Python数据分析（3）Matplotlib

2018-11-27

一、Matplotlib各种图形demo

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#把图画在notebook中
%matplotlib inline

线状图

x = [1,2,3]
y = [5,7,4]

x2 = [1,2,3]
y2 = [10,14,12]
plt.plot(x, y, label='First Line')
plt.plot(x2, y2, label='Second Line')
plt.xlabel('Plot Number')
plt.ylabel('Important var')
plt.title('Interesting Graph\nCheck it out')
plt.legend() #生成默认图例
plt.show()

png

条形图

plt.bar([1,3,5,7,9],[5,2,7,8,2], label="Example one")
plt.bar([2,4,6,8,10],[8,6,2,5,6], label="Example two", color='g')
plt.legend()
plt.xlabel('bar number')
plt.ylabel('bar height')

plt.title('Epic Graph\nAnother Line! Whoa')
plt.show()

png

直方图Histogram

population_ages = [22,55,62,45,21,22,34,42,42,4,99,102,110,120,121,122,130,111,115,112,80,75,65,54,44,43,42,48]

bins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]

plt.hist(population_ages, bins, histtype='bar', rwidth=0.8)

plt.xlabel('x')
plt.ylabel('y')
plt.title('Interesting Graph\nCheck it out')
plt.legend()
plt.show()

png

散点图

x = [1,2,3,4,5,6,7,8]
y = [5,2,4,2,1,4,5,2]

plt.scatter(x,y, label='skitscat', color='k', s=25, marker="o")

plt.xlabel('x')
plt.ylabel('y')
plt.title('Interesting Graph\nCheck it out')
plt.legend()
plt.show()

png

堆叠图

days = [1,2,3,4,5]

sleeping = [7,8,6,11,7]
eating =   [2,3,4,3,2]
working =  [7,8,7,2,2]
playing =  [8,5,7,8,13]

plt.plot([],[],color='m', label='Sleeping', linewidth=5)
plt.plot([],[],color='c', label='Eating', linewidth=5)
plt.plot([],[],color='r', label='Working', linewidth=5)
plt.plot([],[],color='k', label='Playing', linewidth=5)

plt.stackplot(days, sleeping,eating,working,playing, colors=['m','c','r','k'])

plt.xlabel('x')
plt.ylabel('y')
plt.title('Interesting Graph\nCheck it out')
plt.legend()
plt.show()

png

饼图

slices = [7,2,2,13]
activities = ['sleeping','eating','working','playing']
cols = ['c','m','r','b']

plt.pie(slices,
        labels=activities,
        colors=cols,
        startangle=90,
        shadow= True,
        explode=(0,0.1,0,0),
        autopct='%1.1f%%')

plt.title('Interesting Graph\nCheck it out')
plt.show()

png

二、自定义绘图

1	x=np.linspace(0,10,50)

plt.plot(x,np.sin(x),'--')
plt.plot(x,np.cos(x))

[<matplotlib.lines.Line2D at 0x11b64add8>]

png

fig=plt.figure()
plt.plot(x,np.sin(x),'--')

[<matplotlib.lines.Line2D at 0x11b73f6a0>]

png

1	fig.savefig('/Users/iherr/Desktop/a.png')

plt.subplot(2,1,1)
plt.plot(x,np.sin(x),'--')

plt.subplot(2,1,2)
plt.plot(x,np.cos(x))

[<matplotlib.lines.Line2D at 0x11b80bd68>]

png

# 点状样式，颜色
x=np.linspace(0,10,20)
plt.plot(x,np.sin(x),'o',color='red')

[<matplotlib.lines.Line2D at 0x11b926b00>]

png

# 图例
x=np.linspace(0,10,100)
plt.plot(x,np.sin(x),'--',color='red',label='sin(x)')
plt.plot(x,np.cos(x),'o',color='green',label='cos(x)')
#图例位置
plt.legend(loc=1)

<matplotlib.legend.Legend at 0x11b996240>

png

x=np.linspace(0,10,20)
plt.plot(x,np.sin(x),'-*',color='orange',markersize=16,linewidth=4,markeredgecolor='gray')

[<matplotlib.lines.Line2D at 0x11ba6a2e8>]

png

x=np.linspace(0,10,20)
plt.plot(x,np.sin(x),'-*',color='orange',markersize=16,linewidth=4,markeredgecolor='gray')
plt.ylim(-2,2)
plt.xlim(2,8)

(2, 8)

png

#plt.style.use('classic')
x=np.random.randn(100)
y=np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000* np.random.rand(100)
plt.scatter(x,y,c=colors,s=sizes,alpha=0.4)
plt.colorbar()

<matplotlib.colorbar.Colorbar at 0x11bc77cf8>

png

三、pandas自带绘画

线性图

1	df2=pd.DataFrame(np.random.rand(100,4).cumsum(0),columns=['A','B','C','D'])

1
2
3

df2.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x11bcc7d30>

png

1
2
3

df2.A.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x11bd9dd30>

png

1 2	df3=pd.DataFrame(np.random.randint(10,50,(3,4)).cumsum(0),columns=['A','B','C','D'],index=['one','two','three']) df3

	A	B	C	D
one	35	10	45	14
two	71	50	63	36
three	83	62	111	81

柱状图


df3.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x11be48b00>

png

#和上方等价
df3.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x11bf3b0b8>

png

#累加
df3.plot(kind='bar',stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x11c014a58>

png

1
2
3

df3.A.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x11c10a3c8>

png

直方图

df4=pd.DataFrame(np.random.randn(100,4),columns=['A','B','C','D'])
df4.hist(column='A',figsize=(5,4))

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11c1ca9b0>]],
          dtype=object)

png

1
2
3

df4.plot(kind='kde')

<matplotlib.axes._subplots.AxesSubplot at 0x11c273908>

png


N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
ind = np.arange(N)    # the x locations for the groups
width = 0.35       # the width of the bars: can also be len(x) sequence

p1 = plt.bar(ind, menMeans, width, yerr=menStd)
p2 = plt.bar(ind, womenMeans, width,
             bottom=menMeans, yerr=womenStd)

plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))

plt.show()

png

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
plt.figure()
ts.plot()
plt.legend(loc='best')

plt.show()

png

https://zhuanlan.zhihu.com/p/197854613 Python-Matplotlib用户必备的画图速查表

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