Python pandas，多行绘图选项

问题描述

我想从熊猫数据框中绘制多条线，并为每条线设置不同的选项.我想做类似的事情

I want to plot multiple lines from a pandas dataframe and setting different options for each line. I would like to do something like

testdataframe=pd.DataFrame(np.arange(12).reshape(4,3))
testdataframe.plot(style=['s-','o-','^-'],color=['b','r','y'],linewidth=[2,1,1])

这将引发一些错误消息:

This will raise some error messages:

• 线宽不可与列表一起调用

• linewidth is not callable with a list

在样式中，当在列表中定义颜色时，我不能使用's'和'o'或任何其他字母符号

In style I can't use 's' and 'o' or any other alphabetical symbol, when defining colors in a list

还有一些对我来说似乎很奇怪的东西

Also there is some more stuff which seems weird to me

• 当我在上面的代码testdataframe[0].plot()中添加另一个绘图命令时，它将在同一绘图中绘制这条线，如果添加命令testdataframe[[0,1]].plot()，它将创建一个新绘图

• when I add another plot command to the above code testdataframe[0].plot() it will plot this line in the same plot, if I add the command testdataframe[[0,1]].plot() it will create a new plot

如果我打电话给testdataframe[0].plot(style=['s-','o-','^-'],color=['b','r','y'])，可以使用样式列表，但不能使用颜色列表

If i would call testdataframe[0].plot(style=['s-','o-','^-'],color=['b','r','y']) it is fine with a list in style, but not with a list in color

希望有人可以提供帮助，谢谢.

Hope somebody can help, thanks.

推荐答案

您是如此亲密！

您可以在样式列表中指定颜色:

You can specify the colors in the styles list:

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

testdataframe = pd.DataFrame(np.arange(12).reshape(4,3), columns=['A', 'B', 'C'])
styles = ['bs-','ro-','y^-']
linewidths = [2, 1, 4]
fig, ax = plt.subplots()
for col, style, lw in zip(testdataframe.columns, styles, linewidths):
    testdataframe[col].plot(style=style, lw=lw, ax=ax)

还请注意，plot方法可以采用matplotlib.axes对象，因此您可以这样进行多次调用(如果需要):

Also note that the plot method can take a matplotlib.axes object, so you can make multiple calls like this (if you want to):

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

testdataframe1 = pd.DataFrame(np.arange(12).reshape(4,3), columns=['A', 'B', 'C'])
testdataframe2 = pd.DataFrame(np.random.normal(size=(4,3)), columns=['D', 'E', 'F'])
styles1 = ['bs-','ro-','y^-']
styles2 = ['rs-','go-','b^-']
fig, ax = plt.subplots()
testdataframe1.plot(style=styles1, ax=ax)
testdataframe2.plot(style=styles2, ax=ax)

在这种情况下不是很实际，但是稍后该概念可能会派上用场.

Not really practical in this case, but the concept might come in handy later.

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