[英]Getting a legend in a seaborn FacetGrid heatmap plot
我們怎樣才能獲得seaborn FacetGrid
熱圖的傳說? .add_legend()
方法對我不起作用。
使用上一個問題中的代碼:
import pandas as pd
import numpy as np
import itertools
import seaborn as sns
print("seaborn version {}".format(sns.__version__))
# R expand.grid() function in Python
# https://stackoverflow.com/a/12131385/1135316
def expandgrid(*itrs):
product = list(itertools.product(*itrs))
return {'Var{}'.format(i+1):[x[i] for x in product] for i in range(len(itrs))}
methods=['method 1', 'method2', 'method 3', 'method 4']
times = range(0,100,10)
data = pd.DataFrame(expandgrid(methods, times, times))
data.columns = ['method', 'dtsi','rtsi']
data['nw_score'] = np.random.sample(data.shape[0])
def facet(data,color):
data = data.pivot(index="dtsi", columns='rtsi', values='nw_score')
g = sns.heatmap(data, cmap='Blues', cbar=False)
with sns.plotting_context(font_scale=5.5):
g = sns.FacetGrid(data, col="method", col_wrap=2, size=3, aspect=1)
g = g.map_dataframe(facet)
g.add_legend()
g.set_titles(col_template="{col_name}", fontweight='bold', fontsize=18)
你想要的(在matplotlib術語中)是一個顏色條,而不是一個圖例。 在matplotlib中,前者用於連續數據,而后者用於分類數據。 Colort支持未內置於FacetGrid
,但擴展示例代碼以添加FacetGrid
並不困難:
import pandas as pd
import numpy as np
import itertools
import seaborn as sns
methods=['method 1', 'method2', 'method 3', 'method 4']
times = range(0, 100, 10)
data = pd.DataFrame(list(itertools.product(methods, times, times)))
data.columns = ['method', 'dtsi','rtsi']
data['nw_score'] = np.random.sample(data.shape[0])
def facet_heatmap(data, color, **kws):
data = data.pivot(index="dtsi", columns='rtsi', values='nw_score')
sns.heatmap(data, cmap='Blues', **kws) # <-- Pass kwargs to heatmap
with sns.plotting_context(font_scale=5.5):
g = sns.FacetGrid(data, col="method", col_wrap=2, size=3, aspect=1)
cbar_ax = g.fig.add_axes([.92, .3, .02, .4]) # <-- Create a colorbar axes
g = g.map_dataframe(facet_heatmap,
cbar_ax=cbar_ax,
vmin=0, vmax=1) # <-- Specify the colorbar axes and limits
g.set_titles(col_template="{col_name}", fontweight='bold', fontsize=18)
g.fig.subplots_adjust(right=.9) # <-- Add space so the colorbar doesn't overlap the plot
我已經指出了我所做的更改以及它們作為內聯注釋的基本原理。
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