matplotlib必備圖表繪制

官網

http://matplotlib.org/

簡單操作

我們以matplotlab包里面的pyplot 為例介紹，這個模塊仿照matlab中的繪圖，對有matlab繪圖經驗的同學很友好

導包

import matplotlib.pyplot as plt

面板與子圖

面板（figure·）:matplotlib中的影像都是位于figure物件中，

子圖：一個figure影像中可以建立多個subplot物件用于繪制影像

subplot

subplot需要三個引數，前面兩個用于指定整個圖的行列數，第三個說明下面的繪畫是在哪一個子圖完成的

plt.subplot(2, 2, 1)
plt.subplot(221)

以上面代碼為例，2和2表示有4個子圖，2*2的排列，此時是在第一個子圖上進行繪制，如果三個引數均小于10，引數還可以合并為221

線形圖

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

x1 = np.random.randint(-3, 3, 20)
x2 = np.random.randint(-3, 3, 20)
x3 = np.random.randint(-3, 3, 20)
x4 = np.random.randint(-3, 3, 20)
y1 = np.sin(x1)
y2 = np.sin(x2)
y3 = np.sin(x3)
y4 = np.sin(x4)

plt.figure(1)
plt.subplot(2, 2, 1)
plt.plot(x1, y1, 'rH')
plt.title('x1')

plt.subplot(2, 2, 2)
plt.plot(x2, y2, 'bD')
plt.title('x2')

plt.subplot(2, 2, 3)
plt.plot(x3, y3, 'go')
plt.title('x3')

plt.subplot(2, 2, 4)
plt.plot(x4, y4, 'k*')
plt.title('x4')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.show()

直方圖

用于表示資料出現頻率的圖形，這里以正態分布為例

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

plt.figure(1)
x = np.random.randn(10000)
plt.hist(x,bins=20,facecolor='blue',edgecolor='black',alpha=0.7)

散點圖

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

x = np.random.randn(100) 
y = np.random.randn(100)
plt.scatter(x,y,c='r',lw=2)
plt.show()

c指定散點的顏色，lw指定散點的大小

熱力相關圖

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

plt.figure(1)
plt.subplot(221)
data = np.random.randn(10,10)
sns.heatmap(data,linewidths=0.1,cmap='RdBu_r',vmin=-1,vmax=1)

plt.subplot(222)
data = np.random.randn(10,10)
sns.heatmap(data,linewidths=0.1,cmap='rainbow',vmin=-1,vmax=1)

plt.subplot(223)
data = np.random.randn(10,10)
sns.heatmap(data,cmap='rainbow',vmin=-1,vmax=1)

plt.subplot(224)
data = np.random.randn(10,10)
sns.heatmap(data,linewidths=0.1,cmap='rainbow')

柱形圖

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

plt.figure(1) 
x_index = np.arange(5)
x_data = ('A', 'B', 'C', 'D', 'E')
y_data = (12, 23, 34, 45, 56)
bar_width = 0.5
plt.bar(x_index, y_data, width=bar_width, alpha=0.4, color='b', label='data') 

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

plt.figure(1) 
x_index = np.arange(5)
x_data = ('A', 'B', 'C', 'D', 'E')
y1_data = (12, 23, 34, 45, 56)
y2_data = (56, 45, 34, 23, 12)
bar_width = 0.3
r1 = plt.bar(x_index, y1_data, width=bar_width, alpha=0.4, color='b', label='data1') 
r2 = plt.bar(x_index + bar_width, y2_data, width=bar_width, alpha=0.5, color='r',
                 label='data2') 
plt.xticks(x_index+bar_width/2, x_data) # 指定x軸標簽的位置
plt.legend() # 加上標簽
plt.tight_layout()

3D圖

繪制擁有三個維度的影像

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

fig = plt.figure(1)
ax = fig.add_subplot(111, projection='3d')  # 繪制三維圖
x = np.arange(-3, 3, 0.2)
y = np.arange(-3, 3, 0.2)
x, y = np.meshgrid(x, y)  
Z = x * np.exp(-x ** 2 - y ** 2)  
ax.plot_surface(x, y, Z, rstride=2, cstride=1, cmap=plt.cm.coolwarm, alpha=0.8)
ax.set_xlabel('X_index')  
ax.set_ylabel('Y_index')  
ax.set_zlabel('Z_index')