semvec/pypi-packages huggingface dataset:
QuickJSON-nicky 0.0.4
removing all the open(file, 'w') hassle from saving a simple json
Requires Python: >=3.6

# QuickJSON

[QuickJSON](https://pypi.org/project/QuickJSON-nicky/) is a python library that focuses on removing the ```open(file, 'w')``` hassle from your daily json file saving needs

## Installation

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

```bash
pip install QuickJSON
```

## Usage

Import the package to use it.
```python
import QuickJSON
```
\
Create a new QJSON object.
(everything after the "path" parameter will handed over to the ```dict``` super call.
```python
settings = QuickJSON.QJSON('path/to/json.json')
```
The object will behave like a normal python dictionary.

With following method all of the information stored inside the json file at the before 
given path will be stored in the current QJSON object.
Items that are already stored in the object wont be deleted. They will only be overwritten if 
you set no_override to False (by default set to False).
```python
settings.load(no_override=False)
```
\
Storing data works exactly like a normal python dictionary.
```python
settings['a key'] = 'a value'
```
\
Once you are done you can save the QJSON object to the previus path with the following command:

```python
settings.save()
```
\
You can also clear move or copy the JSON file by invoking one of the following functions:
```python
settings.copy_save('new/directory/for/json.json')
settings.merge_save('new/directory/for/json.json')
settings.clear_save()
```
The functions do not modify the content of the dictionary itself, but modify the to file saved content, so make sure to save before copying or moving anything.
``.clear_save`` and ```.move_save``` will also remove any empty folders left behind

\
 ```.save()```, ```.load()```, ```.move_save()``` and ```.copy_save``` will automatically check if any given directory is valid. They will generate any missing directories and files if necessary and return an empty dictionary if file is not found or corrupt. 

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate. (i know, the tests folder is currently empty, but will change soon... hopefully)

## License
No License (yey)
jpmedical 0.0.2
A package for visualization of total of Health expenditures, if people all over the world are Japnanese"
Requires Python: >=3.7

[![Open in Code Ocean](https://codeocean.com/codeocean-assets/badge/open-in-code-ocean.svg)](https://codeocean.com/capsule/7739771/tree)
[![Open in pypi](https://img.shields.io/pypi/v/kotonohagetter)](https://pypi.org/project/kotonohagetter/)
# kotonohatango-getter
ことのはたんごのプレイヤーの集計データを可視化できるPython Package。  
Python Package for visualization of aggregate data of players in "Kotoha Tango".

[ことのはたんご](https://plum-chloride.jp/kotonoha-tango/index.html)

## Example of use
```
pip install kotonohagetter
python kotonohagetter.py 20
```
usage: kg.py [-h] [--F F] Number  
visualization of aggregate data of players in Kotoha Tango  
positional arguments:  
　Number      Get the [Number]th data.(int)  
optional arguments:  
　-h, --help  show this help message and exit  
　--F F       Getting word data?(bool:[y,t,true]or[n,f,false])[Default:True] 

**output**  

![image](https://user-images.githubusercontent.com/70005022/171073813-301307d5-268d-42ec-9fc8-5de011af87aa.png)

1-10 Percentage of users cleared there.  
x Percentage of users who could not clear.
fdce 0.1.2a1
Finite difference coefficient estimator
Requires Python: >=3.8


Finite difference coefficients estimator
========================================

Python implementation of the algorithm presented in:

  Fornberg, B. (1988). Generation of finite difference formulas on arbitrarily
  spaced grids. Mathematics of computation, 51(184), 699-706.

This algorithm can estimate the coefficients of the finite difference formula
used to estimate any derivative of an unidimensional function at a point `x_0`
given a grid of points (mostly neighbors of `x_0`). The accuracy level is
determined by the number of grid points used in each estimation.

Highlights
----------

1. Grid points do not have to be equally spaced.
2. `x_0` does not have to be one of the grid points.
3. As a result of 2., the algorithm can also be used to interpolate a function
   at a point `x_0`, by using the coefficients of the derivative of order zero.
4. In a single `M` order derivative approximation the coefficients needed to
   estimate the derivative at any order from zero to `M` are calculated.