![]() It builds a strong foundation for advanced work with these libraries, covering a wide range of plotting techniques - from simple 2D plots to animated 3D plots with interactive buttons. ✅ Regularly updated for free (latest update in April 2021)ĭata Visualization in Python with Matplotlib and Pandas is a comprehensive book designed to guide absolute beginners with basic Python knowledge in mastering Pandas and Matplotlib. ✅ 30-day no-question money-back guarantee This code limits the view on the X-axis to the data between 25 and 50, as shown in the resulting plot: For example, if we wanted to truncate the view to only show the data in the range of 25-50 on the X-axis, we'd use xlim(): import matplotlib.pyplot as plt Both of these methods accept a tuple containing the left and right limits. Let's first set the X-limit using both the PyPlot and Axes instances. For example, if you want to focus on the range from 2 to 8, you can set the x-axis limits as follows: To set the x-axis range, you can use the xlim function, which takes two arguments: the lower and upper limits of the x-axis. These functions can be accessed either through the PyPlot instance or the Axes instance. ![]() To adjust the axis range, you can use the xlim and ylim functions. However, you might want to modify the axis range for better visualization or to focus on a specific region of the plot. The x-axis currently ranges from 0 to 100, and the y-axis ranges from -1 to 1. Running this code produces the following plot: ![]() The sequence starts at 0 and ends at 10 with a step of 0.1. In this example, we've plotted the values created by applying a sine and cosine function to the sequence generated using NumPy's arange() Function. Optionally, you could add ax.legend() to display the labels for each wave. In the above code, we create a figure and axis object with plt.subplots(), generate x, y, and z data points using NumPy, and then plot the sine and cosine waves on the same axis. Let's first create a simple plot to work with: import matplotlib.pyplot as pltĪx.plot(y, color= 'blue', label= 'Sine wave')Īx.plot(z, color= 'black', label= 'Cosine wave') This can be useful when you want to focus on a particular portion of your data or to ensure consistency across multiple plots. In this tutorial, we'll take a look at how to set the axis range ( xlim, ylim) in Matplotlib, to truncate or expand the view to specific limits. Much of Matplotlib's popularity comes from its customization options - you can tweak just about any element from its hierarchy of objects. Matplotlib is one of the most widely used data visualization libraries in Python.
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