如何从另一个DataFrame的数据中删除一个DataFrame的行? [英] How to remove rows of a DataFrame based off of data from another DataFrame?

问题描述

我是熊猫的新手，我试图弄清楚这种情况: 我有一个带有两个产品的示例DataFrame. df =

I'm new to pandas and I'm trying to figure this scenario out: I have a sample DataFrame with two products. df =

  Product_Num     Date   Description  Price 
          10    1-1-18   Fruit Snacks  2.99
          10    1-2-18   Fruit Snacks  2.99
          10    1-5-18   Fruit Snacks  1.99
          10    1-8-18   Fruit Snacks  1.99
          10    1-10-18  Fruit Snacks  2.99
          45    1-1-18         Apples  2.99 
          45    1-3-18         Apples  2.99
          45    1-5-18         Apples  2.99
          45    1-9-18         Apples  1.49
          45    1-10-18        Apples  1.49
          45    1-13-18        Apples  1.49
          45    1-15-18        Apples  2.99 

我还有另一个看起来像这样的小型DataFrame(显示相同产品的促销价格):df2 =

I also have another small DataFrame that looks like this (which shows promotional prices of the same products): df2=

  Product_Num   Price 
          10    1.99
          45    1.49 

请注意，df2不包含日期"或说明"列.我想做的是使用df1中的数据从df1中删除所有促销价格(针对促销中的所有日期).做这个的最好方式是什么?

Notice that df2 does not contain columns 'Date' nor 'Description.' What I want to do is to remove all promo prices from df1 (for all dates that are on promo), using the data from df1. What is the best way to do this?

所以，我想看看这个:

  Product_Num     Date   Description  Price 
          10    1-1-18   Fruit Snacks  2.99
          10    1-2-18   Fruit Snacks  2.99
          10    1-10-18  Fruit Snacks  2.99
          45    1-1-18         Apples  2.99 
          45    1-3-18         Apples  2.99
          45    1-5-18         Apples  2.99
          45    1-15-18        Apples  2.99 

我当时正在考虑对Price和Product_Num列进行合并，然后看看可以从那里做些什么.但是由于约会太多，我感到困惑.

I was thinking of doing a merge on columns Price and Product_Num, then seeing what I can do from there. But I was getting confused because of the multiple dates.

推荐答案

isin与&

df.loc[~((df.Product_Num.isin(df2['Product_Num']))&(df.Price.isin(df2['Price']))),:]
Out[246]: 
    Product_Num     Date  Description  Price
0            10   1-1-18  FruitSnacks   2.99
1            10   1-2-18  FruitSnacks   2.99
4            10  1-10-18  FruitSnacks   2.99
5            45   1-1-18       Apples   2.99
6            45   1-3-18       Apples   2.99
7            45   1-5-18       Apples   2.99
11           45  1-15-18       Apples   2.99

更新

df.loc[~df.index.isin(df.merge(df2.assign(a='key'),how='left').dropna().index)]
Out[260]: 
    Product_Num     Date  Description  Price
0            10   1-1-18  FruitSnacks   2.99
1            10   1-2-18  FruitSnacks   2.99
4            10  1-10-18  FruitSnacks   2.99
5            45   1-1-18       Apples   2.99
6            45   1-3-18       Apples   2.99
7            45   1-5-18       Apples   2.99
11           45  1-15-18       Apples   2.99

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