如何在Seaborn Heatmap单元中显示多个注释？

Question

我希望Seaborn热图在热图的每个单元格中显示多个值。这是我想看到的手动示例，请清楚一点：

data = np.array([[0.000000,0.000000],[-0.231049,0.000000],[-0.231049,0.000000]])
labels =  np.array([['A\nExtra Stuff','B'],['C','D'],['E','F']])
fig, ax = plt.subplots()
ax = sns.heatmap(data, annot = labels, fmt = '')

以下是获取seaborn.heat以在单元格中显示flightsRoundUp值的示例。

import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

def RoundUp(x):
    return int(np.ceil(x/10)*10)

# Load the example flights dataset and conver to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
flightsRoundUp =  flights.applymap(RoundUp)

# Draw a heatmap with the numeric values in each cell
f, ax = plt.subplots(figsize=(9, 6))
sns.heatmap(flights, annot=flightsRoundUp, fmt="", linewidths=.5, ax=ax)

在所有单元格中同时显示flightsRoundUp和flights的最佳方法是什么？类似于上面的第一个手动示例，但是对于所有单元格都采用矢量化方式...

Answer 1

Rotail的答案对我不起作用，应用该lambda函数时出现错误。

但是，我找到了一个解决方案，该解决方案利用了seaborn在彼此之上绘制连续图的事实。您要做的就是使用一个调用热图来建立图形，然后再调用每个注释。使用annot_kws arg来确保文本不会互相覆盖。

X = pd.DataFrame({'a':[1, 2, 3], 'b':[4, 5, 6]})
Y = pd.DataFrame({'A':['A', 'B', 'C'], 'B':['E', 'F', 'G']})
Z = pd.DataFrame({'A':['(Extra Stuff)', '(Extra Stuff)', '(Extra Stuff)'], 'B':['(Extra Stuff)', '(Extra Stuff)', '(Extra Stuff)']})

sns.heatmap(X, annot=False)
sns.heatmap(X, annot=Y, annot_kws={'va':'bottom'}, fmt="", cbar=False)
sns.heatmap(X, annot=Z, annot_kws={'va':'top'}, fmt="", cbar=False)

Answer 2

以下对我也有用：

X = pd.DataFrame({'a':[1, 2, np.nan], 'b':[10, 20, 30]})
Y = pd.DataFrame({'A':[11, 222, np.nan], 'B':[110, np.nan, 330]})

# convert to string
X_value_ann = (X).astype('|S5').reset_index()
Y_value_ann = (Y).astype('|S5').reset_index()

# define () and new line to glue on later
br = np.char.array(pd.DataFrame('\n(', index=X_value_ann.index, columns=X_value_ann.columns))
cl = np.char.array(pd.DataFrame(')', index=X_value_ann.index, columns=X_value_ann.columns))

# convert to chararray
X_value_ann = np.char.array(X_value_ann)
Y_value_ann = np.char.array(Y_value_ann)

# glue and reshape
my_annotation = pd.DataFrame(X_value_ann+br+Y_value_ann+cl)
my_annotation = my_annotation.applymap(lambda x: x.decode('utf-8')) 
my_annotation = my_annotation.drop(columns=[0])
my_annotation