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Discover how to effectively plot multiple columns from DataFrames in Python using Matplotlib. Learn to visualize temperature and humidity data on the same graph!
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Plotting Multiple Columns from a DataFrame in Python Using a For Loop

Have you ever faced the challenge of plotting multiple columns from a DataFrame in Python? If you're working with data related to weather patterns, for instance, you might be interested in visualizing both temperature and humidity on the same graph. This guide will guide you through the solution step-by-step, so you can easily achieve your data visualization goals.

The Problem: Plotting Data from Multiple Columns

In your scenario, you've got a list of DataFrames that contain hourly data on temperature and humidity, and you'd like to plot both these variables on the y-axis using a for loop. You've managed to plot the temperature successfully, but now you're stumped on how to incorporate humidity into the same plot. Below is the code you’ve been working with:

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

This code effectively generates a plot of temperature against time, but you're unsure how to add the humidity values. Simply trying to plot them together like this:

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

won't work because indexing in this way is not valid in Pandas.

The Solution: Making Two Plot Calls

The good news is that you can easily resolve this issue by making two separate calls to plt.plot() for each of the columns you're interested in plotting. Below is the modified code that incorporates this approach:

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

Explanation of the Code

For Loop: The for loop iterates over each DataFrame in your list li.

First Plot Call: The first plt.plot() function call plots the temperature data against time. The label='Temperature' parameter is optional, but it helps differentiate lines in your plot.

Second Plot Call: The second plt.plot() function call does the same for humidity data, allowing both datasets to be visualized on the same graph.

Adding Legends and Enhancements

To enhance your plot further and make it more informative, consider adding legends, titles, and labels:

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

Conclusion

Now you can easily visualize both temperature and humidity on the same graph! By using two separate plt.plot() calls, you can effectively map multiple columns from your DataFrame onto the y-axis. This technique is handy when dealing with weather data or any scenario where you need to visualize multiple time-dependent variables.

The next time you're plotting with Python and Pandas, remember this straightforward approach for enhancing your data visualizations! Happy plotting!