0401 Simple Line Plots

中文版：简单折线图

Simple Line Plots

Perhaps the simplest ofallplots is the visualization of a single function $y=f(x)$. Herewewilltakeafirst look at creating a simple plotofthistype. Aswithallthe following sections, we’ll start by setting up the notebook for plotting and importing the packages wewilluse:

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

For all Matplotlib plots, we start by creating a figure andanaxes. In their simplest form, a figure andaxescanbe created as follows:

fig = plt.figure()
ax = plt.axes()

In Matplotlib, the figure (an instance oftheclass plt.Figure) can be thought ofasa single container that contains all the objects representing axes, graphics, text, and labels. The axes (an instance oftheclass plt.Axes) iswhatweseeabove: a bounding boxwithticks and labels, which will eventually contain the plot elements thatmakeupour visualization. Throughout this book, we’ll commonly use the variable name fig to refer to a figure instance, and ax to refer toanaxes instance or group of axes instances.

Oncewehave created an axes, wecanusethe ax.plot function toplotsomedata. Let’s start with a simple sinusoid:

fig = plt.figure()
ax = plt.axes()
 
x = np.linspace(0, 10, 1000)
ax.plot(x, np.sin(x));

Ifwewantto create a single figure with multiple lines, we can simply call the plot function multiple times:

plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x));

That’sallthere is to plotting simple functions in Matplotlib! We’llnowdiveintosomemore details about how to control the appearance oftheaxesandlines.

Adjusting the Plot: Line Colors and Styles

plt.plot(x, x + 0, linestyle='solid')
plt.plot(x, x + 1, linestyle='dashed')
plt.plot(x, x + 2, linestyle='dashdot')
plt.plot(x, x + 3, linestyle='dotted');
 
# For short, youcanusethe following codes:
plt.plot(x, x + 4, linestyle='-') # solid
plt.plot(x, x + 5, linestyle='--') # dashed
plt.plot(x, x + 6, linestyle='-.') # dashdot
plt.plot(x, x + 7, linestyle=':'); # dotted

Ifyouwould liketobe extremely terse, these linestyle and color codes can be combined into a single non-keyword argument to the plt.plot() function:

plt.plot(x, x + 0, '-g') # solid green
plt.plot(x, x + 1, '--c') # dashed cyan
plt.plot(x, x + 2, '-.k') # dashdot black
plt.plot(x, x + 3, ':r'); # dotted red

These single-character color codes reflect the standard abbreviations intheRGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.

There aremanyother keyword arguments thatcanbeusedtofine-tune the appearance oftheplot; for more details, I’d suggest viewing the docstring of the plt.plot() function using IPython’shelptools (See Help and Documentation in IPython).

Adjusting the Plot: Axes Limits

Matplotlib does a decent job of choosing default axes limits foryourplot, but sometimes it’snicetohavefiner control. Themostbasic way to adjust axis limits istousethe plt.xlim() and plt.ylim() methods:

plt.plot(x, np.sin(x))
 
plt.xlim(-1, 11)
plt.ylim(-1.5, 1.5);

Ifforsome reason you’d like either axistobe displayed in reverse, you can simply reverse the order of the arguments:

plt.plot(x, np.sin(x))
 
plt.xlim(10, 0)
plt.ylim(1.2, -1.2);

A useful related method is plt.axis() (noteherethe potential confusion between axes with an e, and axis with an i). The plt.axis() method allows youtosetthe x and y limits with a single call, by passing alistwhich specifies [xmin, xmax, ymin, ymax]:

plt.plot(x, np.sin(x))
plt.axis([-1, 11, -1.5, 1.5]);