Given this json:
"World_Regions": {
"Americas": {
"0": {
"Name": "North America",
"Category_Average": "54.53",
"Stocks_%": "55.44",
"Benchmark": "59.02"
},
"1": {
"Name": "Latin America",
"Category_Average": "0.87",
"Stocks_%": "1.14",
"Benchmark": "0.93"
}
},
"Greater Asia": {
"0": {
"Name": "Japan",
"Category_Average": "6.58",
"Stocks_%": "3.74",
"Benchmark": "7.76"
},
"1": {
"Name": "Australasia",
"Category_Average": "1.79",
"Stocks_%": "7.45",
"Benchmark": "2.17"
},
"2": {
"Name": "Asia Developed",
"Category_Average": "5.56",
"Stocks_%": "7.27",
"Benchmark": "4.57"
},
"3": {
"Name": "Asia Emerging",
"Category_Average": "6.63",
"Stocks_%": "2.96",
"Benchmark": "6.58"
}
},
I want to get this result:
Name Category_Average Stocks_% Benchmark 0 North America 54.53 55.44 59.02 1 Latin America 0.87 1.14 0.93 2 Japan 6.58 3.74 7.76 3 Australasia 1.79 7.45 2.17 4 Asia Developed 5.56 7.27 4.57 6 Asia Emerging 6.63 2.96 6.58
but unfortunately the different names for region name(Americas/Greater Asia) is causing a problem. I am trying to this cleanly in one command right now I can get the result by doing this:
pd.DataFrame.from_dict(jsonFile['World_Regions']['Greater Asia']).transpose()
Name Category_Average Stocks_% Benchmark
0 Japan 6.58 3.74 7.76
1 Australasia 1.79 7.45 2.17
2 Asia Developed 5.56 7.27 4.57
3 Asia Emerging 6.63 2.96 6.58
then the same for Americas then merge the dataframes. Is there a way to do it that’s more direct(i.e. one command?)
Answer
You can try concatenate the individual values:
pd.concat([pd.DataFrame(x).T for x in a['World_Regions'].values()],
ignore_index=True)
Or flatten the data before passing to DataFrame:
pd.DataFrame([y for x in a['World_Regions'].values()
for y in x.values() ])
Output:
Name Category_Average Stocks_% Benchmark 0 North America 54.53 55.44 59.02 1 Latin America 0.87 1.14 0.93 2 Japan 6.58 3.74 7.76 3 Australasia 1.79 7.45 2.17 4 Asia Developed 5.56 7.27 4.57 5 Asia Emerging 6.63 2.96 6.58