semvec/pypi-packages huggingface dataset:
c66 0.1.4
A collection of handy Python utilities
Requires Python: >=3.7

# c66

A collection of handy Python utilities.

## Installation

```bash
pip install c66
```

## Usage

- `pp`: Prints the names and values of variables from the calling line.

  ```python
  from c66 import pp

  x = 10
  y = "hello"
  pp(x, y + " world")
  ```

  Output:
  ```
  x: 10
  y + " world": hello world
  ```

- `pps`: Prints the shapes.

  ```python
  from c66 import pps
  import torch
  x = torch.randn(2,5,1)
  y = torch.randn(3,)
  pps(x,y)
  ```

  Output:
  ```
  from c66 import pps
  x's shape: torch.Size([2, 5, 1])
  y's shape: torch.Size([3])
  ```

- `print`: Prints the provided arguments when `c66.show_print` is `True`. If `c66.show_print` is False, it returns `None` without printing.
  The default of `c66.show_print` is `True`

  ```python
  from c66 import print, show_print

  x = 42
  y = "test"

  # Default behavior (show_print = True)
  print(x, y)  # Prints: 42 test

  # Disable printing
  c66.show_print = False
  print(x, y)  # No output

  # Re-enable printing
  c66.show_print = True
  print(x, y)  # Prints: 42 test
  ```

### More Tools
Additional utilities will be added in future updates. Stay tuned!

## Author
ChoCho66

## License
MIT
hefeng-weather 0.1.5
hefeng-weather mcp
Requires Python: >=3.10

和风天气 MCP 服务

项目概述

这是一个基于 FastMCP 框架的和风天气 API 服务封装，提供了完整的天气查询功能套件，包括：

• 全球城市位置代码查询

• 实时天气数据获取

• 未来天气预报查询

• ISO 3166 国家代码参考


功能特性

• 全球城市查询：通过名称或坐标查找城市位置代码

• 实时天气查询：获取指定位置的当前天气状况

• 天气预报查询：支持3天到30天的长期预报和24小时到168小时的短期预报

• 国家代码参考：内置完整的ISO 3166国家代码列表


快速开始



配置服务

```
{
    "mcpServers": {
        hefeng-weather": {
            "command": "uvx",
            "args": [
                "hefeng-weather",
                "--token",
                "eyJhbGciOiAiRWREU0EiLCJraWQiOiAiQUJDRDEyMzQifQ.eyJpc3MiOiJBQkNEMTIzNCIsImlhdCI6MTcwMzkxMjQwMCwiZXhwIjoxNzAzOTEyOTQwfQ.MEQCIFGLmpmAEwuhB74mR04JWg_odEau6KYHYLRXs8Bp_miIAiBMU5O13vnv9ieEBSK71v4UULMI4K5T9El6bCxBkW4BdA,
                "--host",
                "abc.re.qweatherapi.com"
            ]
        }
    }
}
```

API 文档

1. 城市查询

```python
@mcp.tool()
def lookup_city(location: str, number: int = 1, range: str = 'cn') -> str:
    """
    根据城市名称查询城市位置代码
    
    参数:
        location: 城市名称或坐标 (如"北京"或"116.41,39.92")
        number: 返回结果数量 (1-20)
        range: 搜索范围 (默认'cn'表示中国)
    
    返回:
        JSON格式的城市信息，包含:
        - id: 城市ID
        - name: 城市名称
        - lat/lon: 经纬度坐标
        - adm1/adm2: 行政区划信息
        - country: 所属国家
    """
```

2. 实时天气

```python
@mcp.tool()
def weather_now(location: str = '101010100') -> str:
    """
    获取实时天气信息
    
    参数:
        location: 城市ID或坐标
    
    返回:
        JSON格式的实时天气数据，包含:
        - temp: 当前温度(℃)
        - feelsLike: 体感温度
        - text: 天气状况描述
        - windDir/windScale: 风向风力
        - humidity: 湿度(%)
        - 等20+项实时数据
    """
```

3. 天气预报

```python
@mcp.tool()
def weather_furture(location: str = '101010100', furture: str = '3d') -> str:
    """
    获取天气预报
    
    参数:
        location: 城市ID或坐标
        furture: 预报时长 ('3d','7d','10d','15d','30d','24h','72h','168h')
    
    返回:
        JSON格式的预报数据，包含:
        - tempMax/tempMin: 最高/最低温度
        - textDay/textNight: 白天/夜间天气
        - sunrise/sunset: 日出日落时间
        - precip: 降水量
        - uvIndex: 紫外线指数
        - 等30+项预报数据
    """
```

4. 国家代码参考

```python
@mcp.tool()
def get_iso3166() -> str:
    """
    获取ISO 3166国家代码列表
    
    返回:
        格式化文本的国家代码列表，包含:
        - 国家名称
        - 2字母代码
    """
```



[生成JWT Token用于认证](https://dev.qweather.com/docs/configuration/authentication/)



贡献指南

欢迎通过Issue或Pull Request提交改进建议。

许可证

MIT License
dou-utils 0.1.9
The utility package of Dou Inc.
Requires Python: >=3.10

# dou-utils

Composition of utils for Dou Inc. projects.

## DouCLI

Installs files for python formatting options.

### Quickstart

```bash
# initiate project with `uv init`
# add dou-utils with `uv add dou-utils`
dpu install formatting
```

## DouLogger

A simple and flexible Python logging utility with enhanced features for structured logging.

### Features

- **Easy-to-use logging methods**: `info`, `debug`, `warning`, `error`.
- **Structured logging support**: Pass additional metadata with your log messages.
- **Configurable logging levels**: Control the verbosity of your logs.

### Installation

Install DouLogger directly from the GitHub repository:

```bash
pip install git+https://github.com/douinc/dou-python-utils.git@v0.1.0
```

#### Quick Start

Import the logger from the dou package:

```python
from dou import logger
```

#### Basic Logging

Log messages at different severity levels:

```python
logger.info("This is an info message")
logger.debug("This is a debug message")
logger.warning("This is a warning message")
logger.error("This is an error message")
```

#### Structured Logging

Include additional metadata in your logs for better traceability:

```python
logger.info(
    message={
        "event": "user_signup",
        "user_id": 12345,
    },
    search_id="abcde12345",
)
```

## Development

```bash
uv build
uv pip install -e .
```

## Publish

```bash
uv publish
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contact

For questions or suggestions, please open an issue or contact us at team@dou.so
prepropy 0.1.0
A Python package combining essential preprocessing tools
Requires Python: >=3.7
Keywords: preprocessing, data science, machine learning, pandas, scikit-learn

# PreProPy

A Python package combining three essential preprocessing tools: NullSense, DupliChecker, and ScaleNPipe.

## Installation

```bash
pip install prepropy
```

## Features

PreProPy provides three core components:

### 1. NullSense

Intelligently handle missing values in your data based on column types:

```python
import pandas as pd
from prepropy import handle_nulls

# Create a sample dataframe with missing values
df = pd.DataFrame({
    'numeric': [1, 2, None, 4, 5],
    'categorical': ['A', 'B', None, 'B', 'C']
})

# Fill missing values with intelligent defaults
df_filled = handle_nulls(df, strategy="auto")
# Numeric columns filled with median, categorical with mode

# Other available strategies
df_mean = handle_nulls(df, strategy="mean")  # Fill numeric with mean
df_median = handle_nulls(df, strategy="median")  # Fill numeric with median
df_mode = handle_nulls(df, strategy="mode")  # Fill all columns with mode
df_zero = handle_nulls(df, strategy="zero")  # Fill numeric with 0, categorical with ""
```

### 2. DupliChecker

Identify and remove duplicate records with configurable options:

```python
import pandas as pd
from prepropy import drop_duplicates, get_duplicate_stats

# Create a sample dataframe with duplicates
df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3],
    'B': ['x', 'y', 'y', 'z', 'z']
})

# Get duplicate statistics
stats = get_duplicate_stats(df)
print(stats)
# Output: {'duplicate_count': 2, 'duplicate_percent': 40.0, 'unique_count': 3, 'total_count': 5}

# Drop duplicates (keeping first occurrence)
df_unique = drop_duplicates(df)

# Drop duplicates based on specific columns
df_unique_subset = drop_duplicates(df, subset=['B'])

# Keep the last occurrence instead of first
df_unique_last = drop_duplicates(df, keep='last')

# Drop all duplicates
df_no_dups = drop_duplicates(df, keep=False)
```

### 3. ScaleNPipe

Create scikit-learn pipelines with feature scaling and your model:

```python
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from prepropy import scale_pipeline, get_available_scalers

# Load sample data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create a model
model = LogisticRegression()

# Create a pipeline with standard scaling
pipeline = scale_pipeline(model, scaler="standard")

# Train the pipeline
pipeline.fit(X_train, y_train)

# Make predictions
predictions = pipeline.predict(X_test)

# See available scalers
scalers = get_available_scalers()
print(scalers)
```

## Requirements

- pandas >= 1.0.0
- scikit-learn >= 0.24.0

## License

MIT

---

Made with ❤️ by the PreProPy Team
abutils 0.5.3
Utilities for analysis of adaptive immune receptor repertoire (AIRR) data
Requires Python: >=3.10

![](https://img.shields.io/pypi/v/abutils.svg?colorB=blue)
[![tests](https://github.com/briney/abutils/actions/workflows/pytest.yml/badge.svg)](https://github.com/briney/abutils/actions/workflows/pytest.yml)
[![Documentation Status](https://readthedocs.org/projects/abutils/badge/?version=latest)](https://abutils.readthedocs.io/en/latest/?badge=latest)
![](https://img.shields.io/pypi/pyversions/abutils.svg)
![](https://img.shields.io/badge/license-MIT-blue.svg)

# abutils

Models, functions and visualization tools for working with adaptive immune receptore repertoire (AIRR) data. The primary purpose of `abutils` is to provide generalizable tools suitable for direct use analyzing bulk AIRR datasets, and is used by [`scab`](https://github.com/briney/scab) for single cell AIRR analysis. `abutils` is a core component of the ab\[x\] toolkit for AIRR data analysis.
  
  - Source code: [github.com/briney/abutils](https://github.com/briney/abutils)  
  - Documentation: [abutils.readthedocs.org](http://abutils.readthedocs.org)  
  - Download: [pypi.python.org/pypi/abutils](https://pypi.python.org/pypi/abutils)  
  - Docker: [hub.docker.com/r/brineylab/datascience/](https://hub.docker.com/r/brineylab/datascience/)  
  
## install  
``` bash
pip install abutils
```


## api  
We've tried to design the  `abutils` API to be intuitive yet powerful, with the goal of enabling both interactive analyses (via environments like Jupyter notebooks) as well as integration of `abutils` tools into more complex analysis pipelines and/or standalone software tools. See the [documentation](http://abutils.readthedocs.org) for more detail about the API. As always, any feedback is greatly appreciated!!  


### testing  
You can run the complete `abutils` test suite by first installing `pytest`:
``` bash
pip install pytest
```

and then running:

``` bash
git clone https://github.com/brineylab/abutils
cd abutils
pytest
```

This test suite is automatically run against all supported versions of Python following every commit.
  

### requirements  
**python 3.10+**  
  
abstar  
baltic  
biopython  
dnachisel  
fastcluster  
matplotlib  
mnemonic  
natsort  
numpy  
pandas  
parasail  
polars  
prettytable  
pyarrow  
pyfamsa  
pyfastx  
pytest  
python-circos  
pyyaml  
rapidfuzz  
sample-sheet  
scikit-learn  
scipy  
seaborn  
smart_open  
tqdm  
  
`abutils` includes several additional binaries that are required for certain functionality:

* ``abutils.tl.mafft`` uses [MAFFT](https://mafft.cbrc.jp/alignment/software/)
* ``abutils.tl.muscle`` uses [MUSCLE](https://www.drive5.com/muscle/)
* ``abutils.tl.cluster`` uses [CD-HIT](https://cd-hit.org), [MMseqs2](https://github.com/soedinglab/MMseqs2), and [VSEARCH](https://github.com/torognes/vsearch)
* ``abutils.tl.fasttree`` uses [FastTree](http://www.microbesonline.org/fasttree/)

Although these binaries are all packaged into `abutils`, each respective `abutils.tl` function provides the option to supply a alternate binary path in case you'd prefer to use a different version.