semvec/pypi-packages huggingface dataset:
litedfs-client 0.0.12
LiteDFS Client: distributed file system python client
License: MIT

# LiteDFS-Client

A distributed file system, based on Python3, tornado, inspired by HDFS.

It's for data processing cache, not for permanent storage!

All code based on Python3, do not use Python2!

It still under development, so, maybe have some bugs or not stable enough!

See more details at https://github.com/fiefdx/LiteDFS

# Deployment

## Install LiteDFS-Client
```bash
$ pip3 install litedfs-client
```
memedb 0.1.0
MemeDB - the fun and quirky side project for data storage, because sometimes it's good to take a break from the serious stuff!
License: MIT
Requires Python: >=3.10,<4.0

# MemeDB
MemeDB is a minimal in-memory key-value database that doesn't take itself too seriously. It's designed to be simple, lightweight, and fun to use.

## Installation
Install MemeDB from PyPI:

```bash
pip install memedb
```

## Quick Start
Create a new MemeDB instance and add some data:

```python
from memedb import MemeDB

db = MemeDB()
db.set("key1", "value1")
db.set("key2", {"nested_key": "nested_value"})
```

Retrieve data by key:

```python
value = db.get("key1")
print(value)  # output: "value1"

nested_value = db.get("key2")["nested_key"]
print(nested_value)  # output: "nested_value"
Delete data by key:
```

```python
db.delete("key1")
```

Use transactions for atomicity:

```python
with db.transaction():
    db.set("key1", "new_value")
    db.delete("key2")
```

## Advanced Features
MemeDB also includes some more advanced features to make it more powerful:

### Indexing
You can create an index for a specific key to speed up lookups:

```python
db.index("name")
db.set("person:1", {"name": "Alice", "age": 30})
db.set("person:2", {"name": "Bob", "age": 25})
db.set("person:3", {"name": "Charlie", "age": 35})

result = db.lookup("name", "Alice")
print(result)  # output: [{"name": "Alice", "age": 30}]
```

### Querying
You can perform queries on the data using a simple SQL-like syntax:

```python
db.set("person:1", {"name": "Alice", "age": 30})
db.set("person:2", {"name": "Bob", "age": 25})
db.set("person:3", {"name": "Charlie", "age": 35})

result = db.query("SELECT * FROM data WHERE age > 30")
print(result)  # output: [{"name": "Charlie", "age": 35}]
```

### Caching
You can add a cache to MemeDB to speed up access to frequently accessed data:

```python
from memedb import Cache

cache = Cache(max_size=1000, ttl=3600)  # cache up to 1000 items for 1 hour
db = MemeDB(cache=cache)

db.set("key1", "value1")
db.get("key1")  # retrieves from cache
```

## Contributions
Contributions are welcome! If you have a feature request or bug report, please open an issue on GitHub. If you'd like to contribute code, please fork the repository and submit a pull request.
snapmlengine 0.0.2.12
ML Helper
License: MIT
Keywords: python,coding,ml,algorithms

## snapmlengine : snapmlengine helps you with Machine Learning Tasks

- **Website:** https://github.com/midnightbot/snapmlengine
- **Source Code:** https://github.com/midnightbot/snapmlengine
- **Bug Reports:** https://github.com/midnightbot/snapmlengine/issues

## What is it?

**snapmlengine** is a Python package that provides fast and flexible solution to Machine Learning Tasks

## snapmlengine provides
- Functions to perform ML Tasks

## Where to get it

The source code is currently hosted on GitHub at:
https://github.com/midnightbot/snapmlengine

```sh
# conda
conda install snapmlengine
```

```sh
# or PyPI
pip install snapmlengine
```

## Background

Work on ``snapmlengine`` started in 2023 and has been under active development since then.