将json转换为pandas DataFrame

Question

I have a JSON file which has multiple objects such as:

 {"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}

I am trying to convert it to a Pandas DataFrame using the following code:

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)

However, it gives me 'NaN' for nested values ie 'helpful' attribute. I want the output such that both the keys of the nested attribute are a separate column.

EDIT:

PS: I am using try/except because I have '\\' character in some objects which gives me a JSON decode error.

Can anyone help? Is there any other approach I can use?

Thank You.

Answer 1

Use json_normalize on the list of dictionaries which performs reasonably faster on large number of json objects.

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)