如何选择行中至少一个元素中包含特定值的行? [英] How to select the rows that contain a specific value in at least one of the elements in a row?

问题描述

我有一个DataFrame DF和一个列表，例如List1. List1是从DF创建的，具有DF中存在的元素，但没有重复.我需要执行以下操作:
1.从List1中选择包含特定元素的DF行(例如，迭代List1中的所有元素)
2.将它们从0重新索引到任意行数，因为选择的行可能具有不连续的索引.

I have a DataFrame DFand a list, say List1. List1 is created from the DF and it has the elements present in DF but without repetitions. I need to do the following:
1. Select the rows of DF that contain a specific element from List1 (for instance, iterating all the elements in List1)
2. Re-index them from 0 to whatever the number of rows are because the rows selected may have non continuous indices.

样品输入:

List1=['Apple','Orange','Banana','Pineapple','Pear','Tomato','Potato']
Sample DF
  EQ1      EQ2      EQ3
0 Apple    Orange   NaN
1 Banana   Potato   NaN
2 Pear     Tomato   Pineapple
3 Apple    Tomato   Pear
4 Tomato   Potato   Banana

现在，如果我要访问包含Apple的行，则这些行将为0和3.但是我希望将它们重命名为0和1(重新索引).搜索Apple后，应采用List1中的下一个元素，并应执行类似的步骤.此后，我还有其他操作要执行，因此我需要在整个List1中循环整个过程.我希望我已经解释清楚了，这是我的相同代码，该代码无法正常工作:

Now if I want access to the rows that contain Apple, those would be 0 and 3. But I'd like them renamed as 0 and 1(Re-indexing). After Apple is searched, the next element from List1 should be taken and similar steps are to be carried out. I have other operations to perform after this, so I need to loop the whole process throughout List1. I hope I have explained it well and here is my codelet for the same, which is not working:

for eq in List1:
    MCS=DF.loc[MCS_Simp_green[:] ==eq] #Indentation was missing
    MCS= MCS.reset_index(drop=True)
    <Remaining operations>

推荐答案

我认为您需要

I think you need isin with any:

List1=['Apple','Orange','Banana','Pineapple','Pear','Tomato','Potato']

for eq in List1:
    #print df.isin([eq]).any(1)
    #print df[df.isin([eq]).any(1)]
    df1 = df[df.isin([eq]).any(1)].reset_index(drop=True)  
    print df1

     EQ1     EQ2   EQ3
0  Apple  Orange   NaN
1  Apple  Tomato  Pear
     EQ1     EQ2  EQ3
0  Apple  Orange  NaN
      EQ1     EQ2     EQ3
0  Banana  Potato     NaN
1  Tomato  Potato  Banana
    EQ1     EQ2        EQ3
0  Pear  Tomato  Pineapple
     EQ1     EQ2        EQ3
0   Pear  Tomato  Pineapple
1  Apple  Tomato       Pear
      EQ1     EQ2        EQ3
0    Pear  Tomato  Pineapple
1   Apple  Tomato       Pear
2  Tomato  Potato     Banana
      EQ1     EQ2     EQ3
0  Banana  Potato     NaN
1  Tomato  Potato  Banana

要存储值，可以使用dict理解:

For storing values you can use dict comprehension:

dfs = {eq: df[df.isin([eq]).any(1)].reset_index(drop=True) for eq in List1}

print dfs['Apple']
     EQ1     EQ2   EQ3
0  Apple  Orange   NaN
1  Apple  Tomato  Pear

print dfs['Orange']
     EQ1     EQ2  EQ3
0  Apple  Orange  NaN

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