semvec/pypi-packages huggingface dataset:
aiocache-pro 0.0.4
High-performance caching library with gevent and cython speedups
Requires Python: >=3.8

# aiocache-pro

**Fastest cache for production**:

- LRU / MRU / LFU / TTL caching strategies  
- Works with **gevent**  
- Custom keys support  
- Optimized with **C / Cython** for speed  

---

## Features

- **LRU (Least Recently Used)** – automatically removes least recently used items when maxsize is reached.  
- **MRU (Most Recently Used)** – removes most recently used items first.  
- **LFU (Least Frequently Used)** – removes items with the lowest access count.  
- **TTL (Time To Live)** – items expire automatically after a given time.  
- **Custom keys** – generate keys from function arguments.  
- **C optimization** – speed improvements via Cython.

---

## Installation

You can install from you:

```bash
pip install aiocache_pro
pybookkeeper 0.11.0
Utility for small-scale Finnish double-ledger bookkeeping
Requires Python: >=3.10

# Bookkeeper

## Introduction

This is a simple double-ledger bookkeeping tool for a small business, operated from the command line. 

## Installation

```shell
pip install pybookkeeper
```

## Usage

Create a directory for your ledger, and run `bookkeeper` from there. 
Your directory should contain `ledger-defs.txt`, which defines your ledger map, and the outline of the generated statements.

The transactions for accounts are stored in `data-YYYY-MM.txt` files.

For example, for year 2025, you would have:

- directory `2025`
- file `2025/ledger-defs.txt`
- files `2025/data-2025-01.txt`, `2025/data-2025-02.txt`, etc.

Running

```shell
bookkeeper 2025 
```

will generate in directory `output/`, which is created if it does not exist, 
the files `general-ledger.txt`, `statement.txt` and `journal.txt`.

You can change the output directory with `--output-directory`, as follows

```shell
bookkeeper 2025 --output-directory 2025-books
```

## Release process

- [ ] Update version in `pyproject.toml`
- [ ] Update ChangeLog
- [ ] Run tests with `tox`
- [ ] Tag release
datahero 0.1.0
DataHero — quick human-like insights & suggestions for pandas DataFrames
Requires Python: >=3.8
Keywords: data, insights, pandas, analysis

# DataHero

DataHero produces a quick, human-readable explanation of a pandas DataFrame:
summary, missing data, outliers, correlations, and suggested visualizations.

## Author
**Ganeshamoorthy**  
Email: ganeshms110@gmail.com  
LinkedIn: https://www.linkedin.com/in/ganeshamoorthy-s-8466b7332

## Quick example
```python
import pandas as pd
from datahero import explain

df = pd.read_csv("sales.csv")
report = explain(df, save_plots=True, outdir="plots")
print(report["summary"])
fstflow 0.1.0
A Python library to simplify the creation and manipulation of finite state transducers
Requires Python: >=3.12
Keywords: finite state transducer, fst, nlp, text processing

# fstFlow

A Python library to simplify the creation and manipulation of finite state transducers (FSTs).

## Overview

fstFlow makes it easy to build, compose, and work with finite state transducers in Python. Whether you're working on natural language processing, text transformation, or other FST-based applications, fstFlow provides an intuitive API and powerful abstractions.

## Features

- **Simple FST Creation**: Straightforward API to define and build finite state transducers
- **Composition**: Easily compose multiple FSTs together
- **Flexible Operations**: Support for various FST operations and transformations
- **Pythonic Design**: Natural Python syntax and conventions

## Installation

```bash
pip install fstflow
```

## Status

🚧 **Under Development** - fstFlow is actively being developed. The API is subject to change.

## Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Authors

- Pedro José Mora Gallegos
yf-simple-fetcher 0.1.2
A simple package to fetch ready-to-use stock data from yfinance
Requires Python: >=3.9

# yf_simple_fetcher

A simple Python library to fetch historical stock data from Yahoo Finance (`yfinance`) with flexible intervals and clean, ready-to-use DataFrames.

---

## Features

* Fetch **ready-to-use data** for any stock by default.
* Supports **custom intervals** like 1min, 15min, hourly, daily.
* Automatically handles `yfinance` MultiIndex issues.
* Returns clean `pandas.DataFrame` with datetime index and simple column names.
* Easy to extend with custom intervals or periods.

---

## Installation

```bash
pip install yf-simple-fetcher
```

---

## Usage

### Basic Usage (1-year hourly data)

```python
from yf_simple_fetcher import get_data

# 1-year hourly data
df_hourly = get_data("AAPL", "hourly")
print(df_hourly.head())
```

### Custom Intervals

```python
# 2-year daily data
df_daily = get_data("AAPL", "daily")

# Last month, 15-minute data
df_15min = get_data("AAPL", "15min")

# Last week, 1-minute data
df_1min = get_data("AAPL", "1min")
```

### Available Presets

By default, the library includes:

```python
interval_periods = {
    "hourly": {"interval": "1h", "start": datetime.now() - timedelta(days=365), "end": datetime.now()},
    "daily": {"interval": "1d", "start": datetime.now() - timedelta(days=730), "end": datetime.now()},
    "15min": {"interval": "15m", "start": datetime.now() - timedelta(days=30), "end": datetime.now()},
}
```

You can **add your own presets** easily by updating the `interval_periods` dictionary.

---

## Example Output

```
                       Open      High       Low     Close     Volume
2024-01-10 14:00    147.23    148.12    146.80    147.90    4556123
2024-01-10 15:00    147.90    148.55    147.44    148.30    3894411
...
```

---

## Installation for Development (optional)

If you want to edit or extend the library locally:

```bash
git clone https://github.com/devwithmpho/yf_simple_fetcher.git
cd yf_simple_fetcher
pip install -e .
```

---

## Dependencies

* [yfinance](https://pypi.org/project/yfinance/)
* [pandas](https://pypi.org/project/pandas/)

---

## License

MIT License. See [LICENSE](LICENSE) for details.

---

## Author

Mpho
yf-simple-fetcher-fantxxm 0.0.1
A simple package to fetch ready-to-use stock data from yfinance
Requires Python: >=3.8

# yf_simple_fetcher

A simple Python library to fetch historical stock data from Yahoo Finance (`yfinance`) with flexible intervals and clean, ready-to-use DataFrames.

---

## Features

* Fetch **1 year of hourly data** for any stock by default.
* Supports **custom intervals** like 1min, 15min, hourly, daily.
* Automatically handles `yfinance` MultiIndex issues.
* Returns clean `pandas.DataFrame` with datetime index and simple column names.
* Easy to extend with custom intervals or periods.

---

## Installation

```bash
pip install yf_simple_fetcher
```

---

## Usage

### Basic Usage (1-year hourly data)

```python
from yf_simple_fetcher import get_data

# 1-year hourly data
df_hourly = get_data("AAPL", "hourly")
print(df_hourly.head())
```

### Custom Intervals

```python
# 2-year daily data
df_daily = get_data("AAPL", "daily")

# Last month, 15-minute data
df_15min = get_data("AAPL", "15min")

# Last week, 1-minute data
df_1min = get_data("AAPL", "1min")
```

### Available Presets

By default, the library includes:

```python
interval_periods = {
    "hourly": {"interval": "1h", "start": datetime.now() - timedelta(days=365), "end": datetime.now()},
    "daily": {"interval": "1d", "start": datetime.now() - timedelta(days=730), "end": datetime.now()},
    "15min": {"interval": "15m", "start": datetime.now() - timedelta(days=30), "end": datetime.now()},
}
```

You can **add your own presets** easily by updating the `interval_periods` dictionary.

---

## Example Output

```
                       Open      High       Low     Close     Volume
2024-01-10 14:00    147.23    148.12    146.80    147.90    4556123
2024-01-10 15:00    147.90    148.55    147.44    148.30    3894411
...
```

---

## Installation for Development (optional)

If you want to edit or extend the library locally:

```bash
git clone https://github.com/devwithmpho/yf_simple_fetcher.git
cd yf_simple_fetcher
pip install -e .
```

---

## Dependencies

* [yfinance](https://pypi.org/project/yfinance/)
* [pandas](https://pypi.org/project/pandas/)

---

## License

MIT License. See [LICENSE](LICENSE) for details.

---

## Author

Mpho
roi-rectangle 1.0.0
A module for handling rectangular regions of interest (ROI) in images.
Requires Python: >=3.8

# roi_rectangle

A Python module for handling rectangular regions of interest (ROI) in images.

## Features

- Define a rectangular ROI with coordinates.
- Get the center of the ROI.
- Move the ROI to a new center.
- Resize the ROI.
- Get the coordinates and area of the ROI.
- Slice the ROI region from an image.

## Installation

To install the `roi_rectangle` module, use:

```sh
pip install roi_rectangle
```

## Usage

Here's an example of how to use the `RoiRectangle` module:

```python
from roi_rect import RoiRectangle
import numpy as np
import matplotlib.pyplot as plt

# Create a test image
test_image = np.random.random((100, 100))

# Create an instance of RoiRectangle
roi = RoiRectangle(y1=30, y2=80, x1=20, x2=70)

# Print ROI information
print("Initial ROI:")
print(roi)

# Get the center coordinate of the ROI
print("Center Coordinate:", roi.center)

# Get the width and height of the ROI
print("Width:", roi.width)
print("Height:", roi.height)

# Move the ROI to a new center
new_center = (50, 50)
roi.move_to_center(new_center)
print("\nAfter Moving to Center:")
print(roi)

# Resize the ROI
new_width, new_height = 30, 40
roi.resize(new_width, new_height)
print("\nAfter Resizing:")
print(roi)

# Slice the ROI region from the test image
roi_slice = roi.slice(test_image)
print("\nROI Slice:")
print(roi_slice)

# Visualize the original image and the sliced ROI
plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.title('Original Image')
plt.imshow(test_image, cmap='gray')

plt.subplot(1, 2, 2)
plt.title('ROI Slice')
plt.imshow(roi_slice, cmap='gray')

plt.gca().add_patch(plt.Rectangle((roi.x1, roi.y1), roi.width, roi.height, linewidth=2, edgecolor='r', facecolor='none'))
plt.show()
```

## License

This project is licensed under the MIT License.