Saravanan666/pypi-org huggingface dataset:
Package: jsonfield-joinup
django-jsonfielddjango-jsonfield is a reusable Django field that allows you to store validated JSON in your model.It silently takes care of serialization. To use, simply add the field to one of your models.Python 3 & Django 1.11 through 3.0 supported!Use PostgreSQL?1.0.0 introduced a breaking change to the underlying data type, so if you were using < 1.0.0 please readhttps://github.com/dmkoch/django-jsonfield/issues/57before upgrading. Also, consider switching to Django’s native JSONField that was added in Django 1.9.Note:There are a couple of third-party add-on JSONFields for Django. This project is django-jsonfield here on GitHub but is namedjsonfield on PyPI. There is anotherdjango-jsonfield on Bitbucket, but that one isdjango-jsonfield on PyPI. I realize this naming conflict is confusing and I am open to merging the two projects.Note:Since version 1.9, Django added native PostgreSQL JSON support indjango.contrib.postgres.fields.JSONField. This module is still useful if you need to support JSON in databases other than PostgreSQL or are creating a third-party module that needs to be database-agnostic. But if you’re an end user using PostgreSQL and want full-featured JSON support, I recommend using the built-in JSONField from Django instead of this module.Note:Semver is followed after the 1.0 release.InstallationpipinstalljsonfieldUsagefromdjango.dbimportmodelsfromjsonfieldimportJSONFieldclassMyModel(models.Model):json=JSONField()Advanced UsageBy default python deserializes json into dict objects. This behavior differs from the standard json behavior because python dicts do not have ordered keys.To overcome this limitation and keep the sort order of OrderedDict keys the deserialisation can be adjusted on model initialisation:importcollectionsclassMyModel(models.Model):json=JSONField(load_kwargs={'object_pairs_hook':collections.OrderedDict})Other Fieldsjsonfield.JSONCharFieldIf you need to use your JSON field in an index or other constraint, you can useJSONCharFieldwhich subclassesCharFieldinstead ofTextField. You’ll also need to specify amax_lengthparameter if you use this field.Compatibilitydjango-jsonfield aims to support the same versions of Django currently maintained by the main Django project. SeeDjango supported versions, currently:Django 1.11 (LTS) with Python 2.7, 3.5, or 3.6Django 2.2 (LTS) with Python 3.6, 3.7, or 3.8Django 3.0 with Python 3.6, 3.7, or 3.8Testing django-jsonfield LocallyTo test against all supported versions of Django:$docker-composebuild&&docker-composeupOr just one version (for example Django 3.0 on Python 3.8):$docker-composebuild&&docker-composeruntoxtox-epy38-dj30Travis CIContactWeb:http://bradjasper.comTwitter:@bradjasperEmail:contact@bradjasper.comChangesTake a look at thechangelog.
Package: jsonfield-validation
Django JSON Schema Field ValidatorJSON Schema validation for Django JSONField.QuickstartInstallationpipinstalljsonfield-validationUsagefromjsonfield_validationimportJsonSchemaValidatorclassMyModel(models.Model):items=models.JSONField(validators=[JsonSchemaValidator({"maxItems":2})])Like any Django model field validator, validation happens
whenclean_fields()is called on a model instance:>>>instance=MyModel(items=[1,2,3])>>>instance.clean_fields()django.core.exceptions.ValidationError:{'items':["[1, 2, 3] is too long"]}Further documentation is gratiously hosted byRead the DocsCreditsThis package was created withCookiecutterand thepymetrics/cookiecutter-python-libraryproject template.History0.3.0 (2022-06-15)Add path to invalid value to error_list message,0.2.0 (2022-06-15)Add check method.0.1.0 (2022-06-14)First release on PyPI.
Package: jsonfig
=======jsonfig=======Simple autoreloading configuration files in JSON. Will use the very fast UltraJSON library for parsing if available.Installation============$ pip install jsonfigUsage=====Create a JSON file whose root is a dictionary:{"always_load_user_data": true,"use_new_feature_123": false,"feature_456_chance": 0.20}Now it can be loaded as a dictionary:>>> config = jsonfig.from_path("example.json")>>> config["feature_456_chance"]0.20By default, when the file is changed, it will be automatically reloaded within one second:{"always_load_user_data": true,"use_new_feature_123": false,"feature_456_chance": 1.0}Now the new value will show up in the config structure:>>> config["feature_456_chance"]1.0
Package: jsonfile
jsonfile - incrementally generate JSONpip install jsonfile-PyPI linkIncrementally means that, in most places, you can either send in a complete Python
object, or build it a piece at a time.  For example, let’s build the JSON structure[3, {"cool": true, "awesome":[1,2,3,4,5]}]:>>> import jsonfile
>>> jp = jsonfile.JsonProto()
>>> jp.start_list()
'['
>>> jp.list_item(3)
'3'
>>> jp.start_dict()
',{'
>>> jp.dict_item('cool', True)
'"cool":true'
>>> jp.dict_key('awesome')
',"awesome"'
>>> jp.dict_value([1,2,3,4,5])
':[1, 2, 3, 4, 5]'
>>> jp.finish_all()
'}]'That used theJsonProtoobject, asans-ioversion that just returns the
text that you should write to your output.  If you are writing to a file in a
normal synchronous context, you could also use theJsonWriterobject, it
will be slightly more convenient:>>> import io
>>> dest = io.StringIO()
>>> import jsonfile
>>> jw = jsonfile.JsonWriter(dest)
>>> jw.start_list()
>>> jw.list_item(3)
>>> jw.list_item({'things': 'stuff'})
>>> jw.start_dict()
>>> jw.dict_item('cool', True)
>>> jw.dict_key('awesome')
>>> jw.dict_value([1,2,3,4,5])
>>> jw.finish_all()
>>> dest.getvalue()
'[3,{"things": "stuff"},{"cool":true,"awesome":[1, 2, 3, 4, 5]}]'Because the underlyingJsonProtodoesn’t do IO, it should be easy to make a
version ofJsonWriterthat works well in an async context.Many kinds of errors are caught: having more than one object at the root level
of a file, using a list item in a dictionary, etc.TODO:Allow specifying your own JSONEncoder to use on complete objects.Do indentation.Implement anAsyncJsonWriterBetter tests for preventing illegal JSON from being generated (e.g. lists as
keys).Python 2 support?
Package: jsonfilelogger
📖 A Python json-fileloggerA simple way to log data to json-files from within your code.InstallationYou can use the python package manager (pip) to install the file-logger:pipinstalljsonfileloggerUsage: LogWriterCreate aLogWriter:fromjsonfilelogger.loggerimportLogWriterwriter=LogWriter(folder=".",filename="log.json",threshold=10)Parametersfolder: the folder where the logfile should be storedfilename: the name of the logfilethreshold: the threshold for auto-flushing; if set None the writer will not autoflushExampleLog data to the logfile:writer.log({"key_int":1,"key_string":"logger","key_boolean":True,"key_double":1.337,"key_list":["element1","element2"],"key_none":None})Usage: LogReaderCreate aLogReader:fromjsonfilelogger.loggerimportLogReaderreader=LogReader(folder="./",filename="log.json")Parametersfolder: the folder where the logfile should be storedfilename: the name of the logfileExampleRead data from the logfile:data=reader.retrieve()...# do something with the dataMethodsClassMethodExplanationLogReader, LogWriter.reset()This method clears the contents of the logfile.LogReader, LogWriter.remove()This methode removes the logfile.LogWriter.with_datetime(folder: str, threshold: int)This method creates a logfile in the folder with the current timestamp as filename.LogWriter.log(data)This method logs the data (dictionary) in the logfile.LogWriter.flush()This method flushes the buffer in the writer. The threshold of the buffer is given in the constructorLogReader.retrieve()This method retrieves all the data from the logfile and returns it as a list of dictionaries.Demo usageAn example of how to use this logger is given. Imagine one has created amajor breakthrough AI systemthat still has to be trained.
During training one want's to keep an eye on the performance of the progress. To do this, the LogWriter can be added in the learning iterations (e.g. in one Jupyter notebook).
In another process (e.g. in another Jupyter notebook) it is then possible to read in the data and make amazing visualisations (yay, visualisations!) of how your breakthrough model is performing...fromjsonfilelogger.loggerimportLogWriterwriter=LogWriter.with_datetime(folder="./",threshold=10)super_great_ai_model=...iteration=0whileiteration<100_000:score,other_data=super_great_ai_model.do_iteration()writer.log({score:score,other_data:other_data})iteration+=1writer.flush()fromjsonfilelogger.loggerimportLogReaderimportgloblogs_files=sorted(glob.glob("./[0-9]*.json"))reader=LogReader(folder="./",filename=logs_files[-1])data=reader.retrieve()# Make awesome visualisations!...There may also be other use cases, you can use this logger as you want!TestsYou can run the tests yourselves by:cdtests
pip3install-rrequirements.txt
pytestLicenseThis software is licensed under theMITlicense.Bugs/ Ideas?In case of questions, ideas or bugs you can always create an issue orcontact me.
Package: json-files-compare
json-compareJson-compare is a simple package that allows you to easily and fastly compare two .json files. Support key and multi-key comparison.
You can also ignore certain fields' values or perform comparison insensitive to data types.UsageCompare files just as they are:fromjson_compareimportJSONComparatorcomparator=JSONComparator(left_file_path="expected.json",right_file_path="actual.json",)# compare "actual.json" from the perspective of "expected.json"'s structurecomparator.compare_with_right()# / compare_with_left() / full_compare()# save diff logs to ".comparison_logs" foldercomparator.save_diff_logs(path="comparison_logs")# or print them into stdoutdiffs=comparator.diff_log.logprint("\n".join(diffs))# or print only summary. Here's an example:print(comparator.diff_log.get_summary())# OUTPUT example:# ---------------------# TOTAL: 4 differences# -missing_obj_property: 3# -unequal_value: 4Set key property to perform more accurate comparisons of objects in arrays:# expected.json: {"cats": [{"id": 4, "name": "Nyan"}, {"id": 2, "name": "Marx"}, {"id": 8, "name": "Flake"}]}# actual.json: {"cats": [{"id": 2, "name": "Marx"}, {"id": 4, "name": "Naan"}]}comparator=JSONComparator(left_file_path="expected.json",right_file_path="actual.json",key="DATA//cats//<array>//id",# <----- just pass a "path" to needed property with following keywords:)# DATA - points to the root of file# <array> - indicates array with key property's objectIn this case, saved diff log would look like that:actual.json//cats//<array>
lack of items in array: expected 3 items, got only 2
actual.json//cats//<array>//[0]//name
unequal values: expected "Nyan", got "Naan" instead
actual.json//cats//<array>//[2]
missing array item: expected <object> with "id"=8You can go further and add non-important fields toignoreparameter:# expected.json: [{"id": 4, "name": "Nyan", "age": 2}, {"id": 2, "name": "Marx", "age": 7}, {"id": 8, "name": "Flake", "age": 4}]# actual.json: [{"id": 2, "name": "Marx", "age": 7}, {"id": 4, "name": "Naan", "age": "two"}, {"id": 9, "name": "Lol", "age": 1}]comparator=JSONComparator(left_file_path="expected.json",right_file_path="actual.json",key="DATA//<array>//id",ignore="DATA//<array>//age"# <-------)And here the result:actual.json//<array>//[0]//name
unequal values: expected "Nyan", got "Naan" instead
actual.json//<array>//[2]
missing array item: expected <object> with "id"=8If you want to compare ignoring type-differences between similar values
like"1.4"vs1.4or"[\"New Age №1\"]"vs["New Age №1"]- just addignore_types=Trueparam to JSONComparator:comparator=JSONComparator(left_file_path="expected.json",right_file_path="actual.json",key="DATA//<array>//id",ignore_types=True,# <-------)
Package: json-file-split
json-file-splitSplit a json or jsonl file into different chunks.Installationpipinstalljson-file-splitUsagejson-file-split\--input_file_path./tests/data/test.jsonl\--save_to_dir/tmp/output/\--output_file_nametest.jsonl\--split_bynumber_of_buckets\--batch_size10
Package: jsonfind
JSON(python dict) finderInstallpip install jsonfindCLI# jsonfind
Usage: jsonfind [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  find-any
  find-by
  find-eq
  find-is
  find-key
  find-regex
  find-subset

# jsonfind find-any --help
Usage: jsonfind find-any [OPTIONS] [OBJ]

Options:
  --verbose / --no-verbose
  --target TEXT                   query(JSON string)  [required]
  --format [jsonpath|jsonpointer]
  --key [eq|is|in1|in2|match|sub|eval|fnmatch|range]
  --value [eq|is|in1|in2|match|sub|eval|fnmatch|range]
  --mode [sub|super|set]
  --help                          Show this message and exit.jo hello=world a=$(jo b=$(jo c=d)){"hello":"world","a":{"b":{"c":"d"}}}jo hello=world a=$(jo b=$(jo c=d)) | jsonfind find-eq --target $(jo c=d) --format jsonpointer["/a/b"]jo hello=world a=$(jo b=$(jo c=d)) | ./bin/jsonfind find-eq --target $(jo c=d) --format jsonpath["a.b"]jo hello=world a=$(jo b=$(jo c=d)) | ./bin/jsonfind find-by --format jsonpointer --query /a/b{"c": "d"}jo hello=world a=$(jo b=$(jo c=d)) | ./bin/jsonfind find-by --format jsonpath --query a.b[{"c": "d"}]Python>>> from jsonfind import JsonFind
>>> obj = {"a":"b","c":{"d":"e"}}
>>> tgt = obj["c"]
>>> JsonFind.to_jsonpointer(JsonFind.find_eq(obj, tgt))
'/c'
>>> JsonFind.to_jsonpath(JsonFind.find_eq(obj, tgt))
'c'linkspypi repositorycoverage reportlocal pypi repository
Package: jsonfinder
jsonfinderlets you find and extract JSON objects and arrays embedded within a string using a simple iterator. For example:>>> s = 'jack [1,2,3] john'
>>> for start, end, obj in jsonfinder(s):
...     print start, ':', end
...     if obj is None:
...         print 'String:', repr(s[start:end])
...     else:
...         print 'List of length', len(obj)
0 : 5
String: 'jack '
5 : 12
List of length 3
12 : 17
String: ' john'
>>> assert len(s) == endTwo other convenience methods are also provided:has_jsonandonly_json:>>> has_json('stuff {"key": "value"} things')
True
>>> has_json('stuff only')
False
>>> only_json('stuff {"key": "value"} things')[2]
{u'key': u'value'}
>>> only_json('stuff only')
Traceback (most recent call last):
...
ValueError: No JSON object found in argument.
>>> only_json('{}{}')
Traceback (most recent call last):
...
ValueError: More than one JSON object found in the argument.All the methods allow a custom JSONDecoder to be passed in for flexibility.The library also includes a command-line tool to format JSON and filter out parts of the input based on whether JSON is present.
It’s like a more flexible version of python’s built injson.tool, agrep-vfor JSON, and more. For example:$ cat cli_example.txt
This line contains no JSON and will be deleted by the --delete other-lines option.
On the other hand {"json":   ["is", "formatted"]  } and text surrounding it is preserved (but can also be removed if desired).
$ jsonfinder -i cli_example.txt --delete other-lines
On the other hand {
    "json": [
        "is",
        "formatted"
    ]
} and text surrounding it is preserved (but can also be removed if desired).
$ jsonfinder -i cli_example.txt --delete other-lines | jsonfinder --delete context --format tiny
{"json":["is","formatted"]}See the--helpoption in the command-line for more details.Installation is as simple as:pip install jsonfinder
Package: json-fix
What is this?A pip module that let you define a__json__method, that works like thetoJSONfrom JavaScript.(e.g. it magically gets called whenever someone doesjson.dumps(your_object))From a technical perspective, this module is a safe, backwards-compatible, reversable patch to the built-in pythonjsonobject that allows classes to specify how they should be serialized.Why?Because sometimes external code uses something likeimportjsonjson.dumps(list_containing_your_object)And it simply throws an error no matter how you customize your objectHow do I use this for my class?pip install json-fiximportjson_fix# import this before the JSON.dumps gets called# same file, or different fileclassYOUR_CLASS:def__json__(self):# YOUR CUSTOM CODE HERE#    you probably just want to do:#        return self.__dict__return"a built-in object that is natually json-able"How do I change how someone elses class is jsonified?There's 2 ways; the aggressiveoverride_tableor the more collaboration-friendlyfallback_table. Some really powerful stuff can be done safely with the fallback table.Override TableIf a pip module defines a class, you can control how it is json-dumped, even if they defined a.__json__()method, by usingjson.override_table.Note! The "when" (in when-a-rule-is-added) can be very important. Whatever rule was most-recently added will have the highest priority. So, even if a pip module uses the override table, you can override their override by doingimport that_moduleand THEN adding your rule to the override table.Note 2! The override table is capable of changing how built-in types are dumped, be careful!importjson_fix# import this before the JSON.dumps gets calledimportjsonimportpandasaspdSomeClassYouDidntDefine=pd.DataFrame# create a boolean function for identifying the classclass_checker=lambdaobj:isinstance(obj,SomeClassYouDidntDefine)# then assign it to a function that does the convertingjson.override_table[class_checker]=lambdaobj_of_that_class:json.loads(obj_of_that_class.to_json())json.dumps([1,2,SomeClassYouDidntDefine()],indent=2)# dumps as expectedFallback TableLet's say we want all python classes to be jsonable by default, well we can easily do that with the fallback table. The logic isif notthing in override table, and no .__json__ method, then check the fallback table.importjson_fix# import this before the JSON.dumps gets calledimportjson# a checker for custom objectschecker=lambdaobj:hasattr(obj,"__dict__")# use the __dict__ when they don't specify a __json__ methodjson.fallback_table[checker]=lambdaobj_with_dict:obj_with_dict.__dict__classSomeClass:def__init__(self):self.thing=10json.dumps([1,2,SomeClass()],indent=2)# dumps as expectedLike the override table, the most recently-added checker will have the highest priority.
Package: jsonfixer
InstallpipinstalljsonfixerUsageIn[1]:fromhalf_json.coreimportJSONFixerIn[2]:f=JSONFixer()In[3]:f.fix('[{')Out[3]:FixResult(success=True,line='[{}]',origin=False)In[4]:f.fix('{"a')Out[4]:FixResult(success=True,line='{"a":null}',origin=False)In[5]:f.fix('{"a":}')Out[5]:FixResult(success=True,line='{"a":null}',origin=False)目标fix invalid/broken/truncated json修复原理JSONDecoderErrorline contextHitRatio根据 checks 里面的工具来衡量修改效果ABC : autogen --> broken --> check
TSR : run test.sh show.sh ratio.shFixRatio仅判断 result.success == True./runratio.shfix1.  0.4269, 0.4287, 0.4303   # 实现完 12 条规则
2.  0.5037, 0.5084, 0.5077   # string 的 " 补充在末尾
3.  0.5259, 0.5224, 0.5187   # Array 需要 pos - 2
4.  0.5433, 0.5311, 0.5381   # Array 细化一下 [, 的情况
5.  0.7192, 0.7216, 0.7265   # 大改进, FIX 之前的 Bug( parser 被冲掉了)
6.  0.7732, 0.7686, 0.7701   # case: {"a":1 --> 补充 }
7.  0.60  , 0.58             # 去掉了空行
8.  0.6971, 0.6969, 0.6984   # 增加处理 StopIteration
9.  0.7428, 0.7383, 0.7427   # 增加处理 half parse
10. 0.7617, 0.7631, 0.7558   # 细化处理 half parse
11. 0.7608, 0.7612, 0.7650   # 添加从左处理
12. 0.9817, 0.9827, 0.9819   # 增加对字符串的处理
13. 0.8314, 0.8302, 0.8312   # 去掉对字符串的额外处理
14. 0.95X                    # 已不可参考Real HitRatio判断 result.success == True并且解析后的 json 大体和原来一致(equal && dictdiffer)./runratio.shhit1. 0.5610, 0.5563, 0.5529   # origin
2. 0.5593, 0.5532, 0.5587   # fix :} --> :null}TODO目前的缺点 && 发现数字的支持比较弱 --> -02 / 0. / .0还不支持分支回溯 --> [{]突然想到, 应该反思一下, 这个是一个fixer, 而不是一个将任何字符串都转为 json 的东西
应该明确 JSONFixer 的能力范围, 对 runratio.sh 也应该比较前后两个的 json 相似程度。
(听起来像无能者的辩白?)也需要吧 parser 也做成 stack 这样可以解决 ["a] --> ["a"] 这样的 case考虑分支回溯的方式来试探解析缺失的 JSON 常量BadCase1- --> {"1-": null}
Package: json-flat
A Python library to flatten a nested json.Use casesTo get a csv file[1]out of the json document stores like elasticsearch, mongodb, bigquery etc[2]. All of these databases allow their documents to be nested. Out there are plenty of libraries which can convert plain json (not nested) to csv but really suck when the json is nested. This is the real use case we wanted to solve and this library came out as a result.Installationpipinstalljson-flatQuick StartfromjsonflatimportJsonFlat# Load the input json into python data structure.# An input list of rows (nested or plain).rows=[{'a':1,'b':{},'c':{'d':73}},{'a':'hello','b':['thank','you']}]print(JsonFlat().flatten(rows)){'field_names':['a','b','c.d'],'rows':[{'a':1,'b':None,'c.d':73},{'a':'hello','b':'thank'},{'a':'hello','b':'you'}]}field_namesis an array of all the fields (attributes) encountered in the input json. The order of the fields will be based on their appearance in input json. A field appearing first in the input json will be the first in thefield_names, second will be the second and so on.rowsis an array containing the actual flattened rows. This is guaranteed to be always an array of objects irrespective of what the input looks like. No guarantee that the order of keys within each object is the same as that of the input (usefield_namesfor ordering related stuff). Also no guarantee that each of these objects contain all the keys appearing in thefield_names(use null as the value for the non-existing keys) but guaranteed to contain all the keys appearing in the input json corresponding to this object.DocumentationRead thedocumentation.Specification (what in input becomes what in output?)Read thespecification.Footnotes[1]No native functionality in the library to get csv. Use Python’s built in modulecsvon the flattened data.[2]To be specific, Elasticsearch is a search engine, MongoDB is a database and BigQuery is a data warehouse. For simplicity, all of these are considered to be json document stores or simply databases. Defining what these applications exactly are isn’t the goal of this documentation.
Package: json-flatten
json-flattenPython functions for flattening a JSON object to a single dictionary of pairs, and unflattening that dictionary back to a JSON object.Useful if you need to represent a JSON object using a regular HTML form or transmit it as a set of query string parameters.For example:>>>importjson_flatten>>>json_flatten.flatten({"foo":{"bar":[1,True,None]}}){'foo.bar.[0]$int': '1', 'foo.bar.[1]$bool': 'True', 'foo.bar.[2]$none': 'None'}>>>json_flatten.unflatten(_){'foo': {'bar': [1, True, None]}}The top-level object passed toflatten()must be a dictionary.
Package: json-flattening
Json-flatteningFlatten Json to Relational DataframeInstructionsInstallpip install json-flatteningParametersjson_data: Input data to be flattend, should be dict or list of dict
list_split_to_many: If the final node list data should be splitted to one to many relation(i.e to multiple rows)
ignore_parent_key: Parent Keys to be ignored should be string or list
filter_parent_key: Parent Keys only to be considered should be string or list,if none of key specified is in data,will return empty dataframeFlatten jsonfromjson_flatteningimportjson_flattendata=[{"firstName":"Rack","lastName":"Jackon","gender":"man","age":24,"address":{"streetAddress":"126","city":"San Jone","state":"CA","postalCode":"394221"},"phoneNumbers":[{"type":"home","number":"7383627627"}]},{"firstName":"rock","lastName":"Jackon","gender":"man","age":24,"address":{"streetAddress":"126","city":"San Jone","postalCode":"394221"},"phoneNumbers":[{"type":"home","number":"7383627627"}]}]flatten_data=json_flatten(data)firstNamelastNamegenderageaddress_stateaddress_streetAddressaddress_postalCodeaddress_cityphoneNumbers_typephoneNumbers_numberRackJackonman24CA126394221San Jonehome7383627627rockJackonman24NaN126394221San Jonehome7383627627With ignore parent Keyflatten_data = json_flatten(data,ignore_parent_key=['phoneNumbers','age'])firstNamelastNamegenderaddress_stateaddress_streetAddressaddress_postalCodeaddress_cityRackJackonmanCA126394221San JonerockJackonmanNaN126394221San JoneChange Log1.0.0 (12/4/2022)First Release1.0.1 (12/4/2022)Bug fix for complex nested jsonBug fix if key have a null value1.0.2 (12/4/2022)Updated doc string details1.0.3 (20/6/2022)Bug fix if the parent and child node have same key name1.0.4 (15/7/2022)Bug fix if the column name is "id" it was skipping it1.0.5 (18/7/2022)Added Parameter to consider and filter parent keys and ignore rest