DataFrame.drop_duplicates

DataFrame.drop_duplicates(subset=None, keep='first', inplace=False)
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• subset: 默认使用所有columns, 传入column label or sequence of labels
• keep: 默认 first
  • first: 保留第一项
  • last: 保留最后一项
  • False: 删除所有重复项
• inplace:
  • True: 原来的df会被修改, 同时不会返回新的df
  • False: 原来的df不会被修改, 会返回新的df

Example

当subset是传入很多个值时, 要多个字段联合起来都是一样的才删除.

In [48]: df                                                                                                               
Out[48]: 
    a   b   c   d  e  f  g
0  49  75  49  50  1  1  1
1  89  87  27  69  2  1  1
2  41   1  75  99  3  2  1
3   8  19  71   6  4  3  1
4   0  59  92  39  4  4  1

In [49]: dff = df.drop_duplicates(subset=['f', 'g'])                                                                      

In [50]: dff                                                                                                              
Out[50]: 
    a   b   c   d  e  f  g
0  49  75  49  50  1  1  1
2  41   1  75  99  3  2  1
3   8  19  71   6  4  3  1
4   0  59  92  39  4  4  1

In [51]: dff2 = df.drop_duplicates(subset=['e','f', 'g'])                                                                 

In [52]: dff2                                                                                                             
Out[52]: 
    a   b   c   d  e  f  g
0  49  75  49  50  1  1  1
1  89  87  27  69  2  1  1
2  41   1  75  99  3  2  1
3   8  19  71   6  4  3  1
4   0  59  92  39  4  4  1
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In [26]: df = pd.DataFrame(np.random.randint(0, 100, size=(5, 5)), index=list(range(5)), columns=list('abcde'))           

In [27]: df['f'] = [1, 1, 2, 3, 4]                                                                                        

In [28]: df                                                                                                               
Out[28]: 
    a   b   c   d   e  f
0  42  55  55  39  61  1
1  27  51  26  26  64  1
2  87  11  23   2  77  2
3  82  98  61  15  88  3
4  25  21  47  79   4  4

In [29]: dff = df.drop_duplicates(subset=['f'], keep='first')                                                             

In [30]: dff                                                                                                              
Out[30]: 
    a   b   c   d   e  f
0  42  55  55  39  61  1
2  87  11  23   2  77  2
3  82  98  61  15  88  3
4  25  21  47  79   4  4

In [31]: df                                                                                                               
Out[31]: 
    a   b   c   d   e  f
0  42  55  55  39  61  1
1  27  51  26  26  64  1
2  87  11  23   2  77  2
3  82  98  61  15  88  3
4  25  21  47  79   4  4

In [34]: new = df.drop_duplicates(subset=['f'], keep='first', inplace=True)                                               

In [35]: new                                                                                                              

In [36]: df                                                                                                               
Out[36]: 
    a   b   c   d   e  f
0  42  55  55  39  61  1
2  87  11  23   2  77  2
3  82  98  61  15  88  3
4  25  21  47  79   4  4

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