matplotlib 28原则
记下各个简易模板,方便ctrl+c和ctrl+v
子图:
import numpy as np
import matplotlib.pyplot as plt x = np.array(range(1,9))
y = np.array([np.random.randint(1,11) for i in range(8)]) plt.subplot(2,2,1)
plt.plot(x,y,color="b",label="line")
plt.subplot(2,2,2)
plt.plot(x,y,"g",lw=10)
plt.subplot(2,2,3)
plt.plot(x,y,"b")
plt.subplot(2,2,4)
plt.plot(x,y,"g",lw=10)
plt.legend()
plt.show()
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" alt="" />
柱状图:
x = np.array(range(1,9))
y1 = np.array([np.random.randint(1,11) for i in range(8)])
y2 = np.array([np.random.randint(1,11) for i in range(8)]) plt.plot(x,y1,"g",lw=10)
plt.bar(x,y2,0.5,alpha=1,color="b")
plt.show()
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" alt="" />
dataframe生成图表
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt df = pd.DataFrame(np.zeros((10,3)),columns=["c1","c2","c3"]) fig, axs = plt.subplots()
clust_data = df.values
collabel = df.columns
axs.axis('tight')
axs.axis('off')
the_table = axs.table(cellText=clust_data,colLabels=collabel,loc='center')
plt.show()
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