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pythonpandasdictionarydataframe
Andrew Ehsaei
Andrew Ehsaei

Reputation: 13

Python Dataframe contains a list of dictionaries, need to create new dataframe with dictionary items

I have a Python dataframe that contains a list of dictionaries (for certain rows):

In[1]:
cards_df.head()

Out[1]:
    card_id   labels
0   'cid_1'   []
1   'cid_2'   []
3   'cid_3'   [{'id': 'lid_a', 'name': 'lname_a'}, {'id': 'lid_b', 'name': 'lname_b'}]
4   'cid_4'   [{'id': 'lid_c', 'name': 'lname_c'}]

I would like to create a new dataframe that expands the list of dictionary items into separate rows:

    card_id   label_id  label_name
0   cid_3     lid_a     lname_a
1   cid_3     lid_b     lname_b
2   cid_4     lid_c     lname_c

Upvotes: 1

Views: 1082

Answers (2)

piRSquared
piRSquared

Reputation: 294526

Use pd.Series.str.len to produce the appropriate values to pass to np.repeat. This in turn is used to repeat the values of df.card_id.values and make the first column of our new dataframe.

Then use pd.Series.sum on df['labels'] to concatenate all lists into a single list. This new list is now perfect for passing to the pd.DataFrame constructor. All that's left is to prepend a string to each column name and join to the column we created above. 

pd.DataFrame(dict(
    card_id=df.card_id.values.repeat(df['labels'].str.len()),
)).join(pd.DataFrame(df['labels'].sum()).add_prefix('label_'))

  card_id label_id label_name
0   cid_3    lid_a    lname_a
1   cid_3    lid_b    lname_b
2   cid_4    lid_c    lname_c

Setup 

df = pd.DataFrame(dict(
    card_id=['cid_1', 'cid_2', 'cid_3', 'cid_4'],
    labels=[
        [],
        [],
        [
            {'id': 'lid_a', 'name': 'lname_a'},
            {'id': 'lid_b', 'name': 'lname_b'}
        ],
        [{'id': 'lid_c', 'name': 'lname_c'}],
    ]
))

Upvotes: 2

maxymoo
maxymoo

Reputation: 36555

You could do this as a dict comprehension over the rows of your dataframe:

pd.DataFrame({{i: {'card_id': row['card_id'], 
                   'label_id': label['label_id'], 
                   'label_name': label['name']}}
               for i, row in df.iterrows()
               for label in row['labels']

Upvotes: 0

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