python_matplotlib DAY_20(2)数据可视化

学习内容
数据的可视化
TIPS

重点
1.一般画图plot
1）一幅图像

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

s = pd.Series(np.random.randn(100), index=pd.date_range("2020-01-28", periods=100, freq="D"))
s.plot(title="distrbution", figsize=(8,6),style='r--')
#设置标题，设置大小，设置画图风格是红色虚线
plt.show()

2）多幅图像

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

s = pd.DataFrame(np.random.randn(400).reshape(100,4),
                 index=pd.date_range("2020-01-28", periods=100, freq="D"),
                 columns=list("ABCD"))
s.plot(title="distrbution",
       figsize=(8,6),
       subplots=True,#ABCD化成子图，不出现在一幅图上面
       sharex=True,#共享x轴
       sharey=True)#共享y轴
plt.show()

2.柱状图bar
1.一幅图像

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

s = pd.Series(np.random.randn(10), index=pd.date_range("2020-01-28", periods=10, freq="D"))
s.plot.bar()#竖向画图
#s.plot.barh()#横向画图
plt.show()

2）多幅图像

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

s = pd.DataFrame(np.random.randn(40).reshape(10, 4),
                 index=pd.date_range("2020-01-28", periods=10, freq="D"),
                 columns=list("ABCD"))
s.plot.barh(title="distrbution",
            stacked=True)#将四个图像累积起来画
plt.show()

3.直方图hist
1）一幅图像

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

s = pd.DataFrame(np.random.randn(40).reshape(10, 4),
                 index=pd.date_range("2020-01-28", periods=10, freq="D"),
                 columns=list("ABCD"))
s["A"].plot.hist(title="distrbution",
                bins=20,#分割成20等份
                 alpha=0.3)#设置透明度
plt.show()

2）多幅图像

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

s = pd.DataFrame(np.random.randn(40).reshape(10, 4),
                 index=pd.date_range("2020-01-28", periods=10, freq="D"),
                 columns=list("ABCD"))
s.plot.hist(title="distrbution",
            bins=20,
            alpha=0.3,
            stacked=True)#累计画图
plt.show()

4.概率密度图kde

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

s = pd.DataFrame(np.random.randn(40).reshape(10, 4),
                 index=pd.date_range("2020-01-28", periods=10, freq="D"),
                 columns=list("ABCD"))
s['A'].plot.kde(title="distrbution",
                alpha=0.3, )
plt.show()

5.散步图scatter

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

s = pd.DataFrame(np.random.randn(40).reshape(10, 4),
                 index=pd.date_range("2020-01-28", periods=10, freq="D"),
                 columns=list("ABCD"))
s.plot.scatter(title="distrbution",#散步图无法画序列
               alpha=0.3,
               x='A',#必须设置x轴
               y='B',#必须设置y轴
               )
plt.show()

6.饼图pie
饼图的要求比较多，数据不能有负数，不能直接画DataFrame

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

s = pd.Series(np.random.randint(0, 10, 4),
              index=pd.date_range("2020-01-28", periods=4, freq="D"))
s.plot.pie(title="distrbution",
           labels=['aa', 'bb', 'cc', 'dd'],
           autopct='%0.2f',
           colors=['r', 'b', 'g', 'y']
           )
plt.show()