Saravanan666/pypi-org huggingface dataset:
Package: outlier-plotting
outlier_plottingMore graceful handling of outliers in plots. Currently supports most of seaborn categorical scatter, distributional and estimate plots.Functionalityhandle_outliersremove outliers from the plot and show them as text boxes. It can be used with mostseabornplotting function that works with long-form data as well askdeplot.Notable exceptions are:countplotlineplotscatterplotPlease not that only inliers are passed into the plotting function, consequently density estimates and functionals are only computed on that subset andnotrepresentative of the whole data.Examplefromoutlier_plotting.snsimporthandle_outliersimportseabornassnsfrommatplotlibimportpyplotaspltplt.title('Showing Outliers')sns.boxplot(data=df,y='type',x='value')plt.show()plt.title('With Outlier Handling')handle_outliers(data=df,y='type',x='value',plotter=sns.boxplot)plt.show()For more examples, seeexamples.ipynb.Installationcondaconda env create -f environment.yml##PyPIpip install outlier-plotting
Package: outlier-priyank
Topsis AnalysisInstallationUse the package managerpipto install topsis-asharma-3027.pipinstalltopsis-asharma-3027UsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis-asharma-3027 -a "dataset-name" -b "w1,w2,w3,w4,..." -c "i1,i2,i3,i4,..."Do not mention the file format, 'csv', as part of dataset-name.
w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize.
Size of w and i is equal to number of features.Note that the first row and first column of dataset is droppedThe item attributed rank 1 is the best choice.LicenseMIT
Package: outlier-pypck
Outlier Removal by IQR or z-score in pythonProject2 for DATA ANALYSIS AND VISUALISATION(UCS633) 
Nishchay Mahajan COE18
Roll number: 101703377Output is a CSV file with outlier rows removedInstallationpip install outlier-pypckUpgradepip install outlier-pypck --upgradeTo use via command linepython3 outlier.py file_name.csv IQR or z_scoreTo use in python IDLE>>>from outlier_pypck.outlier import outlier
>>>outlier("Filename","IQR" or "z_score")OutputNumber of Rows removed
Package: outlier-python-souravdlboy
outlier-pythonPackage Description :Python package for Outlier Removal Algorithm using z_score or iqr.Motivation :This is a part of project - II made for UCS633 - Data analytics and visualization at TIET.@Author : Sourav Kumar@Roll no. : 101883068Algorithm :Z-SCORE : If the population mean and population standard deviation are known, the standard score of a raw score x is calculated as:z = (x - mean) / std.mean : is the mean of the sample.std : is the standard deviation of the sample.Interquartile range : interquartile range (IQR), also called the midspread, middle 50%, or Hâ€‘spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles.IQR = Q3 âˆ’  Q1The IQR of a set of values is calculated as the difference between the upper and lower quartiles, Q3 and Q1. Each quartile is a median calculated as follows :Given an even 2n or odd 2n+1 number of values.first quartile Q1 = median of the n smallest valuesthird quartile Q3 = median of the n largest valuesThe second quartile Q2 is the same as the ordinary median.Getting started Locally :Run On Terminalpython -m outlier.outlier inputFilePath outputFilePath z_scoreorpython -m outlier.outlier inputFilePath outputFilePath iqrex. python -m outlier outlier C:/Users/DELL/Desktop/train.csv C:/Users/DELL/Desktop/output.csv z_scoreRun In IDLEfrom outlier import outliero = outlier.outlier(inputFilePath, outputFilePath)o.outlier_main('z_score')oro.outlier_main('iqr')Run on JupyterOpen terminal (cmd)jupyter notebookCreate a new python3 file.from outlier import outliero = outlier.outlier(inputFilePath, outputFilePath)o.outlier_main('z_score')oro.outlier_main('iqr')NOTE :outlier_main()doesn't necessarily require anymethodargument , if no argument is provided, it usesz_scoreby default as the algorithm for removal of outliers from the dataset.The algorithm only reports missing data containing columns and not handles them, it assumes that it has been handled already.Also in case of z-score method, it will not affect much, but it may be possible to give wrong output in case of IQR if missing values are found.OUTPUT :Removes all the valid rows contaning outlier values from the dataset and prints the number of rows removed along with the columns which were considered for the algorithm.Also , the final dataframe will be written to the output file path you provided.TESTING :The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.
Package: outlier-removal-101703289
Outlier Removal using Interquartile Range - PythonProject 2 : UCS633Submitted By:Khushnuma Grover 101703289pypi:https://pypi.org/project/outlier-removal-101703289git:https://github.com/khushgrover/outlier-removal-python.gitWhat are Outliers ?There are certain things which, if are not done in the Exploratory Data Analysis phase, can affect further statistical/Machine Learning modelling. One of them is findingOutliers.In statistics, anoutlieris an observation point that is distant from other observations.Interquartile Range MethodA robust method for labelling outliers is the IQR (interquartile range) method of outlier detection developed by John Tukey, the pioneer of exploratory data analysis. This was in the days of calculation and plotting by hand, so the datasets involved were typically small, and the emphasis was on understanding the story the data told. A box-and-whisker plot (also a Tukey contribution), shows this method in action.A box-and-whisker plot uses quartiles (points that divide the data into four groups of equal size) to plot the shape of the data. The box represents the 1st and 3rd quartiles, which are equal to the 25th and 75th percentiles. The line inside the box represents the 2nd quartile, which is the median.The interquartile range, which gives this method of outlier detection its name, is the range between the first and the third quartiles (the edges of the box). Tukey considered any data point that fell outside of either 1.5 times the IQR below the first â€“ or 1.5 times the IQR above the third â€“ quartile to be outside or far out. In a classic box-and-whisker plot, the 'whiskers' extend up to the last data point that is not outside.How to use this package:In this package this method is implemented. It take in the dataset csv file and outputs out a csv file in the rows having outlier values are removed. This package handles univariate datasets as well as multivariate. Each feature having outlier rows are removed.OUTLIER-REMOVAL-KHUSHNUMA-101703289  can be run as in the following example:Installing the packageOn your Command Prompt run the following command:pip install outlier-removal-101703289Sample datasetThe dataset should be constructed with each row representing a data, and each column representing a criterion feature, ending with a target.FeatureTarget100.621000.441000.311000.67100.56In Command Prompt:>> remove-outlier data.csvIn Python IDLE:>>> from outlier_removal.outlier import remove_outlier
>>> file1 = pd.read_csv('data.csv')
>>> remove_outlier(file1)OutputFeatureTarget1000.441000.311000.67
Package: outlier-removal-101703297
Outlier Removal Project 2 UCS633Submitted By:kunal bajaj
roll no : 101703297pypi:https://pypi.org/project/outlier-removal-101703297In statistics, anoutlieris an observation point that is distant from other observations.Interquartile Range Method is used which mean if a data point lies below lower_quartile-1.5iqr or above upper_quartile+1.5iqr then it is a outlierInstalling the packageRun the following command to install from command line:pip install outlier-removal-101703297In Command Prompt:>> remove-outliers data.csvIn Python IDLE:>>> from outlier_removal.outlier import remove_outliers
>>> new_file = pd.read_csv('input_data.csv')
>>> remove_outliers(new_file)

<br>

## Output
Will shows the removed outliers

<br>
Package: outlier-removal3054
OUTLIER REMOVAL USING IQR METHODAuthor :Mehak GargWhat are outliers?An outlier is an observation that lies an abnormal distance from other values in a random sample from a population.
In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.
Before abnormal observations can be singled out, it is necessary to characterize normal observations.What is IQR method?A robust method for labelling outliers is the IQR (interquartile range) method of outlier detection developed by John Tukey, the pioneer of exploratory data analysis.
This was in the days of calculation and plotting by hand, so the datasets involved were typically small, and the emphasis was on understanding the story the data told.
A box-and-whisker plot (also a Tukey contribution), shows this method in action.A box-and-whisker plot uses quartiles (points that divide the data into four groups of equal size) to plot the shape of the data.
The box represents the 1st and 3rd quartiles, which are equal to the 25th and 75th percentiles. The line inside the box represents the 2nd quartile,
which is the median.The interquartile range, which gives this method of outlier detection its name, is the range between the first and the third quartiles (the edges of the box).
Tukey considered any data point that fell outside of either 1.5 times the IQR below the first â€“ or 1.5 times the IQR above the third â€“ quartile to be outside or far out.
In a classic box-and-whisker plot, the 'whiskers' extend up to the last data point that is not outside.How to use this package:It take in the dataset csv file and outputs out a csv file in the rows having outlier values are removed. This package handles univariate datasets as well as multivariate.
Each feature having outlier rows are removed.InstallationThe package can be installed using the following command in anaconda/command prompt.pip install outlier-removal_3054UsageThe package can be used for outlier removal by providing dataset.remove-outlier <dataset>
Package: outlierRemoval-kjindal-101703299
Outlier Removal Using InterQuartile RangeProject 2 : UCS633Submitted By:Kunal Jindal 101703299pypi:https://pypi.org/project/outlierRemoval-kjindal-101703299/InterQuartile Range (IQR) DescriptionAny set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:The minimum or lowest value of the dataset.The first quartile Q1, which represents a quarter of the way through the list of all data.The median of the data set, which represents the midpoint of the whole list of data.The third quartile Q3, which represents three-quarters of the way through the list of all data.The maximum or highest value of the data set.These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.Calculation of IQRIQR = Q3 – Q1MIN = Q1 - (1.5IQR)MAX = Q3 + (1.5IQR)InstallationUse the package managerpipto install outlierRemoval-kjindal-101703299.pipinstalloutlierRemoval-kjindal-101703299How to use this package:outlierRemoval-kjindal-101703299 can be run as shown below:In Command Prompt>> outlierRemoval dataset.csvSample datasetMarksStudents3Student157Student265Student398Student443Student544Student654Student799Student81Student9Output Dataset after RemovalMarksStudents57Student265Student398Student443Student544Student654Student7It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.LicenseMIT
Package: outlierRemoval-kvarshney-101703295
Outlier Removal Using InterQuartile RangeProject 2 : UCS633Submitted By:Kshitiz Varshney 101703295pypi:https://pypi.org/project/outlierRemoval-kvarshney-101703295/InterQuartile Range (IQR) DescriptionAny set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:The minimum value of the dataset.The first quartile Q1, which represents a quarter(25%) of the way through the list of all data.The median of the data set, which represents the midpoint(50%) of the whole list of data.The third quartile Q3, which represents three-quarters(75%) of the way through the list of all data.The maximum or highest value of the data set.These five values helps us to determine outliers present in our dataset.Calculation of IQRIQR = Q3 â€“ Q1MIN = Q1 - (1.5IQR)MAX = Q3 + (1.5IQR)InstallationUse the package managerpipto install outlierRemoval-kvarshney-101703295.pipinstalloutlierRemoval-kvarshney-101703295How to use this package:outlierRemoval-kvarshney-101703295 can be run as shown below:In Command Prompt>> outlierRemoval dataset.csvSample datasetMarksStudents3Student157Student265Student398Student443Student544Student654Student799Student8Output Dataset after RemovalMarksStudents57Student265Student398Student443Student544Student654Student7It is clearly visible that the rows containing Student1 andStudent8 have been removed due to them being Outliers.LicenseMIT
Package: outlier-removal-yash-saxena
Outlier row removal using inter quartile rangeProject 2 : UCS633 DATA ANALYSIS AND VISUALIZATIONSubmitted By:Yash saxena 101703627pypi:https://pypi.org/project/outlier-removal-yash-saxenagit:https://github.com/yashsaxena972/outlier-removalIQR Interquartile range DescriptionAny data can be described by its five-number summary. These five numbers,consist of (in ascending order):The minimum or lowest value of the dataset.The first quartile Q1, which represents a quarter of the way through the list of all data.The median of the data set, which represents the midpoint of the whole list of data.The third quartile Q3, which represents three-quarters of the way through the list of all data.The maximum or highest value of the data set.Calculation of acceptable dataIQR = Q3-Q1
lower=Q1-(1.5*IQR)
upper=Q3+(1.5*IQR)The data values present in between the lower and upper are acceptable and the rest are outliers and hence being removed.InstallationUse the package managerpipto install removal system.pipinstalloutlier-removal-yash-saxenaHow to use this package:outlier-removal-yash-saxena can be run as done below:In Command Prompt>> outliers <dataset.csv>Sample datasetMarksStudents3S157S265S398S443S544S654S799S81S9Output dataset after removalMarksStudents57S265S398S443S544S654S7It is clearly visible that the rows S1,S8 and S9 have been removed from the dataset.LicenseMIT
Package: outlier-remover-101703283
outlier_remover_101703283For :Project-2 (UCS633)Submitted by :Katinder KaurRoll no :101703283Group :3COE13outlier_remover_101703283 is a Python library for dealing with anomalies or outliers in a dataset. The presence of outliers in a dataset is very common, especially in raw data. Outlier removal is an important preprocessing stage since their presence leads to significant hindrance in the performance and prediction accuracies of the model.
There are several methods to detect and remove outliers, this script uses Interquartile Range(IQR) as the method of detection of anomalous data.InstallationUse the package managerpipto install outlier_remover_101703283.pipinstalloutlier_remover_101703283UsageFor command prompt:usage: outlier_remover [-h] [-o OUTPUTDATAFILE] [-c COLUMNSTOSKIP]
                       InputDataFile

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

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUTDATAFILE, --OutputDataFile OUTPUTDATAFILE
                        Enter the name of output CSV file with .csv extention
  -c COLUMNSTOSKIP, --ColumnsToSkip COLUMNSTOSKIP
                        Enter the columns to be left out of analysisEnter the input csv filename followed by.csvextentsionoutlier_removersample_inputfile.csvafter the records with anomalous values are removed, the resultant data will be implicitly stored in sample_input_sansOutliers.csv (i.e. _sansOutliers.csv )Custom output file name:Destination output file name can be provided explicitly by using -o flagoutlier_removersample_inputfile.csv-omy_outputfile.csvthe output data in this case will be stored in a csv file named my_outputfile.csvSkipping out columns:In some cases one may want to leave some features out of analysis(like in case of catagorical data or indices), that can be facilitated by using the -c flagoutlier_removersample_inputfile.csv-c0,2,8oroutlier_removersample_inputfile.csv-c"0,2,8"Note: Column numbers start from 0.View helpTo view usagehelp, useoutlier_remover -hFor Python IDLE:>>>fromoutlier_remover.outlier_removerimportoutlier_remover>>>list_of_columns_to_skip=[]>>>outlier_remover('inputfile.csv','outputfile.csv',list_of_columns_to_skip)Removed2row(s)successfully.Savesuccessful!Checkoutputfile.csvforresults>>>fromoutlier_remover.outlier_removerimportoutlier_removerfn>>>outlier_removerfn('sample2.csv')Removed1row(s)successfully.Savesuccessful!Checksans_outliers.csvforresultsLicenseMIT
Package: outlier-remover-csv
outlier_removerA outlier removal tool, removes outlier row-wise using z-score or InterQuartile Range methodPyPI Project linkhereUsageUse below commands to install and usepip install outlier-remover-csvpython3>>>from outlier import outlier>>>t = outlier.outlier(input_filename,output_filename,[method])method is optional. It can be "z_score" or "iqr", by default it is "iqr"e.g 1. t = outlier.outlier("mydata.csv","out.csv","z_score")e.g 2. t = outlier.outlier("mydata.csv","out.csv")
Package: outliers-101703013-2
With the help of this package, we can remove the outliers present in the dataset.
To do so, the Interquartile range is calculated which is the difference between the third quartile and first quartile.
The target is to remove the rows having values greater than maximum and less than minimum,
where minimum = Q1 - 1.5 * IQR
maximum = Q3 + 1.5 * IQR
Package: outliers-aarshi
This Python package is created to remove outlier rows from a dataset.
To use thi dataset you ned to create a pandas dataframe(you can use
pd.read_csv() to do it), and to to use this package your dataset
should have only numerical values and if you have any strings
then you can use one hot encoding or simple label encoder to do that,
but make sure before using it that it is all done.
Now this function takes one argument that is the dataframe.
and your df variable will now contain the new data which
will not contain outlier rows and this function will also
print the number of rows deleted!
THANKS!
Aarshi Arora
101703005
COE-1
Thapar Institute of Engineering and Technology
Package: outliers-antuanant
ï»¿This Python package is created to remove outlier rows from a dataset.
To use thi dataset you ned to create a pandas dataframe(you can use
pd.read_csv() to do it), and to to use this package your dataset
should have only numerical values and if you have any strings
then you can use one hot encoding or simple label encoder to do that,
but make sure before using it that it is all done.
Anant Chopra 101703063
Aekamjot Singh 101703042
Package: outliers-ashikothari10
A data pre-processing approach applied on a small data to predict the ranks of the model. This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution) approach to select best among many things based on different attributes, such as selecting the best Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy. You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-' where '+' indicates that you need to maximise value of that particular column and '-' indicates that you need to minimise value of that particular column. After passing all these parameters you will get your row rank.
Package: outliers-detection
It takes the dataframe and returns the outliers and other outliers related statsInstallation
pip install detect-outliersHow to use it?This class will initialize Pandas Data framefrom EdaFirstPhase.eda import EDAget_outliers_report() –> This method will print the outliers reportfrom EdaFirstPhase.eda import EDAdata = EDA(data=”train_data”,”y”,path = “/”)You need to pass data=”train_data”,”y”,path = “/”
Package: outliers-filtering
pip install outliers_filteringfrom outliers_filtering.utils import remove_outliers_numeric, plot_distribution_numericTwo functions:remove_outliers_numericplot_distribution_numericLet’s see how to use the functions.First the function remove_coutliers_numeric, it applies an IQR outliers filtering to normal or lognormal numerical pandas columns.remove_coutliers_numeric(df,feature,option = “lognormal”,delta=1.5)df: is the pandas dataframe where to apply the outlier removalfeature: name of the column to apply the functionoption: shape of the distribution, either “normal” or “lognormal”delta: coefficient to remove outliers outside IQR. default value is 1.5, the higher it is the less outliers you remove.Example:Second the function plot_distribution_numeric, it can plot distribution with and without outliers.plot_distribution_numeric(df, feature, option = ‘lognormal’,title = ‘distribution’, mode = ‘with_outliers’)df: is the pandas dataframe where to apply the functionfeature: name of the column to plot the distributionoption: shape of the distribution, either “normal” or “lognormal”title: Title of the plotmode: Two possible modes “with_outliers” to plot the raw distribution and “without_outliers” to plot distribution without outliersExample:plot_distribution_numeric(data,’useful_area’, option = ‘lognormal’,title = ‘distribution useful_area’, mode=’with_outliers’)plot_distribution_numeric(data, ‘useful_area’, option = ‘lognormal’, title = ‘distribution useful_area without extreme values’, mode=’without_outliers’)
Package: outliers-mnv
In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. An outlier can cause serious problems in statistical analyses. Therefore, it is important to detect and remove outliers. This package detects the outliers in the input data file using IQR method and then removes the rows having outliers.How to make it work :
Command for running the package will look like -
python outlier_mnv.py  
e.g. python outlier_mnv.py "mydata.csv" "newdata.csv"
Package: outliers-navkiran
Library for removing outliers from pandas dataframePROJECT 2, UCS633 - Data Analysis and Visualization
Navkiran Singh  
COE17
Roll number: 101703365Update in 1.1.0 - command line script method changed, supports calling from both windows and linux terminalTakes two inputs - filename of input csv, intended filename of output csv.Two optional arguments - which must be provided together or left out.
Third argument is threshold, by default it's 1.5. Fourth argument is method - z_score or IQR.Output is the number of rows removed from the input dataset. Resulting csv is saved as output.csv.Installationpip install outliers_navkiranRecommended - test in a virtual environment.Use via command lineoutliers_navkiran_cli in.csv out.csvDefaults are 1.5 threshold and IQR.When providing custom threshold and method:outliers_navkiran_cli in.csv out.csv 3 z_scoreoutliers_navkiran_cli in.csv out.csv 3 IQRFirst 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 outliers_navkiran import remove_outliers_z,remove_outliers_iqr
# for using z-score
remove_outliers_z('input.csv', 'output.csv',1.5)
# for using IQR
remove_outliers_iqr('input.csv', 'output.csv',1.5)