Saravanan666/pypi-org huggingface dataset:
Package: apache-superset-db
SupersetA modern, enterprise-ready business intelligence web application.Why Superset?|Supported Databases|Installation and Configuration|Release Notes|Get Involved|Contributor Guide|Resources|Organizations Using SupersetWhy Superset?Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.Superset provides:Ano-code interfacefor building charts quicklyA powerful, web-basedSQL Editorfor advanced queryingAlightweight semantic layerfor quickly defining custom dimensions and metricsOut of the box support fornearly any SQLdatabase or data engineA wide array ofbeautiful visualizationsto showcase your data, ranging from simple bar charts to geospatial visualizationsLightweight, configurablecaching layerto help ease database loadHighly extensiblesecurity roles and authenticationoptionsAnAPIfor programmatic customizationAcloud-native architecturedesigned from the ground up for scaleScreenshots & GifsLarge Gallery of VisualizationsCraft Beautiful, Dynamic DashboardsNo-Code Chart BuilderPowerful SQL EditorSupported DatabasesSuperset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena,and more) that has a Python DB-API driver and a SQLAlchemy dialect.Here are some of the major database solutions that are supported:A more comprehensive list of supported databasesalong with the configuration instructions can be foundhere.Want to add support for your datastore or data engine? Read morehereabout the technical requirements.Installation and ConfigurationExtended documentation for SupersetGet InvolvedAsk and answer questions onStackOverflowusing theapache-supersettagJoin our community's Slackand please read ourSlack Community GuidelinesJoin our dev@superset.apache.org Mailing listContributor GuideInterested in contributing? Check out ourCONTRIBUTING.mdto find resources around contributing along with a detailed guide on
how to set up a development environment.ResourcesGetting Started with SupersetSuperset in 2 Minutes using Docker ComposeInstalling Database DriversBuilding New Database ConnectorsCreate Your First DashboardComprehensive Tutorial for Contributing Code to Apache SupersetDocumentation for Superset End-Users (by Preset)Deploying SupersetOfficial Docker imageHelm ChartRecordings of PastSuperset Community EventsLive Demo: Interactive Time-series Analysis with Druid and SupersetLive Demo: Visualizing MongoDB and Pinot Data using TrinoSuperset Contributor BootcampIntroduction to the Superset APIApache Superset 1.3 MeetupBuilding a Database Connector for SupersetVisualizationsBuilding Custom Viz PluginsManaging and Deploying Custom Viz PluginsWhy Apache Superset is Betting on Apache EChartsSuperset API
Package: apache-superset-iteco
SupersetA modern, enterprise-ready business intelligence web application.Why Superset?|Supported Databases|Installation and Configuration|Release Notes|Get Involved|Contributor Guide|Resources|Organizations Using SupersetWhy Superset?Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.Superset provides:Ano-code interfacefor building charts quicklyA powerful, web-basedSQL Editorfor advanced queryingAlightweight semantic layerfor quickly defining custom dimensions and metricsOut of the box support fornearly any SQLdatabase or data engineA wide array ofbeautiful visualizationsto showcase your data, ranging from simple bar charts to geospatial visualizationsLightweight, configurablecaching layerto help ease database loadHighly extensiblesecurity roles and authenticationoptionsAnAPIfor programmatic customizationAcloud-native architecturedesigned from the ground up for scaleScreenshots & GifsLarge Gallery of VisualizationsCraft Beautiful, Dynamic DashboardsNo-Code Chart BuilderPowerful SQL EditorSupported DatabasesSuperset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena,and more) that has a Python DB-API driver and a SQLAlchemy dialect.Here are some of the major database solutions that are supported:A more comprehensive list of supported databasesalong with the configuration instructions can be foundhere.Want to add support for your datastore or data engine? Read morehereabout the technical requirements.Installation and ConfigurationExtended documentation for SupersetGet InvolvedAsk and answer questions onStackOverflowusing theapache-supersettagJoin our community's Slackand please read ourSlack Community GuidelinesJoin our dev@superset.apache.org Mailing listContributor GuideInterested in contributing? Check out ourCONTRIBUTING.mdto find resources around contributing along with a detailed guide on
how to set up a development environment.ResourcesSuperset 2.0!Superset 2.0 MeetupSuperset 2.0 Release NotesUnderstanding the Superset Points of ViewThe Case for Dataset-Centric VisualizationUnderstanding the Superset Semantic LayerGetting Started with SupersetSuperset in 2 Minutes using Docker ComposeInstalling Database DriversBuilding New Database ConnectorsCreate Your First DashboardComprehensive Tutorial for Contributing Code to Apache SupersetResources to master Superset by PresetDeploying SupersetOfficial Docker imageHelm ChartRecordings of PastSuperset Community EventsMixed Time Series ChartsHow the Bing Team Customized Superset for the Internal Self-Serve Data & Analytics PlatformLive Demo: Visualizing MongoDB and Pinot Data using TrinoIntroduction to the Superset APIBuilding a Database Connector for SupersetVisualizationsCreating Viz PluginsManaging and Deploying Custom Viz PluginsWhy Apache Superset is Betting on Apache EChartsSuperset API
Package: apache-toree
This package will install Apache Toree as a Jupyter kernel.Apache Toree is an effort undergoing incubation at the Apache Software
Foundation (ASF), sponsored by the Apache Incubator PMC.Incubation is required of all newly accepted projects until a further review
indicates that the infrastructure, communications, and decision making process
have stabilized in a manner consistent with other successful ASF projects.While incubation status is not necessarily a reflection of the completeness
or stability of the code, it does indicate that the project has yet to be
fully endorsed by the ASF.
Package: apache-tvm
Open Deep Learning Compiler StackDocumentation|Contributors|Community|Release NotesApache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.
TVM works with deep learning frameworks to provide end to end compilation to different backends.LicenseTVM is licensed under theApache-2.0license.Getting StartedCheck out theTVM Documentationsite for installation instructions, tutorials, examples, and more.
TheGetting Started with TVMtutorial is a great
place to start.Contribute to TVMTVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.
Check out theContributor Guide.AcknowledgementWe learned a lot from the following projects when building TVM.Halide: Part of TVM's TIR and arithmetic simplification module
originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.Loopy: use of integer set analysis and its loop transformation primitives.Theano: the design inspiration of symbolic scan operator for recurrence.
Package: apache-virtual-host
##Apache Virtual HostCreating virtual host is a time consuming task for starting new project along with complexity. This tools make this process easy. Within a minute you can create your virtual host without any hassle.##Requirements* Python 3.** Linux OS##Process:* Download virtual_host project directory* Place it in your machine* Open terminal ```ctr+t``` and change directory to downloaded virtual_host directory. ```cd virtual_host```* Run command: ```python virtual_host.py```* Give two questions answer:* Give a directory, where you actually create your project.* Give domain name that you want for browse* Everything complete, visit your project directory and remove html file and place your codebase.* Browse your project following your domain name.
Package: apafea
apafea______     ______   ______     ______   ______     ______    
/\  __ \   /\  == \ /\  __ \   /\  ___\ /\  ___\   /\  __ \   
\ \  __ \  \ \  _-/ \ \  __ \  \ \  __\ \ \  __\   \ \  __ \  
 \ \_\ \_\  \ \_\    \ \_\ \_\  \ \_\    \ \_____\  \ \_\ \_\ 
  \/_/\/_/   \/_/     \/_/\/_/   \/_/     \/_____/   \/_/\/_/DescriptionInstallationUsageHistoryDescriptionapafea is short for "alluvial plots as functional enrichment analysis".And it does just that. Input a set of gene clusters (supports KeyPathwayMiner & BiCoN) and apafea performs gene set enrichment analysis and shows an alluvial plot visualizing the results.For further Information check outusage.InstallationInstall the package with:pip install apafeaDependenciesgseapy == 0.10.8plotly == 5.6.0Usagepython:fromapafeaimportrunrun(mode="KPM",data="/absolute/path/to/directory/",mode_2="BiCoN",data_2="/absolute/path/to/results.csv",gsea_dir="/absolute/path/to/gsea/directory/",GO=["bp","cc","mf","pw"],PW=["KEGG","Reactome","WikiPathways","MSigDB_C","MSigDB_O_S"],all=False,cutoff=0.05,)explanation:"mode" - defines the type of input."KPM" - give the path to your KeyPathwayMiner output directory under "data""BiCoN" - give the path to your BiCoN result .csv file under "data""custom" - give the path to your directory under "data" containing 1 cluster per file like this:gene1
gene2
..."mode_2" - defines the type of input for cluster comparison analysis."KPM" - same as above"BiCoN" - same as above"custom" - same as aboveNone / not set - cluster comparison analysis will not be performed"gsea_dir" - give the path to a directory to be used as gene set enrichment analysis output directory (default: /tmp)"GO" - list of GO-Terms enrichment analysis is being performed for"bp" - Biological process"cc" - Cellular Component"mf" - molecular Function"pw" - KEGG PathwaysNone / [] / not set - analysis will not be performed"PW" - list of pathway databases enrichment analysis is being performed for"KEGG" - KEGG_2016"Reactome" - Reactome_2016"WikiPathways" - WikiPathways_2016"MSigDB_C" - MSigDB_Computational"MSigDB_O_S" - MSigDB_Oncogenic_SignaturesNone / [] / not set - analysis will not be performed"all" - associates all its terms to a gene (default: False)"cutoff" - defines the cutoff for gene set enrichment analysis (default: 0.05)History0.0.0 - initial commit0.0.1 - first official release0.0.2 - added MANIFEST.in and changed relative path handling for sample data0.0.3 - fixed bug with relative path handling for sample data0.0.4 - fixed bug with relative path handling for sample data0.0.5 - fixed bug with relative path handling for sample data0.0.6 - fixed bug with relative path handling for sample data0.0.7 - added dependencies0.0.8 - added adjustable font size feature0.0.9 - added adjustable edge thicness feature
Package: apalis
What is Apalis?Apalis is a python library for parallel computing. It focuses on enabling parallel computing with little overhead. To explore the apalis functionality see theDocumentation.Getting started with ApalisApalis can be installed from PyPI:pipinstallapalisApalis can send an object into a child process and interact with it through a Handler.
To send an object into a parallel process just:importapalisimporttimeclassA:defexpensive(self,x):time.sleep(1)returnxa=A()obj=apalis.Handler(a)# Sends the object to a child process.token=obj.expensive(5)# Sends the task to the object in the child process.token()# Calling the token yields the result of the operation.The same can be done with multiple Handlers at once.objs=[apalis.Handler(A())for_inrange(16)]tokens=[obj.expensive(5)forobjinobjs]aplais.get(tokens)# Gets the results of the operations.More examples can be found in thisJupyter Notebook.
Package: a-pandas-ex-adb-execute-activities
Creates (partial) functions for every Android activity using ADBpipinstalla-pandas-ex-adb-execute-activitiesfroma_pandas_ex_adb_execute_activitiesimportpd_add_adb_execute_activitiespd_add_adb_execute_activities()importpandasaspdpackagename="com.google.android.youtube"adb_path="C:\\Users\\Gamer\\AppData\\Local\\Android\\Sdk\\platform-tools\\adb.exe"deviceserial="localhost:5745"df=pd.Q_ADB_activities_from_package_to_df(adb_path,deviceserial,packagename)df2=pd.Q_ADB_all_activities_to_df(adb_path,deviceserial)#Overviewdf.com_google_android_youtube_com_google_android_apps_youtube_app_application_Shell_UploadActivity__UPLOAD__DEFAULT__videoOut[19]:packagecom.google.android.youtube/com.google.android....Actioncom.google.android.youtube.intent.action.UPLOADAutoVerifyfalseCategoryandroid.intent.category.DEFAULTTypevideomHasPartialTypestruemPriority0Schemecommandamstart-a"com.google.android.youtube.intent...call-a"com.google.android.youtube.intent.action.U...Name:com_google_android_youtube_com_google_android_apps_youtube_app_application_Shell_UploadActivity__UPLOAD__DEFAULT__video,dtype:object#-a / -c /-n / -t will be automatically passed when calling the functiondf.com_google_android_youtube_com_google_android_apps_youtube_app_application_Shell_UploadActivity__UPLOAD__DEFAULT__video.callOut[20]:-a"com.google.android.youtube.intent.action.UPLOAD"-c"android.intent.category.DEFAULT"-n"com.google.android.youtube/com.google.android.apps.youtube.app.application.Shell_UploadActivity"-t"video"package:com.google.android.youtube/com.google.android.apps.youtube.app.application.Shell_UploadActivityAutoVerify:falseType:videomHasPartialTypes:truemPriority:0Scheme:# If you want to upload a video on YouTube, for example, you can complete the adb call like this:df.com_google_android_youtube_com_google_android_apps_youtube_app_application_Shell_UploadActivity__UPLOAD__DEFAULT__video.call('-d file:///storage/emulated/0/DCIM/SharedFolder/xxa.mp4')Executing:amstart-a"com.google.android.youtube.intent.action.UPLOAD"-c"android.intent.category.DEFAULT"-n"com.google.android.youtube/com.google.android.apps.youtube.app.application.Shell_UploadActivity"-t"video"-dfile:///storage/emulated/0/DCIM/SharedFolder/xxa.mp4b'Starting: Intent { act=com.google.android.youtube.intent.action.UPLOAD cat=[android.intent.category.DEFAULT] dat=file:///storage/emulated/0/DCIM/SharedFolder/xxa.mp4 typ=video cmp=com.google.android.youtube/com.google.android.apps.youtube.app.application.Shell_UploadActivity }\r\n'# if you pass stop=True / wait=True, the adb command will be executed with -S / -W respectively
Package: a-pandas-ex-adb-to-df
Use the power of pandas to manage the files on your Android devicepipinstalla-pandas-ex-adb-to-df#################################################Update 2022/12/06:# select individual folderdf=pd.Q_adb_to_df(device=deviceserial,adb_path=adb_path,folder='data/')# will automatically switch to su#################################################Update 2022/12/05:# Added ff_pull_file_cat to pull files that usually need su rights, which is not possible using "adb pull" (Maybe it is, but I haven't# found a way)# Make sure to exclude folders and symlinks when you copy files using cat:df.loc[(df.aa_symlink.isna())&(df.aa_size>0)&(~df.aa_rights.str.contains('^d'))].ff_pull_file_cat.apply(lambdax:x('f:\\gvbadsasww',timeout=15))#################################################################################################Update 2022/11/22:#################################################you can now pull files (folders will be created if they don't exist)forkey,itemindff.loc[(dff.aa_size>10)&(dff.aa_size<200)&(~dff.aa_filename.str.contains('->'))&(~dff.aa_filename.str.contains('/tmp'))].iterrows():try:item.ff_pull_file(f'f:\\keyboardstuffxx2')exceptException:pass#and remove filesforkey,itemindff.loc[dff.aa_fullpath.str.contains(r'tmp.*\.bin')&dff.aa_folder.str.contains(r'sdcard/')].iterrows():try:item.ff_remove_file()exceptException:pass################################################froma_pandas_ex_adb_to_dfimportpd_add_adb_to_dfimportpandasaspdadb_path="C:\\Users\\Gamer\\AppData\\Local\\Android\\Sdk\\platform-tools\\adb.exe"deviceserial="localhost:5745"pd_add_adb_to_df()df=pd.Q_adb_to_df(device=deviceserial,adb_path=adb_path)print(df)print(df.loc[df.aa_fullpath.str.contains(r'download.*_.*\.mp4',na=False,regex=True,flags=re.IGNORECASE)][:5].to_string())aa_filenameaa_folderaa_fullpathaa_idaa_indexaa_rightsaa_linksaa_owneraa_groupaa_sizeaa_date634994von_haus_aus.mp4storage/emulated/0/Downloadstorage/emulated/0/Download/von_haus_aus.mp41403065428-rwxrwx---1u0_a219media_rw55527682022-03-2117:31:00634995ins_haus_stehen.mp4storage/emulated/0/Downloadstorage/emulated/0/Download/ins_haus_stehen.mp41862336328-rwxrwx---1u0_a219media_rw64719582022-03-2117:31:00634996aufs_haus_gehen.mp4storage/emulated/0/Downloadstorage/emulated/0/Download/aufs_haus_gehen.mp41264986588-rwxrwx---1u0_a219media_rw67383742022-03-2117:31:00634997mit_der_tuer_ins_haus_fallen.mp4storage/emulated/0/Downloadstorage/emulated/0/Download/mit_der_tuer_ins_haus_fallen.mp41307736832-rwxrwx---1u0_a219media_rw69893532022-03-2117:31:00635004Alfredo_stiftete_seinen_Lieferwagen_final.mp4storage/emulated/0/Downloadstorage/emulated/0/Download/Alfredo_stiftete_seinen_Lieferwagen_final.mp41367065104-rw-rw----1u0_a219media_rw52199732022-06-0122:58:00
Package: a-pandas-ex-apply-against-all
Apply each value in a column against the whole columnpipinstalla-pandas-ex-apply-against-allfroma_pandas_ex_apply_against_allimportpd_add_apply_eachimportpandasaspdpd_add_apply_each()df=pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")df1=df.PassengerId.s_apply_each(expression="str(x) + str(y)",# use always x/y in your expressionexception_value=pd.NA,diagonal_value=pd.NA,print_exception=True,)print(df1)01234...8868878888898900<NA>12131415...18871888188918901891121<NA>232425...2887288828892890289123132<NA>3435...388738883889389038913414243<NA>45...48874888488948904891451525354<NA>...58875888588958905891...................................88688718872887388748875...<NA>88788888788988789088789188788818882888388848885...888887<NA>88888988889088889188888918892889388948895...889887889888<NA>88989088989188989018902890389048905...890887890888890889<NA>89089189089118912891389148915...891887891888891889891890<NA>[891rowsx891columns]# If you use a non-built-in function, you have to pass it as an argument, and use it as "func" in your expression# An example using shapely (merging different polygons)fromshapely.opsimportunary_unionimportshapelypolyshape=[]forkinrange(10):xmin=k*10+5ymin=k*10+5xmax=k*20+10ymax=k*20+10coordsalls=[[xmin,ymin],[xmax,ymin],[xmax,ymax],[xmin,ymax],[xmin,ymin]]po=shapely.geometry.Polygon(coordsalls)polyshape.append(po)df2=pd.DataFrame(polyshape)df2Out[7]:00POLYGON((55,105,1010,510,55))1POLYGON((1515,3015,3030,1530,1515))2POLYGON((2525,5025,5050,2550,2525))3POLYGON((3535,7035,7070,3570,3535))4POLYGON((4545,9045,9090,4590,4545))5POLYGON((5555,11055,110110,55110,5555))6POLYGON((6565,13065,130130,65130,6565))7POLYGON((7575,15075,150150,75150,7575))8POLYGON((8585,17085,170170,85170,8585))9POLYGON((9595,19095,190190,95190,9595))df1=df2[0].s_apply_each(expression="func([x,y]) if x.intersects(y) else x",# use always x/y in your expressionfunc=unary_union,exception_value=pd.NA,diagonal_value=pd.NA,print_exception=True,ignore_exceptions=True)Out[8]:0...90<NA>...POLYGON((55,105,1010,510,55))1POLYGON((1515,3015,3030,1530,1515))...POLYGON((1515,3015,3030,1530,1515))2POLYGON((2525,5025,5050,2550,2525))...POLYGON((2525,5025,5050,2550,2525))3POLYGON((3535,7035,7070,3570,3535))...POLYGON((3535,7035,7070,3570,3535))4POLYGON((4545,9045,9090,4590,4545))...POLYGON((4545,9045,9090,4590,4545))5POLYGON((5555,11055,110110,55110,5555))...POLYGON((11055,5555,55110,95110,9519...6POLYGON((6565,13065,130130,65130,6565))...POLYGON((13065,6565,65130,95130,9519...7POLYGON((7575,15075,150150,75150,7575))...POLYGON((15075,7575,75150,95150,9519...8POLYGON((8585,17085,170170,85170,8585))...POLYGON((17085,8585,85170,95170,9519...9POLYGON((9595,19095,190190,95190,9595))...<NA>[10rowsx10columns]
Package: a-pandas-ex-apply-ignore-exceptions
Use DataFrame.apply / Series.apply with a default value for Exceptionspipinstalla-pandas-ex-apply-ignore-exceptionsfroma_pandas_ex_apply_ignore_exceptionsimportpd_add_apply_ignore_exceptionsimportpandasaspdpd_add_apply_ignore_exceptions()df=pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")# the Exception value is the first arg, the rest is just like applydf1=df.PassengerId.ds_apply_ignore(pd.NA,lambdax:x/3)df2=df.ds_apply_ignore(pd.NA,lambdax:x["PassengerId"]/3,axis=1)df3=df.PassengerId.ds_apply_ignore(pd.NA,lambdax:x/0)df4=df.ds_apply_ignore(pd.NA,lambdax:x["PassengerId"]/0,axis=1)print(df1)print(df2)print(df3)print(df4)r"""0        0.3333331        0.6666672        1.0000003        1.3333334        1.666667...886    295.666667887    296.000000888    296.333333889    296.666667890    297.000000Name: PassengerId, Length: 891, dtype: float640        0.3333331        0.6666672        1.0000003        1.3333334        1.666667...886    295.666667887    296.000000888    296.333333889    296.666667890    297.000000Length: 891, dtype: float640      <NA>1      <NA>2      <NA>3      <NA>4      <NA>...886    <NA>887    <NA>888    <NA>889    <NA>890    <NA>Name: PassengerId, Length: 891, dtype: object0      <NA>1      <NA>2      <NA>3      <NA>4      <NA>...886    <NA>887    <NA>888    <NA>889    <NA>890    <NA>Length: 891, dtype: object"""
Package: a-pandas-ex-bs4df
One line web scraping by combining pandas and BeautifulSoup4Check out the videoCode from the videopipinstalla-pandas-ex-bs4dffroma_pandas_ex_bs4dfimportpd_add_bs4_to_dfimportpandasaspdpd_add_bs4_to_df()fromPrettyColorPrinterimportadd_printer#optionaladd_printer(True)#optionaldf=pd.Q_bs4_to_df(r'https://github.com/search?l=Python&q=python&type=Repositories')df.loc[(~df.bb_href.isna())&df.aa_attrs_values.str.contains('middle',regex=False,na=False)]df.loc[(~df.bb_href.isna())&df.aa_attrs_values.str.contains('middle',regex=False,na=False)].ff_fetchParents.apply(lambdax:x())df.loc[(~df.bb_src.isna())&(~df.bb_src.str.contains(r'\.png$',regex=True,na=False))]df.loc[(~df.bb_src.isna())&(df.bb_src.str.contains(r'\.png$',regex=True,na=False))]Parameters:htmlcode:Union[str,bytes]filepath,urlorhtmlsourcecodeurlswillbedownloadedwithrequestsdontuse:tuplebs4attributestoexcludefromthedataframedefault=("element_classes","builder","is_xml","known_xml","_namespaces","parse_only","markup","contains_replacement_characters","original_encoding","declared_html_encoding","parser_class","namespace","prefix","cdata_list_attributes","preserve_whitespace_tag_stack","open_tag_counter","preserve_whitespace_tags","interesting_string_types","current_data","string_container_stack","_most_recent_element","currentTag",)parser:strHavealookatthebs4documentation(default='lxml')tags_to_find:Union[bool,str]=Truewillbepassedtosoup.find_all()Havealookatthebs4documentation(default=True)#everythingReturns:df:pd.DataFrame