Konwersja json do Pandy DataFrame

Question

Mam plik JSON, który posiada wiele obiektów, takich jak:

{"reviewerID": "bc19970fff3383b2fe947cf9a3a5d7b13b6e57ef2cd53abc52bb2dfedf5fb1cd", "asin": "a6ed402934e3c1138111dce09256538afb04c566edf37c16b9ba099d23afb764", "overall": 2.0, "helpful": {"nHelpful": 1, "outOf": 1}, "reviewText": "This remote, for whatever reason, was chosen by Time Warner to replace their previous silver remote, the Time Warner Synergy V RC-U62CP-1.12S. The actual function of this CLIKR-5 is OK, but the ergonomic design sets back remotes by 20 years. The buttons are all the same, there's no separation of the number buttons, the volume and channel buttons are the same shape as the other buttons on the remote, and it all adds up to a crappy user experience. Why would TWC accept this as a replacement? I'm skipping this and paying double for a refurbished Synergy V.", "summary": "Ergonomic nightmare", "unixReviewTime": 1397433600} 

{"reviewerID": "3689286c8658f54a2ff7aa68ce589c81f6cae4c4d9de76fa0f66d5c114f79837", "asin": "8939d791e9dd035aa58da024ace69b20d651cea4adf6159d984872b44f663301", "overall": 4.0, "helpful": {"nHelpful": 21, "outOf": 22}, "reviewText": "This is a great truck GPS. I've tried others and nothing seems to come close to the Rand McNally TND-700.Excellent screen size and resolution. The audio is loud enough to be heard over road noise and the purr of my Kenworth/Cat engine. I've used it for the last 8,000 miles or so and it has only glitched once. Just restarted it and it picked up on my route right where it should have.Clean up the minor issues and this unit rates a solid 5.Rand McNally 528881469 7-inch Intelliroute TND 700 Truck GPS", "summary": "Great Unit!", "unixReviewTime": 1280016000} 

Próbuję przekonwertować go do Pandy DataFrame stosując następujący kod:

train_df = pd.DataFrame() 
count = 0; 
for l in open('train.json'): 
    try: 
     count +=1 
     if(count==20001): 
      break 
     obj1 = json.loads(l) 
     df1=pd.DataFrame(obj1, index=[0]) 
     train_df = train_df.append(df1, ignore_index=True) 
    except ValueError: 
     line = line.replace('\\','') 
     obj = json.loads(line) 
     df1=pd.DataFrame(obj, index=[0]) 
     train_df = train_df.append(df1, ignore_index=True) 

jednak , daje mi "NaN" dla wartości zagnieżdżonych, tj. "pomocny" atrybut. Chcę, aby wynik był taki, że oba klucze atrybutu zagnieżdżonego są oddzielną kolumną.

EDIT:

P.S: Używam try/z wyjątkiem, bo mam „\” charakter niektórych przedmiotów, które daje mi błąd dekodowania JSON.

Czy ktoś może pomóc? Czy mogę zastosować inne podejście?

Dziękuję.

Answer 1

Zastosowanie json_normalize na liście słowników, które wykonuje stosunkowo szybciej na dużej liczby obiektów JSON.

from pandas.io.json import json_normalize 

my_list = [] 
with open('train.json') as f: 
    for line in f: 
     line = line.replace('\\','') 
     my_list.append(json.loads(line)) 

# avoid transposing if you want to keep keys as columns of the dataframe 
result_df = json_normalize(my_list).T 

Answer 2

try:

pd.concat([pd.Series(json.loads(line)) for line in open('train.json')], axis=1)