Saravanan666/pypi-org huggingface dataset:
Package: explode
Sometimes you need to drop some data onto a server.YAML and JSON are both great for that, except that they suck to be
consumed in shell scripts.jqis an excellent tool for consuming parts
of a JSON file in a shell script, but similar tofindit has its own syntax
and semantics that need to be learned.Shell scripts are already great at consuming text files and processing
directory trees, and if you need to do fancy things and you’re doing them
in shell, you probably have already learned all of the finer points offind.explodewill take a JSON or a YAML file and turn it into a directory
structure with simple text files containing the values. Dictionary keys
become directories. Lists become directories named for the list index. Booleans
are rendered as True or False. There is no support for anything else.Free software: Apache licenseSource:http://github.com/emonty/explode
Package: exploface
explofaceAuthor: B.L. de Vries (Netherlands eScience Center)IntroducionExploface is a simple python package to work with the output of openface. Openface is a software to analyse faces in images and videos. Please see the website of the authors for more information on openface:https://github.com/TadasBaltrusaitis/OpenFace/wikiThis package works with the output files of openface (csv files). It does some basic inspection and statistics with the data. It also allows to convert the data to a format readable by Elan (a video annotation tool,https://tla.mpi.nl/tools/tla-tools/elan/). Further more it is able to convert the per camera frame format of openface to a format that lists start and end times of a detection.Installation (from the command-line)Please follow the general guidelines for installing python packages and use a virtual environment:Instructions for installing python packages with pip:https://packaging.python.org/tutorials/installing-packages/You can also consider using conda to manage your environments:https://conda.io/docs/When you are in your command-line console and optionally, your virtual environment, install exploface by typing:pip install explofaceTest the installation by starting the python shell (typepythonin your command-line). And then test by runningimport explofaceIf this works, you are ready to do the tutorial.TutorialsIn the directory TUTORIALS you find tutorials on how to use exploface.Tutorial 1: exploring openface csv files and using the exploface packagehttps://github.com/emrecdem/exploface/blob/master/TUTORIALS/tutorial1.ipynbTutorial 2: underconstructionhttps://github.com/emrecdem/exploface/blob/master/TUTORIALS/tutorial2.ipynb
Package: exploit-patterns
Provides Python implementations of thepattern_createandpattern_offsettools from the Metasploit framework. They’re meant to
be used by penetration testers wanting to use them without having to
reach to Metasploit or a Ruby environment every time.usageGenerate a pattern to overflow the buffer usingpattern_create:pattern_createNote the return address on which the PC crashed and pass it topattern_offset:pattern_offset Zz8Z
Package: exploitsearch
Project:    Search Exploits by CVE-IDAuthor:     Nimish ShindeEmail:nimish.v.shinde60@gmail.comRelease:    1.0 (01/17/2019)Reqs:       * Python 3.x* requestsDisclaimer:This script is a enhancement on cvesearch package by Sujit Ghoshal(https://pypi.org/project/cvesearch/) which returns exploit, cve-id data.Please, use this tool at your own risk! The authorshall not be responsible for any misuse done through usage of thistool. Any actions and or activities related to this tool is solelyyour responsibility. The misuse of this tool might result in criminalcharges brought against the persons in question. Please pay respectto the third-party website owners and bandwidth, who makes these typeof structured information available easily to the public.
Package: exploration-stats
Exploration_StatsOverviewThis package provides EDA lifecycle functions like visualizations of missing values, their counts, distribution plots of different numerical columns, their skewness and kurtosis, and many more which will save a lot of timeAn Exploration is a sequence of steps, along with a sequence of decisions indicating where the explorer was at each step, a sequence of transitions indicating which transition was taken at each step, and a sequence of states indicating an extra state at each step. These representations were developed with Metroidvania games in mind.Core capabilities include:Representing exploration processes as a series of steps including partial information about not-yet-explored decisions.
Creating maps and explorations from various text formats, including exploration journal formats.
Reasoning about reachability modulo transition requirements in terms of powers that must be possessed and/or tokens that must be spent for a transition. TODO
The ability to represent fairly sophisticated game logic in the DecisionGraph, and even construct playable maps. Game logic that can't be captured this way can still be represented by making custom changes to maps between exploration steps.TODO
Dependencies:
Python version 3.8+
network For underlying graph structures.
pytest for testing, install with the [test] option to get it automatically.
Installing
Just run pip install exploration-stats. The tool script should be installed along with the module.Getting Started:
after installing the package
import exploration_stats as exp and call the exp. eda() function
pass the data frame and you are all set to goexample:pip install exploration_stats:import exploration_stats as exp:exp. eda(df) ---> df is your data frame and eda is the pre-built function that comes with this packagePlans
Better support for open-source eda guides, where steps are not as closely linked to virtual space structure.Changelog
There are also now a few built-in debug commands available for printing relevant stuff. More may be added as they become popular. It also introduces equivalences, stored in the DecisionGraph, which allow powers (but not tokens) to count as being obtained when one of a set of other requirements is met instead.
v0.1.1 is Still pre-alpha as it's in the process of being re-architected a bit, but some core functionality is present if rough (e.g., core.DecisionGraph and core. Exploration).
v0.1 Initial pre-alpha upload.
Package: exploratium-quant
ETL library to download and pre-process financial data.This tools process and enrichs data screpped from Yahoo Finance into a sets of CSV files. It was conceived as a CI component to generate static data for testing purposes.InstallationRequirementsclickblessedpandas-datareaderPIPpip install exploratium-quantUsageUsage: quant-cli [OPTIONS]Options:-t, --tickers TEXT   Tickers to analyse.-o, --out-dir PATH   Path to save results.--verbose / --quiet  Verbosity on/off.--help               Show this message and exit.ExampleBy calling:quant-cli -t "BTC-USD,SPY,ETH-USD,GME"The tool will save a daily OHLC timeseries for the ticker along the following generated metrics:ma7: 7 days moving averagema30: 30 days moving averagema90: 90 days moving averageupper_bollinger_band: Upper Bollinger Bandlower_bollinger_band: Lower Bollinger Banddaily_returns: Daily returnssharpe: Sharpe ratio
Package: exploratory
exploratoryExploratory Data AnalysisDescriptionThis project explortory was created to perform Exploratory Data Analysis on any structured dataset. Dataset can have categorical or numerical data types.
This project takes pandas dataframe and gives summary statistics and individual plots having categorical count for catagorical variables and PDF's, CDF's with mean, median and mode for numerical variables. The both the results are stored in PDF and CSV file in your current directory/path.Installation:Use the package managerpipto install exploratorypipinstallexploratoryUsage:fromexploratoryimportEDAEDA(df)# df --> pandas dataframe#Please input the DPI value, as DPI value increases runtime would increase. Defualt DPI value: 150Example Run:Expected Outputs:CSV File, DataFrame ContainingColumnDescriptionVariableVariable Name in the dataset providedCardinalityNumber of levels/classes in each variabletotal_countCount of total records (non null)unique_rateCardinality / total_count, Unique Rate of 1 indicates a ID variablepercent_missingPercentage of missing values across each columnmeanAverage of column (Ignores Object/String variables)stdStandard deviation of column (Ignores Object/String variables)minMinimum of column (Ignores Object/String variables)25%25th percentile value of column (Ignores Object/String variables)median50th percentile value of column (Ignores Object/String variables)75%75th percentile value of column (Ignores Object/String variables)maxMaximum of column (Ignores Object/String variables)data_typesData type of column (Int / Float / Object etc)rangeMax Value - Min Value (Ignores Object/String variables)PDF with Statistical Summary and variable distribution graphs  (categorical & continous)ContributingPull requests are welcome. Please use this 'https://github.com/Abhilash-MS/exploratory'
Please feel free to contact authors for any suggestions or issues,  Ramkakarlaramcharan@gmail.com, Abhilashabhilashmaspalli1996@gmail.com
Package: explore
Installingpip3installexploreQuick Usageimportexplore# yummy foods to choose fromfoods=('pasta','glazed ham','salted broccoli','raw anchovy','peking duck')# choosing during a keyboard strokefood=explore.pick(foods,'borlcoki')More Compleximportexplore# lots of yummy food infofoods=food_api.get_all()# match according to these attributesfetch=lambdafood:(food.id,food.name,food.color)# generator of (food, score) pairspairs=explore.generic(fetch,values,'gween peepr')# best matching onefood=explore.lead(pairs)LinksDocumentation
Package: explorecourses
Stanford Explore Courses Python APIThis package is a Python API for Stanford's Explore Courses site, which enables users to search through Stanford's catalog of over13,000 courses.InstallationType the following command in terminal to install:pip install explorecoursesUsageImport the package's classes into your Python program:from explorecourses import *Import the filters module if you would like to use search filters:from explorecourses import filtersCreate a new CourseConnection:connect = CourseConnection()Query the Explore Courses database by department code:courses = connect.get_courses_by_department("MATH", year="2017-2018")Apply filters to your query:courses = connect.get_courses_by_query("all courses", filters.AUTUMN, filters.WAY_AII)Sample Programfromexplorecoursesimport*fromexplorecoursesimportfiltersconnect=CourseConnection()# Print out all courses for 2017-2018.year="2017-2018"forschoolinconnect.get_schools(year):fordeptinschool.departments:courses=connect.get_courses_by_department(dept.code,year=year)forcourseincourses:print(course)ThanksThanks to Jim Sproch who wrote Stanford's Explore Courses Java API.
Package: exploredata
Python Package that performs Exploratory Data Analysisexploredata is built on Pandas and Numpy. It gives an overview of a csv data file.It imports csv file and returns the summary of the data in the file using the Pandas Dataframe.info() function.It contains method (null_cat and null_num) that returns the number of missing categorical and numerical features respectivelyIt also has method that returns the number of numerical and categorical features as well the number of features containing outliersIt also contains method (num_viz and cat_viz) that plot histogram and boxplot for each numerical variable as well as the count plot for each categorical variables.Installingpip install exploredataUsageimport exploredata as ep#imports the exploredata packagenew_variable = ep.EDA(file path)#calls the EDA module in the package. The module import your csv file and assign it as a pandas dataframe to the variable 'train'. It also returns the summarry of the datanew_variable.df#prints the pandas dataframenew_variable.num_var()#returns a tuple containing the number and the list of numerical variables in the dataframetrain.null_var()#returns a tuple containing the number and the list of numerical variables containing null valueshelp(ep.EDA)#provide information on all methods defined in the EDA module
Package: explore-data
ExploratoryDataAnalysisPython Package that performs Exploratory Data AnalysisIt imports csv file, returns the summary of the data in the file using the Pandas Dataframe.info() function.
Contains functions that returns the number of missing features, number of numerical and categorical features as well the number of features containing outliers
It also contain function that returns the histogram plot of each numerical variable as well as the bar plot of each categorical variables.This package is still under construction.
Package: explore-lif
explore_lifLoad 3D confocal images fromliffiles asnumpyarrays.InstallThe most convenient way would be:pip install -e .from the directory where you have cloned or downloaded the code.You can also Include the fileexplore_lifin your project directoryHow to Use ItFor 3D Imagefromexplore_lifimportReaderreader=Reader('lif_file.lif')series=reader.getSeries()chosen=series[0]# choose first image in the lif file# get a numpy array corresponding to the 1st time point & 1st channel# the shape is (x, y, z)image=chosen.getXYZ(T=0,channel=0)For 2D Image with Multiple Channelsfromexplore_lifimportReaderreader=Reader('lif_file.lif')series=reader.getSeries()chosen=series[0]# choose first image in the lif file# get a numpy array corresponding to the 1st time point# the shape is (x, y, channel_number)image=chosen.getXYC(T=0)
Package: explorer-menu
Explorer MenuA python module to help creating explorer menus for Windows Explorer.How to useDefining itemsItems can either be:Action items- items with a command associated to themnode('Label','cmd.exe /K echo action')Container items- items that may contain other container items and/or action itemsnode('Root Label',node(...),node(...),...)Registering itemsSimply call theregistermethod of any item and it will be registered, along with all children items:node('Label','cmd.exe /K echo action').register()node('Root Label',node(...),node(...),node(...)).register()After items are registered you will be able to find them by right clicking
in an empty area of any folder of your Windows Explorer.Removing itemsSimply call theremovemethod of any item and it will be removed, along with all children items:node('Label','cmd.exe /K echo action').remove()node('Root Label',node(...),node(...),node(...)).remove()Building commandsYou can also use thecmd(command, keep_cmd_window=False)method to create commands:fromexplorermenuimportcmd# explicitly invoking cmd.exenode('Label','cmd.exe /K echo action')# same as above, but building command with cmd() methodnode('Label',cmd('echo action',keep_cmd_window=True))Example:fromexplorermenu.menu_itemsimportnodefromexplorermenuimportcmd# a single action itemsingle_action_item=node('Root action',cmd('echo Root action',keep_cmd_window=True))# a nested set of itemsnested_set=\node('Root',node('Child 1',node('Grand child 1',node('Grandgrand child 1',cmd('echo Grandgrand child 1 action',keep_cmd_window=True)),node('Grandgrand child 2',cmd('echo Grandgrand child 2 action',keep_cmd_window=True)),),node('Grand child 2',cmd('echo Grand child 2 action',keep_cmd_window=True))),node('Child 2',cmd('echo Child 2 action',keep_cmd_window=True)),)# Register itemssingle_action_item.register()nested_set.register()# Remove itemssingle_action_item.remove()nested_set.remove()Versionv0.4.0
Package: exploretransform
ExploretransformExplore and transform your datasetsExploretransform is a collection of data exploration functions and custom pipline trasformers.  It aims to streamline exploratory data analysis and extend some of scikit's data transformers.Package GuideExamples of using the exploretransform functions and classes are contained in examples.ipynb located on thegithub repository.  Details about each function or class (docstrings) can be accessed using ?nameInstallationPython PYPI:!pipinstallexploretransformImport the exploretransform package:importexploretransformasetSummary of Functions and ClassesFunction / ClassDescriptionloadbostonloads the Boston housing datasetpeekreturns dtype, levels, # of observations, and first five observations for a dataframeexploreprovides various statistics on a dataframe (zeros, inf, missing, levels, dtypes)nestedtakes a list, series or dataframe and returns the location of nested objectsfreqfor categorical or ordinal features, provides the count, percent, and cumulative percent for each levelplotfreqgenerates a bar plot using the data generated by freqcorrtablegenerates a table of all pairwise correlations and uses the average correlation for the row and column in to decide on potential drop/filter candidatescalcdropanalyzes corrtable output determines which features should be filtered/dropskewstatsreturns the skewness statistics and magnitude for each numeric featureascorescalculates various association scores (kendall, pearson, mic, dcor, spearman) between predictors and targetColumnSelectcustom transformer that selects columns for pipelineCategoricalOtherLevelcustom transformer that creates "other" level in categorical / ordinal data based on thresholdCorrelationFiltercustom transformer that filters numeric features based on pairwise correlationHow to use exploretransformMore examples of using the exploretransform functions and classes are contained in examples.ipynb.  Details about each function or class (docstrings) can be accessed using ??et.exploreloadboston()df,X,y=et.loadboston()explore()et.explore(X)variableobsq_zerp_zerq_nap_naq_infp_infdtype0town50600.0000.000.0object1lon50600.0000.000.0float642lat50600.0000.000.0float643crim50600.0000.000.0float644zn50637273.5200.000.0float645indus50600.0000.000.0float646chas50600.0000.000.0category7nox50600.0000.000.0float648rm50600.0000.000.0float649age50600.0000.000.0float6410dis50600.0000.000.0float6411rad50600.0000.000.0category12tax50600.0000.000.0int6413ptratio50600.0000.000.0float6414b50600.0000.000.0float6415lstat50600.0000.000.0float64ColumnDescriptionvariablename of variableobsnumber of observationsq_zernumber of zerosp_zerpercentage of zerosq_nanumber of missingp_napercentage of missingq_infnumber of infinityp_infpercentage of infinitydtypePython dtypeRelease History1.0.0First release1.0.1 - 1.0.7Minor adjustments to get package working correctlyMetaBrian Pietracatella –bpietrac@gmail.comDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/bxp151/exploretransform
Package: explosig-connect
ExploSig ConnectHelpers for sending data from a Python environment toExploSig(viaExploSig Server) for web-based interactive visualization.DocumentationOpen in ColabInstallationpipinstallexplosig-connectExample Usage - Connecting to an existing session>>>fromexplosig_connectimportconnect>>># Connect using a session ID supplied by ExploSig.>>>session_id='af6242f3'>>>conn=connect(session_id)>>># Obtain the SBS mutation counts matrix associated with the session.>>>sbs_counts_df=conn.get_mutation_category_counts('SBS')>>># You run some custom code to derive better signature exposures.>>>exposures_df=my_exposures_computation_method(sbs_counts_df)>>># Send the new exposures back to ExploSig for visualization.>>>conn.send_exposures('SBS',exposures_df)Example Usage - Starting a new session>>>fromexplosig_connectimportconnect>>># Start a new 'empty' session with no samples, signatures, etc. selected.>>># This will attempt to open ExploSig in a new browser tab that starts the session.>>>conn=connect()>>># Send an SBS mutation counts matrix to visualize.>>>conn.send_mutation_category_counts('SBS',my_sbs_counts_df)>>># Send an exposures matrix to visualize.>>>conn.send_exposures('SBS',my_sbs_exposures_df)>>># Send a signatures matrix to visualize.>>>conn.send_signatures('SBS',my_sbs_signatures_df)DevelopmentBuild and install from the current directory.pythonsetup.pysdistbdist_wheel&&pipinstall.
Package: explosig-data
ExploSig DataHelpers for processing mutation data into standard formats originally developed for theExploSigfamily of tools.DocumentationInstallationpipinstallexplosig-dataExampleWith raw SSM/MAF file from ICGC or TCGA:>>>importexplosig_dataased>>># Step 1: Process into the ExploSig "standard format":>>>data_container=ed.standardize_ICGC_ssm_file('path/to/ssm.tsv')# if ICGC>>>data_container=ed.standardize_TCGA_maf_file('path/to/maf.tsv')# if TCGA>>># Step 2: Process further>>>data_container.extend_df().to_counts_df('SBS_96',ed.categories.SBS_96_category_list())>>># Step 3: Access any processed dataframe of interest:>>>ssm_df=data_container.ssm_df>>>extended_df=data_container.extended_df>>>counts_df=data_container.counts_dfs['SBS_96']>>># Alternatively, use without the chaining API:>>>ssm_df=ed.standardize_ICGC_ssm_file('path/to/ssm.tsv',wrap=False)# if ICGC>>>ssm_df=ed.standardize_TCGA_maf_file('path/to/maf.tsv',wrap=False)# if TCGA>>>extended_df=ed.extend_ssm_df(ssm_df)>>>counts_df=ed.counts_from_extended_ssm_df(extended_df,category_colname='SBS_96',category_values=ed.categories.SBS_96_category_list())With data already in the ExploSig "standard format":>>>importexplosig_dataased>>>importpandasaspd>>># Step 0: Load the data into a dataframe, for example by reading from a TSV file.>>>ssm_df=pd.read_csv('path/to/standard.tsv',sep='\t')>>># Step 1: Wrap the dataframe using the container class to allow use of the chainable functions.>>>data_container=ed.SimpleSomaticMutationContainer(ssm_df)>>># Now see step 2 above (or the alternative steps above).DevelopmentInstall for development (in editable mode):pipinstall-e.Build and push to PyPI:pythonsetup.pysdistbdist_wheel
python-mtwineuploaddist/*
Package: exp-manager
Usage instructions for experiment managerThis document describes to how use the experiment manager.OverviewThe experiment manager is a CLI tool that helps keep track of and annotate training runs. It works by maintaining the following directory structure:out/
|_____current_run.txt
|_____run1/
|_____run2/
|_____run3/
.
.
.whererunNcontains the results and logs for a single training run. The folder names are sortable timestamps to aid navigation and are suffixed by the training run's title. The timestamp for each folder is also the run's ID. The folder name for the current/active run is stored incurrent_run.txt.Each training run folder contains a YAML file with name, description and experiment group name. The experiment manager can:Show run metadataCreate a new runEdit/delete an existing runPick an existing run to be the active runEach run can be annotated with a title, description and group name. The group name is used to group together runs when displaying a list of all runs.Runpython -m exp_manager -hto see a list of commands:usage: exp_manager.py [-h] [--exp_dir EXP_DIR] {show,new,edit,delete,set} ...

positional arguments:
  {show,new,edit,delete,set}
    show                show run(s)
    new                 create new run
    edit                edit existing run
    delete              delete a run
    set                 set a run as the current run

optional arguments:
  -h, --help            show this help message and exit
  --exp_dir EXP_DIR     parent directory containing all runsEach command has a help option as well. e.g. runpython -m exp_manager -husage: exp_manager.py show [-h] [--all | --current | --id ID]

optional arguments:
  -h, --help     show this help message and exit
  --all, -a      show all runs
  --current, -c  show current run
  --id ID        run ID