在收集 pdf 数据时get_xydata很聪明，你现在受到 matplotlib 的渲染/分割算法的支配。拥有x1 and x2跨越不同的范围也可以进行比较y1 and y2难的。
您可以通过自己拟合法线来避免这些问题，而不是
出租sns.distplot做吧。然后你就可以更好地控制你的价值观
寻找。
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
norm = stats.norm
pepe_calories = np.array([361, 291, 263, 284, 311, 284, 282, 228, 328, 263, 354, 302, 293,
254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,
296, 237, 276, 280, 291, 278, 251, 313, 314, 323, 333, 270, 317,
321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,
288, 356, 224, 246, 196, 202, 314, 301, 336, 294, 237, 284, 311,
257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,
313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,
253, 302, 318, 289, 302, 291, 343, 310, 241])
modern_calories = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,
304, 363, 387, 279, 350, 367, 321, 366, 311, 308, 303, 299, 363,
335, 357, 392, 321, 361, 285, 321, 290, 392, 341, 331, 338, 326,
314, 327, 320, 293, 333, 297, 315, 365, 408, 352, 359, 312, 300,
263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,
286, 296, 413, 383, 328, 418, 388, 416, 371, 313, 321, 321, 317,
402, 290, 328, 344, 330, 319, 309, 327, 351, 324, 278, 369, 416,
359, 381, 324, 306, 350, 385, 335, 395, 308])
pepe_params = norm.fit(pepe_calories)
modern_params = norm.fit(modern_calories)
xmin = min(pepe_calories.min(), modern_calories.min())
xmax = max(pepe_calories.max(), modern_calories.max())
x = np.linspace(xmin, xmax, 100)
pepe_pdf = norm(*pepe_params).pdf(x)
modern_pdf = norm(*modern_params).pdf(x)
y = np.minimum(modern_pdf, pepe_pdf)
fig, ax = plt.subplots()
ax.plot(x, pepe_pdf, label="Pepe's", color='blue')
ax.plot(x, modern_pdf, label="Modern", color='orange')
ax.fill_between(x, y, color='red', alpha=0.3)
plt.legend()
plt.show()
如果，比方说，sns.distplot（或其他一些绘图函数）制作了一个您不想重现的绘图，那么您可以使用来自的数据get_xydata这边走：
import numpy as np
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt
pepe_calories = np.array([361, 291, 263, 284, 311, 284, 282, 228, 328, 263, 354, 302, 293,
254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,
296, 237, 276, 280, 291, 278, 251, 313, 314, 323, 333, 270, 317,
321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,
288, 356, 224, 246, 196, 202, 314, 301, 336, 294, 237, 284, 311,
257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,
313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,
253, 302, 318, 289, 302, 291, 343, 310, 241])
modern_calories = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,
304, 363, 387, 279, 350, 367, 321, 366, 311, 308, 303, 299, 363,
335, 357, 392, 321, 361, 285, 321, 290, 392, 341, 331, 338, 326,
314, 327, 320, 293, 333, 297, 315, 365, 408, 352, 359, 312, 300,
263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,
286, 296, 413, 383, 328, 418, 388, 416, 371, 313, 321, 321, 317,
402, 290, 328, 344, 330, 319, 309, 327, 351, 324, 278, 369, 416,
359, 381, 324, 306, 350, 385, 335, 395, 308])
ax = sns.distplot(pepe_calories, fit_kws={"color":"blue"}, kde=False,
fit=stats.norm, hist=None, label="Pepe's");
ax = sns.distplot(modern_calories, fit_kws={"color":"orange"}, kde=False,
fit=stats.norm, hist=None, label="Modern");
# Get the two lines from the axes to generate shading
l1 = ax.lines[0]
l2 = ax.lines[1]
# Get the xy data from the lines so that we can shade
x1, y1 = l1.get_xydata().T
x2, y2 = l2.get_xydata().T
xmin = max(x1.min(), x2.min())
xmax = min(x1.max(), x2.max())
x = np.linspace(xmin, xmax, 100)
y1 = np.interp(x, x1, y1)
y2 = np.interp(x, x2, y2)
y = np.minimum(y1, y2)
ax.fill_between(x, y, color="red", alpha=0.3)
plt.legend()
plt.show()
