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How do you plot multiple data sets or variables on the same chart or graph in Python?

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Python is a powerful and versatile programming language for data analysis and visualization. One of the most common tasks you may encounter as a data presenter is to plot multiple data sets or variables on the same chart or graph to compare, contrast, or explore their relationships. In this article, you will learn how to use some of the popular Python libraries and tools to create different types of charts and graphs with multiple data sets or variables.

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1 Choose your library

The first step to plot multiple data sets or variables on the same chart or graph in Python is to choose the library that suits your needs and preferences. There are many options available, but some of the most widely used ones are matplotlib, seaborn, pandas, and plotly. Each of these libraries has its own advantages and disadvantages, such as ease of use, customization, interactivity, and aesthetics. You can also combine them to leverage their features and functionalities.

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2 Prepare your data

The next step is to prepare your data for plotting. Depending on the type and source of your data, you may need to perform some operations such as cleaning, filtering, aggregating, reshaping, or transforming your data to make it ready for visualization. You can use pandas, numpy, or other libraries to manipulate your data in Python. You should also decide which variables or columns you want to plot and how you want to group, label, or color them.

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3 Select your chart or graph type

The third step is to select the type of chart or graph that best suits your purpose and message. There are many types of charts and graphs you can create with Python, such as line, bar, scatter, pie, histogram, box, heatmap, and more. Each of these types has its own strengths and weaknesses, depending on the nature and distribution of your data, the number and scale of your variables, and the kind of insights or comparisons you want to convey.

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4 Create your chart or graph

The fourth step is to create your chart or graph with the library and the type you have chosen. You can use the built-in functions and methods of the library to create your chart or graph with a few lines of code. You can also customize your chart or graph by adding titles, legends, axes, labels, annotations, colors, styles, and more. You can use the documentation and examples of the library to learn how to use its parameters and options.

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5 Add multiple data sets or variables

The fifth step is to add multiple data sets or variables to your chart or graph. Depending on the library and the type you have chosen, there are various ways to do this. For example, you can use subplots or axes to create multiple charts or graphs in one figure, hue, style, or size parameters to differentiate multiple variables in one chart or graph, overlay or stack options to combine multiple data sets in one chart or graph, and facet or grid functions to create multiple charts or graphs with different subsets of data.

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Annette Dolph

Executive Director | Shaping Strategic Leadership Through Data
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One thing to consider when using seaborn's FacetGrid() or matplotlib's subplot() is how your grid of visualizations will work together to communicate information. Make sure you maintain consistency in terms of color schemes, axis scales, and labeling across all subplots. Consistency allows viewers to easily compare and contrast different facets of the data from one subplot to the next. Additionally, consider the overall composition of the grid, ensuring that the arrangement of visualizations makes logical sense and aids in highlighting patterns or trends. To put it simply, it's essential to strike a balance between individual plot design within each subplot and the collective narrative of the entire grid.

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6 Save or show your chart or graph

The final step is to save or show your chart or graph. You can use the savefig or write_image methods of the library to save your chart or graph as an image file in your desired format and location. You can also use the show or iplot methods of the library to display your chart or graph in your notebook or browser. You can also export or share your chart or graph as an interactive web page or widget with some libraries.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you plot multiple data sets or variables on the same chart or graph in Python?

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1 Choose your library

2 Prepare your data

3 Select your chart or graph type

4 Create your chart or graph

5 Add multiple data sets or variables

6 Save or show your chart or graph

7 Here’s what else to consider

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