semvec/pypi-packages huggingface dataset:
spsspro 0.1.1
统计分析和机器学习工具包
Requires Python: >=3.6

# SPSS Pro

一个用于统计分析和机器学习的Python库，提供简单易用的接口。

## 安装

```bash
pip install -e .
```

## 功能特点

- 监督学习算法
  - 决策树分类
  - 更多算法将陆续添加...

## 使用示例

### 决策树分类

```python
import numpy
import pandas
from spsspro.algorithm import supervised_learning

# 生成案例数据
data_x = pandas.DataFrame({
    "A": numpy.random.random(size=100),
    "B": numpy.random.random(size=100)
})
data_y = pandas.Series(data=numpy.random.choice([1, 2], size=100), name="C")

# 决策树分类
result = supervised_learning.decision_tree_classifier(data_x=data_x, data_y=data_y)

# 查看结果
print(f"模型准确率: {result['accuracy']}")
print("\n特征重要性:")
print(result['feature_importance'])
```

## 依赖项

- numpy
- pandas
- scikit-learn
- matplotlib
- seaborn
PyCSVDataCleaner 0.1.1
A lightweight Python package to clean CSV files
Requires Python: >=3.6

# PyCSVDataCleaner

**PyCSVDataCleaner** is a simple Python package designed to clean CSV files. It helps you preprocess your data by:
- Removing duplicate rows
- Removing rows with missing values
- Removing constant columns

The package is easy to use and works with CSV files containing any kind of data. It is ideal for automating the data cleaning process during your machine learning or data analysis workflow.

---

## Features

- **Remove Duplicate Rows**: Automatically removes duplicate rows from the dataset.
- **Remove Rows with Missing Values**: Cleans your dataset by eliminating rows with empty cells.
- **Remove Constant Columns**: Removes columns that contain constant values across all rows.

## Installation

You can install **PyCSVDataCleaner** via pip:

```bash
pip install PyCSVDataCleaner
```

## Usage

```bash
from PyCSVDataCleaner import PyCSVDataCleaner

input_file = 'path_to_your_input_file.csv'

output_file = 'path_to_your_output_file.csv'

PyCSVDataCleaner(input_file, output_file)
```

## Example Output
When running the script, you'll get output in the terminal indicating how many rows and columns were removed or cleaned:

```bash
Cleaning file: fine_name.csv

--- Initial Data Info ---
Rows (excluding header): 129971
Columns: 14
Removed 0 duplicate rows.
Removed 107584 rows with missing values.
Removed 1 constant columns.

--- Cleaning Done ---
Final Rows: 22387
Final Columns: 13
```