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MinHash implementation in Python

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[#pypi-package]: https://pypi.org/project/pyminhash/
[#conda-forge-package]: https://anaconda.org/conda-forge/pyminhash
[#conda-forge-feedstock]: https://github.com/conda-forge/pyminhash-feedstock
[#docs-package]: https://pyminhash.readthedocs.io/en/latest/

# PyMinHash

MinHashing is a very efficient way of finding similar records in a dataset based on Jaccard similarity. PyMinHash
implements efficient minhashing for Pandas dataframes. See instructions below or look at the example notebook to get
started.

Developed by [Frits Hermans](https://fritshermans.github.io/)

## Documentation

Documentation can be found [here](https://pyminhash.readthedocs.io/en/latest/)

## Installation

### Normal installation
**Using PyPI**

```
pip install pyminhash
```

**Using conda**

```
conda install -c conda-forge pyminhash
```

### Install to contribute

Clone this Github repo and install in editable mode:

```
python -m pip install -e ".[dev]"
python setup.py develop
```

## Usage

Apply record matching to column `name` of your Pandas dataframe `df` as follows:

```python
myHasher = MinHash(n_hash_tables=10)
myHasher.fit_predict(df, 'name')
```

This will return the row pairs from `df` that have non-zero Jaccard similarity.

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