semvec/pypi-packages huggingface dataset:
agi-med-exp 2.1.0
Exp utils for agi-med team
Requires Python: >=3.13

# agi-med-exp

Стандартизированный компонент отдела для экспериментов 

## Ответственный разработчик

@zhelvakov

## Общая информация

- Стандартный конфиг
- Даталоадер для ЦА

## Тесты

- `sudo docker compose up --build`


### Линтеры

```shell
pip install black flake8-pyproject mypy
black .
flake8
mypy .
```
pyaddepar 0.12.0
...
Requires Python: >=3.9

# [pyaddepar](http://lobnek.github.io/pyaddepar/book)

Some utility code for interacting with addepar.
For more information on addepar please check out
<https://addepar.com/>.

## Installing pyaddepar

Install with pip

```bash
pip install pyaddepar
```

## AddeparRequest

AddeparRequest is a class hiding the management of your key(s),
the pagination of requests and conversion of your results to standard pandas containers.

```python
import pandas as pd
from pyaddepar.addeparrequest import AddeparRequest

if __name__ == '__main__':
    r = AddeparRequest(key=..., secret=..., id=..., company=...)
    today = pd.Timestamp("today")

    for key, entity in r.options:
        expiry = pd.Timestamp(entity["expiration_date"])
        if expiry >= today:
            print(expiry)
            print(entity)
            print(entity["option_type"])
            print(entity["node_strike_price"])

            print((expiry-today).days/365.0)

```

## Settings.cfg

We recommend to define a configuration file `(*.cfg)` containing

ADDEPAR = {"key":"A",
           "secret":"B",
           "id":"maffay",
           "company":"maffay"
          }

## Flask-Addepar

A Flask extension that provides integration with Addepar.
In particular this flask extension provides
management of your AddeparRequests.
You can use configuration files such as settings.cfg to
follow standard flask practices.
The configuration is easy, just fetch the extension:

```python
from flask import Flask

from pyaddepar.flask_addepar import addepar

if __name__ == '__main__':
    app = Flask(__name__)
    app.config.from_pyfile('config/settings.cfg')
    addepar.init_app(app)

    with app.app_context():
        for key, entity in addepar.request.entities():
            print(key)
            print(entity)
```
Team-1-library 1.1
Data prerpocess library with missing value imputation and outlier correction functions.
Requires Python: >=3.8

Team_1_library

Python library with two classes: 
-Automatic preprocess, with advaced functions for missing values imputations using clustering and statistical techniques, as well as outlier corrections.
-Manual prerpocess, with manual column statistical imputations, numerical outlier correction, and string focused advanced functions, like normalization and correction.

Characteristics

- Missing value imputation with clustering (K-Means).
- Missing value imputation with statistical values (mean,median,mode).
- Outlier detection and correction to mean with Z-score technique.
- Empty column elimination with customizable threshold.
- Low variance column elimination with customizable threshold.

-String normalization (lowercase and gap elimination)
-String correction depending on the similarity

Usage:

The usage of this library functions are explained in the file 'USER_GUIDE.md'. An example of the use is included in the file 'example_usage.py'. We higly recommend to take a look in those files to understand the functioning of the library.

Instalation:

To install the library, navigate to the folder containing 'Team_1_library' in your preferred environment and install it using 'pip':
```bash
pip install ./path/to/the/library

Make sure you have the required dependencies installed:
```bash
pip install pandas scipy numpy scikit-learn fuzzywuzzy