semvec/pypi-packages huggingface dataset:
pymimic 1.0.1
A pure-Python implementation of linear prediction
Requires Python: >=3.6.9

PyMimic
=======

A pure-Python implementation of linear prediction intended for use in
curve-fitting, kriging, DACE modelling, and Gaussian-process
emulation.

Access the documentation at [Read the Docs](https://pymimic.readthedocs.io/en/latest/).
exacb 0.0.7
A helper script for the exacb project

<!--
- SPDX-License-Identifier: Apache-2.0
- Copyright (C) 2024 Jayesh Badwaik <j.badwaik@fz-juelich.de>
-->

# exacb

Helper scripts for the [exacb](https://go.fzj.de/exacb) project.
BlueScope 1.0.0b1
Library to profile and compare SQL queries for different databases.
Requires Python: >=3.9

<div style="display: flex; justify-content: left; align-items: left;">
  <img src="media/bluescope3.svg" style="width: 100%; max-width: 360px;" alt="BlueScope">
</div>

Introduce science to SQL optimization.

# Description
Profiles the performance of SQL queries by running the N times and measuring the time it takes to execute 
with minimizing any external reasons such as compilation time, queue time e.t.c.

How many times SQL query will be run is determined by confidence level and margin of error. 

Compares results of 2 different queries using Student's t-test. Returns the result of the test (p-value) and the conclusion.

Table of currently supported databases:

| Data Base           | Current status |
|---------------------|---------------|
| Redshift Serverless | Supported  ✅   |
| Redshift Dedicated  | In development ⏳|
| BigQuery            | Planned       |
| PostgreSQL          | Planned       |
| MySQL               | Planned       |
# How to use
## CLI
CLI is in development. For now, you can use the package in your code.

## Package
To install package run
`pip install bluescope`

To use the package in your code:

```python
from bluescope import REDSHIFT_SERVERLESS
from bluescope import get_profiler
from bluescope.statsutils import find_significance

profiler_rs_cls = get_profiler(REDSHIFT_SERVERLESS)
profiler_rs = profiler_cls(host=*host*, port=*port*,
                           db=*db*, user=*user*,
                           password=*password*, agree=True)

pr_1 = profiler_rs.profile(*query_1*)
pr_2 = profiler_rs.profile(*query_2*)

p = find_significance(pr_1['mean'], pr_2['mean'],
                      pr_1['std'], pr_2['std'],
                      pr_1['sample_size'], pr_2['sample_size'])
```

# How to contribute
Any ideas, comments, code improvements are very welcome. 

To contribute to the project, please follow the steps:
1. Fork the repository
2. Create a new branch
3. Make your changes
4. Push your changes to your branch
5. Create a pull request
6. Wait for the review


# License
⚖️ GPL-3.0
# Contact
Maintainer: Mirzabekian Arkadii 📧 [Email](mailto:mirzabekian.arkadii@gmail.com)
pybaseanal 0.1.2
A package to import basic libraries and perform basic data analysis.
Requires Python: >=3.6
Keywords: python,pandas,numpy,basic libraries,Null analysis

# pybaseanal

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 
[![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/)   

## Functionality of the pybaseanal

- Importing basic libraries at once
- Perform basic data processing 
> - Null analysis
> - Graph plots
> - Data type conversion

## Usage
- To read dataframe
- To perform EDA
- To annalyse the null

```
pip install pybaseanal
```
##### For functionality
- null_Analysis(data_df, null_Cuttoff = 25, headcount = 5)
- plot_categorical_frequency(df, output_path='cat_graph.docx')
- convert_columns_to_preferred_types(df)
- generate_visualizations_and_report(df, output_path='output.docx')

## Note 
- Please drop your suggestions on pydev@phaf.in 

## More Information
- Visit [pybaseanal.phaf.in](http://pybaseanal.phaf.in)
ez-validate 0.1.0
Builder-like data validation package
Requires Python: >=2.7

# Ez Validate

Simple package for data validation.

# Examples:

## Valid
```
var = 10.0
ValidateThat(var).is_float().less_than(12.4).greater_than(9.2)
```

## Invalid
```
var = 5.0
ValidateThat(var).is_float().less_than(12.4).greater_than(9.2)
```
This example will raise *ValidationError* with the following message:
> "Untitled var" must be greater than 9.2 but is 5.0

You can change the the name of variable by providing it in *ValidateThat* constractor:
```
ValidateThat(var, name='Your name').is_float().less_than(12.4).greater_than(9.2)
```
> Your name must be greater than 9.2 but is 5.0

To handle the error use try except clause:
```
try:
	...
except ValidationError as e:
	...
```

## Multiple conditions
**And** condition:
```
ValidateThat(var).firts_condition().second_condition().third_condition()
```
**Or** condition:
```
either(
	lambda: ValidateThat(var).first_condition(),
	lambda: ValidateThat(var).second_condition(),
	lambda: ValidateThat(var).third_condition()
)
```
You can make **combination** of **and** and **or** conditions:
```
var = 1.5
either(
	lambda: ValidateThat(var).is_float().less_than(3.0).greater_than(0.0),
	lambda: ValidateThat(var).is_int().less_than(3).greater_than(0),
	lambda: ValidateThat(var).is_none()
)
```
That would mean **var** must be:

 - float **and** greater than 0 **and** less than 3.0
 - **or**
 - int **and** greater than 0 **and** less than 3 
 - **or**
 - None