根据另一个数据框中的数据量删除一个数据框中的行 [英] Delete rows of a data frame based on quantity of data in another data frame

问题描述

我有两个熊猫数据帧A和B. B是A的子集.

I have two pandas data frames A and B. B is a subset of A.

我想从A中删除所有数字，如果它在B中.但是，如果一个数字在A中出现两次，在B中出现1次，那么它只会从A中删除该数字出现1次

I want to delete all numbers from A if it's in B. But, if a number occurs two times in A and 1 time in B then it will only delete 1 occurrence of the number from A.

这是我的示例数据集:

df_A                df_B
[Test]              [Test]
1                   1
2                   2         
3                   5 
2                   5 
4
5
5

操作后，我希望将新的数据帧c作为

After the operation I want new data frame c as

df_C
[Test]
3
2
4

您能建议怎么做吗?

The suggested duplicate removes all occurrences from A if present in B, not just the first N occurrences.

推荐答案

我可能会做些事情(偷取SR的设置):

I might do something (stealing SR's setup):

dfA = pd.DataFrame({'A': [1, 2, 3, 2, 4, 5, 5]})
dfB = pd.DataFrame({'B': [1, 2, 5, 5]})

counts = dfA.groupby('A').cumcount()
limits = dfB['B'].value_counts().reindex(dfA.A).fillna(0).values
dfC = dfA.loc[counts >= limits]

这给了我

In [121]: dfC
Out[121]: 
   A
2  3
3  2
4  4

---

这可以通过使用groupby来获得A中给定值出现之前的次数:

---

This works by using groupby to get the number of times a given value in A has been seen before:

In [124]: dfA.groupby('A').cumcount()
Out[124]: 
0    0
1    0
2    0
3    1
4    0
5    0
6    1
dtype: int64

并使用value_counts来获取限制，然后我们重新索引以匹配计数:

and using value_counts to get the limits which we then reindex to match the counts:

In [139]: dfB['B'].value_counts()
Out[139]: 
5    2
2    1
1    1
Name: B, dtype: int64

In [140]: dfB['B'].value_counts().reindex(dfA.A)
Out[140]: 
A
1    1.0
2    1.0
3    NaN
2    1.0
4    NaN
5    2.0
5    2.0
Name: B, dtype: float64

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