Choosing a visualization

1. Choosing a visualization

Welcome to the section on different plot types! In this section, we'll explore the various types of plots that you can use to present your data. It's important to choose the right plot type for your data to ensure that your message is conveyed effectively. Let's get started!

2. Scatter plots

First up, we have scatter plots. Scatter plots are a great way to show the relationship between two variables. The x-axis represents one variable, and the y-axis represents the other. Each point on the plot represents an individual data point. Let's take a look at an example. This scatterplot shows a point for each region, describing its population and number of verified green businesses.

3. Line plots

Next, we have line plots. Line plots are great for showing trends over time. The x-axis represents time, and the y-axis represents the variable being measured. Each data point is connected by a line, which can help show trends more clearly. Here's an example. This line plot shows the evolution of the number of verified green businesses per each year.

4. Bar plots

Now, let's talk about bar plots. Bar plots are great for showing comparisons between categories. The height of each bar represents the value being measured. The bars can be either horizontal or vertical. Let's take a look at an example of a horizontal bar plot. It shows the number of verified green businesses classified according to the result variable.

5. Pie charts

Moving on to pie charts. Pie charts are great for showing proportions. The size of each slice represents the proportion of the whole. However, pie charts should only be used when there are a small number of categories. This example pie chart shows the most common categories of the verified green businesses. Notice how there is an "Other" category, so to not cram the plot with too many options.

6. Heat maps

Finally, we have heat maps. Heat maps are great for showing how values are distributed across two variables. The x-axis and y-axis represent the two variables being measured, and the color of each cell represents the value being measured. It can be useful to spot trends and visualize the volume of events in a dataset. Let's take a look at an example of a heat map. This heatmap shows the number of green businesses in every combination of year and region. Missing values are colored outside of Datacamp's green color scale.

7. Let's practice!

Great job! Now that you've learned about the different types of plots, let's put that knowledge into practice! Head over to the interactive exercises and see how you can apply what you've learned. Have fun exploring!

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