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https://github.com/dsite42/simple_data_visualizer

This is a simple tool to visualize data for a quick Exploratory Data Analysis (EDA). You can create various plot types as seaborn or plotly plot via a GUI in multiple windows (RelPlot, PairPlot, JointPlot, DisPlot, CatPlot, LmPlot, 3DPlot).
https://github.com/dsite42/simple_data_visualizer

data-analysis data-science data-visualisation data-visualization eda exploratory-data-analysis plotly seaborn

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This is a simple tool to visualize data for a quick Exploratory Data Analysis (EDA). You can create various plot types as seaborn or plotly plot via a GUI in multiple windows (RelPlot, PairPlot, JointPlot, DisPlot, CatPlot, LmPlot, 3DPlot).

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README 

        

## Table of Contents

- [1. About](#1-about)

- [2. Features](#2-features)

  - [Main Window](#main-window)

  - [Plots](#plots)

  - [Window Features](#window-features)

  - [Manipulate Data](#manipulate-data)

- [3. Installation](#3-installation)

  - [Linux](#linux)

  - [Windows](#windows)

- [4. Usage and Examples](#4-usage-and-examples)



### 1. About

This is a simple tool to visualize data for a quick Exploratory Data Analysis (EDA). You can create various plot types as seaborn or plotly plot via a GUI in multiple windows (RelPlot, PairPlot, JointPlot, DisPlot, CatPlot, LmPlot, 3DPlot). This is my first bigger python and first GUI project. The idea was to achieve a practical result with limited time and a good learning curve.



### 2. Features



#### Main Window

- Open CSV file

- Use Seaborn by default

- Check Plotly to use plotly

- Check Multiplot to create kind of subplots (Seaborn)

- Define how much rows and columns the multiplot should have

- Choose one of the loaded dataframes

- Depending on plot type, choose x-axis, y-axis, z-axis

- Choose plot type (RelPlot, PairPlot, JointPlot, DisPlot, CatPlot, LmPlot, 3DPlot, ManualPlot)

- See Console output for errors

- Click show plot to show the plot

- Click refresh to refresh the plot






#### Plots

**RelPlots (Seaborn, Plotly, Multiplot)**  

Create scatter and line relation plots with kwargs kind, hue, size, style, row, col.



 



**PairPlots (Seaborn, Plotly)**  

Create pair plots with kwargs kind, diag_kind, hue, corner.



 



**JointPlot (Seaborn, Plotly, Multiplot)**  

Create joint plots with kwargs kind, hue.



 



**DisPlot (Seaborn, Plotly, Multiplot)**  

Create distribution plots with kwargs kind, hue, rug, row, col.



 



**CatPlot (Seaborn, Plotly, Multiplot)**  

Create categorical plots with kwargs kind, hue, row, col.



 



**LmPlot (Seaborn, Plotly, Multiplot)**  

Create linear model plots with kwargs heu, scatter, x_bins, robust, facet_kws, row, col.



 



**3DPlot (Plotly)**  

Create 3D plots with kwargs hue, size, style.






**ManualPlot (Seaborn, Plotly)**  

You can incert full python code here which will be executed. Create a manual plot. Important is just that you return a figure object.

You can also manipulate the data by manipulating or redifining the df.



**General fields**

- Plot/ x-axis/ y-axis/ z-axis title

- plot with (seaborn/multiplot: inches, plotly: pixels)

- plot height (seaborn/multiplot: inches, plotly: pixels)



#### Window Features  

**Save Plot** (Seaborn, Plotly, Multiplot))  

Click save plot to save the plot as a .png file.



**Copy Plot** (Seaborn, Plotly, Multiplot)

Click copy plot to copy the plot to the clipboard.



**Set Refresh** (Seaborn, Plotly, Multiplot)  

Click set refresh to refresh an existing window with the new plot. A window can just be refreshed with the same window type. (Seaborn, Plotly, Multiplot)



**Auto Scale** (Seaborn)  

Ceck auto scale to resize the plot when changing the window size.



#### Manipulate Data  

Here you can manipulate the data with the following options:

- delete columns

- set data type

- set datetime index

- create new columns by addition, subtraction, multiplication, division

- filter data

- save as csv

- duplicate dataframe

- view datatypes  






  

### 3. Installation



#### Linux

You need python version 3.7.

##### Install pyenv as a python version manager

You can install pyenv via pyenv-installer or the system's package manager.  

A generic way using pyenv-installer is as follows:  

`curl https://pyenv.run | bash`



- After installation, you need to add pyenv initialization lines to the shell configuration file (like ~/.bashrc, ~/.zshrc, etc.):  

`export PATH="$HOME/.pyenv/bin:$PATH"`  

`eval "$(pyenv init --path)"`  

`eval "$(pyenv virtualenv-init -)"`  



- Then, you should restart your shell or re-source the configuration file:  

`source ~/.bashrc`  # Or the respective config file



##### Install Python

- Now, you can install Python 3.7.16 using pyenv:  

`pyenv install 3.7.16`



- After installation, set the desired Python version as global (or local for a specific project directory) using pyenv:  

`pyenv global 3.7.16`



##### Clone the repository

`git clone [email protected]:Dsite42/Simple_Data_Visualizer.git`



##### Install the dependencies

`pip install -r requirements.txt`



##### Running the Simple Data Visualizer

`python main.py`



#### Windows

Use WSL or WSL2 and follow the Linux instructions.



### 4. Usage and Examples

A short youtube video will come soon.