[英]How do you create a legend for kde plot in Seaborn?
我有一个 kdeplot,但我正在努力弄清楚如何创建图例。
import matplotlib.patches as mpatches # see the tutorial for how we use mpatches to generate this figure!
# Set 'is_workingday' to a boolean array that is true for all working_days
is_workingday = daily_counts["workingday"] == "yes"
is_not_workingday = daily_counts['workingday'] == "no"
# Bivariate KDEs require two data inputs.
# In this case, we will need the daily counts for casual and registered riders on workdays
casual_workday = daily_counts.loc[is_workingday, 'casual']
registered_workday = daily_counts.loc[is_workingday, 'registered']
# Use sns.kdeplot on the two variables above to plot the bivariate KDE for weekday rides
sns.kdeplot(casual_workday, registered_workday, color = "red", cmap = "Reds", hue = "workingday", legend = True)
# Repeat the same steps above but for rows corresponding to non-workingdays
casual_non_workday = daily_counts.loc[is_not_workingday, 'casual']
registered_non_workday = daily_counts.loc[is_not_workingday, 'registered']
# Use sns.kdeplot on the two variables above to plot the bivariate KDE for non-workingday rides
sns.kdeplot(casual_non_workday, registered_non_workday, color = 'blue', cmap = "Blues", legend = True, shade = False)
一种方法是将label=
传递给kdeplot
,然后请求显示图例。
geyser = sns.load_dataset("geyser")
long = geyser.loc[geyser['kind']=='long']
short = geyser.loc[geyser['kind']=='short']
sns.kdeplot(x=long["waiting"], y=long["duration"], label='long')
sns.kdeplot(x=short["waiting"], y=short["duration"], label='short')
plt.legend()
另一种方法是使用 seaborn 必须基于hue=
列拆分 dataframe 的内置方法。 在你的情况下,它看起来像下面这样,但不知道你的 dataframe 的结构,所以不可能确定。 有关详细信息,请参阅文档。
sns.kdeplot(x='casual', y='registered', hue='workingday', data=daily_counts, shade=False, legend=True)
当每个 kdeplot 使用一种颜色时,另一个答案很有效。
如果使用诸如“Reds”之类的颜色图,这将显示非常浅的红色。 自定义颜色图可以显示范围中间的颜色:
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
import numpy as np
casual_workday = np.random.randn(100) * 1.2
registered_workday = 0.8 * np.random.randn(100) + casual_workday * 0.2 + 1
sns.kdeplot(x=casual_workday, y=registered_workday, color="red", cmap="Reds", shade=False)
casual_non_workday = np.random.randn(100) * 1.6
registered_non_workday = 0.5 * np.random.randn(100) + casual_non_workday * 0.5 - 1
sns.kdeplot(x=casual_non_workday, y=registered_non_workday, cmap="Blues", shade=False)
handles = [mpatches.Patch(facecolor=plt.cm.Reds(100), label="Workday"),
mpatches.Patch(facecolor=plt.cm.Blues(100), label="Non-workday")]
plt.legend(handles=handles)
plt.show()
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