3 Powerful ways to Visualize Data on Maps using Excel, R, and Python. скачать в хорошем качестве

3 Powerful ways to Visualize Data on Maps using Excel, R, and Python.

In this video, we explore three powerful ways to visualize data on maps using Excel, R, and Python. Whether you’re creating reports, presentations, or just want to better understand geographical patterns in your dataset, these techniques will help you transform raw numbers into meaningful visualizations. We begin with Excel, where you’ll learn how to quickly create both 2D and 3D maps, add labels, and even export map-based videos for presentations. Next, we dive into R, using libraries to plot your Excel data onto a world map, customize colors, and format visuals. Finally, we move into Python with Plotly, generating interactive geographical maps where circle size represents sales and colors represent customers — and learn how to save these as images for your reports. By the end of the video, you’ll have a clear understanding of when to use Excel for speed, R for detailed analysis, and Python for interactive and customizable outputs. If you find this helpful, don’t forget to like, share, and subscribe for more tutorials on data visualization and analysis! ⏱ TIMESTAMPS 00:00 – Introduction 00:05 – Creating 2D maps in Excel 01:38 – Adding labels & customizing data in Excel maps 02:02 – Switching to 3D maps in Excel 03:53 – Exporting map videos from Excel 06:21 – Returning to data & comparing 2D vs 3D maps 07:01 – Mapping data in R (step-by-step code) 08:20 – Formatting and customizing maps in R 09:09 – Saving maps from R for reports 09:23 – Creating maps in Python (Visual Studio + Plotly) 10:38 – Interpreting customers vs sales in Python maps 11:20 – Exporting interactive Python maps 11:44 – Final comparison & conclusion