CODEWITHSUNDEEP
pythonjsonpandasutf
Michael
Michael

Reputation: 353

Error fitting a JSON file into a pandas Dataframe

I am attempting to fit a JSON file into a dataframe. My presently bugged code creates the JSON file through the following method:

fname = 'python.json'
with open(fname, 'r') as f, open('sentiment.json', 'w') as s:
for line in f:
    tweet = json.loads(line)
    # Create a list with all the terms
    tweet_words = tweet['text']
    output = subprocess.check_output(['curl', '-d', "text=" + tweet_words.encode('utf-8'), 'http://text-processing.com/api/sentiment/'])
    s.write(output+"\n")

It writes into 'sentiment.json' output requested from the text-processing.com API. I then load the JSON using:

def load_json(file, skip):
with open(file, 'r') as f:
    read = f.readlines()
    json_data = (json.loads(line) for i, line in enumerate(read) if i%skip==0)
return json_data

And then construct the dataframe using:

sentiment_df = load_json('sentiments.json', 1)

data = {'positive': [], 'negative': [], 'neutral': []}
for s in sentiment_df:
      data['positive'].append(s['probability']['pos'])
      data['negative'].append(s['probability']['neg'])
      data['neutral'].append(s['probability']['neutral'])

df = pd.DataFrame(data)

Error: ValueError: No JSON object could be decoded

I browsed through several related questions, and based on the answer here, from WoodrowShigeru, I suspect it may have something to do with my encoding into 'utf-8' in the first block of code.

Does anyone know a good fix? Or at least provide some directions? Thanks guys!

Edit 1

Screen of 'sentiment.json'

Upvotes: 0

Views: 1321

Answers (1)

Parfait
Parfait

Reputation: 107767

Your screenshot is not a valid json as a container must hold all comma-separated line items. However, the challenge is your command line call returns a string, output, that you then write to text file. You need to create a list of dictionaries that is then dumped to a json file with json.dumps().

Consider doing so by casting command line string into a dictionary with ast.literal_eval() during the first text file read. Then append each dictionary to a list:

import ast

fname = 'python.json'
dictList = []
with open(fname, 'r') as f, open('sentiment.json', 'w') as s:
  for line in f:
      tweet = json.loads(line)
      # Create a list with all the terms
      tweet_words = tweet['text']
      output = subprocess.check_output(['curl', '-d', "text=" + tweet_words.encode('utf-8'), 
                                        'http://text-processing.com/api/sentiment/'])
      # CONVERT STRING TO DICT AND APPEND TO LIST
      dictList.append(ast.literal_eval(output))

  # CONVERT TO JSON AND WRITE TO FILE
  s.write(json.dumps(dictList, indent=4))

From there, read json file into a pandas dataframe with json_normalize. Below uses example data:

Sentiment json file

import json
import pandas as pd

with open('sentiment.json') as f:
    data = json.load(f)

df = pd.io.json.json_normalize(data)
df.columns = [c.replace('probability.', '') for c in df.columns]
print(df)

#       label       neg   neutral       pos
# 0       pos  0.003228  0.204509  0.571945
# 1       pos  0.053094  0.097317  0.912760
# 2       pos  0.954958  0.163341  0.917178
# 3       pos  0.784391  0.646188  0.955281
# 4       pos  0.203419  0.050908  0.490738
# 5       neg  0.122760  0.705633  0.219701
# 6       neg  0.961012  0.923886  0.335999
# 7       neg  0.368639  0.562720  0.124530
# 8       neg  0.566386  0.802366  0.825956
# 9       neg  0.115536  0.512605  0.784626
# 10  neutral  0.202092  0.741778  0.567957
# 11  neutral  0.837179  0.051033  0.509777
# 12  neutral  0.333542  0.085449  0.610222
# 13  neutral  0.798188  0.248258  0.218591
# 14  neutral  0.873109  0.469737  0.005178
# 15      pos  0.916112  0.313960  0.750118
# 16      neg  0.810080  0.852236  0.212373
# 17  neutral  0.748280  0.039534  0.323145
# 18      pos  0.274492  0.461644  0.984955
# 19      neg  0.063772  0.793171  0.631172

Upvotes: 1

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