Searching and replacing in nested dictionary in a Pandas Dataframe column

Question

Have some tricky situation. The Pandas Dataframe df contains a column col which has nested dictionaries as values looking like that:

{'code': 'D104',
  'description': 'Error Occured',
  'id': '5451235d',
  'root_cause': 'Brokage'}

What I need to do is to find the occurrences of certain codes in string and replace the whole string with it, in example above it will be searching for 'D104' and all the string will be just 'D104' instead of dict.

I tried to apply .str.contains () on that column and use replace () function on the column but it seems to be that the trick is not working with dictionaries as values. Can you please guide me the way?

Could you post what your output would look like for the example above? — sacuL, May 22 '18 at 15:32
I'm searching 'D104' in a column and the output is 'D104' instead of all the string which contains 'D104' — Keithx, May 22 '18 at 15:33

score 3 · Accepted Answer · answered May 22 '18 at 15:33

3

If the dictionaries have the same structure, you can split them into separate columns, and then work with those.

codes = df[col].apply(pd.Series)['code']
# add `codes` to `df` or lookup 'D104' in codes
...

answered May 22 '18 at 15:33

w-m

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Searching and replacing in nested dictionary in a Pandas Dataframe column

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