Data Visualization Tutorial
Hands-on Scatter Plot with Matplotlib in Python
A practical guide on how to draw excellent scatter plots with the plot and scatter methods.
A scatter plot is a type of plot used to display the values of two variables. Scatter plot is also called scatter chart, scatter graph, or scatter diagram. In this post, I’ll explain the following topics:
- What is scatter plot?
- Scatter plot with the plot method
- Scatter plot with the scatter method
- The scatter method vs the plot method
Let’s dive in!
What is scatter plot?
A scatter plot is a type of data display that uses dots to represent values for two different numeric variables. You can draw a scatter plot to see the relationship between two numerical variables such as weight and height. You can find the correlations between the variables with the scatter plot.
Let’s draw a scatter plot using the matplotlib. To show this, let’s import the necessary libraries.
You can find the notebook and dataset here. Now, let’s use the %matplotlib inline magic command to enable the inline plotting.
Let’s specify the style of the graphics with seaborn.
I’m going to use the iris dataset to show the scatter plot. Let’s load the iris dataset with seaborn.
Let’s see the first rows of the dataset.
There are 4 numeric variables in the dataset, they are sepal height and width, petal height and width, and one variable indicating three types of the iris flower. You can draw a scatter plot with the plot method and the scatter method. First, I’m going to show how to use the plot method.
Scatter plot with the plot method
To show how to draw a scatter plot with the plot method, let’s create a graphic object and graphic area.
Let’s draw a scatter plot of sepal length and sepal width variables.
You can change the shape of the dots. To do this, let’s use the + symbol.
Let’s see the scatter plot with triangulars using the v option.
The color of the dots is blue by default. You can change the color. For example, let’s use the color red with the rv option.
You can also specify the size of the dots with the marksize parameter as follows:
Scatter plot with the scatter method
You can also draw the scatter plot with the scatter method. This method is similar to the plot method. Let’s draw a scatter plot of the sepal width and petal width with this method.
You can adjust the size of the dots with the s parameter.
The scatter method vs the plot method
The plot method is faster than the scatter method for large samples. In addition, the properties of the dots are entered separately in the scatter method. The c parameter is used for the color of the dots. For example, let’s use the color purple.
You can use the marker parameter for the shape of the dots. For example, let’s draw the scatter plot with the triangles.
You can use the edgecolor parameter for the border color of the dots, and linewidth for the thickness.
You can also adjust the visibility of the dots with the alpha parameter.
You can assign each dot to a color. To show this, let’s create a color variable for this and generate 150 numbers between 0 and 10 with the random.randint method.
Next, let’s set this color variable to the c parameter.
You can adjust the color of the dots with the cmap method. When setting the color of the cmap method, you need to start the first letter with a capital letter and end with the letter s.
For more different colors, you can use the "viridis" option.
viridis optionYou can also add a bar that displays the numerical values of the colors with the colorbar method.
You can also name the color bar with the set_label method.
In addition, you can adjust the size of the dots. For this, let’s create a variable with the random.randint method.
Next, let’s set this variable to the parameter s.
You can name the axes with the xlabel and the ylabel methods.
Finally, let’s give the plot a title with the title method.
Conclusion
You can use a scatter plot to see the relationship between two variables. In this post, I talked about the scatter plot using plot and scatter methods. That’s it. I hope you enjoy it. Thank you for reading. You can find this notebook here. Don’t forget to follow us on YouTube | GitHub | Twitter | Kaggle | LinkedIn
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