查看DA的源代码

=== KDE ===

核密度估计 (Kernel Density Estimate, KDE), 用来估计未知密度函数，属于非参数检验方法之一

<source lang=python>
>>> np.random.normal(loc=(10,20),scale=(4,2),size=(5,2))
array([[15.87305077, 20.3740753 ],
       [14.40449246, 20.73788215],
       [12.51111038, 20.81289712],
       [ 9.55461887, 21.48781844],
       [-0.72336527, 18.81365079]])
>>> dist = pd.DataFrame(np.random.normal(loc=(10,20), scale=(4,2), size=(1000, 2)), columns=['a', 'b'])
>>> dist.agg(['min', 'max', 'mean', 'std']).round(decimals=2)
>>> fig, ax = plt.subplots()
>>> dist.plot.kde(ax=ax, legend=False, title='Histogram: A vs. B')
>>> dist.plot.hist(density=True, ax=ax)
>>> ax.set_ylabel('Probability')
>>> ax.grid(axis='y')
>>> ax.set_facecolor('#d8dcd6')
</source>

<source lang=python>
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

p = pd.read_csv('./data/da03-press.csv',index_col='time')
pp = p['Press']

pp.plot.hist(bins=150, rwidth=.9, density=True, color='C2', alpha=0.8)
pp.plot.kde(bw_method=0.1737, color='C1')

plt.ylabel('Probability'); plt.xlim(xmin=3200,xmax=4200); plt.xlabel('hPa')
plt.grid(linewidth=0.8)
plt.show()
#sns.distplot(pp, color="#ff8000")
#plt.show()
</source>

'''bw_method''' 一般取 n^(-1/5)

更多参考：https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html#Notes

<source lang=python>
>>> s1 = np.random.normal(-1.0, 1, 320)
>>> s2 = np.random.normal(2.0, 0.6, 32)
>>> s = np.hstack([s1, s2])
>>> pdf = stats.kde.gaussian_kde(s)
>>> x = np.linspace(-5, 5, 200)
>>> plt.plot(x, pdf(x), 'r')
>>> plt.hist(s, normed=1, alpha=0.45, color='purple')
>>> plt.show()
</source>

stats.norm.rvs(), ppf(), pdf(), cdf():  https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html

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