semvec/pypi-packages huggingface dataset:
fioo 0.4.1
file and folder operation utility packages

# fioo - Python FileIO and folder Opertation utility package
A Helper package for basic file and folder opetions. For example, determine file format(carriage return),Getting a file size, file extension, file delimeter, compare file, etcs.

## Installation

Install fioo on your system using: [pip](https://pip.pypa.io/en/stable/)

```bash
pip install fioo
```
## Usage

```python
import fioo

# example file path: r'C:\Users\username\Desktop\test.txt'
# example folder path: r'C:\Users\username\Desktop'

fioo.fformat(path)  # return 'Window'    -> based on the file return carriage, get file format, set parm check_all=True to check all lines in file.
fioo.is_empty_file(path)  # return True  -> check if giving file is empty
fioo.size(path)  # return '16B'          -> check the size of a file or folder
fioo.deli(path) # return ','             -> get file delimeter
fioo.ext(path) # return '.txt'           -> get file exention, set parm dot=False to remove dot -> 'txt'
fioo.compare(path1,path2) #		 -> note:path1 and path2 are folder path, will produce all diff files with csv format in the folder2 path location


beta, working in progress:
------using FIOO class-----
f = fioo.FIOO()
f.set_file_path(path) # set path
f.fformat       	          -> same as fioo.fformat(path)
f.is_empty_file 	          -> same as fioo.is_empty_file(path)
f.size          	          -> same as fioo.size(path)
f.deli          	          -> same as fioo.deli(path)
f.ext                             -> same as fioo.ext(path)
f.compare(path1,path2)            -> same as fioo.compare(path1,path2)

```


## License
[MIT](https://choosealicense.com/licenses/mit/)
pyback 0.0.2
An event-driven algorithmic trading framework
Requires Python: >=3.6

# pyback

![Python package](https://github.com/shawnlinxl/pyback/workflows/Python%20package/badge.svg)![Upload Python Package](https://github.com/shawnlinxl/pyback/workflows/Upload%20Python%20Package/badge.svg)[![codecov](https://codecov.io/gh/shawnlinxl/pyback/branch/master/graph/badge.svg)](https://codecov.io/gh/shawnlinxl/pyback)

An Event-Driven Algorithm Trading framework.
aesthetix 0.0.1
A small package for aesthetic output in custom machine learning pipelines
Requires Python: >=3.6

# Aesthetix

This is a python package which facilitates aesthetics in output when using custom training pipelines in machine learning.

## Usage Example

```python
import time
import aesthetix as at

cost  = 3000
acc = 0.9
for i in range(1000):
    at.progress_bar("Progress",i, 1000, num_bars = 40, output_vals={"Cost":cost, "Accuracy":acc})
    cost/=3
    acc *= 1.001
    time.sleep(0.01)
```

### Output

```txt
Progress:[========================================](100.00%)  Cost : 0.00 Accuracy : 2.44
```
problystatpy 0.8.3
Statististical Probability Distributions

# problyStatPy

Python package for computing and visualizing various probability distributions



###  General Module Descriptions:

* Applicable for Gaussian and Binomial distributions
* Read in datasets
* Calculate mean
* Calculate standard deviation
* Compute probability distribution Function
* Plot histogram and bar chart
* Plot probability density function
* Add two distributions
* Return basic statistics of a distribution



### Installation

:link: Instant file download link:
https://pypi.org/project/problystatpy/#files



:sparkles: Command line:
```
$ pip install --user problystatpy
```
​		OR
```
$ python3 -m pip install --user problystatpy
```



### Usage

Recommended for usage accommodation:
```
from problystatpy import *
```

Command line execution:
```
$ python3
>>> from problystatpy import *
>>> gaussian_one = Gaussian()
>>> gaussian_two = Gaussian(25, 2)
>>> gaussian_one.stdev
1
>>> gaussian_one + gaussian_two
mean 25, standard deviation 2.23606797749979
```



### Updates

> 6.22.2020 successfully passed test codes and ready for upload testing
>
> 6.23.2020 problystatpy-0.8.3v release on Test PyPi & pypi.org/project/problystatpy/





### Github

:link: https://github.com/jungsNN/problystatPyP