导入matplotlib
import matplotlib as mpl
import matplotlib.pyplot as plt
设置样式
plt.style.use('classic')
show()在什么时候用
在脚本文件里绘图 需要使用plt.show()
# ------- file: myplot.py ------
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
plt.show()
$ python myplot.py
在ipython命令行里绘图
In [1]: %matplotlib
Using matplotlib backend: TkAgg
In [2]: import matplotlib.pyplot as plt
在笔记本里绘图
%matplotlib notebook
will lead to interactive plots embedded within the notebook
%matplotlib inline
will lead to static images of your plot embedded in the notebook
存储图片到文件
fig.savefig('my_figure.png')
#查看所有的图片
from IPython.display import Image
Image('my_figure.png')
绘画线形
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
fig = plt.figure()
ax = plt.axes()
x = np.linspace(0, 10, 1000)
ax.plot(x, np.sin(x));
plt.plot(x, np.sin(x));
#调整线形和颜色
plt.plot(x, np.sin(x - 0), color='blue') # specify color by name
plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk)
plt.plot(x, np.sin(x - 2), color='0.75') # Grayscale between 0 and 1
plt.plot(x, x + 0, linestyle='solid')
plt.plot(x, x + 1, linestyle='dashed')
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
plt.plot(x, x + 0, '-g') # solid green
plt.plot(x, x + 1, '--c') # dashed cyan
#调整轴
plt.xlim(-1, 11)
plt.ylim(-1.5, 1.5);
plt.axis('tight');
#增加标签
plt.title("A Sine Curve")
plt.xlabel("x")
plt.ylabel("sin(x)");
#也可以同时设置
ax = plt.axes()
ax.plot(x, np.sin(x))
ax.set(xlim=(0, 10), ylim=(-2, 2),
xlabel='x', ylabel='sin(x)',
title='A Simple Plot');
简单的散点图
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
x = np.linspace(0, 10, 30)
y = np.sin(x)
plt.plot(x, y, 'o', color='black');
plt.scatter(x, y, marker='o');
错误可视化
离散偏差
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
x = np.linspace(0, 10, 50)
dy = 0.8
y = np.sin(x) + dy * np.random.randn(50)
plt.errorbar(x, y, yerr=dy, fmt='.k');
连续差
plt.plot(xdata, ydata, 'or')
plt.plot(xfit, yfit, '-', color='gray')
plt.fill_between(xfit, yfit - dyfit, yfit + dyfit,
color='gray', alpha=0.2)
plt.xlim(0, 10);
密度图和等高线图
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 40)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
plt.contour(X, Y, Z, colors='black');
plt.contour(X, Y, Z, 20, cmap='RdGy');
plt.contourf(X, Y, Z, 20, cmap='RdGy')
plt.colorbar();
#类似于matlab contourf imshow
直方图，密度图，条形图
plt.hist(data);
plt.hist(data, bins=30, normed=True, alpha=0.5,
histtype='stepfilled', color='steelblue',
edgecolor='none');
#分幅
x1 = np.random.normal(0, 0.8, 1000)
x2 = np.random.normal(-2, 1, 1000)
x3 = np.random.normal(3, 2, 1000)
kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)
plt.hist(x1, **kwargs)
plt.hist(x2, **kwargs)
plt.hist(x3, **kwargs);
二维直方图
plt.hist2d(x, y, bins=30, cmap='Blues')
counts, xedges, yedges = np.histogram2d(x, y, bins=30)
六边形图
plt.hexbin(x, y, gridsize=30, cmap='Blues')
cb = plt.colorbar(label='count in bin')
核密度图
定制标注
x = np.linspace(0, 10, 1000)
fig, ax = plt.subplots()
ax.plot(x, np.sin(x), '-b', label='Sine')
ax.plot(x, np.cos(x), '--r', label='Cosine')
ax.axis('equal')
leg = ax.legend();
#调整位置
ax.legend(loc='upper left', frameon=False)
fig
#标注分为两列
ax.legend(frameon=False, loc='lower center', ncol=2)
fig
#给标注增加一个背景，有边缘阴影
ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)
fig
#更多请查看帮助
定制颜色条
plt.colorbar();
plt.imshow(I, cmap='gray');
cmap的色系viridiscubehelixRdBujet
多个子图绘制
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np
#不同的水平轴
ax1 = plt.axes() # standard axes
ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])
#通过在图上增加水平坐标轴分幅
fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],
xticklabels=[], ylim=(-1.2, 1.2))
ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],
ylim=(-1.2, 1.2))
x = np.linspace(0, 10)
ax1.plot(np.sin(x))
ax2.plot(np.cos(x));
image.png
image.png
plt.subplot 简单的分图
for i in range(1, 7):
plt.subplot(2, 3, i)
plt.text(0.5, 0.5, str((2, 3, i)),
fontsize=18, ha='center')
#一个图一个图的增加
fig = plt.figure()
fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i in range(1, 7):
ax = fig.add_subplot(2, 3, i)
ax.text(0.5, 0.5, str((2, 3, i)),
fontsize=18, ha='center')
plt.subplots 里整个分图在一个坐标
fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')
plt.GridSpec: More Complicated
grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)
image.png
文本和注释
ax.text('2012-1-1', 3950, "New Year's Day", **style)
# transform=ax.transData is the default, but we'll specify it anyway
ax.text(1, 5, ". Data: (1, 5)", transform=ax.transData)
ax.text(0.5, 0.1, ". Axes: (0.5, 0.1)", transform=ax.transAxes)
ax.text(0.2, 0.2, ". Figure: (0.2, 0.2)", transform=fig.transFigure
箭头和注释
%matplotlib inline
fig, ax = plt.subplots()
x = np.linspace(0, 20, 1000)
ax.plot(x, np.cos(x))
ax.axis('equal')
ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),
arrowprops=dict(facecolor='black', shrink=0.05))
ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle3,angleA=0,angleB=-90"));
设置刻度
#放缩
ax = plt.axes(xscale='log', yscale='log')
#隐藏刻度
ax.yaxis.set_major_locator(plt.NullLocator())
ax.xaxis.set_major_formatter(plt.NullFormatter())
#增加或者减小刻度数量
axi.xaxis.set_major_locator(plt.MaxNLocator(3))
axi.yaxis.set_major_locator(plt.MaxNLocator(3))
#其他刻度格式
ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))
其他知识
Summary of Formatters and Locators
We've mentioned a couple of the available formatters and locators.
We'll conclude this section by briefly listing all the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentaion.
Each of the following is available in the plt namespace:
| Locator class | Description |
|---|---|
NullLocator |
No ticks |
FixedLocator |
Tick locations are fixed |
IndexLocator |
Locator for index plots (e.g., where x = range(len(y))) |
LinearLocator |
Evenly spaced ticks from min to max |
LogLocator |
Logarithmically ticks from min to max |
MultipleLocator |
Ticks and range are a multiple of base |
MaxNLocator |
Finds up to a max number of ticks at nice locations |
AutoLocator |
(Default.) MaxNLocator with simple defaults. |
AutoMinorLocator |
Locator for minor ticks |
| Formatter Class | Description |
|---|---|
NullFormatter |
No labels on the ticks |
IndexFormatter |
Set the strings from a list of labels |
FixedFormatter |
Set the strings manually for the labels |
FuncFormatter |
User-defined function sets the labels |
FormatStrFormatter |
Use a format string for each value |
ScalarFormatter |
(Default.) Formatter for scalar values |
LogFormatter |
Default formatter for log axes |
We'll see further examples of these through the remainder of the book.
配置和样式
plt.rc 的意思就是 configuration配置的意思。
调整样式：
#可获得的样式
plt.style.available[:5]
#使用某种样式
with plt.style.context('stylename'):
make_a_plot()
with plt.style.context('ggplot'):
hist_and_lines()
with plt.style.context('bmh'):
hist_and_lines()
with plt.style.context('dark_background'):
hist_and_lines()
with plt.style.context('grayscale'):
hist_and_lines()
import seaborn
hist_and_lines()