How to use for loop along with if inside lambda python?

Question

I have a dataframe df that has a column tags . Each element of the column tags is a list of dictionary and looks like this:

[
    {
        "id": "leena123",
        "name": "LeenaShaw",
        "slug": null,
        "type": "UserTag",
        "endIndex": 0,
        "startIndex": 0
    },
    {
        "id": "1234",
        "name": "abc ltd.",
        "slug": "5678",
        "type": "StockTag",
        "endIndex": 0,
        "startIndex": 0
    }
]

The list can have any number of elements.

Sample dataset:

 0  some_data  [{'id': 'leena123', 'name': 'leenaShaw', 'slug': None, 'type...
 1  some data  [{'id': '6', 'name': 'new', 'slug': None, 'type...

I want to create a list of all the ids from the tags column where the type is UserTag

sample output: ['leena123', 'saily639,...]

I am trying with this : list(df['tags'].apply(lambda x: d['name'] if any(d['type'] == 'UserTag' for d in x)))

but it doesn't work. Kindly help pn this.

Please share a sample of your dataframe with expected output. — Mayank Porwal
– Mayank Porwal, Commented Apr 5, 2021 at 6:36

Mayank Porwal · Accepted Answer · 2021-04-05 07:07:37Z

1

Use List Comprehension with df.apply:

df['id'] = df.tags.apply(lambda x: [i['id'] for i in x if i.get('type') == 'UserTag'])

Create a list from id column:

import itertools

l = df['id'].values.tolist()
output_id_list = list(itertools.chain(*l))

If you want to drop id column from df, do:

df.drop('id', inplace=True)

edited Apr 5, 2021 at 7:07

answered Apr 5, 2021 at 6:40

Mayank Porwal

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4 Comments

d_b Over a year ago

This is creating a new column with the usertags. I want a list with the user tags as follows ['leena123', 'saily639,...]

Mayank Porwal Over a year ago

You want to replace existing tags column?

d_b Over a year ago

No. I do not add the a new column or replace the existing tags. I want to have a list of the id where type is userTag.

Mayank Porwal Over a year ago

@dipanjana Please check my updated answer.

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