#Python box and whisker plot code#

To remove the tick-marks from top and right spines, or rather to keep ticks only on the bottom and left axes, add the following lines to the code. We can modify the labels using the following line (add it before the line where the figure is saved): # Custom x-axis labelsĪx.set_xticklabels() For example, suppose instead of the default x-axis labels that we see in the plots above, we want labels 'Sample1', 'Sample2', 'Sample3' and 'Sample4'. The list should have the same length as the number of boxes in the boxplot. To do this pass a list of custom labels to ax.set_xticklabels() function. # change the style of fliers and their fillįt(marker='o', color='#e7298a', alpha=0.5)Ĭhange x-axis labels and remove tick marks from the top and right axes. # change color and linewidth of the medians # change color and linewidth of the whiskers # change outline color, fill color and linewidth of the boxes # add patch_artist=True option to ax.boxplot()īp = ax.boxplot(data_to_plot, patch_artist=True)

#Python box and whisker plot code#

Add the following code before the fig.savefig('fig1.png', bbox_inches='tight') line in the above snippet. In addition to changing the colors we also increase the line width to make the differences in color easier to see. To change the colors of boxes, whiskers etc., we need to access these instances from the bp dictionary and change their properties. For example, the values bp are the polygon instances that we see as boxes values bp are the line instances that we see as whiskers. The values in this dictionary are the geometric instances that we see in the plot. The bp variable in the code snippet above is a python dictionary with keys boxes, whiskers, caps, fliers and the median. Matplotlib provides endless customization possibilities.Ĭhange color of the boxes, whiskers, caps and the median Most likely that is not the case and you want to customize these features. If you are satisfied with the default choices for the axes limits, labels and the look of the plot there is nothing more to do. Using data_to_plot we can create the boxplot with the following code: # Create a figure instanceįig.savefig('fig1.png', bbox_inches='tight') The list of arrays that we created above is the only required input for creating the boxplot.

# combine these different collections into a listĭata_to_plot = It takes three arguments, mean and standard deviation of the normal distribution, and the number of values desired. That is, the data for the boxplot is in the form of a list of lists, or list of arrays, or a tuple of arrays etc. Once you have the different collections, one for each box, you combine all these collections together in a list, tuple or a numpy array. A collection can be expressed as a python list, tuple, or as a numpy array. We need to provide a collection of values for each box in the boxplot. The second step is to ensure that your data is in an appropriate format. png fileĬonvert the data to an appropriate format # agg backend is used to create plot as a. The first step is to import the python libraries that we will use. Import the libraries and specify the type of the output file. At the end of the post we will have a boxplot which looks like the following. Let us create some box-and-whisker plots (henceforth, referred to simply as boxplots) using Matplotlib. Creating boxplots with Matplotlib on python | matplotlib | plotting