Python For Data Science Cheat Sheet
Matplotlib
Learn Python Interactively at www.DataCamp.com
Matplotlib
DataCamp
Learn Python for Data Science Interactively
Prepare The Data Also see Lists & NumPy
Matplotlib is a Python 2D plotting library which produces
publication-quality figures in a variety of hardcopy formats
and interactive environments across
platforms.
1
>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)
Show Plot
>>> plt.show()
Save Plot
Save figures
>>> plt.savefig('foo.png')
Save transparent figures
>>> plt.savefig('foo.png', transparent=True)
6
5
>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))
Create Plot
2
Plot Anatomy & Workflow
All plotting is done with respect to an Axes. In most cases, a
subplot will fit your needs. A subplot is an axes on a grid system.
>>> fig.add_axes()
>>> 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)
Customize Plot
Colors, Color Bars & Color Maps
Markers
Linestyles
Mathtext
Text & Annotations
Limits, Legends & Layouts
The basic steps to creating plots with matplotlib are:
1 Prepare data 2 Create plot 3 Plot 4 Customize plot 5 Save plot 6 Show plot
>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]
>>> y = [10,20,25,30]
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.plot(x, y, color='lightblue', linewidth=3)
>>> ax.scatter([2,4,6],
[5,15,25],
color='darkgreen',
marker='^')
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png')
>>> plt.show()
Step 3, 4
Step 2
Step 1
Step 3
Step 6
Plot Anatomy Workflow
4
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
Legends
>>> 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
Ticks
>>> 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',
length=10)
Subplot Spacing
>>> fig3.subplots_adjust(wspace=0.5, Adjust the spacing between subplots
hspace=0.3,
left=0.125,
right=0.9,
top=0.9,
bottom=0.1)
>>> 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
Figure
Axes
>>> 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'))
>>> 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
Plotting Routines
3
1D Data
>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, Colormapped or RGB arrays
cmap='gist_earth',
interpolation='nearest',
vmin=-2,
vmax=2)
2D Data or Images
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 2D vector fields
Data Distributions
>>> ax1.hist(y) Plot a histogram
>>> ax3.boxplot(y) Make a box and whisker plot
>>> ax3.violinplot(z) Make a violin plot
>>> 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
Figure
Axes/Subplot
Y-axis
X-axis
1D Data
2D Data or Images
>>> 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.title(r'$sigma_i=15$', fontsize=20)
>>> ax.text(1,
-2.1,
'Example Graph',
style='italic')
>>> ax.annotate("Sine",
xy=(8, 0),
xycoords='data',
xytext=(10.5, 0),
textcoords='data',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"),)
>>> plt.plot(x,y,linewidth=4.0)
>>> 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)
>>> import matplotlib.pyplot as plt
Close & Clear
>>> plt.cla() Clear an axis
>>> plt.clf() Clear the entire figure
>>> plt.close() Close a window
