
- Matplotlib Tutorial
- Matplotlib - Home
- Matplotlib - Introduction
- Matplotlib - Environment Setup
- Matplotlib - Anaconda distribution
- Matplotlib - Jupyter Notebook
- Matplotlib - Pyplot API
- Matplotlib - Simple Plot
- Matplotlib - PyLab module
- Object-oriented Interface
- Matplotlib - Figure Class
- Matplotlib - Axes Class
- Matplotlib - Multiplots
- Matplotlib - Subplots() Function
- Matplotlib - Subplot2grid() Function
- Matplotlib - Grids
- Matplotlib - Formatting Axes
- Matplotlib - Setting Limits
- Setting Ticks and Tick Labels
- Matplotlib - Twin Axes
- Matplotlib - Bar Plot
- Matplotlib - Histogram
- Matplotlib - Pie Chart
- Matplotlib - Scatter Plot
- Matplotlib - Contour Plot
- Matplotlib - Quiver Plot
- Matplotlib - Box Plot
- Matplotlib - Violin Plot
- Three-dimensional Plotting
- Matplotlib - 3D Contour Plot
- Matplotlib - 3D Wireframe plot
- Matplotlib - 3D Surface plot
- Matplotlib - Working With Text
- Mathematical Expressions
- Matplotlib - Working with Images
- Matplotlib - Transforms
- Matplotlib Useful Resources
- Matplotlib - Quick Guide
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- Matplotlib - Discussion
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Matplotlib - Box Plot
A box plot which is also known as a whisker plot displays a summary of a set of data containing the minimum, first quartile, median, third quartile, and maximum. In a box plot, we draw a box from the first quartile to the third quartile. A vertical line goes through the box at the median. The whiskers go from each quartile to the minimum or maximum.

Let us create the data for the boxplots. We use the numpy.random.normal() function to create the fake data. It takes three arguments, mean and standard deviation of the normal distribution, and the number of values desired.
np.random.seed(10) collectn_1 = np.random.normal(100, 10, 200) collectn_2 = np.random.normal(80, 30, 200) collectn_3 = np.random.normal(90, 20, 200) collectn_4 = np.random.normal(70, 25, 200)
The list of arrays that we created above is the only required input for creating the boxplot. Using the data_to_plot line of code, we can create the boxplot with the following code −
fig = plt.figure() # Create an axes instance ax = fig.add_axes([0,0,1,1]) # Create the boxplot bp = ax.boxplot(data_to_plot) plt.show()
The above line of code will generate the following output −
