semvec/pypi-packages huggingface dataset:
missingvalues-101703297-ucs633 1.0.1
A python package to handle Missing Values using SimpleImputer Class
Requires Python: >=3.6

# Handling Missing Values using SimpleImputer Class

**Project 3 : UCS633**


Submitted By: **Kunal Bajaj 101703297**

***
pypi: <https://pypi.org/project/missingvalues-101703297-thapar/>
***

## SimpleImputer Class

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:
<br>
missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
<br>
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’.
<br>
fill_value : The constant value to be given to the NaN data using the constant strategy.


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install missingvalues-101703297-ucs633

```bash
pip install missingvalues-101703297
```
<br>

## How to use this package:

missingvalues-101703297 can be run as shown below:


### In Command Prompt
```
>> missingvalues-101703297 data.csv
```
<br>


## Sample dataset

a | b | c 
:------------: | :-------------: | :-------------:
NaN | 7 | 0
0 | NaN | 4
2 | NaN | 4
1 | 7 | 0
1 | 3 | 9
7 | 4 | 9
2 | 6 | 9
9 | 6 | 4
3 | 0 | 9
9 | 0 | 1

<br>


## Output Dataset after Handling the Missing Values

a | b | c 
:------------: | :-------------: | :-------------:
3.777778  | 7 | 0
0 | 4.125  | 4
2 |  4.125 | 4
1 | 7 | 0
1 | 3 | 9
7 | 4 | 9
2 | 6 | 9
9 | 6 | 4
3 | 0 | 9
9 | 0 | 1

<br>

It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.


## License
[MIT](https://choosealicense.com/licenses/mit/)
handle-missing-csv 0.0.1
Handle both categorical and non-categorical missing values
Requires Python: >=3.6

# Handle-Missing-Values 
Create and save .csv file with replaced categorical and non-categorical missing values
<H2>Brief</H2>
Replaces missing non-categorical values with mean of respective columns and uses KNN for missing categorical values
<H2>Usage</H2>
Use below commands in python terminal:
<ul>
>>>from missing import missing<br/>
>>>t = missing.missing(input_filename,output_filename,methods)
 </ul>methods can be "replace" or "remove"<br/>
</ul>
e.g . t = missing.missing("mydata.csv","out.csv","replace")
missing-pypck 1.0.0
Outlier removal package
Requires Python: >=3.6

# Library for Handling Missing Data

```
PROJECT 3, UCS633 - Data Analysis and Visualization
Nishchay Mahajan
COE18
Roll number: 101703377
```

Takes two inputs - filename of input csv, Method of handling data. 

Resulting csv is saved with the name OUTPUT.CSV. 


## Installation
`pip install missing-pypck`


## Use via command line

Drop rows with NaN
`python3 missing.py filename dropr` 

Drop columns with NaN
`python3 missing.py filename dropc`

Imputing with mean
`python3 missing.py filename mean`

Imputing with median
`python3 missing.py filename median`

Imputing with mode
`python3 missing.py filename mode`