I have my Dataframe in the below structure. I would like to break them based on the nested values within the details column

cust_id, name, details
101, Kevin, [{"id":1001,"country":"US","state":"OH"}, {"id":1002,"country":"US","state":"GA"}]
102, Scott, [{"id":2001,"country":"US","state":"OH"}, {"id":2002,"country":"US","state":"GA"}]

Expected output

cust_id, name, id, country, state
101, Kevin, 1001, US, OH
101, Kevin, 1002, US, GA
102, Scott, 2001, US, OH
102, Scott, 2002, US, GA

CodePudding user response：

df = df.explode('details').reset_index(drop=True)
df = df.merge(pd.json_normalize(df['details']), left_index=True, right_index=True).drop('details', axis=1)

1. df.explode("details") basically duplicates each row in the details N times, where N is the number of items in the array (if any) of details of that row
2. Since explode duplicates the rows, the original rows' indices (0 and 1) are copied to the new rows, so their indices are 0, 0, 1, 1, which messes up later processing. reset_index() creates a fresh new column for the index, starting at 0. drop=True is used because by default pandas will keep the old index column; this removes it.
3. pd.json_normalize(df['details']) converts the column (where each row contains a JSON object) to a new dataframe where each key unique of all the JSON objects is new column
4. df.merge() merges the new dataframe into the original one
5. left_index=True and right_index=True tells pandas to merge the specified dataframe starting from it's first, row into this dataframe, starting at its first row
6. .drop('details', axis=1) gets rid of the old details column containing the old objects