semvec/pypi-packages huggingface dataset:
auto-reports-sql 1.0.6
This tool automates SQL Reports generation.
License: MIT
Keywords: sql,automation,data science,data analytics,sqlite,mysql,postgres

# SQL Automation

This is a simple tool to run a set of sql queries and generate a HTML report of the results

# How to use the tool

### Install the tool using pip

```bash
pip install auto-reports-sql
```

### Create a json file with the following schema

```json
{
  "type": "array",
  "items": {
    "type": "object",
    "required": ["name", "query"],
    "properties": {
      "name": {
        "type": "string",
        "pattern": "^.*$"
      },
      "query": {
        "type": "array",
        "items": {
          "type": "string",
          "pattern": "^.*$"
        }
      }
    }
  }
}
```

> Example

```json
[
  {
    "name": "First 10 data",
    "query": [
      "select * from Person limit 10",
      "select * from Person order by name desc limit 10"
    ]
  }
]
```

### Run the program with the following command

```
Usage: auto-reports-sql execute [OPTIONS] QUERIES

Options:
  -t, --threads INTEGER           No. of threads used for computation
                                  [default: 10]
  -rt, --report-title TEXT        Title of the generated report.  [default: Report Title]
  -d, --db [sqlite|mysql|postgres]
                                  The database which is used.  [required]
  -dp, --db-path PATH             Sqlite database path. Only required if the
                                  database is set to 'sqlite'
  -h, --host TEXT                 host of the database. Only required if the
                                  database is not set to 'sqlite'
  -u, --username TEXT             username to access the database. Only
                                  required if the database is not set to
                                  'sqlite'
  -p, --password TEXT             password of the database. Only required if
                                  the database is not set to 'sqlite'
  -n, --db-name TEXT              name of the database. Only required if the
                                  database is not set to 'sqlite'
  --config FILE                   Read configuration from FILE.
  --help                          Show this message and exit.
```

# Report Examples

![Screenshot](https://github.com/naveeng2402/auto-reports-sql/raw/master/images/report.jpg)

# Possible Updates

- Errors in the queries are not taken into account
iter-model 2.1.0
iter-model uses a method approach instead of individual functions to work with iterable objects.
License: MIT
Requires Python: >=3.10,<4.0
Keywords: iterator,iterable

<p align="center">
    <a href="https://volodymyrbor.github.io/iter_model">
        <img src="https://volodymyrbor.github.io/iter_model/img/iter_model-logos_transparent.png" alt="IterModel" width="300">
    </a>
</p>


<a href="https://pypi.org/project/iter_model" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/iter_model.svg?color=%2334D058" alt="Supported Python versions">
</a>
<a href="https://pypi.org/project/iter_model" target="_blank">
    <img src="https://img.shields.io/pypi/v/iter_model?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://github.com/VolodymyrBor/iter_model/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster" target="_blank">
    <img src="https://github.com/VolodymyrBor/iter_model/workflows/Test/badge.svg?event=push&branch=master" alt="Test">
</a>

[![Supported Versions](https://img.shields.io/badge/coverage-100%25-green)](https://shields.io/)
[![Supported Versions](https://img.shields.io/badge/poetry-✅-grey)](https://shields.io/)
[![Supported Versions](https://img.shields.io/badge/async-✅-grey)](https://shields.io/)
[![Supported Versions](https://img.shields.io/badge/mypy-✅-grey)](https://shields.io/)

---

**iter_model** - provides a convenient API for interacting with iterable objects ([Iterable]).
iter_model uses a methods approach instead of individual functions.

iter_model also provides **async** analog of all methods. 
This is useful when interacting with asynchronous iterable objects ([AsyncIterable]), 
because python does not have ready functions for these cases.

Therefore, **iter_model** provides **SyncIter** class for [Iterable],
and **AsyncIter** for [AsyncIterable].

---

## Example

```python
from iter_model import SyncIter

it = SyncIter(range(10))  # SyncIter for sync iterables
result = (
    it.where(lambda x: x % 2 == 0)  # filter only odd values
    .take(3)  # take first 3 value
    .map(lambda x: x ** 2)  # square all values
)
print(result.to_list())
```

## Links

**Source code**: [github.com/VolodymyrBor/iter_model](https://github.com/VolodymyrBor/iter_model)

**Documentation**: [iter_model](https://volodymyrbor.github.io/iter_model/)

**Changelog**: [changelog](https://volodymyrbor.github.io/iter_model/changelog)

[Iterable]: https://docs.python.org/3/library/typing.html#typing.Iterable
[AsyncIterable]: https://docs.python.org/3/library/typing.html#typing.AsyncIterable
distopia 0.2.0
Fast distance calculations using explicitly vectorised SIMD
License: MIT
Requires Python: >=3.8
Keywords: molecular dynamics distances simulation SIMD

distopia
--------

Faster distance calculations for the year 2022 🚀 🚀 🚀 using x86 SIMD intrinsics

Get it on PyPi with 

```bash
pip install distopia
```

or get it from conda-forge with


```
conda install -c conda-forge distopia
```

**Documentation can be found at https://www.mdanalysis.org/distopia/**

Follow the instructions in the documentation for building from source! 

A very simple build might look like the following


```bash
python setup.py install

```

You can then use the fast distance functions provided by `distopia`!

---
**NOTE**

Distopia is currently only for x86 processors
---
tsp-algorithms 0.2.0
TSP Algorithms developed as C extension for Python
License: MIT
Requires Python: >3.7
Keywords: tsp,algorithms,c,python

# TSP Algorithms developed as C extensions for Python

## Introduction

In a VRP problem, the objective is to find the best route for a fleet of vehicles to visit a set of customers. The best route is the one that minimizes the total distance traveled by the fleet. The problem is NP-hard, and there are many heuristics to solve it. 

## Install

You can install via pip:
```bash
pip install tsp-algorithms
```

## Usage

Just import the package and use one of the methods available:

```python
import tsp_algorithms as tsp

tsp.nn([[0., 1.], [2., 3.]])
```

## Available methods

- [Nearest Neighbour](https://en.wikipedia.org/wiki/Nearest_neighbour_algorithm) (`.nn`)