semvec/pypi-packages huggingface dataset:
VAMS-pinak47 0.0.8
A small example package
Requires Python: >=3.7

# BMI calculation

### The following are the objective,
    

1) Calculate the BMI (Body Mass Index) using Formula 1, BMI Category and
Health risk from Input data (person_detail.json) of the person and add them as 3 new columns.

            Formula 1 - BMI
            BMI(kg/m2) = mass(kg) / height(m)2


2) Count the total number of overweight people using ranges in the column BMI
Category.

        BMI Category------------BMI Range (kg/m2)------------Health risk

        Underweight------------  18.4 and below ------------Malnutrition risk

        Normal weight------------ 18.5 - 24.9------------------Low risk

        Overweight---------------- 25 - 29.9 ----------------Enhanced risk

        Moderately obese---------- 30 - 34.9 ---------------- Medium risk

        Severely obese ----------- 35 - 39.9  ---------------- High risk

        Very severely obese ---- 40 and above ---------------Very high risk


### There are few major functions used in the whole program,

1. 'fitness_calc.py' (in 'src' directory) has the functions to calculate BMI, Categorization and the count of Overweight persons.
2. 'utility.py' is created to make functions generic. It has the file handling functions.
3. 'test.py' has the function to unit test the code.
4. 'main.py' is used to get the desired output from the input data.
5. 'person_detail.json' has the input data containing persons' Gender, Height and Weight.
6. 'Final_BMI_Results.csv' will give the desired out in .csv format.
7. 'setup.py' has the required package details.
llq-data 0.8.1
Generate sample data
Requires Python: >=3.6

# 生成样例数据
## **pip install -U llq-data**
**sample code as follows:**
###### `from llq.util.data import fakeData
print(fakeData.gen(40))

### 'from llq.util.devices import GPU'

![hah](https://pic.pimg.tw/beigeetemple/1509175446-2818694733_n.jpg)
valeeew 0.0.13
Toolbox made for DATA manipulation
Requires Python: >=3.6

# Valeeew

> ## By Valentin MONTEIRO
___
### Table of contents
- Introduction
- Cleaning
- Outliers
  - IQR
  - Z-score
- Credits
___
## Introduction

Toolbox made for data manipulation, the goal is to reduce time-consuming tasks. If you want to participate, or simply give ideas, I would be happy to discuss, or even work with you on this project ! 

## Cleaning
### watch
function to make a quick overview of the DATAframe
## Outliers
### IQR
function to highlight outliers using the IQR method
### Z-score
function to highlight outliers using the Z-score method
___
## Credits
> People involved in the project