![]() Let’s add some formatting to the above chart. Matplotlib comes with number of different formatting options to customize your charts. Sign up to =1 for access to these, video downloads, and no ads. The scatter plot that we got in the previous example was very simple without any formatting. Figure 4: Matplotlib Scatter plot We can give the graph more meaning by coloring in each data-point by its class. We will also create a figure and an axis using plt.subplots so we can give our plot a title and labels. There exists 3 quiz/question(s) for this tutorial. To create a scatter plot in Matplotlib we can use the scatter method. If we draw multiple lines on one graph, we label them individually using. Next, we can assign the plot's title with plt.title, and then we can invoke the default legend with plt.legend(). To add a legend we use the plt.legend() function. With plt.xlabel and plt.ylabel, we can assign labels to those respective axis. Plt.title('Interesting Graph\nCheck it out') So there are several different types of charts or graphs you can make in matplotlib, including line plots, bar graphs, histograms, pie charts, scatter plots. Let’s now look at some examples of using the above syntax. The rest of our code: plt.xlabel('Plot Number') Now to add labels to each point in the scatter plot, use the () function for each point (x, y) and add its appropriate label. ![]() Here, we plot as we've seen already, only this time we add another parameter "label." This allows us to assign a name to the line, which we can later show in the legend. Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. Notes The plot function will be faster for scatterplots where markers don't vary in size or color. Accepted Answer: scatter plot and line in python R. This way, we have two lines that we can plot. To plot scatter plots when markers are identical in size and color. To start: import matplotlib.pyplot as plt ![]() A lot of times, graphs can be self-explanatory, but having a title to the graph, labels on the axis, and a legend that explains what each line is can be necessary. In this tutorial, we're going to cover legends, titles, and labels within Matplotlib.
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