正确使用numpy searchsorted例程

Question

我有一个输入DataFrame，我想修改它的一个'Spcx'列，为此我定义了一个升序排序列表'Spaces'

import pandas as pd
import numpy as np
if __name__ == "__main__":
Example = [[   0],
[   0],
[0.14],
[0.10],
[0.10],
[0.10],
[0.13],
[0.16],
[0.24],
[0.21],
[0.14],
[0.14]]

Example = pd.DataFrame(data = Example, columns = ['Spcx'])

Spaces = [0, 0.100, 0.125, 0.150, 0.175, 0.200, 0.225, 0.250, 0.275, 0.300]
Spaces = np.array(Spaces)  # convert to numpy array
Example["Spcx"] = Spaces[np.searchsorted(Spaces, Example["Spcx"], side = 'left')]

我要找的是每个示例['Spcx']与'空格'的每个间隔进行比较，并取左侧的值，例如:

0 ->空格[0 - 0.100]->0

0.10 ->[0.100 - 0.125] - ->0.100

0.14 ->[0.125 - 0.150] - ->0.125

应该是这样的:

Spcx    
0       
0       
0.125   
0.1     
0.1        
0.1      
0.125   
0.15
0.225      
0.2        
0.125   
0.125   

Answer 1

一种方法是简单地使用side='right'并减去1:

Spaces = np.array(Spaces)  # convert to numpy array
Example["Spcx"] = Spaces[np.searchsorted(Spaces, Example["Spcx"], side='right') - 1]
print(Example)

Spcx
0   0.000
1   0.000
2   0.125
3   0.100
4   0.100
5   0.100
6   0.125
7   0.150
8   0.225
9   0.200
10  0.125
11  0.125

从np.searchsorted的文档中，假设a是排序数组，它将返回:

tbody> <<tr>

side	返回的索引I满足
左	(张)& lt;V <= a[i]
对	a[i-1] <= v <[我]

您需要numpydigitize函数:

import pandas as pd
import numpy as np
if __name__ == "__main__":
Example = [[   0],
[   0],
[0.14],
[0.10],
[0.10],
[0.10],
[0.13],
[0.16],
[0.24],
[0.21],
[0.14],
[0.14]]

Example = pd.DataFrame(data = Example, columns = ['Spcx'])

Spaces = [0, 0.100, 0.125, 0.150, 0.175, 0.200, 0.225, 0.250, 0.275, 0.300]
Spaces = np.array(Spaces)  # convert to numpy array
Example["Spcx"] = Spaces[np.digitize(Example["Spcx"],Spaces)-1]
print(Example)

输出:

Spcx
0   0.000
1   0.000
2   0.125
3   0.100
4   0.100
5   0.100
6   0.125
7   0.150
8   0.225
9   0.200
10  0.125
11  0.125

您可以尝试pd.cut与right选项:

Spaces[pd.cut(Example['Spcx'], Spaces, right=False, labels=False)]

输出:

array([0.   , 0.   , 0.125, 0.1  , 0.1  , 0.1  , 0.125, 0.15 , 0.225,
0.2  , 0.125, 0.125])