semvec/pypi-packages huggingface dataset:
dexplorize 1.3.4
dexplorize (data exploration) python library use for applying different foundational techniques such as univariate and bivariate analysis.
License: MIT

# DEXPLORIZE DATA EXPLORATION PYTHON LIBRARY

Dexplorize is a Python library tailored to simplify and enhance the journey of data analysis tasks. Whether you're diving into datasets, conducting statistical analysis or visualizing data, dexplorize (Data Exploration) equips you with a comprehensive toolkit to expedite the process and extract valuable insights efficiently.

# FEATURES

UNIVARIATE ANALYSIS
- Entropy Based Binning
- Equal Width Based Binning
- Equal Frequency Based Binning
- Binary Encoding
- Impute Values

1. [CATEGORICAL VALUES]
- Pie Chart
- Bar Chart

2. [NUMERICAL VALUES]
- Statistical Measure
- Histogram

BIVARIATE ANALYSIS
1. [NUMERICAL AND NUMERICAL]
- Scatter Plot
- Linear Correlation

2. [CATEGORICAL AND CATEGORICAL]
- Stacked Column Chart

3. [CATEGORICAL AND NUMERICAL]
- Line Chart w/ Error Bars

# INSTALLATION
You can install DataExploration with pip and required libraries.

```python
pip install dexplorize
pip install matplotlib
pip install numpy
pip install pandas
```

# USAGE
Univariate Function (Data Preprocessing and Visualization)
- entropy_based(data, target, bins)
- equal_width(data, bins)
- ewidth_bin(data, bins)
- ewidth_plot(data)
- equal_freq(data, bins)
- efreq_bin(data, bins)
- efreq_plot(data)
- binary_encoding(data, values)
- target_encoding(data, values, attr)
- impute_values(data)
- pie_chart(data)
- describe(data)
- hist_bar(data, bins)
- bar_chart(data)

Bivariate Function (Data Visualization)
- scatter_plot(data, col1, col2)
- linear_corr(data, col1, col2)
- stacked_column(data, col1, col2)
- line_chart(data, col1, col2)

# IMPORT LIBRARY

```python
from DataExploration import univariate
from DataExploration import bivariate
```


# INFORMATION
Data Exploration is about describing the data by means of statistical and visualization techniques. We explore data in order to bring important aspects of that data into focus for further analysis.
sisl-gui 0.4.1
Graphical interface for the sisl visualization module
License: MIT

This is the graphical interface that you can use to interact with [sisl's](https://github.com/zerothi/sisl) visualization module.

It consists of two parts:
- [Back-end](sisl_gui): The backend is implemented in python. The most important part is the `Session` class which stores
and manages the app state. The `Session` can be connected to the frontend either using pyodide (it runs directly on the browser)
or through sockets (implemented with `flask` and `flask-socketio`).
- [Front-end](frontend): A javascript/typescript UI, which is developed using [React](https://reactjs.org/). The build is
shipped with the python package and also available at https://pfebrer.github.io/sisl-gui/.

# User guide

## Installation

It is extremely simple to install `sisl-gui` with `pip`:

```
pip install sisl-gui
```

## Usage

### From the browser

This is the simplest way, since it doesn't have any requirements. **You don't even need to install it** as mentioned
in the previous section, just go to https://pfebrer.github.io/sisl-gui/.

### From a terminal

You can use the `sisl-gui` command.

### From python

```python
import sisl_gui

app = sisl_gui.launch()
```

#