semvec/pypi-packages huggingface dataset:
kktools 4.28
Tools for finance and treasury specialists

# kktools


========

This library provides python tools for finance and treasury specialists.

## Installation

`pip install kktools`

## Realized Functionality
`get_ecb_rates("CUR", "YYYY-MM-DD")` - returns pandas dataframe with historical values for a given currency ("CUR") starting from the given date.<br>
`get_last_ecb_rates()` - returns pandas dataframe with the latest valid ECB exchange rates for all published currencies.<br>
`df_to_excel("file_name.xlsx", dataframe)` - exports pandas dataframe to an excel file with pretty formatting.<br>
`dfs_to_excel("file_name.xlsx", list_of_dataframes)` - exports list of pandas dataframes to an excel file with pretty formatting.<br>
`Camt053` - class for parsing CAMT053 files (EOD bank statements, ISO20022 standard).<br>
`Pain001` - class for parsing PAIN001 files (payment initiation, ISO20022 standard).

## Basic Usage

Importing history of EUR/GBP exchange rates since 2000-01-01 and exporting it to an excel file with pretty formatting

```
import kktools as kkt

df = kkt.get_ecb_rates("GBP", "2000-01-01")
kkt.df_to_excel("Historical_Rates.xlsx", df)
```

Parsing CAMT053 file and getting list of balances and list of all transactions:
```
from kktools import Camt053

camt = Camt053(file_name) # creates an object of Camt class you need to pass path+file_name of the CAMT053 file to be processed
camt.camt.statements_info # returns list of dictionaries for each statement with the following information: account id and owner name, all available balances (OPBD/CLBD/CLAV/PRCD/FWAV/etc.), 
camt.transactions # returns list of all transactions in the file.

```

### License
MIT licensed. Check the [`LICENSE`](https://github.com/khorevkp/KK_Tools/blob/master/LICENSE) file for full details.
combined-bukmacherska 0.9.0
Tools for analyzing sports statistics and using machine learning to assist in betting strategies.
Requires Python: >=3.6

# Combined Bukmacherska

**Combined Bukmacherska** is a comprehensive project that provides tools for analyzing sports statistics and using machine learning to assist in betting strategies. The package offers utilities for training machine learning models, statistical analysis, and data visualization.

## Features

- **Train Machine Learning Models**: A suite of classifiers, including Random Forest, Gradient Boosting, SVM, and more.
- **Statistical Analysis**: Analyze team performance metrics like average goals scored/conceded.
- **Mathematical Utilities**: Tools for Gamma distribution, Beta distribution, and Poisson probabilities.
- **Visualizations**: Generate line, bar, and 3D plots for data analysis.

## Installation

Clone the repository and install dependencies:

```bash
git clone <repository-url>
cd combined_bukmacherska
pip install -r requirements.txt

Usage Examples
Train Machine Learning Models

from combined_bukmacherska.train_models import train_models, predict_with_models

# Example data
X_train, X_test, y_train, y_test = ...  # Replace with your dataset
models = train_models(X_train, y_train)
predictions = predict_with_models(models, X_test)

from combined_bukmacherska.statistics import analiza_statystyczna, oblicz_statystyki_druzyny

druzyna1 = {'zdobyte': 30, 'stracone': 20}
druzyna2 = {'zdobyte': 25, 'stracone': 15}
mecze = 10

statystyki1, statystyki2 = analiza_statystyczna(druzyna1, druzyna2, mecze)

from combined_bukmacherska.visualizations import rysuj_wykresy

rysuj_wykresy(statystyki1['Ĺ›rednia zdobytych'], statystyki1['Ĺ›rednia straconych'], 
              statystyki2['Ĺ›rednia zdobytych'], statystyki2['Ĺ›rednia straconych'])


git clone https://github.com/your-repository/combined_bukmacherska2.git

from combined_bukmacherska2 import train_models

# Example usage
X_train, y_train = ...  # Your training data
models = train_models(X_train, y_train)

from combined_bukmacherska2 import predict_with_models

# Example usage
X_test = ...  # Your test data
predictions = predict_with_models(models, X_test)

from combined_bukmacherska2 import plot_results

# Example usage
plot_results(predictions, team1_lambda, team2_lambda, team1_avg_conceded, team2_avg_conceded)
bitmask26 0.2.0
A C++ optimized bitmask implementation with Python bindings
Requires Python: >=3.8

# Installation

```bash
pip install bitmask26
```

# Usage

`Bitmask26(word1) <= Bitmask(word2)` return `True` if all letters of `word1` is in `word2`.

```py
from bitmask26 import Bitmask26
Bitmask26('flee') <= Bitmask26('eefl') # True
Bitmask26('elf') <= Bitmask26('eefl')  # True
Bitmask26('left') <= Bitmask26('eefl') # False
Bitmask26('cap') <= Bitmask26('eefl')  # False
Bitmask26('fell') <= Bitmask26('eefl') # True
```

# Building
[py_bitmask26.py](https://github.com/adwaithhs/bitmask26/blob/master/py_bitmask26.py) has the same thing implemented in python.

```bash
pip install setuptools wheel twine pybind11
rm -rf build dist *.egg-info
python setup.py sdist
pip install .
```