Convert JSON file to Pandas dataframe

Question

I would like to convert a JSON to Pandas dataframe.

My JSON looks like: like:

{ 
   "country1":{ 
      "AdUnit1":{ 
         "floor_price1":{ 
            "feature1":1111,
            "feature2":1112
         },
         "floor_price2":{ 
            "feature1":1121
         }
      },
      "AdUnit2":{ 
         "floor_price1":{ 
            "feature1":1211
         },
         "floor_price2":{ 
            "feature1":1221
         }
      }
   },
   "country2":{ 
      "AdUnit1":{ 
         "floor_price1":{ 
            "feature1":2111,
            "feature2":2112
         }
      }
   }
}

I read the file from GCP using this code:

project = Context.default().project_id
sample_bucket_name = 'my_bucket'
sample_bucket_path = 'gs://' + sample_bucket_name
print('Object: ' + sample_bucket_path + '/json_output.json')

sample_bucket = storage.Bucket(sample_bucket_name)
sample_bucket.create()
sample_bucket.exists()

sample_object = sample_bucket.object('json_output.json')
list(sample_bucket.objects())
json = sample_object.read_stream()

My goal to get Pandas dataframe which looks like:

I tried using json_normalize, but didn't succeed.

Answer 1

Nested JSONs are always quite tricky to handle correctly.

A few months ago, I figured out a way to provide an "universal answer" using the beautifully written flatten_json_iterative_solution from here: which unpacks iteratively each level of a given json.

Then one can simply transform it to a Pandas.Series then Pandas.DataFrame like so:

df = pd.Series(flatten_json_iterative_solution(dict(json_))).to_frame().reset_index()

Intermediate Dataframe result

Some data transformation can easily be performed to split the index in the columns names you asked for:

df[["index", "col1", "col2", "col3", "col4"]] = df['index'].apply(lambda x: pd.Series(x.split('_')))

Final result

Answer 2

You could use this:

def flatten_dict(d):
    """ Returns list of lists from given dictionary """
    l = []
    for k, v in sorted(d.items()):
        if isinstance(v, dict):
            flatten_v = flatten_dict(v)
            for my_l in reversed(flatten_v):
                my_l.insert(0, k)

            l.extend(flatten_v)

        elif isinstance(v, list):
            for l_val in v:
                l.append([k, l_val])

        else:
            l.append([k, v])

    return l

This function receives a dictionary (including nesting where values could also be lists) and flattens it to a list of lists.

Then, you can simply:

df = pd.DataFrame(flatten_dict(my_dict))

Where my_dict is your JSON object. Taking your example, what you get when you run print(df) is:

          0        1             2         3     4
0  country1  AdUnit1  floor_price1  feature1  1111
1  country1  AdUnit1  floor_price1  feature2  1112
2  country1  AdUnit1  floor_price2  feature1  1121
3  country1  AdUnit2  floor_price1  feature1  1211
4  country1  AdUnit2  floor_price2  feature1  1221
5  country2  AdUnit1  floor_price1  feature1  2111
6  country2  AdUnit1  floor_price1  feature2  2112

And when you create the dataframe, you can name your columns and index