semvec/pypi-packages huggingface dataset:
nbimageviewer 0.1.0
nbimageviewer is a Python library for rapid and efficient image visualization for Jupyter Notebooks.
License: MIT
Keywords: jupyter,notebook,visualization,images

# nbimageviewer

`nbimageviewer` is a Python library for aesthetic and efficient image visualization in Jupyter Notebooks. It uses [IPython](https://ipython.readthedocs.io/en/stable/api/), ReactJS, and WebSockets to efficiently visualize large amounts of images.

![Carousel](carousel.png)

Visualizing large amounts of images in a Jupyter Notebook is usually done by creating matplotlib plots, which can be a painful and inefficient process with lots of configuring and searching online.

```
import matplotlib.pyplot as plt
import load_images

images = load_images()
fig, ax = plt.subplots()
for i, image in enumerate(len(images)):
  ax[i] = plt.imshow(image)
  ...
```

`nbimageviewer` simplifies this process while providing convenient and beautiful visualizations.

```
import nbimageviewer.carousel as carousel
import load_images

images = load_images()
c = carousel.Carousel(images)
```

## Installation

To install `nbimageviewer` from PyPI:
```
pip install nbimageviewer
```

To  install `nbimageviewer` from source:
```
git clone git@github.com:briankosw/nbimageviewer.git
cd nbimageviewer
pip install .
```

## Usage
`nbimageviewer` currently supports two ways of visualizing images: `Carousel` and `Gallery`.

### Carousel
![Carousel](carousel.png)

### Gallery
![Gallery](gallery.png)


## License

Distributed under the MIT License. See [LICENSE.txt](LICENSE.txt) for more information.
quickplotter 1.0
Instantly generate common EDA plots without cleaning your DataFrame
License: MIT
Keywords: VISUALIZATION,PYTHON,DATA SCIENCE

# Instant EDA
Instantly generate common exploratory data plots without having to worry about cleaning your data.

Note: To find the most updated documentation, visit the Github [repo](https://github.com/jlehrer1/InstantEDA).

Description: The `quickplotter` module provided here is meant to provide common exploratory data plots without having to worry about cleaning your DataFrame or preanalyzing your data. Additionally, these plots can be exported to `.{png, jpeg}` for use in reports and papers. 

## 1. Basic Usage:
```python3
plotter = quickplotter.QuickPlotter(df: pd.DataFrame) #creates a QuickPlotter object with the given DataFrame

plotter.common(subset=['correlation', 'percent_nan']) #plots correlation between features, and percent nan in each column

plotter.distribution(column_subset=df.columns[0:4]) #plots distributions for the first four columns in the DataFrame

plotter.common(column_subset=['body_mass_index', 'blood_type']) #plots common plots for the given columns
```

## 2. Fundamentals

If the number of `NaN` values in the DataFrame is `<= 5%` of the total values, the NaN rows will be dropped and the plots will be generated without them. **Remember, this is meant to be a quick and dirty tool for exploration, and not for being delicate with each data entry.**

### subset & diff lists
The quickplot module works mainly with two specifications, `subset` and `diff`. 

For any `subset`-like list, the items in the list will be used. For any `diff`-like list, all items *except* those in the list will be used. 

The options are as follow:
- `subset`: Use only the plots specified in the list
- `diff`: Use all plots *except* those specified in the list
- `subset_columns`: Use all columns specified in the list. Can either be `df.columns` slicing or by name
- `diff_columns`: Use all columns *except* those specified in the list. Can either be `df.columns` slicing or by name. 

## 3. Contributing

If you have read this far I hope you've found this tool useful. I am always looking to learn more and develop as a collaborative programmer, so if you have any ideas or contributions, feel free to write a feature or pull request.