semvec/pypi-packages huggingface dataset:
Missing-values-101883058 1.0.2
Replacing missing values in the dataset with the mean of that particular column using SimpleImputer class.
License: MIT

# Replacing missing values in a dataset with the mean of that particular column

**Project 3 : UCS633**


Submitted By: **Pritpal Singh Pruthi 101883058**

***
pypi: <https://pypi.org/project/Missing_values_101883058/>
***

## SimpleImputer Class
```
class 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:
<br>
<br>
missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
<br>
<br>
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'.
<br>
<br>
fill_value : The constant value to be given to the NaN data using the constant strategy.
<br>
<br>
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=False
<br>
<br>
add_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. 


## Installation

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

```bash
pip install Missing_values_101883058
```

<br>

## How to use this package:

Outlier-removal-101883058 can be run as done below:



### In Command Prompt
```
>> missing_values dataset.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/)
shd 0.0.12
Show HDD/SSD list
License: MIT

# Show pretty HDD/SSD list

Long time ago when I've switched from Solaris to Linux, I missed "hd" utility.
In Linux "hd" command is used for a hex dump, in Solaris it displayed a pretty
table with HDD info.

I wrote "shd" shell script with a similar functionality. Now I rewrote it in
Python, added options and pretty colors, and releasing it to public.

<img src="https://raw.githubusercontent.com/alttch/shd/master/demo.gif" />

## Installation

Install *smartmontools*, then:

```
python3 -m pip install shd
```

## Usage

Tool should be started under root, to obtain S.M.A.R.T. info

```
shd [-h] [--temp-warn TEMP] [--temp-crit TEMP] [-R] [-y] [-e] [-s] [-J]

  --temp-warn TEMP  Warning temperature, default: 40 C
  --temp-crit TEMP  Critical temperature, default: 45 C
  --fh, --fahrenheit  Temperature in Fahrenheit
  -R, --raw         Suppress colors
  -y, --full        Display full disk info
  -e, --errors      Display only disks with errors / critical temperature
  -s, --no-header   Suppress header
  -J, --json        Output as JSON
```

## Exit codes

* **1** critical temperature
* **2** errors detected

The tool considers drive has an errors, if the following S.M.A.R.T. fields are
greater than zero:

* End-to-End_Error
* Seek_Error_Rate
* Raw_Read_Error_Rate
* UDMA_CRC_Error_Count