Saravanan666/pypi-org huggingface dataset:
Package: quick-csv
Quick CSVRead and write small or large CSV/TXT files in a simple mannerInstallationpip install quick-csvExamples for small filesExample 1: read and write csv or txt filesfromquickcsv.fileimport*# read a csv filelist_model=read_csv('data/test.csv')foridx,modelinenumerate(list_model):print(model)list_model[idx]['id']=idx# save a csv filewrite_csv('data/test1.csv',list_model)# write a text filewrite_text('data/text1.txt',"Hello World!")# read a text fileprint(read_text('data/text1.txt'))Example 2: create dataframe from a list of modelsfromquickcsv.fileimport*# read a csv filelist_model=read_csv('data/test.csv')# create a dataframe from list_modeldf=create_df(list_model)# printprint(df)Examples for large filesExample 1: read large csv filefromquickcsv.largefileimport*if__name__=="__main__":csv_path=r"umls_atui_rels.csv"# a large file (>500 MB)total_count=0defprocess_partition(part_df,i):print(f"Part{i}")defprocess_row(row,i):globaltotal_countprint(i)total_count+=1list_results=read_large_csv(csv_file=csv_path,row_func=process_row,partition_func=process_partition)print("Return: ")print(list_results)print("Total Record Num: ",total_count)Example 2: query from a large csv filefromquickcsv.largefileimport*if__name__=="__main__":csv_path=r"umls_sui_nodes.csv"# a large file (>500 MB)total_count=0# process each partition in the large filedefprocess_partition(part_df,i):print(f"Part{i}")print()# process each row in a partition while readingdefprocess_row(row,i):globaltotal_countprint(row)total_count+=1# field is a field in the csv file, and value is the value you need to find within the csv filelist_results=read_large_csv(csv_file=csv_path,field="SUI",value="S0000004",append_row=True,row_func=process_row,partition_func=process_partition)print("Return: ")print(list_results)print("Total Record Num: ",total_count)Example 3: read top N records from the large csv filefromquickcsv.largefileimport*if__name__=="__main__":csv_path=r"umls_atui_rels.csv"total_count=0# return top 10 rows in the csv filelist_results=read_large_csv(csv_file=csv_path,head_num=10)print("Return: ")print(list_results)print("Total Record Num: ",total_count)LicenseThequick-csvproject is provided byDonghua Chen.
Package: quickD3map
#quickD3map### D3 Point Maps from Pandas DataFramesquickD3map allows you to rapidly generate D3.js maps from data from withinthe pandas/ipython ecosystem by converting Latitude/Longitude Data in to points **Note: this is an experimental repo and not ready for use.**With that said, basic maps can be generated and below are a few examples of how they are made.#### To make the following interactive map:![Examplemap](https://dl.dropboxusercontent.com/u/1803062/quickD3map/map1.png)#### Install and Use from the Examples Directory```pythonimport pandas as pdfrom quickD3map import PointMap#load data and plotstations = pd.read_csv('data/weatherstations.csv')#make a PointMap objectpm = PointMap(stations, columns = ['LAT','LON','ELEV'])#display or write your map to filepm.display_map()pm.create_map(path="map.html")````###Project GoalsThe goal of this project is rather limited in scope:- be able to rapidly plot location data from Pandas dataframes- be able to color by another column- be able to scale by another column- be able to plot connections between plotsI intend to include several map templates (geojson files) and will try tofigure out a template style that can provide the ability to include maps of many types.Just thinking aloud here but the best way to do this would be to standardize thegeojson input types for features so that loading of data (as JSOn strings) can be seperatedfrom everything else (transformation,scaling, etc.)###Thanks.To Mike Bostocks and the D3JS team, as well as to Rob Story and the Folium libary.#History0.1.4 (2014-03-21)++++++++++++++++++* PointClass and LineMap updated. MultiColumnMap rolled into PointMap0.1.2 (2014-03-21)++++++++++++++++++* Updated README with some rudimentary Documentation0.1.1 (2014-03-19)++++++++++++++++++* Fix Manifest file to include template directory0.1.0 (2014-03-19)++++++++++++++++++* First release on PyPI.
Package: quickdataanalysis
quick data analysisThis packages allows you to kick start your data analysis and make faster insights of the data.RequirementsPandas >=1.0.1Documentation1) Creating Dummies for columnsThe create_dummies method will return dummies for the columns. For example you want to dummies for Gender column put the column name in a list and you will get the dataframe with the dummies.>>> df =  create_dummies(df_train,["sex"])
   >>> df
   male female
   0      1
   1      0
   1      0
   1      02) Counting the column valuesThe count_values method will return the number of values in the columns. For example you want to count the number of females.>>> column_value_count(df_train["female"],1,False)
  312ToDoAdd more methodAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome :)Happy Coding!!
Package: quickDatabase
python-dbLight weight database for python, with a simple API and a simple file format.
it is not meant to be a full featured database, but rather a simple way to  store data in a file.
it's written in python, and is compatible with python 3.6 and above.FeaturesSimple APIColorful outputEasy to useCLI interfaceOpen sourceDocumentationThe documentation is available atnot yet availableInstallationYou can install python-db using pip:pip install python-dbUsageTo use python-db, you need to import it:from python_db import Database
Package: quickdate
quickdate快捷处理日期的Python库，支持从字符串解析日期，灵活实现加减日、周、月、年、时、分、秒的时间操作如何安装？pipinstallquickdate如何使用qucickdate默认以今天的起点，开始定位日期，当然你也可以使用DateLocate.parse方法，从字符串解析一个日期，并以此为起点支持的主要函数是add函数，可以动态传入 days、weeks、months、years、hours、minutes、seconds参数，并返回为 DateLocate对象本身这意味着，可以链式定位日期，更为方便快捷>>>fromquickdate.quickdateimportDateLocate>>>DateLocate().format("%Y-%m-%d")2023-08-21>>>fromquickdate.quickdateimportDateLocate>>>DateLocate.parse('20230821').format("%Y-%m-%d")2023-08-21其中， parse 函数还支持以下日期时间格式（不限于）>>>DateLocate.parse('22nd,July,2009').format("%Y-%m-%d")2009-07-22>>>DateLocate.parse('2018-04-20').format("%Y-%m-%d")2018-04-20>>>DateLocate.parse('2018').format("%Y-%m-%d")2018-08-21>>>DateLocate.parse('5,').format("%Y-%m-%d")2023-08-05>>>DateLocate.parse('9:10:8').format("%Y-%m-%d%H:%M:%S")2023-08-2109:10:08>>>DateLocate.parse('02/11/2016').format("%Y-%m-%d%H:%M:%S")2016-02-1100:00:00以下，将以今天为起点，说明 quickdate 库的用法# 按日定位日期>>>DateLocate().format("%Y-%m-%d")2023-08-21>>>DateLocate().add(days=-1).format("%Y-%m-%d")2023-08-20# 按周定位日期>>>DateLocate().add(weeks=-1).format("%Y-%m-%d")2023-08-14# 按月定位日期>>>DateLocate().add(months=-1).format("%Y-%m-%d")2023-07-21# 特别地，若当天为本月最后一天，获取上月日期时，会较正为准确上月最后一天的日期>>>DateLocate.parse("2023-03-31").add(months=-1).format("%Y-%m-%d")2023-02-28# 按年定位日期>>>DateLocate().add(years=-1).format("%Y-%m-%d")2022-08-21定位日期的方式，除了以上传一个参数以外，还可以复合传多个参数（但同一个参数仅能传一次）>>>DateLocate().add(weeks=-1,days=2,months=-2,years=-1).format("%Y-%m-%d")2022-06-16还可以进行链式定位>>>DateLocate().add(days=-1).add(weeks=-3).lastDay().format("%Y-%m-%d")2023-07-31可根据需求，直接设定年月日时分秒的值>>>DateLocate().year(2022).month(12).day(5).hour(10).format("%Y-%m-%d%H:%M:%S")2022-12-0510:41:47通过 week 函数，可获取定位日期同周的周一至周日的日期>>>DateLocate().week(5).format("%Y-%m-%d")2023-08-25通过 lastDay 函数，可获取定位日期当月最后一天的日期>>>DateLocate().lastDay().format("%Y-%m-%d")2023-08-31使用案例获取当天日期>>>fromquickdate.quickdateimportDateLocate>>>DateLocate().format("%Y-%m-%d")2023-08-21获到昨天日期>>>DateLocate().add(days=-1).format("%Y-%m-%d")2023-08-20获取上周三的日期>>>DateLocate().add(weeks=-1).week(3).format("%Y-%m-%d")2023-08-16获取去年今日对应月份的最后一天>>>DateLocate().add(years=-1).lastDay().format("%Y-%m-%d")2022-08-31获取上个月的1号>>>DateLocate().add(months=-1).day(1).format("%Y-%m-%d")2023-07-01获取上上周六的日期>>>DateLocate().add(weeks=-2).week(6).format("%Y-%m-%d")2023-08-12
Package: quickdb
一、模块介绍quickdb 是一个操作合集mysqlpostgresqlkafkamongoredis二 mysql使用 sqlalchemy 为其定义了一个类MysqlSQLAlchemyEngine2.1、MysqlSQLAlchemyEngine该类继承自 SQLAlchemyEngineBase 用来定义连接可接收 sqlalchemy create_engine 的参数engine = MysqlSQLAlchemyEngine(host='localhost', port=3306, user='root', pwd='1234', db='test')

with engine.session() as session, session.begin:
    pass

with engine.connection() as conn, conn.begin:
    pass2.2、MysqlSQLAlchemyEngine 方法其含有以下方法：reverse_table_model：逆向表模型insert：一条或多条upsert：一条或多条deleteexecutemerge主要说一下 reverse_table_model
该方法含有三个参数：path：生成的 model 路径，需含文件名tables：需要的表，可不指定commands：额外的命令method = MysqlSQLAlchemyMethods(engine=engine)
method.reverse_table_model(path='./modules.py')三、postgresql同 mysql四、kafka主要是使用了 with 和方便的 send，会帮助你将 msg 转化为 bytes，也可以同时 flushp = KafkaMsgProducer(server=xxx)
p.send(topic, msg)

with KafkaMsgProducer(server=xxx) as p:
    p.send()五、mongo通过 get_collection 返回的是修改过的 Collection 对象，其有两个新方法iter: 快速迭代数据库upsert_one：插入或更新的便捷写法conn = MongoConn(host, port)
col = conn.get_collection(db, col)

for i in col.iter():
    print(i)其使用了 with，可以自动回收连接conn = MongoConn(host, port)
col = conn.get_collection(db, col)
conn.close()

with MongoConn(host, port) as conn:
    col = conn.get_collection(db, col)六、redis1、redisConnwith RedisConn() as conn:
    pass2、RedisLock这是一个阻塞的 redis 事务锁with RedisLock(lock_name=""):
    pass3、RedisLockNoWait这是一个非阻塞的 redis 事务锁，只有获取到锁的人才执行，获取不到就不会继续等待锁，但是需要使用 lock_success 判断with RedisLockNoWait(lock_name=") as lock:
    if lock.lock_success:
        ...
Package: quickdemo
quickdemoFor when unit tests would be too much!quickdemois a tiny Python library for code demonstrations and small-scale testing.
If your debugging tool of choice is printing tostdout, this might be the right library for you.quickdemoallows you to decorate any module-level function with aninvocationortestingdirective:import quickdemo as qd

@qd.expect([1, 2], [0, 1], 1)
@qd.run([1, 2, 3], 5)
def add_to_list(input_list, number):
    return [x + number for x in input_list]The snippet above produces the following output:add_to_list([1, 2, 3], 5) -> [6, 7, 8]
Passed: add_to_list([0, 1], 1)Check out theshowcase*.pyfiles for more examples!I developedquickdemofor two reasons:Firstly, when presenting solutions to other people, I always wrote severalprintstatements per function to demonstrate the correctness of the solution.
Theprintstatements always added up quickly, making it difficult to discern which output corresponded to which function.
Also, changing any function name implied re-writing the invocation code.
This was especially tedious when working with Jupyter Notebooks, where you have to take great care to re-run a cell each time something changes.Secondly, there is an educational programming language calledPyret, which allows writing short unit tests as part of a function definition.
I believe this to be a sensible choice when targeting beginners.
Unfortunately, Pyret code is neither readable nor particularly fast, so I decided to introduce this feature to Python as a part ofquickdemo.
Package: quick-deploy
Quick-DeployOptimize and deploy machine learning models fast and easy as possible.quick-deploy provide tools to optimize, convert and deploy machine learning models as fast inference API (low latency and high throughput) byTriton Inference ServerusingOnnx Runtimebackend. It support 🤗 transformers, PyToch, Tensorflow, SKLearn and XGBoost models.Get StartedLet's see an quick example by deploying bert transformers for GPU inference. quick-deploy already have support 🤗 transformers so we can specify the path of pretrained model or just the name from the Hub:$quick-deploytransformers\-nmy-bert-base\-ptext-classification\-mbert-base-uncased\-o./models\--model-typebert\--seq-len128\--cudaThe command above created the deployment artifacts by optimizing and converting the model to Onxx. Next just run the inference server:$dockerrun-it--rm\--gpusall\--shm-size256m\-p8000:8000\-p8001:8001\-p8002:8002\-v$(pwd)/models:/modelsnvcr.io/nvidia/tritonserver:21.11-py3\tritonserver--model-repository=/modelsNow we can use tritonclient which uses gRPC calls to consume our model:importnumpyasnpimporttritonclient.httpfromscipy.specialimportsoftmaxfromtransformersimportBertTokenizer,TensorTypetokenizer=BertTokenizer.from_pretrained('bert-base-uncased')model_name="my_bert_base"url="127.0.0.1:8000"model_version="1"batch_size=1text="The goal of life is [MASK]."tokens=tokenizer(text=text,return_tensors=TensorType.NUMPY)triton_client=tritonclient.http.InferenceServerClient(url=url,verbose=False)asserttriton_client.is_model_ready(model_name=model_name,model_version=model_version),f"model{model_name}not yet ready"input_ids=tritonclient.http.InferInput(name="input_ids",shape=(batch_size,9),datatype="INT64")token_type_ids=tritonclient.http.InferInput(name="token_type_ids",shape=(batch_size,9),datatype="INT64")attention=tritonclient.http.InferInput(name="attention_mask",shape=(batch_size,9),datatype="INT64")model_output=tritonclient.http.InferRequestedOutput(name="output",binary_data=False)input_ids.set_data_from_numpy(tokens['input_ids']*batch_size)token_type_ids.set_data_from_numpy(tokens['token_type_ids']*batch_size)attention.set_data_from_numpy(tokens['attention_mask']*batch_size)response=triton_client.infer(model_name=model_name,model_version=model_version,inputs=[input_ids,token_type_ids,attention],outputs=[model_output],)token_logits=response.as_numpy("output")print(token_logits)Note:This does only model deployment the tokenizer and post-processing should be done in the client side. The full tansformers deployment is comming soon.For more use cases please check theexamplespage.InstallBefore install make sure to install just the target model eg.: "torch", "sklearn" or "all". There two options to use quick-deploy, by docker container:$dockerrun--rm-itrodrigobaron/quick-deploy:0.1.1-all--helpor install the python libraryquick-deploy:$pipinstallquick-deploy[all]Note:This will install the full vesionall.ContributingPlease follow theContributingguide.LicenseApache License 2.0
Package: quickder.arpa2
Quick DER precompiled ASN.1 codeMany standards specifications define ASN.1 fragments.
We try to deliver them to you in shrink-wrapped form.Ideally, in Python all you would do to use ASN.1 from a
given standard would be something likefrom quickder.rfc import rfc4511Similarly, in C all you would do to use the same standard
would be#include <quickder/rfc4511>The Quick DER package offers just that.  It comes with
packages likequickder.rfcfor Python andquickder-rfcfor C.  In both cases a namespace for specifications
surrounds the information.  Multiple packages can add
their pre-compiled things here.Have a look in therfc directoryfor an idea
of how this is done.
Package: quickder.itu
Quick DER precompiled ASN.1 codeMany standards specifications define ASN.1 fragments.
We try to deliver them to you in shrink-wrapped form.Ideally, in Python all you would do to use ASN.1 from a
given standard would be something likefrom quickder.rfc import rfc4511Similarly, in C all you would do to use the same standard
would be#include <quickder/rfc4511>The Quick DER package offers just that.  It comes with
packages likequickder.rfcfor Python andquickder-rfcfor C.  In both cases a namespace for specifications
surrounds the information.  Multiple packages can add
their pre-compiled things here.Have a look in therfc directoryfor an idea
of how this is done.
Package: quickder.rfc
Quick DER precompiled ASN.1 codeMany standards specifications define ASN.1 fragments.
We try to deliver them to you in shrink-wrapped form.Ideally, in Python all you would do to use ASN.1 from a
given standard would be something likefrom quickder.rfc import rfc4511Similarly, in C all you would do to use the same standard
would be#include <quickder/rfc4511>The Quick DER package offers just that.  It comes with
packages likequickder.rfcfor Python andquickder-rfcfor C.  In both cases a namespace for specifications
surrounds the information.  Multiple packages can add
their pre-compiled things here.Have a look in therfc directoryfor an idea
of how this is done.
Package: quickdiff
quickdiffquickdiffis a python library for quickly finding nested differences between two python objects.Usage:fromquickdiffimport*a={1:1,2:2,3:[3],4:4}b={1:1,2:4,3:[3,4],5:5,6:6}report=quickdiff(a,b)assertreport==DiffReport(val_changes=[ValChange(path=[2],a=2,b=4)],type_and_val_changes=[],dict_items_added=[DictDiff(path=[],key=5,val=5),DictDiff(path=[],key=6,val=6)],dict_items_removed=[DictDiff(path=[],key=4,val=4)],iter_len_mismatch=[IterLenMismatch(path=[3],a_len=1,b_len=2)])Diff objects (ValChange,DictDiff, etc) are NamedTuples for improved ergonomics and thus can be unpacked as you would any tuple:forpath,a,binreport.val_changes:print(path,a,b)# ([2], 2, 4)Why not DeepDiffI wrote this becauseDeepDiffis quite slow as it's written in pure Python and has a lot of features.Quickdiff on the other hand is simple and written in Rust. The current implementation yields a 16x performance boost on my personal benchmarks.DevelopmentUsematurinfor development:pipinstallmaturinCompile development version with:maturindevelopmentRun tests:python-munittestdiscovertestsRoadmapsupport for sets (currently is treated as an iterator)parallelize for improved performance (by usingpyo3-ffito sidestep the Python runtime)attribute diff checking for python objectssupport custom__eq__()implementations
Package: quick-django
quick-djangoCreate django project quickly single command with all necessary file like djnago app, urls.py, templates folder, static folder and add the default code in view.py,models.py,admin.py and create index.htmlHow to use quick-djangoStep: 1pipinstallquick-djangoStep: 2Windowopen cmd in your porject folder and run this commandpython-mquick-djangomyprojectmyproject_appLinuxopen terminal in your porject folder and run this commandpython3-mquick-djangomyprojectmyproject_appConfiguration# setting.pyINSTALLED_APPS=[....'myproject_app',]For Rest-ApiWindowopen cmd in your porject folder and run this commandpython-mquick-djangomyprojectmyproject_app--restapiLinuxopen terminal in your porject folder and run this commandpython3-mquick-djangomyprojectmyproject_app--restapiConfiguration# setting.pyINSTALLED_APPS=[....'myproject_app','rest_framework']Check Our Site :https://mefiz.compypi site :https://pypi.org/project/quick-django/
Package: quickdocs
QuickdocsCreates HTML docs from a project's readme and sphinx-apidoc.StatusSourceShieldsProjectHealthRepositoryPublishersActivityInstallationpipinstallquickdocsUsageTo create an up to date sphinx configuration:quickdocs.quickdocs.ymlNow we can build the documentation:sphinx-build-EdocsbuildThis will run copy and markup the project's readme at runtime so that you don't need to recompile the sphinx configuration unless any of the settings change.Required settings file fields:project: Quickdocs
version: 1.2.1
author: Joel Lefkowitz
html_title: Quickdocs
github_url: JoelLefkowitz/quickdocsOptional settings:debug: # Default: False
project_root: # Default: os.getcwd()
verbose_name: # Default: Nonemarkup_readme: # Default: True
readme_path: # Default: "README.md"apidoc_module_dir: # Default: NoneIntegrating with readthedocs.readthedocs.yml:version: 2

sphinx:
  configuration: docs/conf.py

formats: all

python:
  version: 3.8
  install:
    - requirements: docs/requirements.txtRemoving old documentationThe sphinx-apidoc plugin generates documentation under docs/api. When running, the sphinx plugin will overwrite but not delete out of date files in this directory. This means if you rename a module you must delete the out of date documentation. This package should not delete the docs/api directory because some developers will add custom documentation to this directory as they write new modules.TestsTo run unit tests and generate a coverage report:grunttests:unitDocumentationThis repository's documentation is hosted onreadthedocs.To generate the sphinx configuration:gruntdocs:generateThen build the documentation:gruntdocs:buildToolingTo run linters:gruntlintTo run formatters:gruntformatBefore committing new code:gruntprecommitThis will run linters, formatters, tests, generate a test coverage report and the sphinx configuration.Continuous integrationThis repository uses Travis CI to build and test each commit. Formatting tasks and writing documentation must be done before committing new code.VersioningThis repository adheres to semantic versioning standards.
For more information on semantic versioning visitSemVer.Bump2version is used to version and tag changes.
For example:bump2versionpatchChangelogPlease read this repository'sCHANGELOGfor details on changes that have been made.ContributingPlease read this repository's guidelines onCONTRIBUTINGfor details on our code of conduct and the process for submitting pull requests.ContributorsJoel Lefkowitz-Initial work-Joel LefkowitzRemarksLots of love to the open source community!
Package: quickdone
Get Things Done Quickly!Usage$pipinstallquickdone>>>importquickdoneasqdfp() short for format_path(file_path)>>>qd.fp(r'C:\Users\user_name\Desktop\test.xlsx')'C:/Users/user_name/Desktop/test.xlsx'etc() short for excel_to_csv(input_path,output_path,input_enc,output_enc)>>>qd.etc(r'C:\Users\user_name\Desktop\test.xlsx',r'C:\Users\user_name\Desktop\test.csv')Packaging$pythonsetup.pysdistbdist_wheel
$twineuploaddist/*For more, seepython packaging-projectsorPackaging and distributing projects
Package: quickdraw
quickdrawGoogle Quick, Draw!is a game which is
training a neural network to recognise doodles.quickdrawis an API for using the Google Quick, Draw! dataquickdraw.withgoogle.com/data,
downloading the data files as and when needed, caching them locally and
allowing them to be used.Getting startedWindowspipinstallquickdrawmacOSpip3installquickdrawLinux / Raspberry Pisudopip3installquickdrawUseOpen the Quick Draw data, pull back ananvildrawing and save it.fromquickdrawimportQuickDrawDataqd=QuickDrawData()anvil=qd.get_drawing("anvil")anvil.image.save("my_anvil.gif")Documentationquickdraw.readthedocs.io
Package: quickdrop
QuickDropThis is a simple CLI for quickly sharing a file or folder via Dropbox
that's already located in a Dropbox folder.It's as simple as$ export DROPBOX_ACCESS_TOKEN=yourdropboxaccesstoken
$ export DROPBOX_ROOT_PATH=yourdropboxrootpath
$ pip install quickdrop

Collecting quickdrop
...
Successfully installed quickdrop-x.y.z

$ url <filepath.whatever>

Okay, <filepath.whatever> is now shared, accessible via
https://www.dropbox.com/sh/bunchofrandomchars/morerandomcharsnstuff?dl=0.
Package: quickdump
quickdumpQuickly store arbitrary Python objects in local files.Library status - this is an experimental work in progress that hasn't been
battle-tested at all. The API will change often between versions, and you may
lose all your data due to silly bugs.FeaturesStore arbitrary objects locallyNo config or boilerplate requiredDump to TCP serverDump to HTTP serverNotes(todo - rewrite this in a coherent manner)Currently, compression is applied per call todump. This isn't very
efficient (probably?)Labels are slugified to prevent errors from invalid characters in the filenameQuickly dump (almost) any object you like:fromquickdumpimportqd,QuickDumperfromdecimalimportDecimalforiinrange(10):result=Decimal(i)**Decimal("0.5")qd(result)And use them whenever it's convenient later:forobjinqd.iter_dumps():print(obj)# 0# 1.000000000000000000000000000# 1.414213562373095048801688724# ...Dump objects assigning a label, or create a dumper with a pre-configured label:qd("Armação","Campeche","Solidão",label="beaches")beach_dumper=QuickDumper("beaches")beach_dumper("Morro das Pedras","Açores","Gravatá")Iterate over multiple labels (including the default):forobjinqd.iter_dumps("beaches","default_dump"):print(obj)Iterate only over objects that match some filter:deffilter_initial_a(obj):returnnotobj.startswith("A")forobjinqd.iter_dumps("beaches",filter_fun=filter_initial_a):print(obj)# Campeche# ...Someday™Enable simple serialization of unpicklable types (e.g. save asockettype property of some object assocket's string representation instead of
just ignoring the object)Quickdump by piping from shellFunction decorator able to log function inputs and/or outputsReal time visualization of dumped data and metadata