semvec/pypi-packages huggingface dataset:
pandas2pgfplots 0.2.0
Library for pgfplots plots from pandas DataFrames
License: MIT
Requires Python: >=3.6.0

# pandas2pgfplots

Pandas2pgfplots supports plotting of data stored in [pandas] DataFrame objects
using [pgfplots] that is based on the amazing [TikZ and PGF] packages. The code
generated by this package can be easily included in LaTeX documents. In Jupyter
notebooks, the plots are rendered using [itikz] (requires working LaTeX
distribution in `$PATH`).

Currently, the package only supports two kinds of plots:
 * scatter plots
 * cactus plots

## Installation

### Requirements
 * [pandas]>=1.0
 * [itikz]>=0.1.5
 * working LaTeX distribution

The Python requirements and pandas2pgfplots itself can be installed from [PyPI]
using pip by
```shell
python3 -m pip install -U pandas2pgfplots
```

## Usage intructions
TBA

[pgfplots]: http://pgfplots.sourceforge.net/
[pandas]: https://pandas.pydata.org/
[itikz]: https://github.com/jbn/itikz
[TikZ and PGF]: https://github.com/pgf-tikz/pgf
[PyPI]: https://pypi.org/
roblopy 5.2
Roblox API built in Python
License: MIT
Keywords: Roblox,Roblox Python,Roblox Api

# Roblopy

![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)

Roblox API built in Python.

Migrating from 4.5 to 5.0, we will now be using snake case to follow PEP8. We are also adding type hints and docstrings.

## Installing

Here is a simple way to install:
```
pip install roblopy
```

Here is a simple way to upgrade the package to the newest version:
```
pip install roblopy --upgrade
```

## Quick Example

```python
from roblopy import Users

user_id = Users.get_id_from_username("ROBLOX")

print("Roblox's User ID is " + str(user_id))
```

## Documentation

https://github.com/jackprogramsjp/Roblopy/wiki

## Requirements

* requests
* Beautiful Soup
visualpriors 0.3.5
The official implementation of visual priors from the paper Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies. Arxiv preprint 2018.
License: MIT
Keywords: computer vision,robotics,perception,midlevel,mid-level,reinforcement learning,machine learning,representation learning

# Visual Priors


# Intro
This package contains the code for the paper:

**Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies**. Arxiv 2018/CoRL 2019. Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir R. Zamir, Silvio Savarese, Leonidas Guibas, Jitendra Malik.

- For a fuller description and useful results and demos, and much more, see the website [http://perceptual.actor](http://perceptual.actor).
- For the code in this package, see [https://github.com/alexsax/midlevel-reps/tree/visualpriors](https://github.com/alexsax/midlevel-reps/tree/visualpriors).
- For the full code from the paper, dockers to reproduce experiments, and more, see [https://github.com/alexsax/midlevel-reps](https://github.com/alexsax/midlevel-reps).


### Citation
If you find this repository or toolkit useful, then please cite:
```
@inproceedings{midLevelReps2018,
 title={Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies.},
 author={Alexander Sax and Jeffrey O. Zhang and Bradley Emi and Amir R. Zamir and Leonidas J. Guibas and Silvio Savarese and Jitendra Malik},
 year={2018},
}
```