matplotlib

本文是在ipython notebook上编写，是matplot的学习笔记

对一些常用的图形做一些简单的介绍
包括线图，柱状图，饼图等
作图我还是服ggplot，蛤蛤
In [18]:
直接写代码，先导入相关包

import matplotlib.pyplot as plt #导入matplotlib库
import random
random.seed(111)
import numpy as np
%matplotlib inline

In [2]:

first plot 第一个简单的图，随便写些数据

x= [1,2,3]
y=[4,5,2]
x2=[1,2,3]
y2=[10,12,11]
plt.plot(x,y,label="first line")
plt.plot(x2,y2,label="senond")
plt.xlabel("plot num")
plt.ylabel("variance")
plt.title("first graph")
plt.legend()

Out[2]:
<matplotlib.legend.Legend at 0x7a7a2d0>

In [3]:

bar plot 柱状图

x3= [2,4,6,8,10]
y3=[6,7,5,7,6]
x4=[1,3,5,7,9]
y4=[3,4,7,8,5]
plt.bar(x3,y3,label="first",color="red")
plt.bar(x4,y4,label="senond")
plt.xlabel("x")
plt.ylabel("y")
plt.title("first\n graph")
plt.legend()

Out[3]:
<matplotlib.legend.Legend at 0x7bc4830>

In [4]:

histgram 直方图

population_ages=[25,44,7,11,16,49,33,54,43,22,77,54,33,52,39,44,50,76,88,67,90,72]
bins=[0,10,20,30,40,50,60,70,80,90,100]
plt.hist(population_ages,bins,histtype="bar",rwidth=0.8,alpha=0.4)#rwidth宽度，alpha透明度
plt.xlabel("x")
plt.ylabel("y")
plt.title("age distribution")
plt.legend()

C:\Anaconda3\lib\site-packages\matplotlib\axes_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots. warnings.warn("No labelled objects found. "

In [5]:

散点图sactter

x5= [1,2,3,5,3,8]
y5=[4,5,2,7,6,5]
plt.scatter(x5,y5,label="first",color="g",s=50,marker="*")
​
plt.xlabel("x")
plt.ylabel("y")
plt.title("graph")
plt.legend()

Out[5]:
<matplotlib.legend.Legend at 0xa02cc90>

In [6]:

堆积图stackplot

days=[1,2,3,4,5]
sleeping=[7,8,6,10,7]
eating=[2,3,4,1,2]
working=[9,8,10,11,8]
playing=[6,5,4,2,7]
stackplot不能加Label，但我们可以用其他方法
plt.plot([],[],color="g",label="sleeping",linewidth=5)
plt.plot([],[],color="b",label="eating",linewidth=5)
plt.plot([],[],color="r",label="working",linewidth=5)
plt.plot([],[],color="y",label="playing",linewidth=5)
plt.stackplot(days,sleeping,eating,working,playing,colors=["g","b","r","y"])
plt.xlabel("x")
plt.ylabel("y")
plt.title("graph")
plt.legend()

Out[6]:
<matplotlib.legend.Legend at 0xa038f50>

In [7]:

饼图pie plot

slices=[7,3,10,5]
activities=["sleeping","eating","working","playing"]
cols=["g","b","r","y"]
plt.pie(slices,labels=activities,colors=cols,startangle=90,labeldistance=1.1,radius=1.2,
 autopct="%1.1f%%" ,explode=(0,0.2,0,0),shadow=True)

注意：startangle 角度90，labeldistance label到中心距离，radius图形大小，autopct显示百分比

Out[7]:
([<matplotlib.patches.Wedge at 0xa0bef70>, <matplotlib.patches.Wedge at 0xa0c4e30>, <matplotlib.patches.Wedge at 0xa0c9cd0>, <matplotlib.patches.Wedge at 0xa0ceb70>], [<matplotlib.text.Text at 0xa0c47d0>, <matplotlib.text.Text at 0xa0c9670>, <matplotlib.text.Text at 0xa0ce510>, <matplotlib.text.Text at 0xa0d53b0>], [<matplotlib.text.Text at 0xa0c4b10>, <matplotlib.text.Text at 0xa0c99b0>, <matplotlib.text.Text at 0xa0ce850>, <matplotlib.text.Text at 0xa0d56f0>])

其他参数：

ax1.grid(True,color="g",linestyle="-",linewidth=5) #网格线和参数  
plt.subplot_adjust(left=,right,bottom,top,wspace)# 图形距上下左右距离

In [9]:

subplot 在一个图里创建多个子图

import random

想要不同的图片风格,下面这条语句一定要记得

from matplotlib import style
style.use("ggplot")

可以使用不同的style,如著名的fivethirtyeight,还有ggplot,查看style.library，还可以自定义style。吼啊！

https://tonysyu.github.io/raw_content/matplotlib-style-gallery/gallery.html

fig=plt.figure()
def create_plot():
 xs =[]
 ys = []
 for i in range(10):
 x=i
 y=random.randrange(10)
 xs.append(x)
 ys.append(y)
 return xs,ys
ax1 = fig.add_subplot(211) #subplot ,2高1宽1第几个
ax2 = fig.add_subplot(212)

如果想要上面两个，下面一个，共三个图形，可以

ax1(221),ax2(222),ax3(212)
x,y=create_plot()
ax1.plot(x,y)
x,y=create_plot()
ax2.plot(x,y)

​
Out[9]:
[<matplotlib.lines.Line2D at 0x4ab8b50>]

In [10]:

另一种subplot方法

ax3=plt.subplot2grid((6,1),(0,0),rowspan=1,colspan=1)
ax4=plt.subplot2grid((6,1),(1,0),rowspan=4,colspan=1)#还可以
sharex=ax3
ax5=plt.subplot2grid((6,1),(5,0),rowspan=1,colspan=1)
x,y=create_plot()
ax3.plot(x,y)
x,y=create_plot()
ax4.plot(x,y)
x,y=create_plot()
ax5.plot(x,y)

Out[10]:
[<matplotlib.lines.Line2D at 0x4b3cf10>]

In [15]:

fill_between 填充颜色到曲线中

from matplotlib import style
style.use("fivethirtyeight")#换个非常著名的fivethirtyeight
a=np.random.randn(100).cumsum()+50
b=np.random.randn(100).cumsum()+50
c=range(100)
fig=plt.figure()
ax6 = fig.add_subplot(211)
ax7 =fig.add_subplot(212)
ax6.plot(c,a)
ax6.plot(c,b)
ax6.fill_between(c,a,b,where=(a<b),facecolor='y',edgecolor='k',alpha=0.5)
ax6.fill_between(c,a,b,where=(a>b),facecolor='b',edgecolor='k',alpha=0.5)
ax7.plot(c,a-b)
#这样看起来更清楚了

Out[15]:
[<matplotlib.lines.Line2D at 0x4eb2410>]

In [20]:

3d plot 3D绘图

from mpl_toolkits.mplot3d import axes3d
fig=plt.figure()
ax01 = fig.add_subplot(111,projection = "3d")
x = [1,2,3,4,5,6,7,8,9]
y = [4,3,5,6,2,5,7,6,3]
z = [4,6,7,3,7,4,8,5,4]
ax01.plot_wireframe(x,y,z)
ax01.set_xlabel("x label")
ax01.set_ylabel("y label")
ax01.set_zlabel("x label")

Out[20]:
<matplotlib.text.Text at 0xa1bb8d0>

还有basemap，暂时不做了 matplotlib里面还有很多toolkit,比如seaborn http://matplotlib.org/mpl_toolkits/index.html?highlight=basemap 想找自己喜欢的colors可以到官网 named_colors
matplotlib 2.0 有个新的更好的colormap "viridis",原来的默认colormap真心不漂亮
以上！ 只是做了一点微小的工作