pandas
代码如下:
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
|
import pandas as pd
import numpy as np
salaries = pd.DataFrame({
'name': ['BOSS', 'Lilei', 'Lilei', 'Han', 'BOSS', 'BOSS', 'Han', 'BOSS'],
'Year': [2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017],
'Salary': [1, 2, 3, 4, 5, 6, 7, 8],
'Bonus': [2, 2, 2, 2, 3, 4, 5, 6]
})
print(salaries)
print(salaries['Bonus'].duplicated(keep='first'))
print(salaries[salaries['Bonus'].duplicated(keep='first')].index)
print(salaries[salaries['Bonus'].duplicated(keep='first')])
print(salaries['Bonus'].duplicated(keep='last'))
print(salaries[salaries['Bonus'].duplicated(keep='last')].index)
print(salaries[salaries['Bonus'].duplicated(keep='last')])
|
输出如下：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
|
Bonus Salary Year name
0 2 1 2016 BOSS
1 2 2 2016 Lilei
2 2 3 2016 Lilei
3 2 4 2016 Han
4 3 5 2017 BOSS
5 4 6 2017 BOSS
6 5 7 2017 Han
7 6 8 2017 BOSS
0 False
1 True
2 True
3 True
4 False
5 False
6 False
7 False
Name: Bonus, dtype: bool
Int64Index([1, 2, 3], dtype='int64')
Bonus Salary Year name
1 2 2 2016 Lilei
2 2 3 2016 Lilei
3 2 4 2016 Han
0 True
1 True
2 True
3 False
4 False
5 False
6 False
7 False
Name: Bonus, dtype: bool
Int64Index([0, 1, 2], dtype='int64')
Bonus Salary Year name
0 2 1 2016 BOSS
1 2 2 2016 Lilei
2 2 3 2016 Lilei
|
非pandas
对于如nunpy中的这些操作主要如下:
假设有数组
a = np.array([1, 2, 1, 3, 3, 3, 0])
想找出 [1 3]
则有
|
1
2
3
4
5
|
方法1
m = np.zeros_like(a, dtype=bool)
m[np.unique(a, return_index=True)[1]] = True
a[~m]
|
|
1
2
3
|
方法2
a[~np.in1d(np.arange(len(a)), np.unique(a, return_index=True)[1], assume_unique=True)]
|
|
1
2
3
|
方法3
np.setxor1d(a, np.unique(a), assume_unique=True)
|
|
1
2
3
4
|
方法4
u, i = np.unique(a, return_inverse=True)
u[np.bincount(i) > 1]
|
|
1
2
3
4
|
方法5
s = np.sort(a, axis=None)
s[:-1][s[1:] == s[:-1]]
|
参考：https://*.com/questions/11528078/determining-duplicate-values-in-an-array
以上这篇Pandas统计重复的列里面的值方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/hguo11/article/details/82556171