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I did a code to generate multiple addresses and export it in csv
import csv
import ipaddress
import random
from random import shuffle
LAN = ipaddress.ip_network('192.168.0.0/16')
WAN1 = ipaddress.ip_network('11.10.8.0/22')
WAN2 = ipaddress.ip_network('12.10.8.0/22')
LAN_IP_Adresses = [ IP_LAN for IP_LAN in LAN.hosts()]
WAN1_IP_Adresses = [ IP_WAN1 for IP_WAN1 in WAN1.hosts()]
WAN2_IP_Adresses = [ IP_WAN2 for IP_WAN2 in WAN2.hosts()]
index_IP_GW = len(WAN1_IP_Adresses)-1
locations_list=['Abidjan','Abu Dhabi','Adana','Adelaide', 'Ahmadabad','Algiers','Amsterdam','Ankara','Anshan','Athens','BANGKOK','BUCHAREST','BUDAPEST','Bagdad','Bandung','Bangalore','Bangkok','Barcelona','Beirut','Belgrade','Bern','Bogota','Brasilia','Brazzaville','Brussels','Bursa','CAIRO','CARACAS','CONAKRY','Canberra','Casablanca','Changchun','Chengdu','Chicago','Copenhagen','Dakar','MINSK','Madrid','Medina','Nairobi','Napoli','Montreal',
'Odessa','Omdurman','Osaka','Ottawa','PYONGYANG','Paris','Pekin', 'Perth','Philadelphia','Phoenix','Pimpri Chinchwad','Porto','Porto Alegre','QUITO','Qingdao','Rabat','Rajkot','Riadh','Rio de Janeiro','Rome','SANTIAGO','Salvador','Samara','San Antonio','San Francisco','Sao Paulo','Sendai','Seongnam','Seoul','Shanghai','Singapore','Sydney','Taiyuan','Tehran','Tijuana','Tokyo','Toronto','Moscou','Moscow','Mumbai (Bombay)','Munich','México','Milan',
'Tripoli','Tunis','Vienna','Warsaw','Wuhan','Xian','Yaounde','Yokohama', 'Zapopan','hong kong','Dallas','Delhi','Doha','Dublin','Durban','Ecatepec','Frankfurt','Fukuoka','Giza','Hamburg','Havana','Helsinki','Houston','Hyderabad','Istanbul','Jaipur','Jakarta','Jeddah','Johannesburg','KIEV','Kaduna','Kano','Kazan','Kuala Lumpur''Kyoto','LUANDA','Lahore','Lanzhou','Le Caire','Leon','Lima','Lisbon','London','Los Angeles','Lyon','MANILA','Melbourne','New York']
#Site_Nmb=1
def initial_Sites_list_generator(filename='SITES_CI.csv', Number_of_Sites=1000):
file_to_output = open(filename,'w',newline='')
csv_writer = csv.writer(file_to_output,delimiter=',')
Site_Nbr=1
index = 0
csv_writer.writerow(["SITE_NAME", "SERIAL_NUMBER",'"LAN_IP_ADDRESS"','"WAN_IP_ADDRESS1"','"WAN_IP_ADDRESS2"','"GATEWAY_IP_ADDRESS1"','"GATEWAY_IP_ADDRESS2"','"ROUTE_REFLECTOR"','"LOCATIONS"','"HARDWAREMODEL"','"LANINT"','"WANINT1"','"WANINT2"','"BW_OUT"','"BW_IN"'])
for i in range(1,Number_of_Sites+1):
shuffle(locations_list)
location = random.choice(locations_list)
csv_writer.writerow(['"SITE'+ str(Site_Nbr)+'"',"2e70129bde9c4426b9213d4408c300",f'"{(LAN_IP_Adresses[index])}"',f'"{str(WAN1_IP_Adresses[index])}"',f'"{str(WAN2_IP_Adresses[index])}"',f'"{str(WAN1_IP_Adresses[index_IP_GW])}"',f'"{str(WAN2_IP_Adresses[index_IP_GW])}"','"False"',f'"{location}"','"ONEv600"','"gigabitethernet0/2"','"gigabitethernet0/0"','"gigabitethernet0/1"','"10"','"20"'])
Site_Nbr = Site_Nbr+1
index = index+1
file_to_output.close()
initial_Sites_list_generator('SITES_OVP.csv', 1000)
but i got unnecessary quotes added in my csv
You are adding the extra quotes yourself. In your for loop, change this line:
csv_writer.writerow(['"SITE'+ str(Site_Nbr)+'"',"2e70129bde9c4426b9213d4408c300",f'"{(LAN_IP_Adresses[index])}"',f'"{str(WAN1_IP_Adresses[index])}"',f'"{str(WAN2_IP_Adresses[index])}"',f'"{str(WAN1_IP_Adresses[index_IP_GW])}"',f'"{str(WAN2_IP_Adresses[index_IP_GW])}"','"False"',f'"{location}"','"ONEv600"','"gigabitethernet0/2"','"gigabitethernet0/0"','"gigabitethernet0/1"','"10"','"20"'])
to this:
csv_writer.writerow(['SITE'+ str(Site_Nbr)+"2e70129bde9c4426b9213d4408c300",
f'{(LAN_IP_Adresses[index])}',
f'{str(WAN1_IP_Adresses[index])}',
f'{str(WAN2_IP_Adresses[index])}',
f'{str(WAN1_IP_Adresses[index_IP_GW])}',
f'{str(WAN2_IP_Adresses[index_IP_GW])}',
'False',
f'{location}',
'ONEv600',
'gigabitethernet0/2',
'gigabitethernet0/0',
'gigabitethernet0/1',
'10',
'20'])
The CSV writer already adds quotes to strings as appropriate.
I did
csv_writer = csv.writer(file_to_output,delimiter=",",quoting=csv.QUOTE_ALL)
and it worked !
How do I extract text from this PDF files where some data is in the form of table while some are key value based data
eg:
https://drive.internxt.com/s/file/78f2d73478b832b2ab55/3edb275967deeca6ad33e7d53f2337c50d5dfb50e0aa525bb7f10d49dff1e2b4
This is what I have tried :
import PyPDF2
import openpyxl
from openpyxl import Workbook
pdfFileObj = open('sample.pdf', 'rb')
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
pdfReader.numPages
pageObj = pdfReader.getPage(0)
mytext = pageObj.extractText()
wb = Workbook()
sheet = wb.active
sheet.title = 'MyPDF'
sheet['A1'] = mytext
wb.save('sample.xlsx')
print('Save')
However I'd like the data to be stored in the following format.
This pdf does not have well defined tables, hence cannot use any tool to extract the entire data in one table format. What we can do is read the entire pdf as text. And process each data fields line by line by using regex to extract the data.
Before you move ahead, please install the pdfplumber package for python
pip install pdfplumber
Assumptions
Here are some assumptions that I made for your pdf and accordingly I have written the code.
First line will always contain the title Account History Report.
Second line will contain the names IMAGE All Notes
Third line will contain only the data Date Created in the form of key:value.
Fourth line will contain only the data Number of Pages in the form of key:value.
Fifth line will only contain the data Client Code, Client Name
Starting line 6, a pdf can have multiple data entity, these data entity for eg in this pdf is 2 but can be any number of entity.
Each data entity will contain the following fields:
First line in data entity will contain only the data Our Ref, Name, Ref 1, Ref 2
Second line line will only contain data in the form as present in pdf Amount, Total Paid, Balance, Date of A/C, Date Received
Third line in data entity will contain the data Last Paid, Amt Last Paid, Status, Collector.
Fourth line will contain the column name Date Notes
The subsequent lines will contain data in the form of table until the next data entity is started.
I also assume that each data entity will contain the first data with key Our Ref :.
I assume that the data entity will be separated on the first line of each entity in the pattern of key values as Our Ref :Value Name: Value Ref 1 :Value Ref 2:value
pattern = r'Our Ref.*?Name.*?Ref 1.*?Ref 2.*?'
Please note that the rectangle that I have created(thick black) in above image, I am calling those as data entity.
The final data will be stored in a dictionary(json) where the data entity will have key as dataentity1, dataentity2, dataentity3 based on the number of entities you have in your pdf.
The header details are stored in the json as key:value and I assume that each key will be present in header only once.
CODE
Here is the simple elegant code, that gives you information from the pdf in the form of json. In the output the first few field contains information from the header part, subsequent data entities can be found as data_entity 1 and 2.
In the below code all you need to change is pdf_path.
import pdfplumber
import re
# regex pattern for keys in line1 of data entity
my_regex_dict_line1 = {
'Our Ref' : r'Our Ref :(.*?)Name',
'Name' : r'Name:(.*?)Ref 1',
'Ref 1' : r'Ref 1 :(.*?)Ref 2',
'Ref 2' : r'Ref 2:(.*?)$'
}
# regex pattern for keys in line2 of data entity
my_regex_dict_line2 = {
'Amount' : r'Amount:(.*?)Total Paid',
'Total Paid' : r'Total Paid:(.*?)Balance',
'Balance' : r'Balance:(.*?)Date of A/C',
'Date of A/C' : r'Date of A/C:(.*?)Date Received',
'Date Received' : r'Date Received:(.*?)$'
}
# regex pattern for keys in line3 of data entity
my_regex_dict_line3 ={
'Last Paid' : r'Last Paid:(.*?)Amt Last Paid',
'Amt Last Paid' : r'Amt Last Paid:(.*?)A/C\s+Status',
'A/C Status': r'A/C\s+Status:(.*?)Collector',
'Collector' : r'Collector :(.*?)$'
}
def preprocess_data(data):
return [el.strip() for el in data.splitlines() if el.strip()]
def get_header_data(text, json_data = {}):
header_data_list = preprocess_data(text)
# third line in text of header contains Date Created field
json_data['Date Created'] = re.search(r'Date Created:(.*?)$', header_data_list[2]).group(1).strip()
# fourth line in text contains Number of Pages, Client Code, Client Name
json_data['Number of Pages'] = re.search(r'Number of Pages:(.*?)$', header_data_list[3]).group(1).strip()
# fifth line in text contains Client Code and ClientName
json_data['Client Code'] = re.search(r'Client Code - (.*?)Client Name', header_data_list[4]).group(1).strip()
json_data['ClientName'] = re.search(r'Client Name - (.*?)$', header_data_list[4]).group(1).strip()
def iterate_through_regex_and_populate_dictionaries(data_dict, regex_dict, text):
''' For the given pattern of regex_dict, this function iterates through each regex pattern and adds the key value to regex_dict dictionary '''
for key, regex in regex_dict.items():
matched_value = re.search(regex, text)
if matched_value is not None:
data_dict[key] = matched_value.group(1).strip()
def populate_date_notes(data_dict, text):
''' This function populates date and Notes in the data chunk in the form of list to data_dict dictionary '''
data_dict['Date'] = []
data_dict['Notes'] = []
iter = 4
while(iter < len(text)):
date_match = re.search(r'(\d{2}/\d{2}/\d{4})',text[iter])
data_dict['Date'].append(date_match.group(1).strip())
notes_match = re.search(r'\d{2}/\d{2}/\d{4}\s*(.*?)$',text[iter])
data_dict['Notes'].append(notes_match.group(1).strip())
iter += 1
data_index = 1
json_data = {}
pdf_path = r'C:\Users\hpoddar\Desktop\Temp\sample3.pdf' # ENTER YOUR PDF PATH HERE
pdf_text = ''
data_entity_sep_pattern = r'(?=Our Ref.*?Name.*?Ref 1.*?Ref 2)'
if(__name__ == '__main__'):
with pdfplumber.open(pdf_path) as pdf:
index = 0
while(index < len(pdf.pages)):
page = pdf.pages[index]
pdf_text += '\n' + page.extract_text()
index += 1
split_on_data_entity = re.split(data_entity_sep_pattern, pdf_text.strip())
# first data in the split_on_data_entity list will contain the header information
get_header_data(split_on_data_entity[0], json_data)
while(data_index < len(split_on_data_entity)):
data_entity = {}
data_processed = preprocess_data(split_on_data_entity[data_index])
iterate_through_regex_and_populate_dictionaries(data_entity, my_regex_dict_line1, data_processed[0])
iterate_through_regex_and_populate_dictionaries(data_entity, my_regex_dict_line2, data_processed[1])
iterate_through_regex_and_populate_dictionaries(data_entity, my_regex_dict_line3, data_processed[2])
if(len(data_processed) > 3 and data_processed[3] != None and 'Date' in data_processed[3] and 'Notes' in data_processed[3]):
populate_date_notes(data_entity, data_processed)
json_data['data_entity' + str(data_index)] = data_entity
data_index += 1
print(json_data)
Output :
Result string :
{'Date Created': '18/04/2022', 'Number of Pages': '4', 'Client Code': '110203', 'ClientName': 'AWS PTE. LTD.', 'data_entity1': {'Our Ref': '2118881115', 'Name': 'Sky Blue', 'Ref 1': '12-34-56789-2021/2', 'Ref 2': 'F2021004444', 'Amount': '$100.11', 'Total Paid': '$0.00', 'Balance': '$100.11', 'Date of A/C': '01/08/2021', 'Date Received': '10/12/2021', 'Last Paid': '', 'Amt Last Paid': '', 'A/C Status': 'CLOSED', 'Collector': 'Sunny Jane', 'Date': ['04/03/2022'], 'Notes': ['Letter Dated 04 Mar 2022.']}, 'data_entity2': {'Our Ref': '2112221119', 'Name': 'Green Field', 'Ref 1': '98-76-54321-2021/1', 'Ref 2': 'F2021001111', 'Amount': '$233.88', 'Total Paid': '$0.00', 'Balance': '$233.88', 'Date of A/C': '01/08/2021', 'Date Received': '10/12/2021', 'Last Paid': '', 'Amt Last Paid': '', 'A/C Status': 'CURRENT', 'Collector': 'Sam Jason', 'Date': ['11/03/2022', '11/03/2022', '08/03/2022', '08/03/2022', '21/02/2022', '18/02/2022', '18/02/2022'], 'Notes': ['Email for payment', 'Case Status', 'to send a Letter', '845***Ringing, No reply', 'Letter printed - LET: LETTER 2', 'Letter sent - LET: LETTER 2', '845***Line busy']}}
Now once you got the data in the json format, you can load it in a csv file, as a data frame or whatever format you need the data to be in.
Save as xlsx
To save the same in a xlsx file in the format as shown in the image in the question above. We can use xlsx writer to do the same.
Please install the package using pip
pip install xlsxwriter
From the previous code, we have our entire data in the variable json_data, we will be iterating through all the data entities and write the data to appropriate cell specified by row, col in the code.
import xlsxwriter
workbook = xlsxwriter.Workbook('Sample.xlsx')
worksheet = workbook.add_worksheet("Sheet 1")
row = 0
col = 0
# write columns
columns = ['Account History Report', 'All Notes'] + [ key for key in json_data.keys() if 'data_entity' not in key ] + list(json_data['data_entity1'].keys())
worksheet.write_row(row, col, tuple(columns))
row += 1
column_index_map = {}
for index, col in enumerate(columns):
column_index_map[col] = index
# write the header
worksheet.write(row, column_index_map['Date Created'], json_data['Date Created'])
worksheet.write(row, column_index_map['Number of Pages'], json_data['Number of Pages'])
worksheet.write(row, column_index_map['Client Code'], json_data['Client Code'])
worksheet.write(row, column_index_map['ClientName'], json_data['ClientName'])
data_entity_index = 1
#iterate through each data entity and for each key insert the values in the sheet
while True:
data_entity_key = 'data_entity' + str(data_entity_index)
row_size = 1
if(json_data.get(data_entity_key) != None):
for key, value in json_data.get(data_entity_key).items():
if(type(value) == list):
worksheet.write_column(row, column_index_map[key], tuple(value))
row_size = len(value)
else:
worksheet.write(row, column_index_map[key], value)
else:
break
data_entity_index += 1
row += row_size
workbook.close()
Result :
The above code creates a file sample.xlsx in the working directory.
I have lists that are formatted like so:
order_ids = ['Order ID', '026-2529662-9119536', '026-4092572-3574764', '026-4267878-0816332', '026-5334006-4073138', '026-5750353-4848328', '026-5945233-4883500', '026-5966822-8160331', '026-8799392-8255522', '202-5076008-9615516', '202-5211901-8584318', '202-5788153-3773918', '202-6208325-9677946', '203-1024454-3409960', '203-1064201-9833131', '203-4104559-7038752', '203-5013053-9959554', '203-5768187-0573905', '203-8639245-4145958', '203-9473169-4807564', '204-1577436-4733125', '204-7025768-1965915', '204-9196762-0226720', '205-6427246-2264368', '205-9028779-8764322', '206-0703454-9777135', '206-0954144-1685131', '206-3381432-7615531', '206-3822931-6939555', '206-4658913-5563533', '206-5213573-9997926', '206-5882801-0583557', '206-7158700-9326744', '206-7668862-3913143', '206-8019246-1474732', '206-8541775-0545153']
one = [['Order ID', 'Amount'], ['026-2529662-9119536', '10.42'], ['026-4092572-3574764', '10.42'], ['026-4267878-0816332', '1.75'], ['026-5334006-4073138', '17.990000000000002'], ['026-5750353-4848328', '16.25'], ['026-5945233-4883500', '1.83'], ['026-5966822-8160331', '11.92'], ['026-8799392-8255522', '8.5'], ['202-5076008-9615516', '1.83'], ['202-5211901-8584318', '1.83'], ['202-5788153-3773918', '8.08'], ['202-6208325-9677946', '11.33'], ['203-1024454-3409960', '8.08'], ['203-1064201-9833131', '1.5'], ['203-4104559-7038752', '8.5'], ['203-5013053-9959554', '9.67'], ['203-5113131-7525963', '-8.5'], ['203-5768187-0573905', '3.66'], ['203-8639245-4145958', '5.08'], ['203-9473169-4807564', '3.66'], ['204-1577436-4733125', '1.83'], ['204-7025768-1965915', '1.83'], ['204-9196762-0226720', '11.33'], ['205-8348990-1889964', '-11.33'], ['205-9028779-8764322', '6.91'], ['206-0703454-9777135', '23.84'], ['206-0954144-1685131', '22.66'], ['206-3381432-7615531', '8.08'], ['206-3822931-6939555', '11.92'], ['206-4658913-5563533', '9.67'], ['206-5213573-9997926', '3.66'], ['206-5882801-0583557', '13.92'], ['206-7158700-9326744', '27.5'], ['206-7668862-3913143', '6.58'], ['206-8541775-0545153', '1.83']]
What I want to do is cycle through every item inside order_ids, and if the order_id is present in one - get the "value"
So far what I have tried is:
with open('test.csv', mode='w', newline='') as outfile:
writer = csv.writer(outfile)
i = 0
while i < len(order_ids):
for order in order_ids:
try:
if order == one[i][0]:
value_a = one[i][1]
print(order, value_a)
writer.writerow([order, value_a])
i += 1
else:
i += 1
pass
except IndexError:
i += 1
This is working somewhat - but there are 36 items inside "order_ids" and 36 lists inside "one", however only 18 rows are being wrote to my outfile.
An example of one order_id that isn't being wrote is "206-7668862-3913143", even though this clearly has a value of "6.58" inside "one"
What is stopping the rest of my rows being written?
You can do this simply with a dictionary. The dict() constructor will accept a nested list of pairs and create a dictionary mapping order_id to amount. Then we can just loop over the order_ids list, and write out any order_id that appears to test.csv.
Code:
import csv
d = dict(one)
with open('test.csv', mode='w', newline='') as outfile:
writer = csv.writer(outfile)
for order_id in order_ids:
if order_id in d:
writer.writerow([order_id, d[order_id]])
test.csv:
Order ID,Amount
026-2529662-9119536,10.42
026-4092572-3574764,10.42
026-4267878-0816332,1.75
026-5334006-4073138,17.990000000000002
026-5750353-4848328,16.25
026-5945233-4883500,1.83
026-5966822-8160331,11.92
026-8799392-8255522,8.5
202-5076008-9615516,1.83
202-5211901-8584318,1.83
202-5788153-3773918,8.08
202-6208325-9677946,11.33
203-1024454-3409960,8.08
203-1064201-9833131,1.5
203-4104559-7038752,8.5
203-5013053-9959554,9.67
203-5768187-0573905,3.66
203-8639245-4145958,5.08
203-9473169-4807564,3.66
204-1577436-4733125,1.83
204-7025768-1965915,1.83
204-9196762-0226720,11.33
205-9028779-8764322,6.91
206-0703454-9777135,23.84
206-0954144-1685131,22.66
206-3381432-7615531,8.08
206-3822931-6939555,11.92
206-4658913-5563533,9.67
206-5213573-9997926,3.66
206-5882801-0583557,13.92
206-7158700-9326744,27.5
206-7668862-3913143,6.58
206-8541775-0545153,1.83
I am trying to sort a column from a .txt file. I am going do a point-register system where I am going to save the name of the player and his three laps.
I am saving my values in the text-file like so:
1. name;lap_1;lap_2;lap_3;
2. name;lap_1;lap_2;lap_3;
3. name;lap_1;lap_2;lap_3;
In my code I write them to the file like so:
for result in results:
my_file.write("{}:{}:{}:{}:{}:{};\n".format(result["name"],
result["lap1"],
result["lap2"],
result["lap3"],
result["total"],
result["average"]))
How do I sort each column, for example "name"? And how do I print it out?
first of all, as #sgrg suggested, use CSV-file format, e. g. we can write simply with
import csv
def write_results(results, fields_names):
# or use mode="a" if you want to append
with open("my_file.csv", mode="w", newline="") as my_file:
csv_writer = csv.DictWriter(my_file, fieldnames=fields_names, delimiter=";")
# remember: you don"t need to add headers in "append" mode
csv_writer.writeheader()
for result in results:
csv_writer.writerow(result)
then read with
def read_results(fields_names):
with open("my_file.csv", mode="r") as my_file:
# ignoring headers
next(my_file)
csv_reader = csv.DictReader(my_file, fieldnames=fields_names, delimiter=";")
return list(csv_reader)
sorting of results by name can be done with
sorted_results = sorted(results, key=lambda result: result["name"])
usage
fields_names = ["name", "lap1", "lap2", "lap3", "total", "avarage"]
results_tuples = [("Luke", "lap1_value", "lap2_value", "lap3_value", 100, 96.3),
("Stephen", "lap1_value", "lap2_value", "lap3_value", 100, 96.3),
("Adrian", "lap1_value", "lap2_value", "lap3_value", 100, 96.3)]
results = [dict(zip(fields_names, result_tuple)) for result_tuple in results_tuples]
write_results(results,
fields_names=fields_names)
results = read_results(fields_names)
sorted_results = sorted(results, key=lambda result: result["name"])
in given example results is a list object which looks like
[{'avarage': 96.3,
'lap1': 'lap1_value',
'lap2': 'lap2_value',
'lap3': 'lap3_value',
'name': 'Luke',
'total': 100},
{'avarage': 96.3,
'lap1': 'lap1_value',
'lap2': 'lap2_value',
'lap3': 'lap3_value',
'name': 'Stephen',
'total': 100},
{'avarage': 96.3,
'lap1': 'lap1_value',
'lap2': 'lap2_value',
'lap3': 'lap3_value',
'name': 'Adrian',
'total': 100}]
and sorted_results is a list object which looks like
[OrderedDict([('name', 'Adrian'),
('lap1', 'lap1_value'),
('lap2', 'lap2_value'),
('lap3', 'lap3_value'),
('total', '100'),
('avarage', '96.3')]),
OrderedDict([('name', 'Luke'),
('lap1', 'lap1_value'),
('lap2', 'lap2_value'),
('lap3', 'lap3_value'),
('total', '100'),
('avarage', '96.3')]),
OrderedDict([('name', 'Stephen'),
('lap1', 'lap1_value'),
('lap2', 'lap2_value'),
('lap3', 'lap3_value'),
('total', '100'),
('avarage', '96.3')])]
More info about csv module at docs
More info about OrderedDict at docs
Hi I'm trying to access a json to save it in a list to perform a sort of append and create a pdf in ReportLab, I have the following code but I have several problems the first is that I would like to have a list of 2xn to always it has columns and rows be dynamic according to the json.
If anyone can help me be grateful much
import json
json_data = []
attributesName = []
testTable = { "attributes":[] }
attributesValue = []
path="prueba2.pdf"
doc = SimpleDocTemplate(path, pagesize=letter)
styleSheet = getSampleStyleSheet()
text = []
with open("prueba.json") as json_file:
document = json.load(json_file)
for item in document:
for data_item in item['data']:
attributesName.append([str(data_item['name'])
attributesValue.append([data_item['value']])
testTable[attributesName].extend({data_item['name'], data_item['value']})
print attributesName[0]
print testTable[0]
parts = []
p = Paragraph('''<para align=left fontsize=9>{0}</para>'''.format(text), styleSheet["BodyText"])
parts.append(p)
doc.build(parts)
I implemented the following,but it prints the list
[[['RFC', 'NOMBRE', 'APELLIDO PATERNO', 'APELLIDO MATERNO', 'FECHA NACIMIENTO', 'CALLE', 'No. EXTERI
OR', 'No. INTERIOR', 'C.P.', 'ENTIDAD', 'MUNICIPIO', 'COLONIA', 'DOCUMENTO']], [['MORR910304FL2', 'R
JOSE', 'MONTIEL', 'ROBLES', '1992-02-04', 'AMOR', '4', '2', '55064', 'EDO DE MEX', 'ECATEPEC', 'INDUSTRIAL', 'Documento']]]
I want some like this
[['RFC'], ['22232446']]
[['NOMBRE'], ['22239952']]
[['APELLIDO'], ['22245430']]
if you change your code with the next code
with open("prueba.json") as json_file:
document = json.load(json_file)
for item in document:
for data_item in item['data']:
attributesName.append(str(data_item["name"]))
attributesValue.append(str(data_item["value"]))
tabla.append([[attributesName],[attributesValue]])
print attributesName
print attributesValue
for Y in tabla:
print(Y)