CODEWITHSUNDEEP
pythonpandas
Kevin Nash
Kevin Nash

Reputation: 1561

Pandas - Extracting values from a Dataframe column

I have a Dataframe in the below format:

cust_id, cust_details
101, [{'self': 'https://website.com/rest/api/2/customFieldOption/1', 'value': 'Type-A', 'id': '1'}, 
      {'self': 'https://website.com/rest/api/2/customFieldOption/2', 'value': 'Type-B', 'id': '2'}, 
      {'self': 'https://website.com/rest/api/2/customFieldOption/3', 'value': 'Type-C', 'id': '3'}, 
      {'self': 'https://website.com/rest/api/2/customFieldOption/4', 'value': 'Type-D', 'id': '4'}]
102, [{'self': 'https://website.com/rest/api/2/customFieldOption/5', 'value': 'Type-X', 'id': '5'}, 
      {'self': 'https://website.com/rest/api/2/customFieldOption/6', 'value': 'Type-Y', 'id': '6'}]

I am trying to extract for every cust_id all cust_detail values

Expected output:

cust_id, new_value
101,Type-A, Type-B, Type-C, Type-D
102,Type-X, Type-Y

Upvotes: 0

Views: 133

Answers (1)

ASGM
ASGM

Reputation: 11381

Easy answer:

df['new_value'] = df.cust_details.apply(lambda ds: [d['value'] for d in ds])

More complex, potentially better answer:

Rather than storing lists of dictionaries in the first place, I'd recommend making each dictionary a row in the original dataframe.

df = pd.concat([
        df['cust_id'], 
        pd.DataFrame(
            df['cust_details'].explode().values.tolist(), 
            index=df['cust_details'].explode().index
        )
     ], axis=1)

If you need to group values by id, you can do so via standard groupby methods:

df.groupby('cust_id')['value'].apply(list)

This may seem more complex, but depending on your use case might save you effort in the long-run.

Upvotes: 1

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