![]() # Now use the axes object to add stuff to plot # Use similar to plt.figure() except use tuple unpacking to grab fig and axes The plt.subplots() object will act as a more automatic axis manager. ,Īxes = fig.add_axes() # left, bottom, width, height (range 0 to 1)Īxes.set_xlabel('Set X Label') # Notice the use of set_ to begin methodsĬode is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure:Īxes1 = fig.add_axes() # main axesĪxes2 = fig.add_axes() # inset axes This approach is nicer when dealing with a canvas that has multiple plots on it. The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. Introduction to the Object Oriented Method This means we will instantiate figure objects and then call methods or attributes from that object. Now that we've seen the basics, let's break it all down with a more formal introduction of Matplotlib's Object Oriented API. Plt.plot(x, y, 'b') # More on color options later Plt.plot(x, y, 'r') # More on color options later To create a 2×2 grid of plots, you can use this code: Plt.plot(x, y, 'r-') # More on color options laterīy changing the subplot parameters we can create a vertical plot Use the code below to create a horizontal subplot Plt.plot(x, y, 'y') # 'r' is the color red We can create a very simple line plot using the following: We created 2 set of numbers **x** and **y**. You can also use lists, but most likely you'll be passing numpy arrays or pandas columns (which essentially also behave like arrays). Let's walk through a very simple example using two numpy arrays. Note:** If you are using Colaboratory **plt.show()** at the end of all the plooting commands to have the figure pop up in another window. To import matplotlib in Colaboratory under the name **plt** from module **matplotlib.pyplot** type the following: * Great control of every element in a figure * Generally easy to get started for simple plots Some of the major Pros of Matplotlib are: It is an excellent 2D and 3D graphics library for generating scientific figures. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. ![]() Matplotlib is a plotting library for Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you. He created it to try to replicate MatLab's (another programming language) plotting capabilities in Python. ![]() Matplotlib is the "grandfather" library of data visualization with Python. The Python tutorials are written as Jupyter notebooks and run directly in Google Colab-a hosted notebook environment that requires no setup. ![]()
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