Youtor
The EASIEST way to insert a NAVIGATION into your Streamlit app
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=6Eu2b34alsE
In the following video, you'll be guided through the creation of a Python Adidas Sales Dashboard using Streamlit and Plotly. This will enable you to create a dashboard similar to what's achievable with tools like PowerBI and Tableau. • Streamlit is a tool that allows for the rapid development and sharing of data applications. • • This video covers follows: • β π§ππ ππ¦π§ππ π£π¦: • 00:00 – Introduction • 01:00 – Exploring the Data • 01:30 – About Streamlit • 02:23 – Importing Necessary Packages • 03:45 – Reading Data from File • 04:08 – Setting the Streamlit Page and Dashboard Heading • 08:40 – Adding Last Updated Date • 10:42 – Creating a Bar Chart Using Plotly (Retailer by Sales) • 13:03 – Viewing and Downloading Bar Chart Source Data • 17:03 – Creating a Time Series Chart for Sales and Data Viewing • 22:22 – Adding a White Line to the Dashboard • 23:10 – Creating a Dual-Axis Chart Based on Total Sales and Units Sold • 32:25 – Viewing and Downloading Dual-Axis Chart Source Data • 34:35 – Creating a TreeMap Chart Based on Region, City, and Sales with Data Set Features View and Download • 45:15 – Viewing and Downloading Adidas Sales Source Data • 47:10 – Final Dashboard Overview and Its Features with Final Touch π • • π π₯ππ¦π’π¨π₯πππ¦: • Source Code: https://github.com/AbhisheakSaraswat/... • Raw Data: https://github.com/AbhisheakSaraswat/... • This video serves as the second installment in our series on Python Streamlit Dashboards. If you haven't already, we recommend watching the first video on Python Interactive Dashboard Development using Streamlit and Plotly. • π Python Interactive Dashboard Development using Streamlit and Plotly. • • Python Interactive Dashboard Developm... • • Pandas and Plotly are powerful libraries that play essential roles in dashboard development. • ββββββββ ββββββββ • π Pandas: • 1.) Data Manipulation • 2.) Data Cleaning and Preprocessing • 3.) Data Integration • 4.) Data Transformation • π Plotly: • 1.) Interactive Data Visualization • 2.) Dynamic Updating • 3.) Intuitive Interactivity • 4.) Dash Integration • In summary, Pandas and Plotly complement each other in dashboard development. Pandas helps with data manipulation, cleaning, and preprocessing, while Plotly enables interactive and visually appealing data visualizations. Together, they empower you to build powerful and insightful dashboards that effectively present and analyze data. • βΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈβΌοΈ • ππ’π‘π‘πππ§ πͺππ§π π π: • π GitHub: https://github.com/AbhisheakSaraswat • LinkedinβΊ / abhisheak-saraswat-0b1b4a105 • Telegram: https://t.me/+32-TodtiOvo2Njk9 • Python Excel Automation: • Excel Automation Using Python • Python Teaser: • A Beautiful Python Programming Teaser... • Playlists: • Python Pandas Data Science Tutorial: • Python Pandas Tutorial | What is Pand... • Python Playlist: • Python Tutorial for Beginners • Python Data Structure Playlist: • Python Data Structure • Python OOPs Playlist: • Object Oriented Programming Tutorials... • • #python • #programming • #datascience • #machinelearning • #webdevelopment • #code • #developer • #softwareengineering • #opensource • #tutorial • #tech • #coding • #computerprogramming • #pandas • #numpy • #matplotlib • #StreamlitTutorial • #PythonDashboard • #InteractivePlots • #DataVisualization • #StreamlitAndPlotly • #DataAnalysis • #TimeSeriesAnalysis • #PandasTutorial • #PlotlyVisualization • #DataTables • #DashboardDevelopment • #PythonProgramming • #StreamlitExamples • #DataManipulation • #DataFiltering • #DataTransformation • #HierarchicalView • #DataExploration • #DashboardDesign • #StreamlitTips
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