semvec/pypi-packages huggingface dataset:
tablepy-lib 0.8.0
This is a versatile and user-friendly Python table library that can quickly render any Dictionary{key, []} or DataFrame into a visually appealing markdown or sql insert
Requires Python: >=3.9,<4.0

# tablepy Lib

This is a versatile and user-friendly Python table library that can quickly render any Dictionary{key, []} or DataFrame into a visually appealing markdown or sql insert

## Download Stats

https://pypistats.org/packages/tablepy-lib

## Notebook for testing

https://github.com/JordiCorbilla/tablepy-lib/blob/main/Test%20Package.ipynb

## Usage: Output as Markdown/console

```python
from tablepy_lib import markdown

data = {
    "Name": ["John", "Emily", "Tom", "JC"],
    "Age": [-28, 3002.6, 25, 2],
    "Country": ["USA", "Canada", "UK", "DE"],
    "Data": ["USA", "Canada", "UK", "3434243"]
}

table = markdown(data)
print(table)    
```

Sample output:

```
| Name    | Age      | Country   | Data      | 
| ------- | -------- | --------- | --------- | 
| John    | -28.0    | USA       | USA       | 
| Emily   | 3002.6   | Canada    | Canada    | 
| Tom     | 25.0     | UK        | UK        | 
| JC      | 2.0      | DE        | 3434243   | 
```

| Name    | Age      | Country   | Data      | 
| ------- | -------- | --------- | --------- | 
| John    | -28.0    | USA       | USA       | 
| Emily   | 3002.6   | Canada    | Canada    | 
| Tom     | 25.0     | UK        | UK        | 
| JC      | 2.0      | DE        | 3434243   | 

## Usage: Output as SQL Insert

```python
from tablepy_lib import sql_insert

data = {
    "Name": ["John", "Emily", "Tom", "JC"],
    "Age": [-28, 3002.6, 25, 2],
    "Country": ["USA", "Canada", "UK", "DE"],
    "Data": ["USA", "Canada", "UK", "3434243"]
}

data_frame = pd.DataFrame(data)
table = sql_insert(data_frame, 'dd')
print(table)

```

Sample output:

```sql
INSERT INTO dd (Name, Age, Country, Data) VALUES ('John', -28.0, 'USA', 'USA');
INSERT INTO dd (Name, Age, Country, Data) VALUES ('Emily', 3002.6, 'Canada', 'Canada');
INSERT INTO dd (Name, Age, Country, Data) VALUES ('Tom', 25.0, 'UK', 'UK');
INSERT INTO dd (Name, Age, Country, Data) VALUES ('JC', 2.0, 'DE', 3434243);
```
partitionpy 1.0.0
A collection of PARTITION algorithms
Requires Python: >=3.7

# partition package

This is a collection of several algorithms to solve [PARTITION](https://en.wikipedia.org/wiki/Partition_problem).

![test](https://github.com/llueder/partitionpy/actions/workflows/python-app.yml/badge.svg) ![lint](https://github.com/llueder/partitionpy/actions/workflows/pylint.yml/badge.svg) ![mypy](https://github.com/llueder/partitionpy/actions/workflows/mypy.yml/badge.svg) [![coverage](https://llueder.github.io/partitionpy/badges/coverage.svg)](https://github.com/llueder/partitionpy/actions)

The package offers two heuristics:
* the [naive greedy heuristic](https://github.com/llueder/partitionpy/tree/main/src/partitionpy/naive_greedy.py)
* the [Karmakar-Karp heuristic](https://github.com/llueder/partitionpy/tree/main/src/partitionpy/kk.py) or Largest Differencing Method (LDM)
and a complete algorithm,
* the [Complete Karmarkar-Karp algorithm](https://github.com/llueder/partitionpy/tree/main/src/partitionpy/ckk.py).

The latter is also available in a more efficient [version](https://github.com/llueder/partitionpy/tree/main/src/partitionpy/kckk.py) for some instances.

## Resources on the partition problem

The implementation stem from a project during my master's degree.
The resulting thesis is [available](https://github.com/llueder/partitionpy/tree/main/doc/partition_llueder.pdf), feel free to read especially the sections 1 to 3.
From the references, I would like to recommend the following publications in particular:

R. E. Korf. A complete anytime algorithm for number partitioning. Artif. Intell., 106(2):
181–203, dec 1998. ISSN 0004-3702. [doi:10.1016/S0004-3702(98)00086-1](https://doi.org/10.1016/S0004-3702(98)00086-1).

B. Hayes. Computing science: The easiest hard problem. American Scientist, 90(2):113–117,
2002. ISSN 00030996. URL [http://www.jstor.org/stable/27857621](http://www.jstor.org/stable/27857621).

S. Mertens. The easiest hard problem: number partitioning. In Computational
Complexity and Statistical Physics. Oxford University Press, 12 2005. ISBN 9780195177374.
[doi:10.1093/oso/9780195177374.003.0012](https://doi.org/10.1093/oso/9780195177374.003.0012).

## Installation and usage

Run `pip install partitionpy` to install.

Example code:
```
import partitionpy

partitionpy.karmarkar_karp([4,5,6,7,8])
partitionpy.naive_greedy([4,5,6,7,8])
partitionpy.complete_karmarkar_karp([4,5,6,7,8])
partitionpy.kernelized_complete_karmarkar_karp([4,5,6,7,8])
partitionpy.complete_greedy([4,5,6,7,8])
```

## Alternatives

This is by far not the only package for PARTITION. You may want to check out [numberpartitioning](https://pypi.org/project/numberpartitioning/) and [prtpy](https://pypi.org/project/prtpy/) as well.
pyhop 0.0.6
This project is a Python script that utilizes built-in trace tools like tracert for Windows and traceroute for Linux to perform a traceroute to a given hostname or IP address. It traces the path that packets take from your device to the destination host, providing ip addresses
Keywords: python,traceroute,tracert,hops,ip address,server hops


pyhop is a Python package that simplifies the process of performing traceroutes by utilizing the built-in trace tools, tracert for Windows and traceroute for Linux. This package provides a consistent interface and reliable results across different platforms, allowing network administrators, developers, and users to diagnose network issues and optimize performance with ease. 

With pyhop, you can effortlessly trace the path of packets from your device to a destination host, gaining valuable insights into network connectivity.

Usage Example:

from pyhop import traceroute

print(traceroute('google.com'))