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Python Matplotlib Cheat Sheet

This Python Matplotlib Cheat Sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

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.

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You’ll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.

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, and the documentation.

Also, don’t miss out on our other cheat sheets for data science that cover Numpy, Pandas, and Python basics.


Matplotlib is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data

1D Data

import numpy as np
x = np.linspace(0, 10, 100)
y = np.cos(x)
z = np.sin(x)

2D Data or Images

data = 2 * np.random.random((10, 10))
data2 = 3 * np.random.random((10, 10))
Y, X = np.mgrid[-3:3:100j, -3:3:100j]
U = 1 X** 2 + Y
V = 1 + X Y**2
from matplotlib.cbook import get_sample_data
img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

import matplotlib.pyplot as plt


fig = plt.figure()
fig2 = plt.figure(figsize=plt.figaspect(2.0))


ax1 = fig.add_subplot(221) #row-col-num
ax3 = fig.add_subplot(212)
fig3, axes = plt.subplots(nrows=2,ncols=2)
fig4, axes2 = plt.subplots(ncols=3)

Save Plot

plt.savefig('foo.png') #Save figures
plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot


Plotting Routines

1D Data

fig, ax = plt.subplots()
lines = ax.plot(x,y) #Draw points with lines or markers connecting them
ax.scatter(x,y) #Draw unconnected points, scaled or colored
axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
axes[1,1].axhline(0.45) #Draw a horizontal line across axes
axes[0,1].axvline(0.65) #Draw a vertical line across axes
ax.fill(x,y,color='blue') #Draw filled polygons
ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data

fig, ax = plt.subplots()
im = ax.imshow(img, #Colormapped or RGB arrays
cmap= 'gist_earth',
interpolation= 'nearest',
axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
CS = plt.contour(Y,X,U) #Plot contours
axes2[2].contourf(data1) #Plot filled contours
axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields

axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
axes[1,1].quiver(y,z) #Plot a 2D field of arrows
axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions

ax1.hist(y) #Plot a histogram
ax3.boxplot(y) #Make a box and whisker plot
ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow

Plot Anatomy


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The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

import matplotlib.pyplot as plt
x = [1,2,3,4]  #Step 1
y = [10,20,25,30]
fig = plt.figure() #Step 2
ax = fig.add_subplot(111) #Step 3
ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
color= 'darkgreen',
marker= '^' )
ax.set_xlim(1, 6.5)
plt.savefig('foo.png' ) #Step 5
plt.show() #Step 6

Close and Clear

plt.cla()  #Clear an axis
plt.clf(). #Clear the entire figure
plt.close(). #Close a window

Plotting Customize Plot

Colors, Color Bars & Color Maps

plt.plot(x, x, x, x**2, x, x** 3)
ax.plot(x, y, alpha = 0.4)
ax.plot(x, y, c= 'k')
fig.colorbar(im, orientation= 'horizontal')
im = ax.imshow(img,
cmap= 'seismic' )


fig, ax = plt.subplots()
ax.scatter(x,y,marker= ".")
ax.plot(x,y,marker= "o")


plt.plot(x,y,ls= 'solid')
plt.plot(x,y,ls= '--')
plt.plot(x,y,'--' ,x**2,y**2,'-.' )
plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations

'Example Graph',
style= 'italic' )
xy=(8, 0),
xycoords= 'data',
xytext=(10.5, 0),
textcoords= 'data',
arrowprops=dict(arrowstyle= "->",


plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts

Limits & Autoscaling

ax.margins(x=0.0,y=0.1) #Add padding to a plot
ax.axis('equal')  #Set the aspect ratio of the plot to 1
ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
ax.set_xlim(0,10.5) #Set limits for x-axis


ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
ylabel= 'Y-Axis',
xlabel= 'X-Axis')
ax.legend(loc= 'best')  #No overlapping plot elements


ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
ticklabels=[3,100, 12,"foo" ])
ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
direction= 'inout',

Subplot Spacing

fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines

ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward


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Download Python Matplotlib Cheat Sheet

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