Load multiple Jason data in one Data Frame

[vijays3]

Test of code using your files.

import json
import os
import pandas as pd
pd.set_option('display.max_column', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_seq_items', None)
pd.set_option('display.max_colwidth', 500)
pd.set_option('expand_frame_repr', False)

path_to_json = r'G:\div_code\JsonFiles\\'
def file_path(path_to_json):
    json_files = [path_to_json + pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('.json')]
    return  json_files

df = pd.DataFrame()
for jf in file_path(path_to_json):
    with open(jf,'r') as f:
        data = json.loads(f.read())
        # Flatten data
        df_nested_list = pd.json_normalize( data, record_path =['attributes'], meta=['ORDERNUMBER', 'PRODUCTCODE'])
        df = df.append(df_nested_list)

Look at 20 first and 20 last entries,look ok to me.

>>> df.head(20)
    QUANTITYORDERED  PRICEEACH    SALES       ORDERDATE   STATUS       PRODUCTLINE  MSRP ORDERNUMBER PRODUCTCODE
0                50      67.80  3390.00   1/6/2003 0:00  Shipped      Vintage Cars    60       10100    S18_2248
1                22      86.51  1903.22   1/6/2003 0:00  Shipped      Vintage Cars    92       10100    S18_4409
2                49      34.47  1689.03   1/6/2003 0:00  Shipped      Vintage Cars    41       10100    S24_3969
3                45      31.20  1404.00   1/9/2003 0:00  Shipped      Vintage Cars    33       10101    S24_1937
4                46      53.76  2472.96   1/9/2003 0:00  Shipped      Vintage Cars    44       10101    S24_2022
5                41      50.14  2055.74  1/10/2003 0:00  Shipped      Vintage Cars    53       10102    S18_1367
6                22      54.09  1189.98  1/29/2003 0:00  Shipped  Trucks and Buses    60       10103    S18_2432
7                27      83.07  2242.89  1/29/2003 0:00  Shipped      Vintage Cars   101       10103    S18_2949
8                35      57.46  2011.10  1/29/2003 0:00  Shipped      Vintage Cars    62       10103    S18_2957
9                41      47.29  1938.89  1/29/2003 0:00  Shipped      Vintage Cars    50       10103    S18_4668
10               45      75.63  3403.35  1/29/2003 0:00  Shipped  Trucks and Buses    64       10103    S32_3522
11               35      55.49  1942.15  1/31/2003 0:00  Shipped      Classic Cars    57       10104    S24_1444
12               44      39.60  1742.40  1/31/2003 0:00  Shipped      Classic Cars    35       10104    S24_2840
13               35      47.62  1666.70  1/31/2003 0:00  Shipped  Trucks and Buses    54       10104    S32_2509
14               49      65.87  3227.63  1/31/2003 0:00  Shipped            Trains    62       10104    S32_3207
15               32      53.31  1705.92  1/31/2003 0:00  Shipped            Trains    58       10104    S50_1514
16               41      82.50  3382.50  2/11/2003 0:00  Shipped      Vintage Cars    87       10105    S18_4522
17               44      72.58  3193.52  2/11/2003 0:00  Shipped      Vintage Cars    88       10105    S24_3151
18               50      79.67  3983.50  2/11/2003 0:00  Shipped      Vintage Cars    83       10105    S24_3816
19               41      70.67  2897.47  2/11/2003 0:00  Shipped             Ships    66       10105   S700_1138
>>> 
>>> 
>>> df.tail(20)
     QUANTITYORDERED  PRICEEACH    SALES       ORDERDATE      STATUS       PRODUCTLINE  MSRP ORDERNUMBER PRODUCTCODE
244               55      96.30  5296.50  5/29/2005 0:00  In Process      Classic Cars   117       10420    S24_2887
245               35      96.74  3385.90  5/29/2005 0:00  In Process      Classic Cars    85       10420    S24_3191
246               15      43.49   652.35  5/29/2005 0:00  In Process      Vintage Cars    41       10420    S24_3969
247               40      45.70  1828.00  5/29/2005 0:00  In Process      Vintage Cars    44       10421    S24_2022
248               51      95.55  4873.05  5/30/2005 0:00  In Process      Vintage Cars   102       10422    S18_1342
249               25      51.75  1293.75  5/30/2005 0:00  In Process      Vintage Cars    53       10422    S18_1367
250               10      88.14   881.40  5/30/2005 0:00  In Process      Vintage Cars   101       10423    S18_2949
251               31      53.72  1665.32  5/30/2005 0:00  In Process      Vintage Cars    62       10423    S18_2957
252               21      84.82  1781.22  5/30/2005 0:00  In Process      Vintage Cars   104       10423    S18_3136
253               21      89.29  1875.09  5/30/2005 0:00  In Process      Vintage Cars    99       10423    S18_3320
254               28      78.89  2208.92  5/30/2005 0:00  In Process      Vintage Cars    97       10423    S24_4258
255               26      59.87  1556.62  5/31/2005 0:00  In Process      Vintage Cars    50       10424    S18_4668
256               44      61.41  2702.04  5/31/2005 0:00  In Process  Trucks and Buses    64       10424    S32_3522
257               46      80.92  3722.32  5/31/2005 0:00  In Process      Classic Cars   101       10424   S700_2824
258               38      99.41  3777.58  5/31/2005 0:00  In Process  Trucks and Buses   122       10425    S18_2319
259               19      49.22   935.18  5/31/2005 0:00  In Process  Trucks and Buses    60       10425    S18_2432
260               55      46.82  2575.10  5/31/2005 0:00  In Process      Classic Cars    57       10425    S24_1444
261               31      33.24  1030.44  5/31/2005 0:00  In Process      Classic Cars    35       10425    S24_2840
262               41      86.68  3553.88  5/31/2005 0:00  In Process  Trucks and Buses    96       10425    S32_1268
263               11      43.83   482.13  5/31/2005 0:00  In Process  Trucks and Buses    54       10425    S32_2509

Amazing.. Thank you very much snippsat. It has worked as expected. You have saved my day.