How to Normalize Json Correctly by Python Pandas

I am a beginner in Python. What I want to do is load a json file of forex historical price data by Pandas and do statistic with the data. I have go through many topics on Pandas and parsing json file. I want to pass a json file with extra value and nested list to a pandas data frame. I got a problem stuck here.

I got a json file 'EUR_JPY_H8.json'

First I import the lib that required,

import pandas as pd
import json
from pandas.io.json import json_normalize

Then load the json file,

with open('EUR_JPY_H8.json') as data_file:    
data = json.load(data_file)

I got a list below:

[{u'complete': True,
u'mid': {u'c': u'119.743',
  u'h': u'119.891',
  u'l': u'119.249',
  u'o': u'119.341'},
u'time': u'1488319200.000000000',
u'volume': 14651},
{u'complete': True,
u'mid': {u'c': u'119.893',
  u'h': u'119.954',
  u'l': u'119.552',
  u'o': u'119.738'},
u'time': u'1488348000.000000000',
u'volume': 10738},
{u'complete': True,
u'mid': {u'c': u'119.946',
  u'h': u'120.221',
  u'l': u'119.840',
  u'o': u'119.888'},
u'time': u'1488376800.000000000',
u'volume': 10041}]

Then I pass the list to json_normalize. Try to get price which is in the nested list under 'mid'

result = json_normalize(data,'time',['time','volume','complete',['mid','h'],['mid','l'],['mid','c'],['mid','o']])

But I got such result, json_normalize output

The 'time' data got breakdown into each integer row by row. I have checked related document. I have to pass a string or list object to the 2nd parameter of json_normalize. How can I pass the timestamp there without breaking down.

My expected output is:

column = 
  index  |  time  | volumn  |  completed  |  mid.h  |  mid.l  |  mid.c  |  mid.o