Transform text file with part json objects into json file

You could use datetime and json module. Open the file and iterate over lines, you may need to adapt some parts of the code.

strptime behavior

Working example:

import datetime
import json

in_text = """14/10/2019 13:00:19 | www.google.com | {"type":"click", "user":"root", "ip":"0.0.0.0"}
14/10/2019 13:02:19 | www.google.com | {"type":"click", "user":"root", "ip":"0.0.0.0"}
14/10/2019 13:05:19 | www.google.com | {"type":"click", "user":"root", "ip":"0.0.0.0"}"""

item_list = []
for line in in_text.split("\n"):
    date, url, json_part = line.split("|")
    item = {
        "date_time": datetime.datetime.strptime(date.strip(), "%d/%m/%Y %H:%M:%S"),
        "url": url.strip(),
    }
    item.update(json.loads(json_part))
    item_list.append(item)

print(item_list)

To read lines from a file:

with open("your/file/path.txt") as fh:
    for line in fh:
        # Copy the code from the above example.
        ...

import json
from ast import literal_eval

def transform_to_json(row):

    d = literal_eval(row[2].strip())
    d["date_time"] = row[0]
    d["url"] = row[1]

    return d


with open('example.txt', 'r') as file:
    json_objs = [transform_to_json(row.split('|')) for row in file.readlines()]

single_json_result = json.dumps(json_objs)

Use pandas:

• Given your data, as described, in a .txt file.
• .to_json has various parameters to customize the final look of the JSON file.
• Having the data in a dataframe has the advantage of allowing for additional analysis
• The data has a number of issues that can easily be fixed
  • No column names
  • Improper datatime format
  • Whitespace around the URL

import pandas as pd

# read data
df = pd.read_csv('test.txt', sep='|', header=None, converters={2: eval})

# convert column 0 to a datatime format
df[0] = pd.to_datetime(df[0])

# your data has whitespace around the url; remove it
df[1] = df[1].apply(lambda x: x.strip())

# make column 2 a separate dataframe
df2 = pd.DataFrame.from_dict(df[2].to_list())

# merge the two dataframes on the index
df3 = df.merge(df2, left_index=True, right_index=True, how='outer')

# drop old column 2
df3.drop(columns=[2], inplace=True)

# name column 0 and 1
df3.rename(columns={0: 'date_time', 1: 'url'}, inplace=True)

# dataframe view
          date_time               url   type  user       ip
2019-10-14 13:00:19   www.google.com   click  root  0.0.0.0
2019-10-14 13:02:19   www.google.com   click  root  0.0.0.0
2019-10-14 13:05:19   www.google.com   click  root  0.0.0.0

# same to a JSON
df3.to_json('test3.json', orient='records', date_format='iso')

JSON file

[{
        "date_time": "2019-10-14T13:00:19.000Z",
        "url": "www.google.com",
        "type": "click",
        "user": "root",
        "ip": "0.0.0.0"
    }, {
        "date_time": "2019-10-14T13:02:19.000Z",
        "url": "www.google.com",
        "type": "click",
        "user": "root",
        "ip": "0.0.0.0"
    }, {
        "date_time": "2019-10-14T13:05:19.000Z",
        "url": "www.google.com",
        "type": "click",
        "user": "root",
        "ip": "0.0.0.0"
    }
]