semvec/pypi-packages huggingface dataset:
pylib-searchalgo 0.1.0
Search & sort algorithms with performance metrics. Essential for AI and ML applications. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, algorithms, data-processing, data-science, machine-learning, ml, nlp, search, sorting, utilities

# pysearchalgo

Search & sort algorithms

## Installation

```bash
pip install pysearchalgo
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_searchalgo import binary_search, linear_search, quick_sort, merge_sort

# Binary search in sorted array
arr = [1, 3, 5, 7, 9]
index = binary_search(arr, 5)
# 2

# Linear search
index = linear_search([1, 2, 3, 4, 5], 3)
# 2

# Quick sort
sorted_arr = quick_sort([3, 1, 4, 1, 5])
# [1, 1, 3, 4, 5]

# Merge sort
sorted_arr = merge_sort([3, 1, 4, 1, 5])
# [1, 1, 3, 4, 5]
```

### AI/ML Use Cases

```python
from pylib_searchalgo import binary_search, linear_search, quick_sort, merge_sort

# Search sorted feature vectors
features = sorted([0.1, 0.5, 0.9, 0.3])
target_index = binary_search(features, 0.5)

# Sort training data
sorted_data = quick_sort(training_data, key=lambda x: x['score'])
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-datastruct 0.1.0
Educational DSA implementations (Stack, Queue, Graph, Tree). Core algorithms library.
License: MIT
Requires Python: >=3.8
Keywords: ai, algorithms, computer-science, data-processing, data-science, data-structures, machine-learning, ml, nlp, utilities

# pydatastruct

Educational DSA implementations

## Installation

```bash
pip install pydatastruct
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_datastruct import Stack, Queue, Graph, Tree

# Stack
stack = Stack()
stack.push(1)
stack.push(2)
stack.pop()  # 2

# Queue
queue = Queue()
queue.enqueue(1)
queue.enqueue(2)
queue.dequeue()  # 1

# Graph
graph = Graph()
graph.add_edge("A", "B")
graph.add_edge("B", "C")
neighbors = graph.get_neighbors("A")

# Tree
tree = Tree(5)
tree.insert(3)
tree.insert(7)
inorder = tree.inorder()
```

### AI/ML Use Cases

```python
from pylib_datastruct import Stack, Queue, Graph, Tree

# Use stack for DFS in ML graph algorithms
stack = Stack()
stack.push(start_node)
# Process nodes...

# Use queue for BFS
queue = Queue()
queue.enqueue(root)
# Process nodes...
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-streams 0.1.0
Chainable functional API for list processing (map/filter/reduce). Great for data pipelines. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, functional-programming, machine-learning, ml, nlp, streams, utilities

# pystreams

Chainable functional API for list processing

## Installation

```bash
pip install pystreams
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_streams import Stream, map, filter, reduce

# Using Stream class
result = Stream([1, 2, 3, 4, 5])\
    .map(lambda x: x * 2)\
    .filter(lambda x: x > 5)\
    .to_list()
# [6, 8, 10]

# Using functional API
doubled = map(lambda x: x * 2, [1, 2, 3])
filtered = filter(lambda x: x > 2, [1, 2, 3, 4])
summed = reduce(lambda a, b: a + b, [1, 2, 3, 4])
# 10
```

### AI/ML Use Cases

```python
from pylib_streams import Stream, map, filter, reduce

# Process ML predictions
predictions = Stream([0.1, 0.5, 0.9, 0.3])\
    .filter(lambda p: p > 0.5)\
    .map(lambda p: "high" if p > 0.8 else "medium")\
    .to_list()

# Transform training data
features = Stream(raw_data)\
    .map(extract_features)\
    .filter(is_valid)\
    .to_list()
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-dictutils 0.1.0
Deep merge, flatten, pick, omit, and transform nested dicts. Essential for data processing.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, dictionaries, machine-learning, ml, nlp, utilities

# pydictutils

Deep merge, flatten, pick, omit

## Installation

```bash
pip install pydictutils
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_dictutils import deep_merge, flatten_dict, pick, omit

# Deep merge dictionaries
dict1 = {"a": 1, "b": {"c": 2}}
dict2 = {"b": {"d": 3}, "e": 4}
merged = deep_merge(dict1, dict2)
# {'a': 1, 'b': {'c': 2, 'd': 3}, 'e': 4}

# Flatten nested dictionary
nested = {"a": 1, "b": {"c": 2, "d": 3}}
flat = flatten_dict(nested)
# {'a': 1, 'b.c': 2, 'b.d': 3}

# Pick specific keys
data = {"name": "John", "age": 30, "city": "NYC"}
picked = pick(data, ["name", "age"])
# {'name': 'John', 'age': 30}

# Omit specific keys
omitted = omit(data, ["age"])
# {'name': 'John', 'city': 'NYC'}
```

### AI/ML Use Cases

```python
from pylib_dictutils import deep_merge, flatten_dict, pick, omit

# Merge API responses with defaults
default_config = {"model": "gpt-4", "temperature": 0.7}
user_config = {"temperature": 0.9}
final_config = deep_merge(default_config, user_config)

# Flatten ML model parameters
model_params = {"layers": {"hidden": 128, "output": 10}}
flat_params = flatten_dict(model_params)
# {'layers.hidden': 128, 'layers.output': 10}
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-summarize 0.1.0
Summarize long text using frequency-based or AI-based summarizers. Perfect for AI agents. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: agent, ai, data-processing, data-science, llm, machine-learning, ml, nlp, summarization, text-processing, utilities

# pysummarize

Summarize long text using frequency-based or AI-based summarizers.

## Installation

```bash
pip install pysummarize
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_summarize import summarize_frequency, extract_keywords, summarize_by_ratio

text = "Python is great. Machine learning is fun. Python and ML are powerful."

# Summarize by word frequency
summary = summarize_frequency(text, num_sentences=2)
# 'Python is great. Machine learning is fun.'

# Extract keywords
keywords = extract_keywords(text, num_keywords=3)
# ['python', 'learning', 'machine']

# Summarize by ratio
short_summary = summarize_by_ratio(text, ratio=0.5)
```

### AI/ML Use Cases

```python
from pylib_summarize import summarize_frequency, extract_keywords

# Summarize AI-generated content
ai_response = "Long AI-generated text..."
summary = summarize_frequency(ai_response, num_sentences=3)

# Extract key topics from text
topics = extract_keywords(ai_response, num_keywords=10)
```

## 📚 API Reference

### `summarize_frequency(text: str, num_sentences: int = 3) -> str`
Summarizes text based on word frequency. Returns top N sentences.

### `extract_keywords(text: str, num_keywords: int = 5) -> List[str]`
Extracts top keywords by frequency, excluding stop words.

### `summarize_by_ratio(text: str, ratio: float = 0.3) -> str`
Summarizes text to a ratio of original length.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
zsx-pack 1.0.10
自用包，主要是为了logger无法屏蔽第三方包日志而开发的
License: MIT
Requires Python: >=3.6

# Example Package

#在此文件内，可使用markdown撰写对包的详细介绍和说明，便于别人熟悉和使用，在此不再赘述
This is a simple example package. You can use
[Github-flavored Markdown](https://github.com/ziyueguyi/zsx_pack)
本函数主要作用是开发一个日志系统，本日志系统不使用logger包，可以有效屏蔽第三方日志信息，
函数：Logger
参数：

`title:日志标题（字符串）date_rotate:是否将日期添加到日志路径里面（false：不加，true：加）
log_dir:日志路径（如果填写日志路径，表示需要永久化保存日志，不填写的话不会将日志保存到本地）
print_level:输出日志的等级，默认为notset
file_level:储存日志默认的最低等级
is_color:是否启用才是输出
file_encoding:文件编码
datetime_format:日期格式
format:日志格式，默认为"[datetime] [[class_name]-[func_name]]|<[log_level]>|([lineno]):[message|extra]"`