semvec/pypi-packages huggingface dataset:
memory-foam 0.3.2
Performant iterators for loading files from S3, GCS and Azure into memory for easy processing.
License: Apache-2.0
Requires Python: >=3.9

# memory-foam


[![pypi](https://img.shields.io/pypi/v/memory-foam.svg)](https://pypi.org/project/memory-foam/)
[![tests](https://github.com/mattseddon/memory-foam/actions/workflows/tests.yml/badge.svg)](https://github.com/mattseddon/memory-foam/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/mattseddon/memory-foam/graph/badge.svg?token=7TT8YRWTV9)](https://codecov.io/gh/mattseddon/memory-foam)

`memory-foam` is a Python package that provides performant iterators for loading files from S3, GCS and Azure into memory for easy processing.

## Features

- **Unified Interface**: Seamlessly interact with files stored in S3, GCS and Azure.
- **Asynchronous Support**: Efficiently load files using asynchronous iterators.

## Installation

You can install `memory-foam` using pip:

```bash
pip install memory-foam
```

## Example usage

```python
from memory_foam import iter_files

...

for pointer, contents in iter_files(uri, glob, modified_after, client_config):
    results = process(contents)
    data = pointer.to_dict_with(results)
    save(data)
```

## Credentials

This package uses the `fsspec` implementations for S3, GCS and Azure. Entries from the `client_config` dictionary are passed to the respective `fsspec` clients.

However, you can use the same authentication setup as the individual `fsspec` packages.

The documentation for each of the clients is linked below:

- S3:  https://s3fs.readthedocs.io/en/latest/#credentials
- GCS: https://gcsfs.readthedocs.io/en/latest/#credentials
- Azure: https://github.com/fsspec/adlfs?tab=readme-ov-file#setting-credentials
rhoPy 0.0.0
A (currently) small Python library of statistics functions which are aimed to be more efficient and expansive compared to the default library.
License: Apache-2.0
Requires Python: >=3.13
Keywords: statistics, math

Welcome to rhoPy.

This is my first project. I plan on making it continuous. Please do not hesitate to make suggestions to my code.
I wrote the first 4 functions on the same day I learned to code in Python. Expect some errors.

More detailed descriptions will be found wherever I put the documentation later.

WHAT I DON'T HAVE THAT statistics DOES:
    mode
    multimode
    quantiles
    kde
    kde_random
    covariance
    correlation
    linear_regression

I will slowly
yet eventually
make this list empty.

Contents are not thorough.
Tests are not comprehensive.
Objects are incomplete.

So be it.

The code will run.

For pull requests:
    Generally follow my pattern/style.
    If i'm not following convention, just tell me in the request.
    Otherwise, if it's valid statistics, you're good.

Honestly, I have no idea what I'm doing so I'm trying to keep stuff familiar as I learn.

For the record: ChatGPT has coded some of this. If stuff doesn't follow how I normally code, it's probably not me.
Also for the record: It is hardly more than 5% of the codebase, if any significant amount at all. If it's obvious, I beg you change it.