Pandas统计重复的列里面的值方法


						pandas

代码如下:





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：





 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





方法1



m = np.zeros_like(a, dtype=bool)

m[np.unique(a, return_index=True)[1]] = True

a[~m]









方法2



a[~np.in1d(np.arange(len(a)), np.unique(a, return_index=True)[1], assume_unique=True)]









方法3



np.setxor1d(a, np.unique(a), assume_unique=True)









方法4



u, i = np.unique(a, return_inverse=True)

u[np.bincount(i) > 1]









方法5



s = np.sort(a, axis=None)

s[:-1][s[1:] == s[:-1]]





参考：http://stackoverflow.com/questions/11528078/determining-duplicate-values-in-an-array

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