![]() ![]() ![]() Sign up to =1 for access to these, video downloads, and no ads. There exists 3 quiz/question(s) for this tutorial. ![]() Next, we can assign the plot's title with plt.title, and then we can invoke the default legend with plt.legend(). Adding an x-axis and labels When it comes to the x-axis in matplotlib, there’s two important pieces to that axis: x-ticks: The place in the dataset where you want a label to be applied x-ticks-labels: The label you want to put at the tick Practically in our case, I believe it would be interesting to show 10 dates along the x-axis. With plt.xlabel and plt.ylabel, we can assign labels to those respective axis. Plt.title('Interesting Graph\nCheck it out') The rest of our code: plt.xlabel('Plot Number') 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. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data. This way, we have two lines that we can plot. The first way is to use the ax.set () function, which uses the following syntax: ax.set(xlabel'x-axis label', ylabel'y-axis label') The second way is to use matplotlib functions, which use the following syntax: plt.xlabel('x-axis label') plt. To start: import matplotlib.pyplot as plt There are two ways to change the axis labels on a seaborn plot. 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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