semvec/pypi-packages huggingface dataset:
outlierRemoval-kvarshney-101703295 1.0.3
A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Requires Python: >=3.6

# Outlier Removal Using InterQuartile Range

**Project 2 : UCS633**


Submitted By: **Kshitiz Varshney 101703295**

***
pypi: <https://pypi.org/project/outlierRemoval-kvarshney-101703295/>
***

## InterQuartile Range (IQR) Description

Any 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.
<br>
The first quartile Q1, which represents a quarter(25%) of the way through the list of all data.
<br>
The median of the data set, which represents the midpoint(50%) of the whole list of data.
<br>
The third quartile Q3, which represents three-quarters(75%) of the way through the list of all data.
<br>
The maximum or highest value of the data set.
<br>
<br>
These five values helps us to determine outliers present in our dataset.

## Calculation of IQR

IQR = Q3 â€“ Q1
<br>
MIN = Q1 - (1.5*IQR)
<br>
MAX = Q3 + (1.5*IQR)
<br>

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install outlierRemoval-kvarshney-101703295.

```bash
pip install outlierRemoval-kvarshney-101703295
```
<br>

## How to use this package:

outlierRemoval-kvarshney-101703295 can be run as shown below:


### In Command Prompt
```
>> outlierRemoval dataset.csv
```
<br>


## Sample dataset

Marks | Students 
:------------: | :-------------:
3 | Student1
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7
99 | Student8

<br>


## Output Dataset after Removal

Marks | Students 
:------------: | :-------------:
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7

<br>

It is clearly visible that the rows containing Student1 andStudent8 have been removed due to them being Outliers.


## License
[MIT](https://choosealicense.com/licenses/mit/)
101703573-Outlier-pkg-suruchipundir 0.0.1
Outlier detection and removal using iqr
Requires Python: >=3.6

In statistics, an outlier is an observation point that is distant from other observations. The outliers can be a result of a mistake during data collection or it can be just an indication of variance in your data. The package uses interquartile range method to detect and remove outliers from a csv file. 

To run on command line 
python3 outlier.py filename
outlier-remover-101703283 0.0.0
python package for removing outliers from multi-variate data
Requires Python: >=3.6

# outlier_remover_101703283

_For : **Project-2 (UCS633)**_  
_Submitted by : **Katinder Kaur**_  
_Roll no : **101703283**_  
_Group : **3COE13**_  


outlier_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.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install outlier_remover_101703283.

```bash
pip install outlier_remover_101703283
```

## Usage

### For 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 analysis
```

Enter the input csv filename followed by _.csv_ extentsion
```bash
outlier_remover sample_inputfile.csv
```
after the records with anomalous values are removed, the resultant data will be implicitly stored in sample_input_sansOutliers.csv (i.e. <InputFileName>_sansOutliers.csv )

#### Custom output file name:
Destination output file name can be provided explicitly by using -o flag
```bash
outlier_remover sample_inputfile.csv -o my_outputfile.csv
```
the output data in this case will be stored in a csv file named my_outputfile.csv

#### Skipping 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 flag
```bash
outlier_remover sample_inputfile.csv -c 0,2,8
```
or
```bash
outlier_remover sample_inputfile.csv -c "0,2,8"
```
__Note__ : Column numbers start from 0. 

#### View help
To view usage __help__, use
```
outlier_remover -h
```

### For Python IDLE:

```python
>>> from outlier_remover.outlier_remover import outlier_remover
>>> list_of_columns_to_skip=[]
>>> outlier_remover('inputfile.csv','outputfile.csv',list_of_columns_to_skip)
Removed 2 row(s) successfully.
Save successful!
Check outputfile.csv for results


>>> from outlier_remover.outlier_remover import outlier_removerfn
>>> outlier_removerfn('sample2.csv')
Removed 1 row(s) successfully.
Save successful!
Check sans_outliers.csv for results
```

## License
[MIT](https://choosealicense.com/licenses/mit/)