python - Parsing a column of JSON strings -


i have tab seperated flatfile, 1 column of json data stored string, e.g.

col1        col2                    col3 1491109818  2017-08-02 00:00:09.250 {"type":"tipper"} 1491110071  2017-08-02 00:00:19.283 {"type":"hgv"} 1491110798  2017-08-02 00:00:39.283 {"type":"tipper"} 1491110798  2017-08-02 00:00:39.283 \n ...

what want load table pandas dataframe, , col3 change data string information type key. there no json or json without type key want return none.

e.g.

col1        col2                    col3 1491109818  2017-08-02 00:00:09.250 tipper 1491110071  2017-08-02 00:00:19.283 hgv 1491110798  2017-08-02 00:00:39.283 tipper 1491110798  2017-08-02 00:00:39.283 none ...

the way can think iterrows, slow when dealing large files.

for index, row in df.iterrows():     try:         df.loc[index, 'col3'] = json.loads(row['col3'])['type']     except:         df.loc[index, 'col3'] = none

any suggestions on quicker approach?

using np.vectorize , json.loads

import json  def foo(x):     try:         return json.loads(x)['type']     except (valueerror, keyerror):         return none  v = np.vectorize(foo) df.col3 = v(df.col3)

note never recommended use bare except, can inadvertently catch , drop errors didn't mean to. 

df           col1                     col2    col3 0  1491109818  2017-08-02 00:00:09.250  tipper 1  1491110071  2017-08-02 00:00:19.283     hgv 2  1491110798  2017-08-02 00:00:39.283  tipper 3  1491110798  2017-08-02 00:00:39.283    none

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