基于一列列表访问数据框的行。 如果列表中存在唯一字符串，则将查看该行

Question

This is difficult to explain, so I haven't been able to google my problem.

I have a dataframe. A column of that dataframe contains lists. Each row has a list of strings. The lists are of various size. Some rows don't have a list, but a NaN value.

I want to be able to view rows of the dataframe that contain an arbitrary string in their list. So if I want to find all rows that have a list that contains "arbitrary_string" as an element of the list, those rows will be selected.

Here is an image indicating an example dataframe.

I want to use the term "corndog" to return a view of row 1 and 2. The location in the list of the string does not matter. My associates suggested I try to use lambdas and apply and a special function together. Their examples haven't worked for me.

They propose:

def find_id(inpList:list,inpstr):  
    print(inpList)
    for x in inpList:
        if inpstr in x:
            return(1)
    return(0)

Df[list_of_strings].apply(lambda x: find_id(x, cust string))

I'm not really sure what I'm doing. I don't understand how these things could be pieced together.

Answer 1

IIUC, I think you can use this:

Original df:

+----+-----------+--------------+----------------------------+
|    | some_int  | some_string  |      List_of_strings       |
+----+-----------+--------------+----------------------------+
| 0  |       84  | something    | [‘cat’,’dog’,’corndog’]    |
| 1  |       74  | etc          | [‘qwetry’,’celphone’]      |
| 2  |       64  | etc          | [‘dog’,corndog’]           |
| 3  |       89  | etc          | [‘etc’,’catfish’,’purple’] |
+----+-----------+--------------+----------------------------+

df[df['List_of_strings'].str.contains('corndog')]

Output:

    some_int    some_string  List_of_strings
0   84          something   [‘cat’,’dog’,’corndog’]
2   64              etc     [‘dog’,corndog’]

EDIT considering column value are of list type and not string you can use following:

df[df['List_of_strings'].apply(lambda x: 'corndog' in x)]