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Learn how to plot timeseries data in VSCode with pandas and visualize stock data effortlessly.
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This video is based on the question https://stackoverflow.com/q/75385586/ asked by the user 'elksie5000' ( https://stackoverflow.com/u/386861/ ) and on the answer https://stackoverflow.com/a/75388180/ provided by the user 'Ashwani Kumar' ( https://stackoverflow.com/u/3943564/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Plotting Timeseries Data in VSCode Using Pandas

Visualizing data is a key aspect of data analysis and manipulation, and one of the most commonly used libraries for this task in Python is pandas. If you’re using Visual Studio Code (VSCode) to work with Jupyter notebooks, you might encounter a simple yet frustrating issue when trying to plot timeseries data. In this guide, we’ll walk through the steps to effectively plot BP share data using the yfinance library and ensure that your plots display correctly. Let’s dive in!

The Problem: Getting No Plot Output

You might have run into the scenario where, after executing your code to create a plot, you see the message indicating that a figure was created, but the plot itself does not appear on the screen. This can be bewildering and, if you're new to data visualization, may lead you to think there’s an error in your code. Here’s a common code snippet you might find yourself using:

[[See Video to Reveal this Text or Code Snippet]]

You then try to create a plot with something like:

[[See Video to Reveal this Text or Code Snippet]]

However, you find that despite everything looking correct in your console, the plot does not display as expected.

The Solution: Adjusting Your Code

Don’t worry; the solution is quite simple! Follow the steps below to ensure your plot appears as expected when working in VSCode with Jupyter notebooks.

1. Comment Out %matplotlib inline

The key to your plotting issue is the line containing %matplotlib inline. This command is specific to Jupyter notebooks and allows inline plotting; however, if you want to plot your data in a separate window (as is the case with Visual Studio Code), you need to comment it out or remove it. Here’s how your code will look after this modification:

[[See Video to Reveal this Text or Code Snippet]]

2. Consider Additional Plot Customizations

While it’s great to just plot the data, adding some customizations can enhance the readability and aesthetics of your plot. Here are a few suggestions:

Title: Add a title to your plot using plt.title('BP Stock Closing Prices').

Labels: Use plt.xlabel('Date') and plt.ylabel('Price') to label your axes clearly.

Legend: Enable a legend using plt.legend() to make the graph more informative.

Example with Customizations

Here’s a more polished version of your plot code:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Plotting stock data can be straightforward once you know the necessary adjustments to make. By commenting out %matplotlib inline, your plots should now display correctly in separate windows when using VSCode. Be sure to experiment with additional plot customizations to enhance the visual impact of your data. Happy plotting!