Python For Data Science Cheat Sheet

Plot Anatomy & Workflow

Plot Anatomy

Workflow

Matplotlib

The basic steps to creating plots with matplotlib are:

Axes/Subplot

Learn Python

Interactively

Prepare data

Create plot

Plot

Customize plot

Save plot

Show plot

>>> import matplotlib.pyplot as plt

>>> x = [1,2,3,4]

Step 1

>>> y = [10,20,25,30]

Matplotlib

>>> fig = plt.figure()

Step 2

>>> ax = fig.add_subplot(111)

Step 3

Y-axis

Figure

>>> ax.plot(x, y, color='lightblue', linewidth=3)

Step 3, 4

Matplotlib is a Python 2D plotting library which produces

>>> ax.scatter([2,4,6],

publication-quality figures in a variety of hardcopy formats

[5,15,25],

color='darkgreen',

and interactive environments across

marker='^')

>>> ax.set_xlim(1, 6.5)

platforms.

X-axis

>>> plt.savefig('foo.png')

>>> plt.show()

Step 6

Prepare The Data

Also see Lists & NumPy

Customize Plot

1D Data

>>> import numpy as np

Colors, Color Bars & Color Maps

Mathtext

>>> x = np.linspace(0, 10, 100)

>>> y = np.cos(x)

>>> plt.plot(x, x, x, x**2, x, x**3)

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

>>> z = np.sin(x)

>>> ax.plot(x, y, alpha = 0.4)

>>> ax.plot(x, y, c='k')

Limits, Legends & Layouts

2D Data or Images

>>> fig.colorbar(im, orientation='horizontal')

>>> im = ax.imshow(img,

Limits & Autoscaling

>>> data = 2 * np.random.random((10, 10))

cmap='seismic')

>>> ax.margins(x=0.0,y=0.1)

Add padding to a plot

>>> data2 = 3 * np.random.random((10, 10))

>>> ax.axis('equal')

Set the aspect ratio of the plot to 1

>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]

Markers

>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])

Set limits for x-and y-axis

>>> U = -1 - X**2 + Y

>>> ax.set_xlim(0,10.5)

Set limits for x-axis

>>> V = 1 + X - Y**2

>>> fig, ax = plt.subplots()

Legends

>>> from matplotlib.cbook import get_sample_data

>>> ax.scatter(x,y,marker=".")

>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

>>>

ax.set(title='An Example

Axes',

>>> ax.plot(x,y,marker="o")

Set a title and x-and y-axis labels

ylabel='Y-Axis',

Linestyles

xlabel='X-Axis')

Create Plot

>>> ax.legend(loc='best')

No overlapping plot elements

>>> plt.plot(x,y,linewidth=4.0)

Ticks

>>> plt.plot(x,y,ls='solid')

>>> import matplotlib.pyplot as plt

>>> ax.xaxis.set(ticks=range(1,5),

Manually set x-ticks

>>> plt.plot(x,y,ls='--')

ticklabels=[3,100,-12,"foo"])

>>> plt.plot(x,y,'--',x**2,y**2,'-.')

Figure

>>> ax.tick_params(axis='y',

Make y-ticks longer and go in and out

>>> plt.setp(lines,color='r',linewidth=4.0)

direction='inout',

>>> fig = plt.figure()

length=10)

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

Text & Annotations

Subplot Spacing

Axes

>>> fig3.subplots_adjust(wspace=0.5,

>>> ax.text(1,

Adjust the spacing between subplots

-2.1,

hspace=0.3,

All plotting is done with respect to an Axes. In most cases, a

'Example

Graph',

left=0.125,

style='italic')

right=0.9,

subplot will fit your needs. A subplot is an axes on a grid system.

>>> ax.annotate("Sine",

top=0.9,

xy=(8, 0),

>>> fig.add_axes()

bottom=0.1)

xycoords='data',

>>> fig.tight_layout()

>>> ax1 = fig.add_subplot(221)

# row-col-num

Fit subplot(s) in to the figure area

xytext=(10.5, 0),

>>> ax3 = fig.add_subplot(212)

textcoords='data',

Axis Spines

>>> fig3, axes = plt.subplots(nrows=2,ncols=2)

arrowprops=dict(arrowstyle="->",

>>> ax1.spines['top'].set_visible(False)

Make the top axis line for a plot invisible

>>> fig4, axes2 = plt.subplots(ncols=3)

connectionstyle="arc3"),)

>>> ax1.spines['bottom'].set_position(('outward',10)) Move the bottom axis line outward

Plotting Routines

Save Plot

Save figures

1D Data

Vector Fields

>>> plt.savefig('foo.png')

>>> fig, ax = plt.subplots()

>>> axes[0,1].arrow(0,0,0.5,0.5)

Add an arrow to the axes

Save transparent figures

>>> lines = ax.plot(x,y)

Draw points with lines or markers connecting them

>>> axes[1,1].quiver(y,z)

Plot a 2D field of arrows

>>> ax.scatter(x,y)

>>> plt.savefig('foo.png', transparent=True)

Draw unconnected points, scaled or colored

>>> axes[0,1].streamplot(X,Y,U,V)

Plot a 2D field of arrows

>>> 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)

Data Distributions

Show Plot

>>> axes[1,1].axhline(0.45)

Draw a horizontal line across axes

>>> axes[0,1].axvline(0.65)

Draw a vertical line across axes

>>> ax1.hist(y)

Plot a histogram

>>> plt.show()

>>> ax.fill(x,y,color='blue')

Draw filled polygons

>>> ax3.boxplot(y)

Make a box and whisker plot

>>> ax.fill_between(x,y,color='yellow') Fill between y-values and 0

>>> ax3.violinplot(z)

Make a violin plot

Close & Clear

2D Data or Images

>>> plt.cla()

Clear an axis

>>> fig, ax = plt.subplots()

>>> axes2[0].pcolor(data2)

Pseudocolor plot of 2D array

>>> plt.clf()

Clear the entire figure

>>> im = ax.imshow(img,

Colormapped or RGB arrays

>>> axes2[0].pcolormesh(data)

Pseudocolor plot of 2D array

>>> plt.close()

Close a window

cmap='gist_earth',

>>> CS = plt.contour(Y,X,U)

Plot contours

interpolation='nearest',

>>> axes2[2].contourf(data1)

Plot filled contours

vmin=-2,

DataCamp

>>> axes2[2]= ax.clabel(CS)

Label a contour plot

vmax=2)

Learn Python for Data Science

Interactively

Matplotlib 2.0.0 - Updated on: 02/2017

Python For Data Science Cheat Sheet - Datacamp

Related Articles

Related forms

Related Categories