擅长:python、mysql、java
<p>这将是解决问题的一种方法-</p>
<pre><code>import numpy as np
# Input array
A = np.array([[1,2,4.0],
[1,1,5.0],
[1,2,3.0],
[0,1,9.0]])
# Extract xy columns
xy = A[:,0:2]
# Perform lex sort and get the sorted indices and xy pairs
sorted_idx = np.lexsort(xy.T)
sorted_xy = xy[sorted_idx,:]
# Differentiation along rows for sorted array
df1 = np.diff(sorted_xy,axis=0)
df2 = np.append([True],np.any(df1!=0,1),0)
# OR df2 = np.append([True],np.logical_or(df1[:,0]!=0,df1[:,1]!=0),0)
# OR df2 = np.append([True],np.dot(df1!=0,[True,True]),0)
# Get unique sorted labels
sorted_labels = df2.cumsum(0)-1
# Get labels
labels = np.zeros_like(sorted_idx)
labels[sorted_idx] = sorted_labels
# Get unique indices
unq_idx = sorted_idx[df2]
# Get counts and unique rows and setup output array
counts = np.bincount(labels, weights=A[:,2])
unq_rows = xy[unq_idx,:]
out = np.append(unq_rows,counts.ravel()[:,None],1)
</code></pre>
<p>输入和输出-</p>
^{pr2}$