Saravanan666/pypi-org huggingface dataset:
Package: chars2vec
Chars2vec library could be very useful if you are dealing with the texts                         containing abbreviations, slang, typos, or some other specific textual dataset.                         Chars2vec language model is based on the symbolic representation of words –                         the model maps each word to a vector of a fixed length.                         These vector representations are obtained with a custom neural netowrk while                         the latter is being trained on pairs of similar and non-similar words.                         This custom neural net includes LSTM, reading sequences of characters in words, as its part.                         The model maps similarly written words to proximal vectors.                         This approach enables creation of an embedding in vector space for any sequence of characters.                        Chars2vec models does not keep any dictionary of embeddings,                         but generates embedding vectors inplace using pretrained model.                         There are pretrained models of dimensions 50, 100, 150, 200 and 300 for the English language.                        The library provides convenient user API to train a model for an arbitrary set of characters.
Package: charset
What is this packageThecharsetdetects character encodings using theUniversal Charset Detectorimplemented
by Mozilla. If the text cannot be converted with the charset detected using theUniversal Charset Detectorthen it uses thechardetpackage.IntallationYou can install using pip:$ pip install charsetExampleIn [1]: from charset import Detector, text_to_unicode, text_to_utf8

In [2]: det = Detector()

In [3]: input_text = open('input.txt').read()

In [3]: text1 = text_to_unicode(input_text)

In [4]: text2 = text_to_utf8(input_text)ChangelogVersion 1.0.1 (2013-11-20)Modified setup.py.Added a README.txt.Added a MANIFEST.in to include data files missing in version 1.0.Removed dependencies form cython and setuptools-cython.Add dependency of chardet.Version 1.0 (2013-04-21)Initial version.Support for character encoding detection.
Package: charsi
CharsiA command-line tool to help game modders build string resources
forDiablo II: Resurrected.IntroductionIn the classic version of Diablo II, there was a very famous hacking tool calledMaphack, which could easily configure texts displayed in game. Now in
Diablo II: Resurrected, you can directly modify the JSON files to change texts
in game, but the workload is high, and it's easy to make mistakes.Charsiis the blacksmith in the Rogue Encampment in Act I of Diablo II, and
can imbue any normal item of the player's choice.This tool named charsi, can imbue game strings with your recipes which are in a
format similar to Maphack configurations.LicenseCopyright (C) 2022 HE Yaowenhe.yaowen@hotmail.comThe GNU General Public License (GPL) version 3, seeCOPYING.
Package: char-similar
char-similar汉字字形/拼音/语义相似度(单字, 可用于数据增强, CSC错别字检测识别任务(构建混淆集))一、安装0. 注意事项


   默认不指定numpy版本(标准版numpy==1.22.4), 过高或者过低的版本可能不支持


   标准版本的依赖包详见 requirements-all.txt


   


1. 通过PyPI安装


   pip install char-similar


   使用镜像源, 如：


   pip install -i https://pypi.tuna.tsinghua.edu.cn/simple char-similar二、使用方式2.1 快速使用fromchar_similarimportstd_cal_simchar1="我"char2="他"res=std_cal_sim(char1,char2)print(res)# output:# 0.58212.2 详细使用fromchar_similarimportstd_cal_sim# "all"(字形:拼音:字义=1:1:1)  # "w2v"(字形:字义=1:1)  # "pinyin"(字形:拼音=1:1)  # "shape"(字形=1)kind="shape"rounded=4# 保留x位小数char1="我"char2="他"res=std_cal_sim(char1,char2,rounded=rounded,kind=kind)print(res)# output:# 0.58212.3 多线程使用fromchar_similarimportpool_cal_sim# "all"(字形:拼音:字义=1:1:1)  # "w2v"(字形:字义=1:1)  # "pinyin"(字形:拼音=1:1)  # "shape"(字形=1)kind="shape"rounded=4# 保留x位小数char1="我"char2="他"res=pool_cal_sim(char1,char2,rounded=rounded,kind=kind)print(res)# output:# 0.58212.4 多进程使用(不建议, 实现得较慢)if__name__=='__main__':fromchar_similarimportmulti_cal_sim# "all"(字形:拼音:字义=1:1:1)  # "w2v"(字形:字义=1:1)  # "pinyin"(字形:拼音=1:1)  # "shape"(字形=1)kind="shape"rounded=4# 保留x位小数char1="我"char2="他"res=multi_cal_sim(char1,char2,rounded=rounded,kind=kind)print(res)# output:# 0.5821三、技术原理char-similar最初的使用场景是计算两个汉字的字形相似度(构建csc混淆集), 后加入拼音相似度,字义相似度,字频相似度...详见源码.





# 四角码(code=4, 共5位), 统计四个数字中的相同数/4


# 偏旁部首, 相同为1


# 词频log10, 统计大规模语料macropodus中词频log10的 1-(差的绝对值/两数中的最大值)


# 笔画数, 1-(差的绝对值/两数中的最大值)


# 拆字, 集合的与 / 集合的并


# 构造结构, 相同为1


# 笔顺(实际为最小的集合), 集合的与 / 集合的并


# 拼音(code=4, 共4位), 统计四个数字中的相同数(拼音/声母/韵母/声调)/4


# 词向量, char-word2vec, cosine四、参考(部分字典来源以下项目)https://github.com/contr4l/SimilarCharacterhttps://github.com/houbb/nlp-hanzi-similarhttps://github.com/mozillazg/python-pinyinhttps://github.com/CNMan/UnicodeCJK-WuBihttps://github.com/yongzhuo/Macropodushttps://github.com/kfcd/chaiziReferenceFor citing this work, you can refer to the present GitHub project. For example, with BibTeX:@misc{Macropodus,


    howpublished = {https://github.com/yongzhuo/char-similar},


    title = {char-similar},


    author = {Yongzhuo Mo},


    publisher = {GitHub},


    year = {2024}


}
Package: charSn
Characters_SnFunctions for calculation group characters of the symmetric group SnThis code was written by Lizzie Hernandez
for Math 68 Algebraic Combinatorics course in Fall 2021
at Darmouth College[GitHub Link] (https://github.com/lizziehv/math68-final)
[Google Colab Link] (https://colab.research.google.com/drive/1GMeXdseX97ZfMtfsO6Tvf5pijP4Zslc6?usp=sharing)
Package: chart
A zero-dependency python package that prints basic charts to a Jupyter outputCharts supported:Bar graphsScatter plotsHistograms🍑📊👏ExamplesBar graphs can be drawn quickly with thebarfunction:fromchartimportbarx=[500,200,900,400]y=['marc','mummify','chart','sausagelink']bar(x,y)marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
    mummify: ▇▇▇▇▇▇▇                       
      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇And thebarfunction can accept columns from apd.DataFrame:fromchartimportbarimportpandasaspddf=pd.DataFrame({'artist':['Tame Impala','Childish Gambino','The Knocks'],'listens':[8_456_831,18_185_245,2_556_448]})bar(df.listens,df.artist,width=20,label_width=11,mark='🔊')Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
 The Knocks: 🔊🔊🔊Histograms are just as easy:fromchartimporthistogramx=[1,2,4,3,3,1,7,9,9,1,3,2,1,2]histogram(x)▇        
▇        
▇        
▇        
▇ ▇      
▇ ▇      
▇ ▇      
▇ ▇     ▇
▇ ▇     ▇
▇ ▇   ▇ ▇And they can accept objects created byscipy:fromchartimporthistogramimportscipy.statsasstatsimportnumpyasnpnp.random.seed(14)n=stats.norm(loc=0,scale=10)histogram(n.rvs(100),bins=14,height=7,mark='🍑')🍑              
            🍑   🍑          
            🍑 🍑 🍑          
            🍑 🍑 🍑          
        🍑   🍑 🍑 🍑          
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑Scatter plots can be drawn with a simplescattercall:fromchartimportscatterx=range(0,20)y=range(0,20)scatter(x,y)••••••••••••••••••••And at this point you gotta know it works with anynp.array:fromchartimportscatterimportnumpyasnpnp.random.seed(1)N=100x=np.random.normal(100,50,size=N)y=x*-2+25+np.random.normal(0,25,size=N)scatter(x,y,width=20,height=9,mark='^')^^                  
 ^                  
    ^^^             
    ^^^^^^^         
       ^^^^^^       
        ^^^^^^^     
            ^^^^    
             ^^^^^ ^
                ^^ ^In fact, allchartfunctions work with pandas, numpy, scipy and regular python objects.PreprocessorsIn order to create the simple outputs generated bybar,histogram, andscatterI had to create a couple of preprocessors, namely:NumberBinarizerandRangeScaler.I tried to adhere to the scikit-learn API in their construction. Although you won't need them to usecharthere they are for your tinkering:fromchart.preprocessingimportNumberBinarizernb=NumberBinarizer(bins=4)x=range(10)nb.fit(x)nb.transform(x)[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]fromchart.preprocessingimportRangeScalerrs=RangeScaler(out_range=(0,10),round=False)x=range(50,59)rs.fit_transform(x)[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]InstallationpipinstallchartContributeFor feature requests or bug reports, please useGithub IssuesInspirationI wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea oftermgraph, it didn't really fit well or integrate with my Jupyter workflow. Here's tochart🥂 (still can't believe I got it onPyPI)
Package: charta
chartaA package for building realtime dashboards. Originally designed for monitoring the progress of optimization problems.Installationcharta is available on PyPi. You canpip install chartaQuickstartRun the server in the terminal with:charta-serverThen open your browser tolocalhost:8889.
The following code should draw a sine wave to the dashboard.importnumpyasnpfromchartaimportDashboard,Series,Chart# Create some data.x=np.linspace(0,np.pi)y=np.sin(x)# Get a reference to the dashboard.dash=Dashboard.default()# Add data to the dashboard.dash.add_series(Series("x",x))dash.add_series(Series("y",y))dash.add_chart(Chart("chart",["x","y"]))
Package: chartart
ChartArt Package is under developmentUseGithub-flavored Markdownto write your content.Change Log0.0.1 => 0.0.2
Added support for =>
-Publish post without javascript package from notebook
-Pulish post from standalone python script (Non jupyter environment)0.0.2 => 0.0.3
Added support for features at the time of publishing post. Features will get used as filters.0.0.3.dev5 => 0.0.3.dev6
-Support for status posts compatible with new low code platform.
Example:filters = {
    "dateAdded": datetime.datetime.now().isoformat() + 'Z',
    'rowSubscriptionCol': 'vendor',
    'type': 'aggregatePost'
}

data = {
    'count': 5,
    'status': 'Warning',
    'vendor': 'GCP'
}

post = Post(postId='GCP_aggregatePost', postTitle='# Aggregated Post')
post.addFilters(filters)
post.createPost(data=data)0.0.3.dev6 => 0.0.3.dev7
-Added Exception handling code if response is not in json format0.0.3.dev7 => 0.0.3.dev8
-Added code to retry 5xx error code0.0.3.dev7 => 0.0.3.dev10
-Resolved unauthorized error and changed sleep time0.0.3.dev12 => 0.0.3.dev13Added line/bar/scatter chart subtypesline type : line, area, spline, stepLine, splineArea, stepArea, stackedLine, stackedArea, stackedArea100, stackedLine100bar type : bar,column,stackedBar,stackedColumn,stackedBar,stackedBar100,stackedColumn100scatter : bubble , if size is specified as list of float scatter chart will get ploted as bubble chart0.0.3.dev14 => 0.0.3.dev15Tree Map : c (color) param is optionalMap : shapeDataField is new optional param (defaults to key 'Name'). primaryValueMapper and data is optional parameters now. This will be usefull for markers/bubbles where we dont need these inputs.Map : added support fo sub layers.0.0.3.dev18Added startAngle, endAngle, radius, groupTo in circular chartAdded number format on axis0.0.3.dev19added support to specify axis typeMade default bin interval is 0 . 0 means syncfusion will calculate internally.0.0.3.dev20preview and previewPost url should route should start with /  e.g  earlierhttp://localhost:port/#previewnowhttp://localhost:port/#/preview0.0.3.dev21Fix for communicate with Jupyter kernel0.0.3.dev22Fix for setting axis type on figure object0.0.3.dev23Added tooltip related parameters for map0.0.3.dev25Added datetime.date as supported type for datetime axis0.0.3.dev26Inmemory chart inside post0.0.3.dev29Inmemory aggregate posts0.0.3.dev30for table chart now we can specify column types.by default we will not show index column in table. User can specify it if needed.0.0.3.dev31Resolved issue of technical indicator in charts0.0.3.dev32Moved candle chart under cartesian chart ubrellaAdded generic method to add charts mainly required for drilldown table0.0.3.dev33Multi axis support0.0.3.dev34Date time axis x support for bar chart. Removed check of check_not_datetime(x)0.0.3.dev35support for isVisibleInLegend on series0.0.3.dev36support for drilldown details at figure level.
Package: chartart-appbuilder
ChartArt Package is under developmentUseGithub-flavored Markdownto write your content.Change Log0.0.1 => 0.0.2
Added support for =>
-Publish post without javascript package from notebook
-Pulish post from standalone python script (Non jupyter environment)0.0.2 => 0.0.3
Added support for features at the time of publishing post. Features will get used as filters.0.0.3.dev5 => 0.0.3.dev6
-Support for status posts compatible with new low code platform.
Example:filters = {
    "dateAdded": datetime.datetime.now().isoformat() + 'Z',
    'rowSubscriptionCol': 'vendor',
    'type': 'aggregatePost'
}

data = {
    'count': 5,
    'status': 'Warning',
    'vendor': 'GCP'
}

post = Post(postId='GCP_aggregatePost', postTitle='# Aggregated Post')
post.addFilters(filters)
post.createPost(data=data)0.0.3.dev6 => 0.0.3.dev7
-Added Exception handling code if response is not in json format0.0.3.dev7 => 0.0.3.dev8
-Added code to retry 5xx error code0.0.3.dev7 => 0.0.3.dev10
-Resolved unauthorized error and changed sleep time0.0.3.dev12 => 0.0.3.dev13Added line/bar/scatter chart subtypesline type : line, area, spline, stepLine, splineArea, stepArea, stackedLine, stackedArea, stackedArea100, stackedLine100bar type : bar,column,stackedBar,stackedColumn,stackedBar,stackedBar100,stackedColumn100scatter : bubble , if size is specified as list of float scatter chart will get ploted as bubble chart0.0.3.dev14 => 0.0.3.dev15Tree Map : c (color) param is optionalMap : shapeDataField is new optional param (defaults to key 'Name'). primaryValueMapper and data is optional parameters now. This will be usefull for markers/bubbles where we dont need these inputs.Map : added support fo sub layers.0.0.3.dev18Added startAngle, endAngle, radius, groupTo in circular chartAdded number format on axis0.0.3.dev19added support to specify axis typeMade default bin interval is 0 . 0 means syncfusion will calculate internally.0.0.3.dev20preview and previewPost url should route should start with /  e.g  earlierhttp://localhost:port/#previewnowhttp://localhost:port/#/preview0.0.3.dev21Fix for communicate with Jupyter kernel0.0.3.dev22Fix for setting axis type on figure object0.0.3.dev23Added tooltip related parameters for map0.0.3.dev25Added datetime.date as supported type for datetime axis0.0.3.dev26Inmemory chart inside post0.0.3.dev29Inmemory aggregate posts0.0.3.dev30for table chart now we can specify column types.by default we will not show index column in table. User can specify it if needed.0.0.3.dev31Resolved issue of technical indicator in charts0.0.3.dev32Moved candle chart under cartesian chart ubrellaAdded generic method to add charts mainly required for drilldown table0.0.3.dev33Multi axis support0.0.3.dev34Date time axis x support for bar chart. Removed check of check_not_datetime(x)0.0.3.dev35support for isVisibleInLegend on series0.0.3.dev36support for drilldown details at figure level.0.0.1.dev1support for App Builder.
Package: chartbeatpy
A simple, synchronous charbeat API wrapper.
This python package creates a simple “pythonic” entry point
for the Chartbeat API.  Originally created by Timothee Peignier,
we are adapting it to our needs here at Detroit Media Partnership.Any requests, issues should be directed towards us in this forked
repository, or email me atneurosnap@gmail.comChartbeat APIExplorerInstallationTo install charbeatpy, use pip:$pipinstallchartbeator$pythonsetup.pyinstallQuick How TofromchartbeatpyimportChartbeatbeat=Chartbeat("<your chartbeat api key>","<your host>")# live databeat.quickstats()beat.geo()# some API endpoints require extra parametersbeat.histogram(keys=[user,title],breaks=[1,2,10])beat.path_summary(keys=[pagetimer,time_spent,new])beat.summary(keys=[domain,title,read,write,idle])beat.recent()beat.referrers()beat.top_pages()# historical databeat.engage_series()beat.engage_stats()beat.social_series()beat.social_stats()beat.traffic_series()beat.traffic_stats()# specify API version numberoldbeat=Chartbeat("<API>","<host>",2)oldbeat.quickstats()
Package: chartfactor
ChartFactor for PythonChartFactor Pyis built on top ofChartFactor, the lightweight visual analytics platform. It includesChartFactor Studioand theChartFactor Toolkitbetween other components.Installing Chartfactor PyVisit theinstallation sectionin the ChartFactor Py documentation for a detailed guide.UsageCreate a new notebook and do the followingCell 1fromchartfactorimport*importpandasaspdCell 2titanic=pd.read_csv('https://chartfactor.com/resources/tutorials/titanic.csv')Cell 3cf.studio("My titanic app")The output will display an empty Studio app, select the dataframe in the Data Sources windowThen, add some widgets like Raw Data Table, Bars, Tree Map, etc...
Package: chartfactor-jlab-ext
chartfactor-jlab-extRun chartfactor-py python commands in Jupyter Lab KernelsRequirementsJupyterLab >= 3.0Installpipinstallchartfactor-jlab-extContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the chartfactor-jlab-ext directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallchartfactor-jlab-ext
Package: chartgpt
ChartGPT is a lightweight and user-friendly tool designed to assist you in visualizing your Pandas dataframes. Whether you are working in a Jupyter notebook or developing a Dash application, ChartGPT makes it effortless to generate stunning charts and plots. 📈Features 🌟Intuitive integration with Pandas dataframes 🐼Supports both Jupyter notebooks and Dash applications 📓🚀Simple installation and setup ⚙️Installation ⬇️You can install ChartGPT using pip:pipinstallchartgptExample Usage 🎉Jupyter Notebook 📔importchartgptascgdf=pd.read_csv('data.csv')chart=cg.Chart(df,api_key="YOUR_API_KEY")chart.plot("Pop vs. State")Generated graph after inputting 'Pop vs. State'Dash App 🚀Documentation 📚For detailed information on how to use ChartGPT, please refer to thedocumentation.License 📜This project is licensed under the MIT License - see theLICENSEfile for details.
Package: charticle
charticleis anMIT-licensed Python package
designed to make it very easy to build new diagrams like the Drew
Conway Data Science diagram.This may not be wise.Release Information0.0.3 (3 July 2016)Changes:Addcharticle.hierarchydiagrams and examples.Supports general text and polygon arguments for outer triangle & layers.Cleanups:wishlist, release procedures added to repo.Jupyter notebooks removed from repo.py27 testing included intox.ini.refactoring validators.cleaned up release procedure.0.0.2 (30 June 2016)Changes:Support Venn2 diagrams (and Venn3).Improve documentation examples and testing.Full changelog.Creditscharticleis written and maintained byJeremy G. Kahn.Collaboration and kibitzing due toBill McNeill.A full list of contributors can be found inGitHub’s overview.
Package: chartify-helpers
chartify-helpersSome lightweight helper functions which add readability/functionality to Spotify'schartifylibraryInstallation:via pip:pip install chartify-helpersvia github:git clone https://github.com/KristofPusztai/chartify-helpers.gitnavigate to cloned directory and run setup.pysudo python setup.py installUsage:Custom Callouts:Method:custom_callouts(chart,custom_callout,xs,ys,labels,colors,legend=True,label_text=False,sizes=None,alphas=None,text_offset=0.0005)fromchartify_helpersimportcustom_callouts# Generate example datadata=chartify.examples.example_data()# Sum price grouped by dateprice_by_date=(data.groupby('date')['total_price'].sum().reset_index()# Move 'date' from index to column)# Plot the datach=chartify.Chart(blank_labels=True,x_axis_type='datetime')ch.set_title("Line charts")ch.set_subtitle("Custom Callouts")ch.plot.line(# Data must be sorted by x columndata_frame=price_by_date.sort_values('date'),x_column='date',y_column='total_price')# Diamond Calloutcustom_callouts(ch,ch.figure.diamond,[[price_by_date['date'][54]]],[[price_by_date['total_price'][54]]],['diamond callout'],['orange'],legend=True,label_text=True,sizes=[10],alphas=[1],text_offset=0.3)# Circular Calloutcustom_callouts(ch,ch.figure.circle,[[price_by_date['date'][103]]],[[price_by_date['total_price'][103]]],['circular callout'],['green'],legend=True,label_text=True,sizes=[10],alphas=[1],text_offset=0.3)# Square Calloutcustom_callouts(ch,ch.figure.square,[[price_by_date['date'][146]]],[[price_by_date['total_price'][146]]],['square callout'],['red'],legend=True,label_text=True,sizes=[10],alphas=[1],text_offset=0.3)More callout styles foundhereStacked Bar Chart Top Labels:Method:add_stacked_label(chart,categories,labels,y,colors=None,sizes='10pt')fromchartify_helpersimportadd_stacked_label# Generate example datadata=chartify.examples.example_data()quantity_by_fruit_and_country=(data.groupby(['fruit','country'])['quantity'].sum().reset_index())# Plot the datach=chartify.Chart(blank_labels=True,x_axis_type='categorical')ch.set_title("Stacked bar chart")ch.set_subtitle("Stack columns by a categorical factor, with top labels")ch.plot.bar_stacked(data_frame=quantity_by_fruit_and_country,categorical_columns=['fruit'],numeric_column='quantity',stack_column='country',normalize=False)ch.set_legend_location('top_right')# Adding numerical labels above each baradd_stacked_label(ch,data.groupby('fruit').sum()['quantity'].index,data.groupby('fruit').sum()['quantity'].values,data.groupby('fruit').sum()['quantity'].values)ch.show('png')