semvec/pypi-packages huggingface dataset:
tf-pydf 0.1.0
A simple implimentation of TF-IDF
License: MIT
Requires Python: >=3.10,<4.0

# TF_pyDF

TF_pyDF is a Python package that provides a module for calculating [TF-IDF](https://en.wikipedia.org/wiki/Tf–idf) (Term Frequency-Inverse Document Frequency). It allows you to create a document model and perform various operations such as adding and removing documents, searching for documents based on queries, and computing TF-IDF scores.

## Usage Warning

The document contents and search query have to be pre-tokenized into list[str] or in a Tokenizer Iterator.
You can use your own tokenizer or use one related to this package - [LexiPy](https://pypi.org/project/lexipy/), it provides a simple way to tokenize a string of text into tokens.

## Installation

You can install TF_pyDF using pip:

```shell
pip install tf_pydf
```

## Usage

```python
from tf_pydf import Model

documents = {
    "fruits":
        ["apple", "banana", "orange"],
    "vegetables":
        ["tomato", "cucumber", "radish"],
    "pasta":
        ["tagliatelle", "rotini", "rigatoni"],
    }

# Create a new instance of the document model
model = Model()

# Add documents to the model
for doc_id, doc_content in documents.items():
    model.add_doc(doc_id, doc_content)

# Or use convenience method "from_dict"
model = Model.from_dict(documents)

# Remove a document from the model
doc_id = "pasta"
model.remove_doc(doc_id)

# Check if a document is in the model
if doc_id in model:
    ...

# Search the model for documents matching a query
results = model.search_query(query)

results
>>> [('fruits', 0.10034333188799373), ('vegetables', 0.0)]
```

## Contributing

Contributions to LexiPy are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the [GitHub repository](https://github.com/t4skmanag3r/tf_pydf).

## License

This package is licensed under the [MIT License](https://opensource.org/licenses/mit).

## References

- [Github Repository](https://github.com/t4skmanag3r/tf_pydf)
- [PyPI package](https://pypi.org/project/tf_pydf/)
- [LexiPy](https://pypi.org/project/lexipy/) - tokenizer
- [TF-IDF](https://en.wikipedia.org/wiki/Tf–idf) - wikipedia
csvsdataset 0.0.7
Memory frugal torch dataset from a csv collection
License: MIT

# csvsdataset

`csvsdataset` is a Python library designed to simplify the process of working with multiple CSV files as a single dataset. The primary functionality is provided by the `CsvsDataset` class in the `csvsdataset.py` module.

This was written by ChatGPT4 as mentioned [here](https://www.linkedin.com/posts/petercotton_chatgpt4-opensource-python-activity-7047184874163597312-JTr3?utm_source=share&utm_medium=member_desktop). Issues will be cut and paste into a session. It is an experiment in semi-autonomous code maintenance.

## Installation

To install the `csvsdataset` library, simply run:

```bash
pip install csvsdataset
```

## Usage

        from csvsdataset.csvsdataset import CsvsDataset
        
        # Initialize the CsvsDataset instance
        dataset = CsvsDataset(folder_path="path/to/your/csv/folder",
                              file_pattern="*.csv",
                              x_columns=["column1", "column2"],
                              y_column="target_column")
        
        # Iterate over the dataset
        for x_data, y_data in dataset:
            # Your processing code here
            pass
        
        # Access a specific item in the dataset
        x_data, y_data = dataset[42]

### Memory frugality
Only data from a small number of csv files are maintained in memory. The
rest is discarded on a LRU basis. This class is intended for use
when a very large number of data files exist which cannot be loaded into
memory conveniently.
mknsmPy 0.4
mekanism material calculator
License: MIT
Keywords: mekanism,mechanism,mknsm,mknsmPy

MekanismCalcLiblary
===================

.. figure:: https://user-images.githubusercontent.com/122292089/236683530-15675752-b36e-428e-9e9f-1a1f292853f0.png  

mekanismの材料計算をもっと簡単に
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

|MIT License| |PyPI version|

   pip install mknsmPy

作りたいものの名前と個数を引数に代入すると材料を辞書型で返します。

・使用例

   mknsmPy.Calc(“アイテム名”, 42)

::

   {"材料1": "個数",
    "材料2": "個数",
    "材料3": "個数",
    ...}

.. |MIT License| image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat
   :target: https://github.com/hihimamuLab/MekanismCalcLibrary/blob/main/LICENSE
.. |PyPI version| image:: https://badge.fury.io/py/mknsmPy.svg
   :target: https://badge.fury.io/py/mknsmPy