Showing information in a way that's easy to grasp is important for seeing patterns. In this post, we'll go through the steps to make an interactive Tableau dashboard to look at London bike rental information. This project has elements like total bike rides, rolling averages, a heatmap displaying temperature versus wind speed, and charts based on weather and time of day. Let's get into it!

Step 1 - Data Exploration and Preparation

• Obtain the London bike sales dataset from Kaggle using the Kaggle API.

• Use Python's Pandas library to explore, assess, and manipulate the dataset.

• Save the cleaned dataset as 'London_Bikes_Final.xlsx' for Tableau visualization.

Step 2 - Creating Visualizations

1. Total Bike Rides Visualization

   • Utilize Tableau to create a line chart showcasing total bike rides over time.

   • Apply a moving average to smooth out fluctuations and trends.

2. Moving Average Chart

   • Generate a line chart illustrating the moving average trend.

   • Enable dynamic adjustments of the moving average duration via user-defined parameters.

3. Heatmap: Temperature vs. Wind Speed

   • Build a heatmap displaying temperature against wind speed.

   • Use bins to categorize temperature and wind speed for effective visualization.

4. Weather and Hour Charts

   • Develop visualizations showcasing bike rides by weather type and hour.

   • Use bar charts to depict trends and distributions.

5. Implementing Tooltips for Additional Insights

   • Configure tooltips to provide detailed information about weather, hours, and more when hovering over the charts.

If you wish to try out the visualization for yourself, here are the links to the resources and the dashboard.

Step 3 - Dashboard Creation

1. Assembling the Dashboard

   • Create a dashboard layout in Tableau and integrate the visualizations.

   • Ensure the dashboard elements are appropriately aligned and sized for a clear view.

2. Adding Interactivity

   • Implement filters and set actions to enable users to interact with the data effectively.

   • Link the moving average period set for dynamic updates across visualizations.

3. Customization and Formatting

   • Adjust colours, fonts, and borders to enhance visual appeal and readability.

   • Refine chart titles, labels, and tooltips for clarity.

4. Final Touches and Testing

   • Review the dashboard to ensure all elements are working correctly.

   • Validate user-defined parameters, set actions, and tooltips for accurate information display.

Conclusion

In this post, we used Tableau to turn London bike data into an easy-to-understand dashboard. We cleaned the data, created charts and graphs, and made it interactive. This project shows how Tableau can make even complex data feel simple and approachable. The same method can be used to understand any data better.