Question

我拖了三个有重复行的DataFrame。

In [31]: df1
Out[31]: 
    member           time
0       0 2009-09-30 12:00:00
1       0 2009-09-30 18:00:00
2       0 2009-10-01 00:00:00
3       1 2009-09-30 12:00:00
4       1 2009-09-30 18:00:00
5       2 2009-09-30 12:00:00
6       3 2009-09-30 12:00:00
...

In [32]: df2
Out[32]: 
    member           time
0       0 2009-09-30 12:00:00
1       0 2009-09-30 18:00:00
3       1 2009-09-30 12:00:00
4       2 2009-09-30 12:00:00
5       2 2009-09-30 18:00:00
6       2 2009-10-01 00:00:00
...

我想从df1和df2过滤出具有'member'和'time'唯一值的行，并获得一个只有行具有'member'和'time'公共值的行的DataFrame在df1和df2中，即

In [33]: df_duplicated_1_and_2
Out[33]: 
    member           time
0       0 2009-09-30 12:00:00
1       0 2009-09-30 18:00:00
3       1 2009-09-30 12:00:00
4       2 2009-09-30 12:00:00
...

有没有一种高效优雅的方法来做到这一点？

更新如果可能，我想要的不是新的合并DataFrame，而是过滤后的DataFrame。例如，

In [34]: df1
Out[34]: 
    member           time           value
0       0 2009-09-30 12:00:00  a
1       0 2009-09-30 18:00:00  b
2       0 2009-10-01 00:00:00  c
3       1 2009-09-30 12:00:00  d
4       1 2009-09-30 18:00:00  e
5       2 2009-09-30 12:00:00  f
6       3 2009-09-30 12:00:00  g
...

In [35]: df1_filtered_out
Out[35]: 
    member           time           value
0       0 2009-09-30 12:00:00  a
1       0 2009-09-30 18:00:00  b
3       1 2009-09-30 12:00:00  d
4       2 2009-09-30 12:00:00  g
...

并且也过滤了df2。

Answer 1

在member和time列上进行内部联接：

>>> df1.merge(df2, on=['member', 'time'], how='inner')
   member                time
0       0 2009-09-30 12:00:00
1       0 2009-09-30 18:00:00
2       1 2009-09-30 12:00:00
3       2 2009-09-30 12:00:00

这将生成一个结果，该结果只包含两个DataFrame中具有相同member和time值的行。

<强>更新

>>> df1.merge(df2[['member', 'time']])
   member                time value
0       0 2009-09-30 12:00:00     a
1       0 2009-09-30 18:00:00     b
2       1 2009-09-30 12:00:00     d
3       2 2009-09-30 12:00:00     f

Python / Pandas过滤掉DataFrames中的唯一行

1 个答案: