将鼠标悬停在数据上时如何显示数据标签

只需插入我自己的项目，看看mpldatacursor: https://github.com/joferkington/mpldatacursor

Just to plug my own project, have a look at mpldatacursor: https://github.com/joferkington/mpldatacursor

作为一个基本示例，只需调用datacursor(hover=True, point_labels=df['E'])即可获得90％的所需方式.例如，将您的代码段放在上面:

As a basic example, just calling datacursor(hover=True, point_labels=df['E']) will get you 90% of the way to what you want. For example, taking your code snippet above:

from StringIO import StringIO
import pandas as pd
import matplotlib.pyplot as plt
from mpldatacursor import datacursor

f = StringIO(
"""931,Oxfordshire,9314125,123255,Larkmead School,Abingdon,125,124,20,SUPP,8
931,Oxfordshire,9314126,123256,John Mason School,Abingdon,164,164,25,6,16
931,Oxfordshire,9314127,123257,Fitzharrys School,Abingdon,150,149,9,0,11
931,Oxfordshire,9316076,123298,Our Lady's Abingdon,Abingdon,57,57,SUPP,SUPP,16
""")
df = pd.read_csv(f, names=['A','B','C','D','E','F','G', 'H','I','J', 'K'],
                 header=None)
df.replace('SUPP', 3.0, inplace=True)
df = df.convert_objects(convert_numeric=True)
df['KG'] = df['K']*1.0/df['G']
plt.plot(df['KG'], marker='o')

datacursor(hover=True, point_labels=df['E'])

plt.show()

每当鼠标悬停在行上时，我们都会获得一个弹出标签.

We'll get a popup label whenever the line is hovered over.

但是，根据设计，默认行为是每当鼠标悬停在行上/单击行时，都会显示弹出窗口.因此，使用point_labels选项时，结果可能与您的想法不尽相同:

However, by design, the default behavior is to display the pop-up whenever the line is hovered over / clicked on. Therefore, when using the point_labels option, the result may not quite be what you had in mind:

如果仅希望将鼠标悬停在顶点上时显示弹出窗口，则可以使用类似的解决方法:(在下一个版本中，将有一个选项仅在顶点上显示弹出窗口，因此以后将不再需要此解决方法.)

If you only want the pop-up to be displayed when the vertexes are hovered over, you can use a workaround similar to this: (There will be an option for only displaying the pop-up at vertexes in the next release, so this workaround won't be required in the future.)

from StringIO import StringIO
import pandas as pd
import matplotlib.pyplot as plt
from mpldatacursor import datacursor

f = StringIO(
"""931,Oxfordshire,9314125,123255,Larkmead School,Abingdon,125,124,20,SUPP,8
931,Oxfordshire,9314126,123256,John Mason School,Abingdon,164,164,25,6,16
931,Oxfordshire,9314127,123257,Fitzharrys School,Abingdon,150,149,9,0,11
931,Oxfordshire,9316076,123298,Our Lady's Abingdon,Abingdon,57,57,SUPP,SUPP,16
""")
df = pd.read_csv(f, names=['A','B','C','D','E','F','G', 'H','I','J', 'K'],
                 header=None)
df.replace('SUPP', 3.0, inplace=True)
df = df.convert_objects(convert_numeric=True)
df['KG'] = df['K']*1.0/df['G']
plt.plot(df['KG'], marker='o')

l, = plt.plot(df['KG'], marker='o', linestyle='', visible=False)
datacursor(l, hover=True, point_labels=df['E'])

plt.show()

此外，您可能只想显示有问题的学校，而不是x，y坐标等.要更改此设置，请使用自定义的formatter函数:

Also, you might want to only display the school in question, instead of the x,y coordinates, etc. To change this, use a custom formatter function:

datacursor(l, hover=True, point_labels=df['E'],
           formatter=lambda **kwargs: kwargs['point_label'][0])

最后，您可能想要一个带有更奇特的箭头和不同的相对位置的白框:

And finally, you might want a white box with a fancier arrow and a different relative position:

datacursor(l, hover=True, point_labels=df['E'], bbox=dict(fc='white'),
           formatter=lambda **kwargs: kwargs['point_label'][0], xytext=(0, 25),
           arrowprops=dict(arrowstyle='simple', fc='white', alpha=0.5))

对于最后一个示例，只需将它们全部组合成可运行的版本即可:

Just to put it all together into a runnable version for the last example:

from StringIO import StringIO
import pandas as pd
import matplotlib.pyplot as plt
from mpldatacursor import datacursor

f = StringIO(
"""931,Oxfordshire,9314125,123255,Larkmead School,Abingdon,125,124,20,SUPP,8
931,Oxfordshire,9314126,123256,John Mason School,Abingdon,164,164,25,6,16
931,Oxfordshire,9314127,123257,Fitzharrys School,Abingdon,150,149,9,0,11
931,Oxfordshire,9316076,123298,Our Lady's Abingdon,Abingdon,57,57,SUPP,SUPP,16
""")
df = pd.read_csv(f, names=['A','B','C','D','E','F','G', 'H','I','J', 'K'],
                 header=None)
df.replace('SUPP', 3.0, inplace=True)
df = df.convert_objects(convert_numeric=True)
df['KG'] = df['K']*1.0/df['G']
plt.plot(df['KG'], marker='o')

l, = plt.plot(df['KG'], marker='o', linestyle='', visible=False)
datacursor(l, hover=True, point_labels=df['E'], bbox=dict(fc='white'),
           formatter=lambda **kwargs: kwargs['point_label'][0], xytext=(0, 25))

plt.show()