Converting string file into json format file

Question

Ok , let say that I have a string text file named "string.txt" , and I want to convert it into a json text file. What I suppose to do? I have tried to use 'json.loads()' ,but it never works with me! here is a part from my text file :

rdian","id":"161428670566653"},{"category":"Retail and consumer merchandise","category_list":[{"id":"187937741228885","name":"Electronics Store"},{"id":"191969860827280","name":"Photographic Services & Equipment"}

any help please? edit: I have use this code:

import json

f = open("string.txt", 'w')
f1 = open("stringJson.txt", 'r')


f.write(json.dumps(json.loads(f), indent=1))


f.close()

the error is like this:

obj, end = self.raw_decode(s, idx=_w(s, 0).end()) TypeError: expected string or buffer

enter image description here

For one, you have two double-quotes before string.txt => f = open(""string.txt", 'w'); Second, your JSON snippet (first code block) is truncated/missing some of it. — user2839610
– user2839610, Commented Jan 3, 2014 at 10:18
Googled your error - this was the first result. See if it helps you. stackoverflow.com/questions/12326370/… — user2839610
– user2839610, Commented Jan 3, 2014 at 10:22
@arzeramade Take it easy with the tone - just trying to better understand what you are asking. — user2839610
– user2839610, Commented Jan 3, 2014 at 10:27
Does the file contain line breaks? Post a few complete lines in that case. — Janne Karila
– Janne Karila, Commented Jan 3, 2014 at 10:28
That snapshot doesn't help because it could be anywhere in the file where it would be out of format for JSON and therefore not load properly or at all. — user2839610
– user2839610, Commented Jan 3, 2014 at 10:39

igrinis · Accepted Answer · 2018-07-02 07:49:29Z

10

import json
with open("string.txt", "rb") as fin:
    content = json.load(fin)
with open("stringJson.txt", "wb") as fout:
    json.dump(content, fout, indent=1)

See http://docs.python.org/2/library/json.html#basic-usage

edited Jul 2, 2018 at 7:49

igrinis

14k27 silver badges53 bronze badges

answered Jan 3, 2014 at 10:22

Shaung

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10 Comments

arze ramade Over a year ago

Extra data: line 1 column 102663 - line 1 column 106497 (char 102662 - 106496) this is error

user2839610 Over a year ago

@arzeramade It seems pretty obvious from the errors that the text file is not completely in the appropriate format to be loaded as JSON. You should review the file - fix the issue and retry Shaung's solution.

Janne Karila Over a year ago

@arzeramade What is there in the indicated range? Use eg. this to check: print open("string.txt","rb").read()[102662:106497]

arze ramade Over a year ago

@Nicarus: this what I was telling you , do I have to do this manually?in order to be appropriate ti loaded as json?

arze ramade Over a year ago

@Nicarus: yea I know that but how to make a text to be valid for json loads?

|

Nayanexx.py · Accepted Answer · 2020-11-26 14:04:34Z

It really depends on how your txt file is structured. But suppose you have a structured txt file like the following:

BASE|30-06-2008|2007|2|projected
BASE|30-06-2007|2010|1|projected
BASE|30-06-2007|2009|3|projected
BASE|30-06-2007|2020|2|projected
...

You could use a script like this:

import codecs
import json

import numpy as np
import pandas as pd

raw_filepath = "your_data.txt"

field_names = [
    "Scenario",
    "Date",
    "Year",
    "Quarter",
    "Value"
]

data_array = np.genfromtxt(raw_filepath, delimiter="|", dtype=None, encoding="utf-8")
df = pd.DataFrame.from_records(data_array)
df.columns = field_names
result = df.to_json(orient="records")
parsed = json.loads(result)
out_json_path = "your_data.json"

### saves pandas dataframe in .json format
json.dump(
    parsed, codecs.open(out_json_path, "w", encoding="utf-8"), sort_keys=False, indent=4
)

Explanation

To load a dataset in Numpy, we can use the genfromtxt() function. We can specify data file name, delimiter (which is optional but often used), and number of rows to skip if we have a header row. The genfromtxt() function has a parameter called dtype for specifying data types of each column(this parameter is optional). Without specifying the types, all types will be casted the same to the more general/precise type and numpy will try infer the type of a column.

In this part df.to_json(orient="records") we are Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding. This, way, we can have an output like this, as described in the Pandas Documentation:

>>>result = df.to_json(orient="records")
>>>parsed = json.loads(result)
>>>json.dumps(parsed, indent=4)  
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

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