| Name | bloomlib JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | https://www.mikehuls.com |
| Summary | Library that offers all types of Bloom filters, implemented in Rust |
| upload_time | 2024-02-04 19:29:38 |
| maintainer | |
| docs_url | None |
| author | Mike Huls |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
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No coveralls.
|
[//]: # (<div align="center">)
[//]: # ( <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>)
[//]: # (</div>)
```commandline
______ ______ ___________ ______
___ /_ ___ /______ ______ _______ ___ ___ /___(_)___ /_
__ __ \__ / _ __ \_ __ \__ __ `__ \__ / __ / __ __ \
_ /_/ /_ / / /_/ // /_/ /_ / / / / /_ / _ / _ /_/ /
/_.___/ /_/ \____/ \____/ /_/ /_/ /_/ /_/ /_/ /_.___/
```
-----------------
# bloomlib: superfast Bloom filters for Python, optimized in Rust
| | |
|---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Package | [](https://pypi.org/project/bloomlib/) [](https://pypi.org/project/bloomlib/) |
| Meta |  |
**bloomlib** is a Python package that provides superfast Bloom filters, designed to
optimize your applications in an easy and intuitive way.
It aims to be the go-to package to build and use Bloom Filters that make your applications
superfast, memory-efficient and user-friendly.
```shell
pip install bloomlib
```
## Table of Contents
- [Main Features](#main-features)
- [Usage Example](#usage-example)
- [Installation](#installation)
- [Dependencies](#dependencies)
- [License](#license)
- [Documentation](#documentation)
- [Development](#development)
- [Contributing to bloomlib](#contributing-to-bloomlib)
## Main Features
- 🦀 Built in Rust
- ⚡ Highly optimized for speed and memory-efficiency
- 👨🎨 User-friendly
## Usage Example
```python
from bloomlib import BloomFilter
# 1. Create the filter
bf = BloomFilter(expected_number_of_items=1_000, desired_false_positive_rate=0.05)
# 2. Add items
for i in range(100):
bf.add(item=i)
# 3. Check if an item is contained; False means definitely not, True means "maybe"
if (bf.contains(item=42)):
print("This item may be in filter")
else:
print("This item is definitely not in the filter")
```
## Installation
```sh
pip install bloomlib
```
The source code is currently hosted on GitHub at:
https://github.com/mike-huls/bloomlib
Binary installers for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/bloomlib).
## Dependencies
Bloomlib has no Python dependencies
## License
[MIT](LICENSE.txt)
## Documentation
🔨 Under construction
## Development
Find the changelog and list of upcoming features [here](doc/CHANGELOG.md).
<br>
**Contributions** are always welcome; feel free to submit bug reports, bug fixes, feature requests, documentation improvements or enhancements!
<hr>
[Go to Top](#table-of-contents)
Raw data
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"description": "[//]: # (<div align=\"center\">)\r\n[//]: # ( <img src=\"https://pandas.pydata.org/static/img/pandas.svg\"><br>)\r\n[//]: # (</div>)\r\n\r\n```commandline\r\n______ ______ ___________ ______ \r\n___ /_ ___ /______ ______ _______ ___ ___ /___(_)___ /_ \r\n__ __ \\__ / _ __ \\_ __ \\__ __ `__ \\__ / __ / __ __ \\\r\n_ /_/ /_ / / /_/ // /_/ /_ / / / / /_ / _ / _ /_/ /\r\n/_.___/ /_/ \\____/ \\____/ /_/ /_/ /_/ /_/ /_/ /_.___/ \r\n```\r\n-----------------\r\n\r\n# bloomlib: superfast Bloom filters for Python, optimized in Rust\r\n\r\n| | |\r\n|---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\r\n| Package | [](https://pypi.org/project/bloomlib/) [](https://pypi.org/project/bloomlib/) |\r\n| Meta |  |\r\n\r\n**bloomlib** is a Python package that provides superfast Bloom filters, designed to \r\noptimize your applications in an easy and intuitive way.\r\nIt aims to be the go-to package to build and use Bloom Filters that make your applications \r\nsuperfast, memory-efficient and user-friendly.\r\n```shell\r\npip install bloomlib\r\n```\r\n\r\n## Table of Contents\r\n- [Main Features](#main-features)\r\n- [Usage Example](#usage-example)\r\n- [Installation](#installation)\r\n- [Dependencies](#dependencies)\r\n- [License](#license)\r\n- [Documentation](#documentation)\r\n- [Development](#development)\r\n- [Contributing to bloomlib](#contributing-to-bloomlib)\r\n\r\n## Main Features\r\n- \ud83e\udd80 Built in Rust\r\n- \u26a1 Highly optimized for speed and memory-efficiency\r\n- \ud83d\udc68\u200d\ud83c\udfa8 User-friendly\r\n\r\n## Usage Example\r\n```python\r\nfrom bloomlib import BloomFilter\r\n\r\n# 1. Create the filter\r\nbf = BloomFilter(expected_number_of_items=1_000, desired_false_positive_rate=0.05)\r\n\r\n# 2. Add items\r\nfor i in range(100):\r\n bf.add(item=i)\r\n\r\n# 3. Check if an item is contained; False means definitely not, True means \"maybe\" \r\nif (bf.contains(item=42)):\r\n print(\"This item may be in filter\")\r\nelse:\r\n print(\"This item is definitely not in the filter\")\r\n```\r\n\r\n\r\n## Installation\r\n```sh\r\npip install bloomlib\r\n```\r\nThe source code is currently hosted on GitHub at:\r\nhttps://github.com/mike-huls/bloomlib\r\n\r\nBinary installers for the latest released version are available at the [Python\r\nPackage Index (PyPI)](https://pypi.org/project/bloomlib).\r\n\r\n## Dependencies\r\nBloomlib has no Python dependencies\r\n\r\n## License\r\n[MIT](LICENSE.txt)\r\n\r\n## Documentation\r\n\ud83d\udd28 Under construction\r\n\r\n## Development\r\nFind the changelog and list of upcoming features [here](doc/CHANGELOG.md).\r\n<br>\r\n**Contributions** are always welcome; feel free to submit bug reports, bug fixes, feature requests, documentation improvements or enhancements!\r\n\r\n<hr>\r\n\r\n[Go to Top](#table-of-contents)\n",
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