你可以使用scatter它，但是这需要为你提供数值key1，并且你不会注意到图例。
最好只plot对像这样的离散类别使用。例如：
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(1974)
# Generate Data
num = 20
x, y = np.random.random((2, num))
labels = np.random.choice(['a', 'b', 'c'], num)
df = pd.DataFrame(dict(x=x, y=y, label=labels))
groups = df.groupby('label')
# Plot
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for name, group in groups:
ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name)
ax.legend()
plt.show()
如果你希望外观看起来像默认pandas样式，则只需rcParams使用pandas样式表进行更新，并使用其颜色生成器即可。（我也略微调整了图例）：
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(1974)
# Generate Data
num = 20
x, y = np.random.random((2, num))
labels = np.random.choice(['a', 'b', 'c'], num)
df = pd.DataFrame(dict(x=x, y=y, label=labels))
groups = df.groupby('label')
# Plot
plt.rcParams.update(pd.tools.plotting.mpl_stylesheet)
colors = pd.tools.plotting._get_standard_colors(len(groups), color_type='random')
fig, ax = plt.subplots()
ax.set_color_cycle(colors)
ax.margins(0.05)
for name, group in groups:
ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name)
ax.legend(numpoints=1, loc='upper left')
plt.show()
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