擅长:python、mysql、java
<p>我会用<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.digitize.html">^{<cd1>}</a>为你做垃圾箱分类。通过这种方式,您可以轻松地应用任何函数并设置您感兴趣的范围。在</p>
<pre><code>import numpy as np
import pylab as plt
N = 2000
total_bins = 10
# Sample data
X = np.random.random(size=N)*10
Y = X**2 + np.random.random(size=N)*X*10
bins = np.linspace(X.min(),X.max(), total_bins)
delta = bins[1]-bins[0]
idx = np.digitize(X,bins)
running_median = [np.median(Y[idx==k]) for k in range(total_bins)]
plt.scatter(X,Y,color='k',alpha=.2,s=2)
plt.plot(bins-delta/2,running_median,'r--',lw=4,alpha=.8)
plt.axis('tight')
plt.show()
</code></pre>
<p><img src="https://i.stack.imgur.com/OzYTG.png" alt="enter image description here"/></p>
<p>作为方法多功能性的一个例子,让我们添加由每个箱子的标准偏差给出的误差条:</p>
^{pr2}$
<p><img src="https://i.stack.imgur.com/Iuj5M.png" alt="enter image description here"/></p>