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Matplotlib cheat sheet

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib. DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. (By the way, if you want to get started with this Python package, you might want to consider our Matplotlib tutorial.)You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. Check out the infographic by clicking on the button below:With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation and our tutorial. Also, don't miss out on our other cheat sheets for data science that cover SciPy, Numpy, Scikit-Learn, Bokeh, Pandas and the Python basics. Skip to ContentThe Python library Matplotlib contains the pyplot module, which provides users with an interface for graphing data. Pyplot contains over 100 functions, from acorr to yticks. You must import the module, and plt is the standard variable name used. from matplotlib import pyplot as plt Here are some of the most common pyplot functions: Function Description Plot plots y versus x as lines and/or markers Show displays a figure Axis sets some axis properties Xlabel sets the label for the x-axis Ylabel sets the label for the y-axis Title sets a title for the axes Subplot adds a subplot to the current figure Subplots_adjust tunes the subplot layout Legend places a legend on the axes Figure creates a new figure Savefig saves the current figure Matplotlib's pyplot.axis function takes one parameter, which must be a four-item array, and returns the current axes' limits. The four items should contain enough info to define an x axis and a y axis by minimum and maximum values. The array must order these values as follows: x-axis minimum, x-axis maximum, y-axis minimum, y-axis maximum. x = range(12) y = [2,8,20,40,70,300,930,7000,68000,500000,4000000, 2000000] plt.plot(x, y) #x-axis minimum is 0, x-axis maximum is 11; y-axis minimum is 300, y-axis maximum is 500000 plt.axis([0,11,300,500000]) plt.show()In Python's Matplotlib, the pyplot.plot() function can accept parameters to set the color(color), linestyle(linestyle) and marker(marker) for line graph. Color values can be HTML color names or HEX codes. Line styles can be dashed('--') or dotted('..'). Markers can be circles('o'), squares('s'), stars('*'), or other shapes. pyplot.plot(days, money_spent, color='green', linestyle='--') pyplot.plot(days, money_spent_2, color='#AAAAAA', marker='o')In Python's Matplotlib, subplots can overlap, either horizontally or vertically. The function pyplot.subplots_adjust() can set better spacing around each subplot in a figure. It can set values for left, right, bottom and top margins, plus the horizontal(wspace) and vertical(hspace) spaces between adjacent subplots. import matplotlib.pyplot as plt # Left Plot plt.subplot(1, 2, 1) plt.plot([-2, -1, 0, 1, 2], [4, 1, 0, 1, 4]) # Right Plot plt.subplot(1, 2, 2) plt.plot([-2, -1, 0, 1, 2], [4, 1, 0, 1, 4]) # Subplot Adjust plt.subplots_adjust(wspace=1.3) plt.show()In Python's Matplotlib, the x-tick and y-tick marks of the plot can be changed using functions ax.set_xticks() and ax.set_yticks(). These functions accepts an array of values representing tick mark positions. import matplotlib.pyplot as plt ax = plt.subplot() plt.plot([0, 1, 2, 3, 4], [0, 1, 4, 9, 16]) plt.plot([0, 1, 2, 3, 4], [0, 1, 8, 27, 64]) ax.set_xticks([1, 2, 4])In Python, the Matplotlib's pyplot.subplot() function can be used to create a figure with a grid of subplots. The function accepts number of rows, number of columns, and the current index as arguments. import matplotlib.pyplot as plt # Datasets x = [1, 2, 3, 4] y = [1, 2, 3, 4] # First Subplot plt.subplot(1, 2, 1) plt.plot(x,y, color='green') plt.title('First Subplot') # Second Subplot plt.subplot(1, 2, 2) plt.plot(x,y, color='blue') plt.title('Second Subplot') # Display both subplots plt.show()In Python's Matplotlib, a figure is a container that holds plots. It can hold a single plot or multiple plots. When a figure holds multiple separate plots, those are called subplots. nbviewer.urls/g...Page 2nbviewer.urls/g...

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