semvec/pypi-packages huggingface dataset:
pycudahll 0.1.0
A GPU implementation of HyperLogLog
Requires Python: >3.10,<3.12
Keywords: cupy,gpu,hll,hyperloglog

# PyCudaHLL

This is a GPU accelerated implementation of HyperLogLog using the CuPy library. It was created for the class "Algorithmic Techniques for Taming Big Data" at the department of Computing and Data Science at Boston University.

## Using the Code

To use this code, you can either get the library from PyPI or build it from source.

### Get from PyPI (Recommended)

- Install using pip: `pip install pycudahll`
- In your code, import the library: `from pycudahll.CudaHLL import CudaHLL`

### Building from Source

- Clone the repository
- Install dependencies: `poetry install`
- In your code, import the library: `from pycudahll.CudaHLL import CudaHLL`
- See `test.py` for examples. (Note: `test.py` is most likely in a broken state, but should give you an idea of how to use the library.)

## API

The main class of the library is CudaHLL. It can be imported in your code with:
```python
from pycudahll.CudaHLL import CudaHLL
```

CudaHLL also includes a helper function to hash data to use with the main class:
```python
from pycudahll.CudaHLL import hashDataGPUHLL
```

A short example of how to use the library is as follows:
```python
from pycudahll.CudaHLL import CudaHLL, hashDataGPUHLL

with open('data.csv', 'r') as file:
    data = file.read().split(',')
    hashedData = hashDataGPUHLL(data)

    threads = 64
    p = 14
    cudaDevice = 0 # optional
    roundThreads = True # optional
    hll = CudaHLL(p, threads, cudaDevice, roundThreads)

    hll.add(hashedData)
    print(hll.card()) # print unrounded cardinality estimate
    print(len(hll)) # print rounded cardinality estimate
```


## Test Data

Text of Shakespeare plays obtained from https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt. Original text can be found in t8.shakespeare.txt and the modified text can be found in shakespeare.csv.

Total number of items = 899300
Exact cardinality = 34065
plotgpt 0.1.0
A tiny ai to help you visualize data
Requires Python: >=3.10,<4.0

Introducing PlotGPT, a lightweight AI assistant to help you visualize your data.

![](demo.gif)

Built on top of langchain and openai


Currently it will (most likely) only support plotting in matplotlib and seaborn

# Quickstart

To get started, all you need to do is give PlotGPT a pandas dataframe to inspect.

```python
import seaborn as sns
df = sns.load_dataset('iris')

ai = PlotGPT()
ai.inspect(df)
```

And then ask it to start plotting.

```python
ai.ask("plot sepal width vs sepal length")
```


PlotGPT will remember previous prompts (up to a certain point), so you can build off of previous plots, mimicking iterative explorate data analysis.

```python
ai.ask("now colored it by specied")
```


By default, PlotGPT will return the plotting code. You can turn it off by setting the `show_code` flag.
```python
ai = PlotGPT(show_code=False)
```
> However, it is this author's recommendation to leave this setting on be able to sanity check the plots.


# Questions

## Should this completely replace my data analysis?

Probably not. Since this is an LLM, it's definitely not perfect. I'm more envisioning this as empowering initial discovery, where we use the AI to create a series of initial exploratory visualizations, and then have the human take over once you want to go deeper / more nuanced.
ezcode 1.0.28
Easy Algorithm & Data Structure
Requires Python: >=3.10


# ezcode
[![PyPi](https://img.shields.io/pypi/v/ezcode.svg)](https://pypi.python.org/pypi/ezcode)
[![Github](https://img.shields.io/badge/github-master-brightgreen)](https://github.com/zheng-gao/ez_code)
## Installation
```bash
pip3 install --upgrade ezcode
```
## Documentation
* [docs](https://github.com/zheng-gao/ez_code/blob/main/docs)
## Development Workflow
```bash
git clone https://github.com/zheng-gao/ez_code.git
ez_code/ez.sh -d/--development
```
## Release Workflow
```bash
git clone https://github.com/zheng-gao/ez_code.git
ez_code/ez.sh -r/--release
```
## Unit Test
### Run all the tests
```bash
ez_code/ez.sh -o/--operations test
```
### Run a single test
```bash
ez_code/ez.sh -o/--operations test -a/--arguments test_array.py::test_binary_search
```