semvec/pypi-packages huggingface dataset:
agenthub-tools-core 0.0.7
AgentHub core package
Requires Python: >=3.7

# AgentHub Core

Core package for the AgentHub framework, providing base classes and utilities.

## Installation

```bash
pip install agenthub-tools-core
```

## Usage

```python
from agenthub_tools.core import BaseTool

class MyTool(BaseTool):
    name = "my-tool"
    description = "My custom tool"
    
    def execute(self, **kwargs):
        # Implement tool logic
        pass
```
PythonFileLib 0.0.3
The file processing collection for Python
Requires Python: >=3.7

This is a powerful package for manipulatinf files.
It contains all the attribute and functions in model sky-txt.
So model sky-txt won't be updated from now on.

#Warnings
This packages was called PFI before version 0.0.2.
And now it is called PFL.

#History
-------------------
0.0.1 (2024-11-09}
-------------------
I have written packages since 2022.And I uplowded txt in Feb,2023.
This time I add more files in to this model.

-------------------
0.0.2 (2024-11-16}
-------------------
Fixed a major issue with incompleted packages.

-------------------
0.0.3 (2024-11-16锝
-------------------
Fixed a major issue with syntax errors packages.

# How to use PFL

1. Install PFL
```bash
pip install PythonFileLib
```

2. You're done! Now all there is to do is read the documentation.

# Documentation



1. Load Package
```python
import PFL

dir(PFL)
```

2. get files(examples)
```python
from PFL import file
from file import file
f = file.file('C:/123.py',file.AP)
f.change_extension('.txt')  #it would become 123.txt
f.movefile('D:/')
```

3. Get text in .txt files
```python
from PFL import file
from file import txt

f = txt.txt('C:/123.txt')
f.write("123")
f.write("456")
print(f.read_first_line)  #return 123
f.change_txt_line(2,'123')  #line2 will become 123
```

4. Read .csv files
```python
from PFL import file
from file import csv

f = csv.csv('C:/123.csv')
f.read()
```

# Functions and classes are updating......
gaozhong-shuju-leixing 0.1.3
A collection of fundamental data structures implemented in Python
Requires Python: >=3.6

# 高中数据类型

这是一个包含基本数据结构的Python实现项目，包括栈、队列、链表、双向链表、循环队列和二叉树。

## 使用方法

请参考 `高中数据类型.py` 文件中的示例用法以了解如何使用这些数据结构。
timeseries-shaper 0.0.0.18
timeseries-shaper filters, transforms and engineer your timeseries dataframe
Requires Python: >=3.10

# timeseries-shaper

Timeseries-Shaper is a Python library for efficiently filtering and preprocessing time series data using pandas. It provides a set of tools to handle various data transformations, making data preparation tasks easier and more intuitive.

Besides that multiple engineering specific methods are utilized to make it fast and easy to work with time series data.

## Features | Structure

```
├── timeseries_shaper
│   ├── base.py
│   ├── calculator
│   │   └── numeric_calc.py
│   ├── cycles
│   │   ├── cycle_processor.py
│   │   └── cycles_extractor.py
│   ├── events
│   │   ├── outlier_detection.py
│   │   ├── statistical_process_control.py
│   │   ├── tolerance_deviation.py
│   │   └── value_mapping.py
│   ├── filter
│   │   ├── boolean_filter.py
│   │   ├── custom_filter.py
│   │   ├── datetime_filter.py
│   │   ├── numeric_filter.py
│   │   └── string_filter.py
│   ├── functions
│   │   └── lambda_func.py
│   ├── loader
│   │   ├── metadata
│   │   │   ├── metadata_api_loader.py
│   │   │   └── metadata_json_loader.py
│   │   └── timeseries
│   │       ├── parquet_loader.py
│   │       ├── s3proxy_parquet_loader.py
│   │       └── timescale_loader.py
│   ├── stats
│   │   ├── boolean_stats.py
│   │   ├── numeric_stats.py
│   │   ├── string_stats.py
│   │   └── timestamp_stats.py
│   ├── time_stats
│   │   └── time_stats_numeric.py
```


## Installation

Install timeseries-shaper using pip:

```bash
pip install timeseries-shaper
```

## Useage

Here is a quick example to get you started:

```python
import pandas as pd
from timeseries_shaper.filters import IntegerFilter, StringFilter

# Sample DataFrame
data = {
    'value_integer': [1, 2, None, 4, 5],
    'value_string': ['apple', 'banana', None, 'cherry', 'date']
}
df = pd.DataFrame(data)

# Initialize the filter object
integer_filter = IntegerFilter(df)
string_filter = StringFilter(df)

# Apply filters
filtered_integers = integer_filter.filter_value_integer_not_match(2)
filtered_strings = string_filter.filter_value_string_not_match('banana')

print(filtered_integers)
print(filtered_strings)
```

## Documentation

For full documentation, visit GitHub Pages or check out the docstrings in the code.

## Contributing

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.

Please ensure to update tests as appropriate.

## License

Distributed under the MIT License. See LICENSE for more information.