{"id":20229993,"url":"https://github.com/dsite42/simple_data_visualizer","last_synced_at":"2025-03-03T13:26:06.790Z","repository":{"id":229730629,"uuid":"720110433","full_name":"Dsite42/Simple_Data_Visualizer","owner":"Dsite42","description":"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).","archived":false,"fork":false,"pushed_at":"2024-03-25T23:20:08.000Z","size":2647,"stargazers_count":0,"open_issues_count":5,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-03-26T02:28:05.311Z","etag":null,"topics":["data-analysis","data-science","data-visualisation","data-visualization","eda","exploratory-data-analysis","plotly","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Dsite42.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-11-17T15:47:07.000Z","updated_at":"2024-03-26T02:28:09.983Z","dependencies_parsed_at":"2024-03-26T02:28:08.953Z","dependency_job_id":null,"html_url":"https://github.com/Dsite42/Simple_Data_Visualizer","commit_stats":null,"previous_names":["dsite42/simple_data_visualizer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dsite42%2FSimple_Data_Visualizer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dsite42%2FSimple_Data_Visualizer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dsite42%2FSimple_Data_Visualizer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dsite42%2FSimple_Data_Visualizer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Dsite42","download_url":"https://codeload.github.com/Dsite42/Simple_Data_Visualizer/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241671820,"owners_count":20000669,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","data-visualisation","data-visualization","eda","exploratory-data-analysis","plotly","seaborn"],"created_at":"2024-11-14T07:37:49.168Z","updated_at":"2025-03-03T13:26:06.769Z","avatar_url":"https://github.com/Dsite42.png","language":"Python","readme":"\n## Table of Contents\n- [1. About](#1-about)\n- [2. Features](#2-features)\n  - [Main Window](#main-window)\n  - [Plots](#plots)\n  - [Window Features](#window-features)\n  - [Manipulate Data](#manipulate-data)\n- [3. Installation](#3-installation)\n  - [Linux](#linux)\n  - [Windows](#windows)\n- [4. Usage and Examples](#4-usage-and-examples)\n\n\n### 1. About\nThis 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.\n\n### 2. Features\n\n#### Main Window\n- Open CSV file\n- Use Seaborn by default\n- Check Plotly to use plotly\n- Check Multiplot to create kind of subplots (Seaborn)\n- Define how much rows and columns the multiplot should have\n- Choose one of the loaded dataframes\n- Depending on plot type, choose x-axis, y-axis, z-axis\n- Choose plot type (RelPlot, PairPlot, JointPlot, DisPlot, CatPlot, LmPlot, 3DPlot, ManualPlot)\n- See Console output for errors\n- Click show plot to show the plot\n- Click refresh to refresh the plot\n\n\u003cimg src=\"readmeFiles/MainWindow.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n#### Plots\n**RelPlots (Seaborn, Plotly, Multiplot)**  \nCreate scatter and line relation plots with kwargs kind, hue, size, style, row, col.\n\n\u003cimg src=\"readmeFiles/RelPlot.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e \u003cimg src=\"readmeFiles/RelPlot_plotly.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**PairPlots (Seaborn, Plotly)**  \nCreate pair plots with kwargs kind, diag_kind, hue, corner.\n\n\u003cimg src=\"readmeFiles/PairPlot.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e \u003cimg src=\"readmeFiles/PairPlot_plotly.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**JointPlot (Seaborn, Plotly, Multiplot)**  \nCreate joint plots with kwargs kind, hue.\n\n\u003cimg src=\"readmeFiles/JointPlot.png\" width=\"500\" height=\"auto\" style=\"border: 2px solid black;\"\u003e \u003cimg src=\"readmeFiles/JointPlot_plotly.png\" width=\"450\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**DisPlot (Seaborn, Plotly, Multiplot)**  \nCreate distribution plots with kwargs kind, hue, rug, row, col.\n\n\u003cimg src=\"readmeFiles/DisPlot.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e \u003cimg src=\"readmeFiles/DisPlot_plotly.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**CatPlot (Seaborn, Plotly, Multiplot)**  \nCreate categorical plots with kwargs kind, hue, row, col.\n\n\u003cimg src=\"readmeFiles/CatPlot.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e \u003cimg src=\"readmeFiles/CatPlot_plotly.png\" width=\"370\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**LmPlot (Seaborn, Plotly, Multiplot)**  \nCreate linear model plots with kwargs heu, scatter, x_bins, robust, facet_kws, row, col.\n\n\u003cimg src=\"readmeFiles/LmPlot.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e \u003cimg src=\"readmeFiles/LmPlot_plotly.png\" width=\"470\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**3DPlot (Plotly)**  \nCreate 3D plots with kwargs hue, size, style.\n\n\u003cimg src=\"readmeFiles/3DPlot_plotly.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n**ManualPlot (Seaborn, Plotly)**  \nYou can incert full python code here which will be executed. Create a manual plot. Important is just that you return a figure object.\nYou can also manipulate the data by manipulating or redifining the df.\n\n**General fields**\n- Plot/ x-axis/ y-axis/ z-axis title\n- plot with (seaborn/multiplot: inches, plotly: pixels)\n- plot height (seaborn/multiplot: inches, plotly: pixels)\n\n#### Window Features  \n**Save Plot** (Seaborn, Plotly, Multiplot))  \nClick save plot to save the plot as a .png file.\n\n**Copy Plot** (Seaborn, Plotly, Multiplot)\nClick copy plot to copy the plot to the clipboard.\n\n**Set Refresh** (Seaborn, Plotly, Multiplot)  \nClick 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)\n\n**Auto Scale** (Seaborn)  \nCeck auto scale to resize the plot when changing the window size.\n\n#### Manipulate Data  \nHere you can manipulate the data with the following options:\n- delete columns\n- set data type\n- set datetime index\n- create new columns by addition, subtraction, multiplication, division\n- filter data\n- save as csv\n- duplicate dataframe\n- view datatypes  \n\n\u003cimg src=\"readmeFiles/ManipulateData.png\" width=\"400\" height=\"auto\" style=\"border: 2px solid black;\"\u003e\n\n  \n### 3. Installation\n\n#### Linux\nYou need python version 3.7.\n##### Install pyenv as a python version manager\nYou can install pyenv via pyenv-installer or the system's package manager.  \nA generic way using pyenv-installer is as follows:  \n`curl https://pyenv.run | bash`\n\n- After installation, you need to add pyenv initialization lines to the shell configuration file (like ~/.bashrc, ~/.zshrc, etc.):  \n`export PATH=\"$HOME/.pyenv/bin:$PATH\"`  \n`eval \"$(pyenv init --path)\"`  \n`eval \"$(pyenv virtualenv-init -)\"`  \n\n- Then, you should restart your shell or re-source the configuration file:  \n`source ~/.bashrc`  # Or the respective config file\n\n##### Install Python\n- Now, you can install Python 3.7.16 using pyenv:  \n`pyenv install 3.7.16`\n\n- After installation, set the desired Python version as global (or local for a specific project directory) using pyenv:  \n`pyenv global 3.7.16`\n\n##### Clone the repository\n`git clone git@github.com:Dsite42/Simple_Data_Visualizer.git`\n\n##### Install the dependencies\n`pip install -r requirements.txt`\n\n##### Running the Simple Data Visualizer\n`python main.py`\n\n#### Windows\nUse WSL or WSL2 and follow the Linux instructions.\n\n### 4. Usage and Examples\nA short youtube video will come soon.\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdsite42%2Fsimple_data_visualizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdsite42%2Fsimple_data_visualizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdsite42%2Fsimple_data_visualizer/lists"}