Saravanan666/pypi-org huggingface dataset:
Package: cncd-package
Downloadpip install cncd-packageLoading the QC checks classfromcncd_package.QCimportQC_CheckReading datasharing paths of different datasets:\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_cad_2nd_entry_april_2023.csv\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_dm_2nd_entry_april_2023.csv\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_hf_1st_entry_april_2023.csv\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_nafld_1st_entry_april_2023.csv\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_pgr_1st_entry_april_2023.csv\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_promis_1st_entry_april_2023.csv\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_stroke_1st_entry_april_2023.csvimportpandasaspdimportwarningswarnings.simplefilter('ignore')promis_data=pd.read_csv(r"\\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_promis_1st_entry_april_2023.csv")Doing QCqc=QC_Check(dataframe=promis_data,project_id="PROMIS")Age Checkqc.age_check().head()study_idage0D969106.01LY66485.02LY675399.03LZ639496.04V36290.0Gender Checksqc.gender_check(gender='male')study_idgenderresult0LY70802.0MR1LY70942.0MR2LY74702.0MR3V21252.0MR4V21342.0MR...............65R8912.0MR66R10522.0MR67R15602.0MR68R15772.0MR69R16582.0MR70 rows Ã— 4 columnsqc.gender_check(gender='female')study_idgenderresult0K18841.0BIBI1LY28981.0BIBI2LY29031.0BIBI3LY29101.0BANO4LY38551.0BIBI...............85HZ931.0BIBI86PZ61.0BIBI87PY271.0BIBI88SM1911.0BB89RZ91.0BIBI90 rows Ã— 4 columnsCheck QC statusqc.check_qc_status()study_idstatusmipre_mist_elevationtroponin_positive0D11.00.00.01.00.01D21.00.00.01.0NaN2D41.00.00.01.00.03D51.00.00.01.02.04D61.01.01.01.00.0.....................82820RZ1111.00.0NaN1.0NaN82827RZ1181.00.0NaN0.01.082828RZ1191.00.0NaN0.01.082830RZ1211.00.0NaN1.0NaN82831RZ1221.00.0NaN0.01.031966 rows Ã— 6 columns
Package: cnceye
cnceyecnceye analyzes 3D models from a stl file and find measuring lines and curves.InstallationpipinstallcnceyeUsagefromcnceyeimportShapeshape=Shape("tests/fixtures/stl/sample.stl")lines,arcs=shape.get_lines_and_arcs()Simulation with BlenderCreate test dataPrerequisitesBlender 3.6.1 or laterblender"blender/measure.blend"--background--pythonscripts/demo.py--tests/fixtures/gcode/edge.gcode
Package: cncrd
ConcordMiddleware-based event processing library for Discord. Usesdiscord.pyunder the hood.Concordis nota library for accessing Discord API. If you're here for an
API library, seediscord.pyordisco, orDiscord Developer Documentationpage with a list of libraries for different languages.PurposeThe library aims to provide a more convenience way to handle Discord gateway
events, with code reusing where it's possible, including separating
functionality into extensions.Event processing is done using the programmer-defined handlers tree. Like in web
applications, due to similarity of the concepts of processing requests, Concord
calls these handlers as middleware as well.Concord doesn't try to be either afastor aslowlibrary. For it's
customization ability, it had to pay the speed.ExampleHugo- example bot, built on the Concord.
Take a note, that there's no so much code. It just registers extensions -
third-party middleware sets.Actually, Concord - is a successor of Hugo. You can figure this out by the code
history.Example extensions:concord-ext-audio,concord-ext-player,concord-ext-stats.InstallationVia PoetryConcord usesPoetryfor it's dependency
management. You can add Concord to your project using Poetry:poetryaddcncrdPoetry will handle the rest for you.Take a note, thatcncrdhas no vowels. Concord's and extensions' distribution
name iscncrd.Viapip/ other package managerConcord is hosted on PyPI and can be installed via other package managers:pipinstallcncrdConcord has a specific requirement -rewritebranch ofdiscord.pythat is handled by Poetry,
but not by other package managers. Take care of installing it too:pipinstall-Uhttps://github.com/Rapptz/discord.py/archive/rewrite.zip#egg=discord.pyDevelopmentConcord's development version is located in thedevbranch, and, in most
cases, it's a pretty stable to use in case you're a bot developer.poetryaddcncrd--githttps://github.com/narimanized/concordDocumentationI'm really sorry, but there's no online documentation yet.But. Concord is a small library, the code is well documented, and, with a
mentioned examples, you can quickly understand everything. Feel free to open an
issue on GitHub, if you need some help.LicenseMIT.See LICENSE file for more information.
Package: cndi
Installpip install -U git+https://github.com/mayank31313/python-dependency-injection.gitDocumentationDocumentation is shiftedhereExampleFollow the below code to simplify understanding, or can also refer tomain.pyfrom cndi.annotations import Bean, Autowired, AppInitializer

class TestBean:
    def __init__(self, name):
        self.name = name


@Bean()
def getTestBean() -> TestBean:
    return TestBean("Test 123")

testBean = None

app = AppInitializer()
if __name__ == "__main__":
    @Autowired()
    def setTestBean(bean: TestBean):
        global testBean
        testBean = bean

    app.run()

    print(testBean.name)
Package: cndprint
CndPrintCndPrint is a simple and elegant log display library. It allow you from one place to display more or less log message.>>>>importcndprint>>>>cnd_print=CndPrint()>>>>cnd_print.log_e("Hello")25/07/202222:59:54#|#Helloinstanciate the classuuidUUID will be display in beginning of each log message (after date). It can allow you to identify a unique run ID (very usefull if you use a log agregator)levelSwitch to log, info or trace to display more or less messagesilent modeuse this for example in automated testing mode, when you don't need any messageMultiple event familly with colorYou can use 5 event familly :e : Usually use forError (red)s : AnySuccess event (green)d : Use when i decide to goDown into a method(blue)c : Use when youCall another method (cyan)v : use for warning of simplyVerbose (yellow)Some exemple :>>>>importcndprint>>>>cnd_print=CndPrint()cnd_print.log_e("You failed ! that's not good !")cnd_print.log_s("You got it baby !")cnd_print.log_d("Start looping into for")cnd_print.log_c("Now calling sub method")cnd_print.log_v("what the waether today ?")Multiple level for printingYou can define 3 level for log filtering : 'trace', 'log', 'info'trace error can be identify with trace_
trace display trace, log and info
log display log and info
info display only infoBy default Info is actif, to use another level simplye use thislevel=log (default)importcndprintcnd_print=CndPrint()cnd_print.log_e("Hello Log")cnd_print.info_e("Hello Info")cnd_print.trace_e("Hello Trace")will result in this25/07/202222:59:54#|#Hello Loglevel=infoBut the same code, if you just replaceimportcndprintcnd_print=CndPrint(level='info')cnd_print.log_e("Hello Log")cnd_print.info_e("Hello Info")cnd_print.trace_e("Hello Trace")the print is now25/07/202222:59:54#|#Hello Log25/07/202222:59:54#|#Hello Infolevel=traceBut the same code, if you just replaceimportcndprintcnd_print=CndPrint(level='trace')cnd_print.log_e("Hello Log")cnd_print.info_e("Hello Info")cnd_print.trace_e("Hello Trace")the print is now25/07/202222:59:54#|#Hello Log25/07/202222:59:54#|#Hello Info25/07/202222:59:54#|#Hello Trace
Package: cndversion
cndversionThe definitive tools to manage VERSION and CHANGES files (based on semver)Installationpip install cndversionUsageHelp accessible from herecndversion --helpSetup a new project in a foldercndversion-fmylibif will createproject
│   VERSION
│   CHANGES  
└───mylib
│   │   __init__.py
│   │   __version__.py
│   │   VERSIONBump to major versioncndversion-Mit will increase the major version in mylib/VERSION file and add commit message to CHANGESBump to minor versioncndversion-mit will increase the minor version in mylib/VERSION file and add commit message to CHANGESBump to patch versioncndversion-pit will increase the patch version in mylib/VERSION file and add commit message to CHANGESWhat in each file ?VERSIONThis file content is the path for the "real" VERSION file (use for compatibilities with somes old tools)>>>>catVERSION
mylib/VERSIONCHANGESThis file is just empty>>>>catCHANGEmylib/init.pySimply include in the beginning of the file theversionfile>>>>catmylib/__init__.py
from.__version__import(__version__)# noqa: F401mylib/version.pyAllow you to include VERSION into the package (usefull for lib)>>>>catmylib/__version__.py
importpkg_resourcespath=pkg_resources.resource_filename('mylib','VERSION')__version__=open(path).read()mylib/VERSIONThis file is the real version number>>>>catmylib/VERSION0.1.1
Package: cne-learn
CNE is a probabilistic self-supervised deep learning model for compressing high-dimensional data to a low-dimensional embedding. CNE is a general-purpose algorithm that works with multiple types of data including images, time series, and tabular data. It uses the InfoNCE objective, a variational bound on mutual information, to improve local structure preservation in the compressed latent space and simultaneously learns a cluster distribution (a prior over the latent embedding) during optimization. Overlapping clusters are automatically combined by optimizing a variational upper bound on entropy, so the number of clusters does not have to be specified manually — provided the number of initial clusters is large enough. CNE produces embeddings with similar quality to existing dimensionality reduction methods; can detect outliers; scales to large, out-of-core datasets; and can easily add new data to an existing embedding/clustering.
Package: cnes-pylint-extension
cnes-pylint-extensioncnes-pylint-extension is a python checker, which adds CNES specific checks to pylint.cnes-pylint-extension checks the following rules :R5101 - multiple-exit-statementsR5102 - too-many-decoratorsR5103 - bad-exit-conditionR5104 - builtin-name-usedR5105 - recursive-callR5106 - use-context-managerR5401 - sys-exit-usedR5402 - os-environ-usedR5403 - sys-argv-usedW9095 - missing-docstring-fieldW9096 - malformed-docstring-fieldW9097 - missing-docstring-descriptioncnes-pylint-extension checks the following metrics :R5301 - too-high-complexity (default < 25)R5302 - too-high-complexity-simplified (default < 20)R5201 - too-few-comments (default > 20%)Available versions :Version 1.0 - compatible pylint 1.5Version 2.0 - compatible pylint 1.6Version 3.0 - compatible pylint 1.7.4 and 1.9.1Version 4.0 - compatible pylint 2.1.1Version 5.0 - compatible pylint >=2.5.0,<2.12.0Version 6.0 - compatible pylint >=2.12,<3.0.0warning: At 6.0.0 release, latest pylint was 2.13.5. If you encounter issue with pylint>2.13.5 and <3.0.0 please open an issue.To use these checkers:Install from PIPpip install cnes-pylint-extensionInstall from sourcesInstall Pylintpip install pylint==2.13.5Install CNES Pylint extension checkersDownload the project's code source then add the checkers subdirectory to your PYTHONPATH :unzip cnes-pylint-extension.zip
cd cnes-pylint-extension
echo export PYTHONPATH=$PYTHONPATH:$PWD/checkers >> ~/.bashrc
source ~/.bashrcTo enable Pylint to use the extension, you need to edit your pylintrc file, and add "cnes_checker" to the plugins list.[MASTER]
load-plugins=cnes_checker
...UsagePylint is now able to use the extension.Otherwise, add--load-plugins=cnes_checkerto your pylint command line in order to activate it.
Package: cnewtonprop
cnewtonpropCython implementation of theNewton propagator, for QuTiPQobj’sDevelopment ofcnewtonprophappens onGithub.⚠️WARNING: This implementation is work in progress. No public release is
available at this time, nor should the current development version (master)
be considered functional.InstallationTo install the latest development version ofcnewtonpropfromGithub.$pipinstallgit+https://github.com/qucontrol/cnewtonprop.git@master#egg=cnewtonpropUsageThecnewtonpropcan be used as a drop-in replacement for thenewtonproppackage:import cnewtonprop as newtonpropHistory
Package: cnext
CNextOpen source workspace designed for the DS & AI workflowBuilt with ❤︎ bycontributors🔮 OverviewCNext is a workspace for DS and AI workflows. This workspace is meant to consolidate the most common tasks performed by data scientists and ML engineers. At a high level our workspace allows for:Data exploration & transformationModel development / explorationProduction code generationDashboard & App GenerationExperiment Management📢 FeaturesInteractive Python coding envrionment with native Python output (think Jupyter replacement)Smart code suggestion (categorical values and column names)Interactive data explorationAutomative visualizationsExperiment and model managementInstant dashboarding🚀Requests:We're actively developing features based off user feedback, if you'd like to make any suggestions please feel free to hit us up on Slack.📄 Installation via PipPLEASE NOTE: CNext requires npm >= 18.4 and Python >= 3.9.7 . Please ensure your environment meets the minimum requirements before beginning the installation.Step 1: Make sureNodejsis available in your computer (trynpm --version)Step 2:runcommandpip install -U cnextStep 3:runcommandcnextInputEnter path to the cnext sample project created in Step 1and hitEnter(ExampleC:/Skywalker)Web application will launch at :http://localhost:CLIENT_PORTorhttp://127.0.0.1:CLIENT_PORT/(CLIENT_PORT default is 4000)Stop application:Ctrl + c | Command + cNote: Pay attention atCLIENT_PORT, andSERVER_PORTin.envfile (you will have to change these ports if you already use them on your machine)📄 Installation via Dockercnext is also available via pre-built Docker images. To get started, you can simply run the following command:dockerrun--rm-it-p4000:4000-p5000:5000-p5011:5011-p5008:5008-p5005:5005cycai/cnextThe web application will launch at:http://localhost:4000orhttp://127.0.0.1:4000/LicenseCopyright 2022 CycAI Inc. Distributed under MIT License.
​
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
​
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
​
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: cnext-test
CNextOpen source workspace designed for DS & AI workflowsBuilt with ❤︎ bycontributors🔮 OverviewCNext is a workspace for DS and AI workflows. This workspace is meant to consolidate the most common tasks performed by data scientists and ML engineers. At a high level our workspace allows for:Data exploration & transformationModel development / explorationProduction code generationDashboard & App GenerationExperiment Management📢 FeaturesInteractive Python coding envrionment with native Python output (think Jupyter replacement)Smart code suggestion (categorical values and column names)Interactive data explorationAutomative visualizationsExperiment and model managementInstant dashboarding🚀Requests:We're actively developing features based off user feedback, if you'd like to make any suggestions please feel free to hit us up on Slack.📄 Installation via PipPLEASE NOTE: CNext requires npm >= 18.4 and Python >= 3.9.7 . Please ensure your environment meets the minimum requirements before beginning the installation.Step 1: Make sureNodejsis available in your computer (trynpm --version)Step 2:runcommandpip install -U cnextStep 3:runcommandcnextInputEnter path to the cnext sample project created in Step 1and hitEnter(ExampleC:/Skywalker)Web application will launch at :http://localhost:CLIENT_PORTorhttp://127.0.0.1:CLIENT_PORT/(CLIENT_PORT default is 4000)Stop application:Ctrl + c | Command + cNote: Pay attention atCLIENT_PORT, andSERVER_PORTin.envfile (you will have to change these ports if you already use them on your machine)📄 Installation via Dockercnext is also available via pre-built Docker images. To get started, you can simply run the following command:dockerrun--rm-it-p4000:4000-p5000:5000-p5011:5011-p5008:5008-p5005:5005cycai/cnextThe web application will launch at:http://localhost:4000orhttp://127.0.0.1:4000/LicenseCopyright 2022 CycAI Inc. Distributed under MIT License.
​
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
​
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
​
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: cnf
CNF is short for Conjunctive Normal Form, which is a way of grouping logical statements; namely, but grouping or statements together separated by and statements:(a or b or ...) and (x or y or...) and ...For simplicity, let us refer to each statement as a "filter", as most likely one will be applying CNF to some data to find what passes the statements.Currently, we implement a filter inpython3via thedicttype:{
  'logic':    a string which is either 'and' or 'or'
  'lambda':   a lambda function to be applied to something
  'name':     a name which can help with keeping things clear
}Thus given alistof such filters, one can call:cnf.apply(cnf.group(filters), *lambda_args)
Package: cnfg
cnfgSimple configuration should be simple. Here’sexample.py:import cnfg
settings = cnfg.load('.examplerc')
print(settings['message'])Relative paths are awful. Not all systems have/etc. The only
reasonable place to put configuration is in your home directory.JSON and YAML are not Python.evalis not so bad. Here’s.examplerc:# It's Python, so you can use comments (and more)
{"message": "My custom message."}Now all of these work, with relative directories as implied:./example.py
python ../example.py
cat example/example.py | pythonWhat about default settings?It’s up to you, but it’s easy, especially if you’re keeping all your
configuration in a dict:settings = {"message": "Default message!"}
settings.update(cnfg.load('.examplerc'))What about environment variables?It’s up to you, but it’s easy:import os
some_var = os.getenv("SOME_VAR", "some default value")What about “from config import settings,” likeconfire?It’s up to you, but it’s easy. Just make a file calledconfig.pyand definesettingsin it.Where’s my home directory?On a Mac or Linux machine,echo ~. On Windows,echo %userprofile%.This is so abstract!Check out theexample.This is so trivial!Yes, it’s as simple as possible, and it’s useful all over the place
and the right level of complexity for a lot of projects.
Package: cnfhash
Author:Lukas ProkopDate:August 2015, May 2016Version:2.1.2license:CC-0A python3 implementation to hash DIMACS CNF files.
Seethe technical reportfor more details.
This implementation was pushed toPython Package Index PyPI.How to useTo install use pip3:pip3 install cnfhashThen you can use the following API in python3:import cnfhash

# reading blockwise from a DIMACS file

def read_blockwise(filepath):
    with open(filepath) as fd:
        while True:
            buf = fd.read(4096)
            if len(buf) == 0:
                break
            yield buf

reader = read_blockwise('test.cnf')
print(cnfhash.hash_dimacs(reader))

# or use integers directly

print(cnfhash.hash_cnf([3, 2, 1, -3, 0, -1, 2, 0]))Testing the softwareDownloadthe testsuite.
Provide the folder location as environment variable:export TESTSUITE="/home/prokls/Downloads/cnf-hash/tests1/"Then run all files in thetestsdirectory:python3 tests/test_testsuite.pyThe testsuite has been run successfully, if the exit code has always been 0.DIMACS file assumptionsA DIMACS file is valid iffAny line starting with “c” or consisting only of whitespace is considered ascomment lineand content is not interpreted until the next newline character occurs.The remaining file is a sequence of whitespace separated values.The first value is required to be “p”The second value is required to be “cnf”The third value is required to be a non-negative integer and callednbvars.The fourth value is required to be a non-negative integer and callednbclauses.The remaining non-zero integers are calledlits.The remaining zero integers are calledclause terminators.A DIMACS file must be terminated by a clause terminator.Every lit must satisfy-nbvars≤ lit ≤ nbvars.The number of clause terminators must equate nbclauses.termASCII mapping“c”U+0063“p”U+0070“cnf”U+0063 U+006E U+0066 U+0020signU+002Dnonzero digitU+0031 – U+0039digitsU+0030 – U+0039whitespaceU+0020, U+000D, U+000A, U+0009zeroU+0030Formal specificationA valid DIMACS file is read in and a SHA1 instance is fed with bytes:The first four values are dropped.Lits are treated as integers without leading zeros. Integers are submitted as base 10 ASCII digits with an optional leading sign to the SHA1 instance.Clause terminators are submitted as zero character followed by a newline character to the SHA1 instance.Performance and memoryThe DIMACS parser uses OS’ page size as default block size.
A few constant values and the python runtime is also stored in memory.
So for a python program, this implementation is very memory-friendly.The technical report shows that 45 DIMACS files summing up to 1 GB memory
can be read in 2989 seconds. In terms of performance, the equivalentGo
implementationis recommended.Example% cat test.cnf
p cnf 5 6
1 2 3 0
2 3 -4 0
1 -2 0
-1 2 0
1 3 5 0
1 -4 -5 0
% cnf-hash-py test.cnf
cnf-hash-py 2.1.2 2016-05-29T12:27:13.991260 /root
cnf2$776d81a0c805104e265667917b22ffefe9f39433  test.cnf
%Cheers!
Package: cnforpy
一个中文编程语言Cn-打造全中文编程简介下载使用打印引用变量随机数网络通信询问终端控制简介Cn是由双叶团队开发的一种中文编程语言官网:heikeling.github.io开发人员:Rainwangpupil, Heikeling, Code X下载终端输入一下命令：pip3 install cnforpy使用终端输入以下命令：cnforpy此时你会看到一个窗口，即为cn的编程页面打印类似于Python的print()语法：打印：你好CN!输出:你好CN!引用类似于Python的import语法：引用：随机变量CN的变量语法：新建变量：
设置变量名：我最喜欢的编程语言
设置变量值：CN等同于以下Python代码我最喜欢的编程语言="CN"随机数随机数需要引用随机模块CN随机数语法:引用：随机
随机数一：52
随机数二：78另外，你还可以打印随机数引用：随机
随机数一：52
随机数二：78
打印随机数终端输出：62网络通信网络通信套用了Python的requests模块，语法：引用：请求
请求网站源码：https://baidu.com
请求网页状态：https://heikeling.github.io询问询问后，会弹出一个窗口，输入内容，并存储在一个变量中语法：询问：你最喜欢的编程语言是什么？你还可以获取他人的答复打印：获取答复终端输出：（询问你最喜欢的编程语言）
（用户写了CN)
CN终端控制这个功能使用了Python的os.system()函数语法：引用：终端
终端输入：echo CN是我最喜欢的语言输出:CN是我最喜欢的语言关于CN的介绍就到这里，如果想了解更多，可以到CN语言的官网(https://heikeling.github.io)查看，我就介绍到这里，希望您快乐的使用CN!