson-includewill be registerd as an entry point,
use it as follows:usage: json-include [-h] DIR FILE

Command line tool to build JSON file by include syntax.

positional arguments:
  DIR         The directory path of source json files
  FILE        The name of the source json file

optional arguments:
  -h, --help  show this help message and exitFor example:json-include test/source_json/ c.jsonThe parsed and built result ofc.jsonwill be printed.Further usage ofjson_includepackage is documented in its source code,
andtest/can also give you better understandings of how it works.TODOs[ ] Prevent circluar inclusion[ ] Test circluar inclusion[ ] Test invalid syntax