如何过滤包含 Python dataframe 中的元素列表的列

Question

I have a data frame like this:

df
ID     Col_1
1      Apple, Cherry, Banana
2      Apple, Mango
3      Kiwi, Cherry
4      Apple, Cherry, Pear
5      Apple, Melon
6      Papaya, Cherry

I want to filter the data frame in these 3 ways:

1. Col_1 has both Apple & Cherry
2. Col_1 has Apple but not Cherry
3. Col_1 has Cherry but not Apple

This is how my output looks like:

1. Col_1 has both Apple & Cherry   

Output:
ID     Col_1
1      Apple, Cherry, Banana
4      Apple, Cherry, Pear


2. Col_1 has Apple but not Cherry

Output:
ID     Col_1
2      Apple, Mango
5      Apple, Melon

3. Col_1 has Cherry but not Apple

Output:
ID     Col_1
3      Kiwi, Cherry
6      Papaya, Cherry

Can anyone help me with this?

Answer 1

Let's first start by creating OP's dataframe

df = pd.DataFrame({'ID': [1, 2, 3, 4, 5, 6],
                        'Col_1': ['Apple, Cherry, Banana', 'Apple, Mango', 'Kiwi, Cherry', 'Apple, Cherry, Pear', 'Apple, Melon', 'Papaya, Cherry']})

[Out]:
   ID                  Col_1
0   1  Apple, Cherry, Banana
1   2           Apple, Mango
2   3           Kiwi, Cherry
3   4    Apple, Cherry, Pear
4   5           Apple, Melon
5   6         Papaya, Cherry

Based on what OP shared, considering that the constraints are always dependent on apple and cherry , one can create a function, let's call it filter_df , that takes as input a dataframe and two strings as follows

def filter_df(df, s1, s2):

    # Col_1 has both Apple & Cherry
    df1 = df[df['Col_1'].str.contains(s1) & df['Col_1'].str.contains(s2)]

    # Col_1 has Apple but not Cherry
    df2 = df[df['Col_1'].str.contains(s1) & ~df['Col_1'].str.contains(s2)]

    # Col_1 has Cherry but not Apple
    df3 = df[df['Col_1'].str.contains(s2) & ~df['Col_1'].str.contains(s1)]

    return df1, df2, df3

Then, if one applies the function filter_df to the dataframe df , with the strings Apple and Cherry , one gets the following results

df1, df2, df3 = filter_df(df, 'Apple', 'Cherry')

# df1 - Col_1 has both Apple & Cherry

[Out]:
   ID                  Col_1
0   1  Apple, Cherry, Banana
3   4    Apple, Cherry, Pear


# df2 - Col_1 has Apple but not Cherry

[Out]:
   ID         Col_1
1   2  Apple, Mango
4   5  Apple, Melon


# df3 - Col_1 has Cherry but not Apple

[Out]:
   ID           Col_1
2   3    Kiwi, Cherry
5   6  Papaya, Cherry

If one wants to change the strings to consider, for example, Kiwi and Mango , or other strings, one can do that as well. Also, if the conditions change in the future, one can easily adjust the function filter_df accordingly.