Pandas read json error trailing data

import os basePath = os. abspath( _ _ file_ _ ) ) df = pandas. read_ json( basePath + ' / ut1. json', orient = ' records', dtype= { " A" : str, " B" : list} ). You have to read the file line by line, you can find a detailed answer in this question of n your file through jsonlint. com to see if your file is formatted correctly for json. Your first attempt was written correctly: df= pd. read_ json( ' example_ json. I think you can use modul json for reading file. json and then DataFrame constructor : import pandas as pd import json with open( ' file. json' ) as f: data = json.

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    Error trailing json

    load( f) print data [ { u' city' : u' ab', u' medium' : u' iPhone', u' request_ date' :. How can I fix the error? import pandas as pd # read the entire file into a python array with open( ' C: / Users/ Alberto/ nutrients. json', ' rb' ) as f: data = f. readlines( ) # remove the trailing " \ n" from each line data = map( lambda x: x. rstrip( ), data) # each. with open( datafilename, encoding= " utf8" ) as f: # Reading file as list of lines data = f. readlines( ) # Removing useless whitespaces data. for line in data] # Joining lines together data = ' '. join( data) # Loading dataframe from json str df = pandas. read_ json( datafile). got an error ValueError: Trailing data – Abhishek Pathak Jan 31 ' 17 at 18: 07. As the error says, you have an issue with the encoding.

    import pandas as pd # read the entire file into a python array with open( ' your. rstrip( ), data) # each element of ' data' is an. You can do something like this: import pandas as pd # results is the JSON tweet data. # Define the columns you want to extract resultFrame = pd. DataFrame( columns= [ " username", " created_ at", " tweet" ] ) print len( results) for i in. import pandas as pd import json import os os. chdir( ' / Users/ nicolas/ Downloads' ) # Reading the json as a dict with open( ' json_ example. json' ) as json_ data: data = json. load( json_ data) # using the from_ dict load function. Note that the ' orient' parameter # is not using the default value ( or it will give the same error than you had) # We transpose the resulting df and set index column as its.