Learning to unfold a nested column within a pandas data frame and rejoin it with the original dataset in Python

I currently have a dataframe structured like this:

Input

df.head(3)

groupId  Gourpname  totalItemslocations
    7494732   A         {'code': 'DEHAM', 'position': {'lat': 53.551085, 'lon': 9.993682}}
    7494733   B         {'code': 'DEHAM', 'position': {'lat': 53.551086, 'lon': 9.993687}}
    7494734   A         {'code': 'DEHAM', 'position': {'lat': 53.552084, 'lon': 9.993682}}

Expected Output

roupId   Gourpname  totalItemslocations.code     totalItemslocations.position.lat    totalItemslocations.position.lon 
7494732   A              DEHAM                        53.551085                           9.993682
7494733   B              DEHAM                        53.551086                           9.993687
7494734   A              DEHAM                        53.552084                           9.993682

Can someone help me convert this into a pandas dataframe?

pythonpython-3.xpandasdataframe

Answer №1

Utilize the method pd.Series to expand the dictionary and then use pd.concat to combine it with other columns.

df = pd.concat([df.drop(['totalItemslocations'], axis=1), df['totalItemslocations'].apply(pd.Series)], axis=1)
df = pd.concat([df.drop(['position'], axis=1), df['position'].apply(pd.Series)], axis=1)

print(df)

groupId   groupname          code      lat         lon 
7494732       A              DEHAM    53.551085   9.993682
7494733       B              DEHAM    53.551086   9.993687
7494734       A              DEHAM    53.552084   9.993682

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