python multiple for loop question (pandas, dataframe)

Question

idx_list = []
for idx, row in df_quries_copy.iterrows():
    for brand in brand_name:
        if row['user_query'].contains(brand):
            idx_list.append(idx)
        else:
            continue

brand_name list looks like below

brand_name = ['Apple', 'Lenovo', Samsung', ... ]

I have df_queries data frame which has the query the user used the table is looks like below

user_query	user_id
Apple Laptop	A
Lenovo 5GB	B

and also I have a brand name as a list i want to find out the users who uses related with brand such as 'Apple laptop'

but when I run the script, I got a message saying that 'str' object has no attribute 'contains'

how am I supposed to do to use multiple for loop ?

Thank you in advance.

for brand in brand_name[:100]:
if len(copy_df[copy_df['user_query'].str.contains(brand)]) >0:
    ls.append(copy_df[copy_df['user_query'].str.contains(brand)].index)
    
else:continue

I tried like answer but the whole dataframe came out in a sudden as a result

Ynjxsjmh · Accepted Answer · 2022-04-13 05:34:50Z

1

You can use df_quries_copy[df_quries_copy['user_query'].str.contrains(brand)].index to get index directly.

for brand in brand_name:
    df_quries_copy[df_quries_copy['user_query'].str.contrains(brand)].index

Or in your code, use brand in row['user_query'] since row['user_query'] is a string value.

edited Apr 13, 2022 at 5:34

answered Apr 13, 2022 at 5:10

Ynjxsjmh

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10 Comments

Nini Over a year ago

I have a quick question. no need to use iterrows to iterate the row of dataframe? I don't understand the logic even though the script is running :) and also i got a error unbalanced parenthesis at position 4

Ynjxsjmh Over a year ago

@Nini Yes, no need to iterate the dataframe. The unbalanced parenthesis might be some parenthesis is not closed.

d_frEak Over a year ago

yes, you can just put the condition what you want to filter like this, df[condition], you can read this too pandas.pydata.org/docs/getting_started/intro_tutorials/…

Nini Over a year ago

for brand in brand_name[:100]: print(copy_df[copy_df['user_query'].str.contains(brand)].index) the result is like below

Nini Over a year ago

Int64Index([], dtype='int64') Int64Index([], dtype='int64') Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], dtype='int64')

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