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How to extract text and save as excel file using python or JavaScript

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.

Convert many complex txt files into excel.xls and save their names in python script

I have many txt files with the standard format in the same folder:
Name: 321;
Score:100; Used Time: 1:09:308;
GTime: 6/28/2024 10:04:18 PM;
Core Version : 21.0.0.0;
Software Version : 21.0.0.0;
AppID: 0S0; MapDispName: Future City; MapName:MapName123;
Key:A0000-abcde-Q0000-F0000-00H00; REG Date : 2/27/2021 1:16:34 PM; Expiry : 7/7/2024 12:00:00 AM
I would like to convert those text files into an excle.xls (table) using a python script. At the same time, I would like to save text filenames as well.
Team ID, Result, Used Time,Software Ver, Core Ver, AppID, Key, REG Date, Expiry,MapName,TXTName
321,100,69.308s,21.0.0.0,21.0.0.0,0S0,A0000-abcde-Q0000-F0000-00H00,2/27/2021 1:16:34 PM,7/7/2024 12:00:00 AM,MapName123,TXTName1
Part of my code as below, but it's not working. TypeError: unsupported operand type(s) for +: 'dict' and 'str' in the penultimate line.
list_drr=[]
xls_name=None
for path, file_dir, files in os.walk(file_name_path):
for file_name in files:
list_drr.append(os.path.join(path, file_name))
for dir in file_dir:
list_drr.append(os.path.join(path, dir))#
excel_data= ExcelData(xls_name,"")
excel_datas= excel_data.readExcel()
print(excel_datas)
excel_header=['Team ID', 'Result', 'Used Time', 'Software Ver', 'Core Version', 'AppID', 'Key', 'REG Date', 'Expiry','MapName','TXTName']
file= WFile(excel_header)
for drr in list_drr:
file_datas= getFileData(drr)
file_datas=file_datas[:7]
data_list=[]
for data in file_datas:
lis= data.split(";")
for li in lis:
data_list.append(li)
data_dic={}
for data in data_list:
pos= data.find(":")
ddq=data[:pos].strip()
data_dic[ddq]=data[pos+1:].strip()
file.write((data_dic) + (os.path.basename(file_name)))
file.save("excel.xls")
Please advise what should I do, thanks.
Updated wri_file.py as below.
import xlwt
class WFile():
def __init__(self,head_name):
super().__init__()
self.head_name=head_name
self.index=0
self. workbook =xlwt. Workbook(encoding='utf-8')
self. worksheet = self.workbook.add_sheet('Sheet1')
self.line =len( self.head_name)
for i in range( self.line):
self.worksheet.write( self.index,i ,self.head_name[i])
self.index+=1
def write(self,d):
for i in range( self.line):
name=d.get(self.head_name[i])
self.worksheet.write( self.index,i ,(name))
self.index += 1
def writes(self, d):
for i in range(self.line):
self.worksheet.write(self.index, i,d[i])
self.index += 1
def save(self,name):
self.workbook.save(name)

You are trying to add file_name as another entry to be written. As you have constructed a dictionary, a better approach would be to add a new key value pair before writing:
data_dic['TXTName'] = os.path.basename(drr)
file.write(data_dic)
As your output header is not consistent with the keys in your text files, you need to create a dictionary to map between the two, e.g. field_mapping
You were also storing directory names into your list of file? A simpler approach is to just use glob.glob() which also can work recursively and also just on .txt files if needed.
import glob
import xlwt
# {Your header : Key in text file}
field_mapping = {
'Team ID' : 'Name',
'Result' : 'Score',
'Used Time' : 'Used Time',
'Core Version' : 'Core Version',
'Software Ver' : 'Software Version',
'MapName' : 'MapName',
'AppID' : 'AppID',
'Key' : 'Key',
'REG Date' : 'REG Date',
'Expiry' : 'Expiry',
'TXTName' : 'TXTName',
}
def getFileData(drr):
with open(drr) as f_input :
return f_input.readlines()
class WFile():
def __init__(self,head_name):
super().__init__()
self.head_name = head_name
self.index = 0
self.workbook = xlwt.Workbook(encoding='utf-8')
self.worksheet = self.workbook.add_sheet('Sheet1')
self.line = len(self.head_name)
for i in range(self.line):
self.worksheet.write(self.index, i, self.head_name[i])
self.index += 1
def write(self, d):
for i, v in enumerate(self.head_name):
name = d.get(field_mapping[v])
self.worksheet.write(self.index, i, (name))
self.index += 1
def save(self,name):
self.workbook.save(name)
file_name_path = r"\my file\location\*.txt" # or just * for all files
excel_header = ['Team ID', 'Result', 'Used Time', 'Software Ver', 'Core Version', 'AppID', 'Key', 'REG Date', 'Expiry','MapName','TXTName']
file = WFile(excel_header)
for file_name in glob.glob(file_name_path, recursive=True):
file_datas = getFileData(file_name)
file_datas = file_datas[:7]
data_list = []
for data in file_datas:
for li in data.split(";"):
data_list.append(li)
data_dic = {}
for data in data_list:
pos = data.find(":")
ddq = data[:pos].strip()
data_dic[ddq] = data[pos+1:].strip()
data_dic['TXTName'] = os.path.basename(file_name)
file.write(data_dic)
file.save("excel.xls")
Giving you an old style output .xls file of:
You should consider using the newer .xlsx format, with a library such as openpyxl.
import openpyxl
from itertools import islice
import glob
import os
field_mapping = {
'Name' : 'Team ID',
'Score' : 'Result',
'Used Time' : 'Used Time',
'Core Version' : 'Core Ver',
'Software Version' : 'Software Ver',
'AppID' : 'AppID',
'Key' : 'Key',
'REG Date' : 'REG Date',
'Expiry' : 'Expiry',
'TXTName' : 'TXTName'
}
wb = openpyxl.Workbook()
ws = wb.active
ws.append(list(field_mapping.values())) # write a header
for filename in glob.glob('/my folder/*.txt', recursive=True):
key_values = {}
with open(filename) as f_input:
for field in ''.join(islice(f_input, 0, 7)).replace('\n', '').split(';'):
key, value = map(str.strip, field.split(':', 1)) # only split on the first colon
key_values[field_mapping.get(key, None)] = value
key_values['TXTName'] = os.path.basename(filename)
output_row = [value for key, value in key_values.items() if key] # Ignore key=None
ws.append(output_row)
wb.save('excel.xlsx')

Is it possible for a python script to check whether row exists in google sheets before writing that row?

I have a python script that searches for vehicles on a vehicle listing site and writes the results to a spreadsheet. What I want is to automate this script to run every night to get new listings, but what I don't want is to create numerous duplicates if the listing exists each day that the script is run.
So is it possible to get the script to check whether that row (potential duplicate) already exists before writing a new row?
To clarify the code I have works perfectly to print the results exactly how I want them into the google sheets document, what I am trying to do is to run a check before it prints new lines into the sheet to see if that result already exists. Is that clearer? With thanks in advance.
Here is a screenshot of an example where I might have a row already existing with the specific title, but one of the column cells may have a different value in it and I only want to update the original row with the latest/highest price value.
UPDATE:
I am trying something like this but it just seems to print everything rather than only if it doesn't already exist which is what I am trying to do.
listing = [title, img['src'], video, vin,loc, exterior_colour, interior_colour, 'N/A', mileage, gearbox, 'N/A', 'Live', auction_date,'', '£' + bid.attrs['amount'][:-3], 'The Market', '', '', '', '', year, make, model, variant]
list_of_dicts = sheet2.get_all_records()
# Convert listing into dictionary output to be read by following statement to see if listing exists in sheet before printing
i = iter(listing)
d_listing = dict(zip(i, i))
if not d_listing in list_of_dicts:
print(listing)
#print(title, img['src'], video, vin,loc, exterior_colour, interior_colour, 'N/A', mileage, gearbox, 'N/A', 'Live', auction_date,'', '£' + bid.attrs['amount'][:-3], 'The Market', '', '', '', '', year, make, model, variant)
index = 2
row = [title, img['src'], video, vin,loc, exterior_colour, interior_colour, 'N/A', mileage, gearbox, 'N/A', 'Live', auction_date,'', '£' + bid.attrs['amount'][:-3], 'The Market', '', '', '', '', year, make, model, variant]
sheet2.insert_row(row,index)
My code is:
import requests
import re
from bs4 import BeautifulSoup
import pandas
import gspread
from oauth2client.service_account import ServiceAccountCredentials
# use creds to create a client to interact with the Google Drive API
scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
creds = ServiceAccountCredentials.from_json_keyfile_name('creds.json', scope)
client = gspread.authorize(creds)
sheet = client.open("CAR AGGREGATOR")
sheet2 = sheet.worksheet("Auctions - Live")
url = "https://themarket.co.uk/live.xml"
get_url = requests.get(url)
get_text = get_url.text
soup = BeautifulSoup(requests.get(url).text, 'lxml')
for loc in soup.select('url > loc'):
loc = loc.text
r=requests.get(loc)
c=r.content
hoop = BeautifulSoup(c, 'html.parser')
soup = BeautifulSoup(c, 'lxml')
current_bid = soup.find('div', 'bid-step__header')
bid = soup.find('bid-display')
title = soup.find('h2').text.split()
year = title[0]
if not year:
year = ''
if any(make in 'ASTON ALFA HEALEY ROVER Arnolt Bristol Amilcar Amphicar LOREAN De Cadenet Cosworth'.split() for make in title):
make = title[1] + ' ' + title[2]
model = title[3]
try:
variant = title[4]
except:
variant = ''
else:
make = title[1]
model = title[2]
try:
variant = title[3]
if 'REIMAGINED' in variant:
variant = 'REIMAGINED BY SINGER'
if 'SINGER' in variant:
variant = 'REIMAGINED BY SINGER'
except:
variant = ''
title = year + ' ' + make + ' ' + model
img = soup.find('img')
vehicle_details = soup.find('ul', 'vehicle__overview')
try:
mileage = vehicle_details.find_all('li')[1].text.split()[2]
except:
mileage = ''
try:
vin = vehicle_details.find_all('li')[2].text.split()[2]
except:
vin = ''
try:
gearbox = vehicle_details.find_all('li')[4].text.split()[2]
except:
gearbox = 'N/A'
try:
exterior_colour = vehicle_details.find_all('li')[5].text.split()[1:]
exterior_colour = "-".join(exterior_colour)
except:
exterior_colour = 'N/A'
try:
interior_colour = vehicle_details.find_all('li')[6].text.split()[1:]
interior_colour = "-".join(interior_colour)
except:
interior_colour = 'N/A'
try:
video = soup.find('iframe')['src']
except:
video = ''
tag = soup.countdown
try:
auction_date = tag.attrs['formatted_date'].split()
auction_day = auction_date[0][:2]
auction_month = auction_date[1]
auction_year = auction_date[2]
auction_time = auction_date[3]
auction_date = auction_day + ' ' + auction_month + ' ' + auction_year + ' ' + auction_time
except:
continue
print(title, img['src'], video, vin,loc, exterior_colour, interior_colour, 'N/A', mileage, gearbox, 'N/A', 'Live', auction_date,'', '£' + bid.attrs['amount'][:-3], 'The Market', '', '', '', '', year, make, model, variant)
index = 2
row = [title, img['src'], video, vin,loc, exterior_colour, interior_colour, 'N/A', mileage, gearbox, 'N/A', 'Live', auction_date,'', '£' + bid.attrs['amount'][:-3], 'The Market', '', '', '', '', year, make, model, variant]
sheet2.insert_row(row,index)

I would load all data in two dictionaries, one representing the freshly scraped information, the other one the full information of the GoogleSheet. (To load the information from GoogleSheet, use its API, as described in Google's documentation.)
Both dictionaries, let's call them scraped and sheets, could have the titles as keys, and all the other columns as value (represented in a dictionary), so they would look like this:
{
"1928 Aston Martin V8": {
"Link": "...",
"Price": "12 $",
},
...
}
Then update the Sheets-dictionary with dict.update():
sheets.update(scraped)
and rewrite the Google Sheet with the data in sheets.
Without exactly knowing your update logic, I cannot give a more specific advice than this.

How to flag if a dictionary key is absent

I have files with CommonChar is some of them and my python code works on them to build a dictionary. While building there are some required keys which users might forget to put in. The code should be able to flag the file and the key which is missing.
The syntax for python code to work on is like this:
CommonChar pins Category General
CommonChar pins Contact Mark
CommonChar pins Description 1st line
CommonChar pins Description 2nd line
CommonChar nails Category specific
CommonChar nails Description 1st line
So for above example "Contact" is missing:
CommonChar nails Contact Robert
I have a list for ex: mustNeededKeys=["Category", "Description", "Contact"]
mainDict={}
for dirName, subdirList, fileList in os.walk(sys.argv[1]):
for eachFile in fileList:
#excluding file names ending in .swp , swo which are creatied temporarily when editing in vim
if not eachFile.endswith(('.swp','.swo','~')):
#print eachFile
filePath= os.path.join(dirName,eachFile)
#print filePath
with open(filePath, "r") as fh:
contents=fh.read()
items=re.findall("CommonChar.*$",contents,re.MULTILINE)
for x in items:
cc, group, topic, data = x.split(None, 3)
data = data.split()
group_dict = mainDict.setdefault(group, {'fileLocation': [filePath]})
if topic in group_dict:
group_dict[topic].extend(['</br>'] + data)
else:
group_dict[topic] = data
This above code does its job of building a dict like this:
{'pins': {'Category': ['General'], 'Contact': ['Mark'], 'Description': ['1st', 'line', '2nd', 'line'] } , 'nails':{'Category':['specific'], 'Description':['1st line']}
So when reading each file with CommonChar and building a group_dict , a way to check all the keys and compare it with mustNeededKeys and flag if not there and proceed if met.

Something like this should work:
# Setup mainDict (equivalent to code given above)
mainDict = {
'nails': {
'Category': ['specific'],
'Description': ['1st', 'line'],
'fileLocation': ['/some/path/nails.txt']
},
'pins': {
'Category': ['General'],
'Contact': ['Mark'],
'Description': ['1st', 'line', '</br>', '2nd', 'line'],
'fileLocation': ['/some/path/pins.txt']
}
}
# check for missing keys
mustNeededKeys = {"Category", "Description", "Contact"}
for group, group_dict in mainDict.items():
missing_keys = mustNeededKeys - set(group_dict.keys())
if missing_keys:
missing_key_list = ','.join(missing_keys)
print(
'group "{}" ({}) is missing key(s): {}'
.format(group, group_dict['fileLocation'][0], missing_key_list)
)
# group "nails" (/some/path/nails.txt) is missing key(s): Contact
If you must check for missing keys immediately after processing each group, you could use the code below. This assumes that each group is stored as a contiguous collection of rows in a single file (i.e., not mixed with other groups in the same file or spread across different files).
from itertools import groupby
mainDict={}
mustNeededKeys = {"Category", "Description", "Contact"}
for dirName, subdirList, fileList in os.walk(sys.argv[1]):
for eachFile in fileList:
# excluding file names ending in .swp , swo which are created
# temporarily when editing in vim
if not eachFile.endswith(('.swp','.swo','~')):
#print eachFile
filePath = os.path.join(dirName,eachFile)
#print filePath
with open(filePath, "r") as fh:
contents = fh.read()
items = re.findall("CommonChar.*$", contents, re.MULTILINE)
split_items = [line.split(None, 3) for line in items]
# group the items by group name (element 1 in each row)
for g, group_items in groupby(split_items, lambda row: row[1]):
group_dict = {'fileLocation': [filePath]}
# store all items in the current group
for cc, group, topic, data in group_items:
data = data.split()
if topic in group_dict:
group_dict[topic].extend(['</br>'] + data)
else:
group_dict[topic] = data
# check for missing keys
missing_keys = mustNeededKeys - set(group_dict.keys())
if missing_keys:
missing_key_list = ','.join(missing_keys)
print(
'group "{}" ({}) is missing key(s): {}'
.format(group, filePath, missing_key_list)
)
# add group to mainDict
mainDict[group] = group_dict

data = '''CommonChar pins Category General
CommonChar pins Contact Mark
CommonChar pins Description 1st line
CommonChar pins Description 2nd line
CommonChar nails Category specific
CommonChar nails Description 1st line'''
from collections import defaultdict
from pprint import pprint
required_keys = ["Category", "Description", "Contact"]
d = defaultdict(dict)
for line in data.splitlines():
line = line.split()
if line[2] == 'Description':
if line[2] not in d[line[1]]:
d[line[1]][line[2]] = []
d[line[1]][line[2]].extend(line[3:])
else:
d[line[1]][line[2]] = [line[3]]
pprint(dict(d))
print('*' * 80)
# find missing keys
for k in d.keys():
for missing_key in set(d[k].keys()) ^ set(required_keys):
print('Key "{}" is missing "{}"!'.format(k, missing_key))
Prints:
{'nails': {'Category': ['specific'], 'Description': ['1st', 'line']},
'pins': {'Category': ['General'],
'Contact': ['Mark'],
'Description': ['1st', 'line', '2nd', 'line']}}
********************************************************************************
Key "nails" is missing "Contact"!

Reading a dynamic table with pandas

I'm using conda 4.5.11 and python 3.6.3 to read a dynamic list, such as this:
[['Results:',
'2',
'Time:',
'16',
'Register #1',
'Field1:',
'999999999999999',
'Field2:',
'name',
'Field3:',
'some text',
'Field4:',
'number',
'Fieldn:',
'other number',
'Register #2',
'Field1:',
'999999999999999',
'Field2:',
'name',
'Field3:',
'type',
'Field4:',
'some text'
'FieldN:',
'some text',
'Register #N',
...
]]
Here is the code for my best try:
data = []
header = []
data_text = []
for data in res:
part = data.split(":")
header_text = part[1]
data_t = part[2]
header.append(header_text)
data_text.append(data_t)
df_data = pd.DataFrame(data_text)
df_header = pd.DataFrame(header)
Output
Field1 Field2 Field3 Field4 Fieldn1 Fieldn2 Fieldn
999999999999999 name sometext number number text number
999999999999999 name sometext number number number NAN
999999999999999 name number NAN number text number
Is it possible to read from a list and concat in one DataFrame?