Saravanan666/pypi-org huggingface dataset:
Package: missing-data-navkiran
Library for Handling Missing DataPROJECT 3, UCS633 - Data Analysis and Visualization
Navkiran Singh  
COE17
Roll number: 101703365Takes two inputs - filename of input csv, intended filename of output csv.Two optional arguments - which must be provided together or left out.Resulting csv is saved with the name you provide.Installationpip install missing_data_navkiranRecommended - test in a virtual environment.Use via command lineDefaults are drop NaN with parameter along = 0 (drops rows containing NaN)missing_data_navkiran_cli in.csv out.csvDrop rows with NaNmissing_data_navkiran_cli in.csv out.csv DROP 0Drop columns with NaNmissing_data_navkiran_cli in.csv out.csv DROP 1Forward fillingmissing_data_navkiran_cli in.csv out.csv FILL 0Backward fillingmissing_data_navkiran_cli in.csv out.csv FILL 1Imputing with meanmissing_data_navkiran_cli in.csv out.csv IMPUTE 0Imputing with medianmissing_data_navkiran_cli in.csv out.csv IMPUTE 1Imputing with modemissing_data_navkiran_cli in.csv out.csv IMPUTE 2First argument after outcli is the input csv filename from which the dataset is extracted. The second argument is for storing the final dataset after processing.Use in .py scriptfrom missing_data_navkiran import dropval,filler,impute
input_df = pd.read_csv('in.csv')axis = 0output_df = dropval(input_df,along=0)axis = 1output_df = dropval(input_df,along=1)backward-fillingoutput_df = filler(input_df,0)forward-fillingoutput_df = filler(input_df,1)Meanoutput_df = impute(input_df,0)Medianoutput_df = impute(input_df,1)Modeoutput_df = impute(input_df,2)There are also stand alone functions to fill numerical data and fill categorical data.from missing_data_navkiran import fill_numerical,fill_categorical
fill_numerical(input_df,list_of_numerical_columns)
fill_categorical(input_df,list_of_categorical_columns)
Package: missing-diff-lines
missing_diff_linesRunmissing_diff_lines()after you rancoverage.pyin a git-repo, e.g. withcoverage run -m unittest discover.
It will output a set with tuples in the form(filename, line-number)for every line in the current diff, that has no test that
covers it.ExampleSay you have this code incalc.pyand no tests yet:def mul(a, b):
    return a * bYou add some more cod:def mul(a, b):
    return a * b

def div(a, b):
    if b == 0:
        return 0
    return a / bAs well as a test-suite:class TestDiv(TestCase):

    def test_simple_div(self):
        self.assertEqual(3, div(9, 3))Note, that the branchb == 0is not covered.
And indeed. if you run:$ coverage run -m unittest discover && python3 report.pyWhere this is the content ofreport.py:from missing_diff_lines import missing_diff_lines
from pprint import pprint

print(missing_diff_lines())You get{('calc.py', 6)}Which is correct, as this line is not covered. But note, that
the not covered line 2 is not in the current diff and thus is
not reported. This is the very purpose of this package.
Package: missing-mnv
There may be some missing values in your dataset which are very important to be dealt with. This package helps in handling the missing values in your dataset. It replaces the missing values with the mean of that column. It also prints the dataset after imputing the missing values.How to make it work :
Command for running the package will look like -
python missing_mnv 
e.g. python missing_mnv "mydata.csv"
Package: missing-pypck
Library for Handling Missing DataPROJECT 3, UCS633 - Data Analysis and Visualization
Nishchay Mahajan
COE18
Roll number: 101703377Takes two inputs - filename of input csv, Method of handling data.Resulting csv is saved with the name OUTPUT.CSV.Installationpip install missing-pypckUse via command lineDrop rows with NaNpython3 missing.py filename droprDrop columns with NaNpython3 missing.py filename dropcImputing with meanpython3 missing.py filename meanImputing with medianpython3 missing.py filename medianImputing with modepython3 missing.py filename mode
Package: missingval-asharma-3027
Handling Missing Values using SimpleImputer ClassProject 3 : UCS633Submitted By: **Abhishek Sharma - 101703027 **SimpleImputer ClassSimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.
It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments:missing_data : The missing_data placeholder which has to be imputed. By default is NaN.stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values – ‘mean'(default), ‘median’, ‘most_frequent’ and ‘constant’.fill_value : The constant value to be given to the NaN data using the constant strategy.How to use this package:missingval-asharma-3027 can be run as shown below:In Command Prompt>> missingValues dataset.csvSample datasetabcNaN700NaN42NaN4170139749269964309901Output Dataset after Handling the Missing Valuesabc3.7777787004.125424.1254170139749269964309901It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.License[MIT]
Package: missing-values
UCS633 Project SubmissionName-Kartikey TiwariRoll no.-101703282missing_valuesmissing_values is a Python package for handling missing values from a dataset.Missing valuesHereâ€™s some typical reasons why data is missing:
User forgot to fill in a field.
Data was lost while transferring manually from a legacy database.
There was a programming error.
Users chose not to fill out a field tied to their beliefs about how the results would be used or interpreted.
As you can see, some of these sources are just simple random mistakes. Other times, there can be a deeper reason why data is missing.
Itâ€™s important to understand these different types of missing data from a statistics point of view. The type of missing data will influence how you deal with filling in the missing values.Getting StartedThese instructions will help you to install and use this package for general use.PrerequisitesYour csv file should not have categorical dataInstallationUse the package managerpipto install missing_values.pipinstallmissing_valuesUsageYou can import it either in Python IDLE or run directly through command promptFor Command PromptIf you want to use this package on "data.csv" file. You need to change the directory where "data.csv" is stored then pass the name of csv file ("data.csv") as an input,your new csv file without missing values will be stored as "MissingValuesRemovedata.csv"missing_valuesdata.csvFor Python IDLEfrommissing_values.missingimportmissing_valuesmissing_values(file1)#file1 is name of your csv file on which you will perform operationSample datasetTK104TK105TK107254263338440NA470501NA558368451426697709733476542539188223240525659628451689517517509564370321435NA403306NA690558NA460358396492429ResultTK104TK105TK107254.0263.0338440.011.434782608695652470501.011.434782608695652558368.0451.0426697.0709.0733476.0542.0539188.0223.0240525.0659.0628451.0689.0517517.0509.0564370.0321.043511.043478260869565403.030611.043478260869565690.055811.043478260869565460.0358396.0492.0429ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: missing-values-101703283
missing_values_101703283For :Project-3 (UCS633)Submitted by :Katinder KaurRoll no :101703283Group :3COE13missing_values_101703283 is a Python library for dealing with missing values(NaN) in a numeric dataset. This simple package uses column mean to replcae the NaN values.InstallationUse the package managerpipto install missing_values_101703283.pipinstallmissing_values_101703283UsageFor command prompt:usage: missing_val [-h] InputDataFile

positional arguments:
  InputDataFile  Enter the name of input CSV file with .csv extension

optional arguments:
  -h, --help     show this help message and exitEnter the input csv filename followed by.csvextentsionmissing_valuessample_inputfile.csvafter the records with NaN values are removed, the resultant data will be implicitly stored in the same file as input.View helpTo view usagehelp, usemissing_values -hFor Python IDLE:>>>frommissing_values.missing_valuesimportmissing_values_fn>>>missing_values_fn('inputfile.csv')Missingvaluessuccessullyreplacedwithcolumnaverage.LicenseMIT
Package: missingvalues-101703297-ucs633
Handling Missing Values using SimpleImputer ClassProject 3 : UCS633Submitted By:Kunal Bajaj 101703297pypi:https://pypi.org/project/missingvalues-101703297-thapar/SimpleImputer ClassSimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.
It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments:missing_data : The missing_data placeholder which has to be imputed. By default is NaN.stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values – ‘mean'(default), ‘median’, ‘most_frequent’ and ‘constant’.fill_value : The constant value to be given to the NaN data using the constant strategy.InstallationUse the package managerpipto install missingvalues-101703297-ucs633pipinstallmissingvalues-101703297How to use this package:missingvalues-101703297 can be run as shown below:In Command Prompt>> missingvalues-101703297 data.csvSample datasetabcNaN700NaN42NaN4170139749269964309901Output Dataset after Handling the Missing Valuesabc3.7777787004.125424.1254170139749269964309901It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.LicenseMIT
Package: missingValues-kjindal-101703299
Handling Missing Values using SimpleImputer ClassProject 3 : UCS633Submitted By:Kunal Jindal 101703299pypi:https://pypi.org/project/missingValues-kjindal-101703299/SimpleImputer ClassSimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.
It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments:missing_data : The missing_data placeholder which has to be imputed. By default is NaN.stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values – ‘mean'(default), ‘median’, ‘most_frequent’ and ‘constant’.fill_value : The constant value to be given to the NaN data using the constant strategy.InstallationUse the package managerpipto install missingValues-kjindal-101703299.pipinstallmissingValues-kjindal-101703299How to use this package:missingValues-kjindal-101703299 can be run as shown below:In Command Prompt>> missingValues dataset.csvSample datasetabcNaN700NaN42NaN4170139749269964309901Output Dataset after Handling the Missing Valuesabc3.7777787004.125424.1254170139749269964309901It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.LicenseMIT
Package: missingValues-kvarshney-101703295
Handling Missing Values using SimpleImputer ClassProject 3 : UCS633Submitted By:Kshitiz Varshney 101703295pypi:https://pypi.org/project/missingValues-kvarshney-101703295/SimpleImputer ClassSimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.
It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments:missing_data : The missing_data placeholder which has to be imputed. By default is NaN.stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values – ‘mean'(default), ‘median’, ‘most_frequent’ and ‘constant’.fill_value : The constant value to be given to the NaN data using the constant strategy.InstallationUse the package managerpipto install missingValues-kvarshney-101703295.pipinstallmissingValues-kvarshney-101703295How to use this package:missingValues-kvarshney-101703295 can be run as shown below:In Command Prompt>> missingValues dataset.csvInput datasetabc0NaN42NaN4170139749269964309901Output Dataset after Handling the Missing Valuesabc044244170139749269964309901It is clearly visible that the rows,columns containing Null Values have been Handled Successfully using median values.LicenseMIT
Package: missing-values-yash-saxena
Replacing missing values in a dataset with the mean of that particular columnProject 3 : UCS633 DATA ANALYTICS AND VISUALIZATIONSubmitted By:Yash Saxena 101703627pypi:https://pypi.org/project/missing-values-yash-saxena/SimpleImputer Classclass sklearn.impute.SimpleImputer(missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False)SimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.It replaces the NaN values with a specified placeholder.It is implemented by the use of the SimpleImputer() method which takes the following arguments:missing_data : The missing_data placeholder which has to be imputed. By default is NaN.stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values â€“ 'mean'(default),'median', 'most_frequent' and 'constant'.fill_value : The constant value to be given to the NaN data using the constant strategy.copy : boolean, default=True
If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=Falseadd_indicator : boolean, default=False
If True, a MissingIndicator transform will stack onto output of the imputerâ€™s transform. This allows a predictive estimator to account for missingness despite imputation.InstallationUse the package managerpipto install removal system.pipinstallmissing-values-yash-saxenaHow to use this package:missing-values-yash-saxena can be run as done below:In Command Prompt>> missing_values dataset.csvSample datasetabcNaN700NaN42NaN4170139749269964309901Output Dataset after Handling the Missing Valuesabc3.7777787004.125424.1254170139749269964309901It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.LicenseMIT
Package: mission-control2
A project launcher for MarathonInstallationTo install using a terminal:$ virtualenv ve
$ source ve/bin/activate
(ve)$ pip install mission-control2
(ve)$ export DJANGO_SETTINGS_MODULE="mc2.settings"
(ve)$ ve/bin/django-admin migrate --noinputRunningBecause this system uses Google Accounts with OAuth2 for authentication, in dev, it’s easiest to just login using your superuser:Create a super user:(ve)$ ./manage.py createsuperuserStart the application on local address127.0.0.1:8000:(ve)$ DEBUG=True ./manage.py runserver
Package: misskey-python
This project has been discontinued.When installing, keep in mind that support is not provided by this library.MiPCMiPC (Misskey Python Client) is a Python client for using MisskeyAPI.ExamplesMiAuthfromMiPC.MiAuthimportMiAuthmia=MiAuth(server="misskey.io",name="TestApp")url=mia.generate_url()whileTrue:try:auth_token=mia.get_token()breakexceptMisskeyMiAuthFailedException:awaitasyncio.sleep(0.5)passquestionQ. Why is the pypi registered under a different name(misskey-python) than MiPC?A. Because the registration was denied due to the name being similar to an existing pypi project.
Package: misso
MISSOInstallationUsingpippip install missoNote:In our benchmarks, multi-core version was always faster than the GPU accelerated version. So, we
highly recommend installing just the CPU version and using multi-core computation.Installing from sourceUsagefrom misso import MISSOFor a more detailed usage, check out the Tutorials folder.Benchmarks** Benchmarks were run on a machine with the following configurationCPU:       6 core Intel Core i7-8750H (-MT-MCP-) [12 core with Hyperthreading]
           arch: Skylake rev.10 cache: 9216 KB
           flags: (lm nx sse sse2 sse3 sse4_1 sse4_2 ssse3 vmx) bmips: 26399
           clock speeds: max: 4100 MHz 1: 2479 MHz 2: 3013 MHz 3: 3211 MHz
           4: 3098 MHz 5: 3362 MHz 6: 3769 MHz 7: 3082 MHz 8: 3290 MHz
           9: 3090 MHz 10: 3141 MHz 11: 3055 MHz 12: 3650 MHz
Graphics:  Card-1: Intel Device 3e9b bus-ID: 00:02.0
           Card-2: NVIDIA Device 1f10 bus-ID: 01:00.0
           Display Server: x11 (X.Org 1.19.6 )
           drivers: modesetting,nvidia (unloaded: fbdev,vesa,nouveau)
           Resolution: 3840x1600@59.99hz
           OpenGL: renderer: GeForce RTX 2070 with Max-Q Design/PCIe/SSE2
           version: 4.6.0 NVIDIA 440.100 Direct Render: YesLicenseTODOTry gradient-based solversConjugate-gradient descentMulti-processing forlsmicomputationReduce interprocess overheadTry other methods to parallelize the codeBenchmarksMultiprocessingGPU benchmarksSolver BenchmarksRun benchmarks on multiple machines and put in benchmark reportsDetailed comparison with graphical Lasso (Tutorials)Toy ExampleTime Series: Stationary & DynamiclinkComparison of MISSO and GLASSO on indirect couplinglinkGPU AccelerationUse Cupy for solvingReduce GPU overheadVerify correctness (Still an issue)Try torch for GPU accelerationtqdm for Notebook and ScriptPandas DataFrame supportPackagingpip packageTravis CIReadme
Package: misspell
misspellSome functions that introduce random typographical errors in provided text.make one typo:frommisspellimportmake_typomake_typo('test')# tedt, tesw etc...make typos in 50% of the characters:frommisspellimportmake_typosmake_typos('Hello, world!',percent=50)# 'Jell8, woelf!', 'Hdllo, sorld!' etc...
Package: misspellings
Spell checker for codeThis is a Python library and tool to check for misspelled words in
source code. It does this by looking for words from a list of
common misspellings. The dictionary it uses to do this is based
on the Wikipedia list of common misspellings.http://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machinesThe list has been slightly modified to remove some changes that
cause a number of false positives. In particularok->OKwas
removed (ok is frequently used in perl tests for instance).ExampleTo try it out, merely run the following (using an old coreutils
source tree as an example):$ git clone git://git.sv.gnu.org/coreutils -b v8.10 coreutils
$ find coreutils -name '*.c' | misspellings -f -
coreutils/src/cat.c:754: efficency -> "efficiency"
coreutils/src/comm.c:198: funtion -> "function"
coreutils/src/expr.c:21: seperate -> "separate"
coreutils/src/pr.c:1417: accomodate -> "accommodate"
coreutils/src/tac.c:342: unneccessary -> "unnecessary"
coreutils/src/test.c:91: supressed -> "suppressed"ContributionsContributions are welcome! Please add unit tests for new features
or bug fixes. To run all the unit tests run./setup.py test.
If you havetoxinstalled, just runtox.Note that tests are run onTravisfor all supported python
versions whenever the tree on github is pushed to.TODOSome items on the TODO list:Implement option to interactively fix files.Give some thought to supporting multiple languages?Might a “common misspellings” list be different for different English
users - might an American make one set of mistakes while a German
writing English make another? Source of this data?Fix pip upgrade bug.Fix sed flag.CreditsKevin Lyda: Initial shell goo and python version.myint: Better python idioms and style. Mixed case support.Travis support.Maciej Blizinski: Potential use in opencsw pushed move to python.Ville Skyttä: Sped up wordification, editor-friendly reporting.
Package: misspellings_hurkman
Spell checker for codeThis is a Python library and tool to check for misspelled words in
source code. It does this by looking for words from a list of
common misspellings. The dictionary it uses to do this is based
on the Wikipedia list of common misspellings.http://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machinesThe list has been slightly modified to remove some changes that
cause a number of false positives. In particularok->OKwas
removed (ok is frequently used in perl tests for instance).Opinionated versionThis includes custom additions that should be considered opinionated, such as
cancellation vs. cancelation. This is not recommended for anyone’s use.ExampleTo try it out, merely run the following (using an old coreutils
source tree as an example):$ git clone git://git.sv.gnu.org/coreutils -b v8.10 coreutils
$ find coreutils -name '*.c' | misspellings -f -
coreutils/src/cat.c:754: efficency -> "efficiency"
coreutils/src/comm.c:198: funtion -> "function"
coreutils/src/expr.c:21: seperate -> "separate"
coreutils/src/pr.c:1417: accomodate -> "accommodate"
coreutils/src/tac.c:342: unneccessary -> "unnecessary"
coreutils/src/test.c:91: supressed -> "suppressed"ContributionsContributions are welcome! Please add unit tests for new features
or bug fixes. To run all the unit tests run./setup.py test.
If you havetoxinstalled, just runtox.You can reviewcoverageof added tests by runningcoverage run setup.py testand then runningcoverage report-m.Note that tests are run onTravisfor all supported python
versions whenever the tree on github is pushed to.The packaged version is available viapiporeasy_installasmisspellings. The project page is onpypi:The source code is available in the following locations:Bitbucket:https://bitbucket.org/lyda/misspell-check/srccode.google:http://code.google.com/p/misspell-check/Github:https://github.com/lyda/misspell-checkGitorious:https://gitorious.org/uu/misspell-checkSourceforge:https://sourceforge.net/p/misspell-checkPull requests on any of those platforms or emailed patches are fine.To doSome items on the TODO list:Implement option to interactively fix files.Give some thought to supporting multiple languages?Might a “common misspellings” list be different for different English
users - might an American make one set of mistakes while a German
writing English make another? Source of this data?Fix sed flag.  Have it support sed -i optionally, have it output all
unambiguous sed commands, have it be more careful on what it
replaces. It might also be an idea to have a perl output option.Use generators to allow finding errors as you go. Currently misspellings
grabs all files first, then checks them, which can take a while.Lacking tests for misspellings cli.CreditsKevin Lyda: Initial shell goo and python version.Steven Myint: Better python idioms and style. Mixed case support.
Travis/tox support.Maciej Blizinski: Potential use inOpenCSWpushed move to python.Ville Skyttä: Sped up wordification, editor-friendly reporting.