Develop Reference

Extract Text into Column using Regex

I want to extract data (S no, Item Code, Price and Size) from the attached PDF Document in to columns.
The re.compile works for the S no, Item Code and Price, but as soon as I put the Size - it gives a limited output. I am unable to figure out why? Can you please help
(Attached picture of the PDF page)
Import pandas as pd
Import re
Import PyPDF2
file = open("Petchem.pdf", "rb")
pdfReader = PyPDF2.PdfFileReader(file)
my_dict = {"S no":[], "Item Code":[], "Price":[], "Size":[]}
for page in range (1,25):
pageObj = pdfReader.getPage(page)
data = pageObj.extractText()
size = re.compile(r'((\d{2,4}?)(\d{10})EA\s(\d?\d?,?\d?\d?\d.\d\d)[\s\w\d,:/.()-])')
for number in size.findall(data):
S_No = my_dict["S No"].append(number[1])
Item_Code = my_dict["Item Code"].append(number[2])
Price = my_dict["Price"].append(number[3])
Size = my_dict["Size"].append(number[4])
print(number[1])
a_file = open("Column_Breakup.csv", "w")
datadf = pd.DataFrame(my_dict)
datadf.to_csv("Column_Breakup.csv")
a_file.close()

Separating large PDF document into smaller documents based on content

I have a large pdf file with very specific formatting, a bunch of reports if you will, all in one big pdf document. I'm using pdfplumber to extract specific text within a bounding box on each page. I've called this variable scene_text. The value of scene_text changes throughout the document, but many pages contain the same value for scene_text. I want to separate the large pdf into multiple smaller pdf files named according to their scene_text value with each pdf file containing all of the pages with matching scene_text. I'm terribly stuck, any help would be appreciated.
import pdfplumber
from PyPDF2 import PdfFileWriter, PdfFileReader
import os
file = 'report.pdf'
with pdfplumber.open(file) as pdf:
for i, page in enumerate(pdf.pages):
# get scene text for current page
bounding_box = (880, 137, 1048, 180)
scene_text = page.within_bbox(bounding_box, relative=True).extract_text()
previous_page_text = pdf.pages[i-1].within_bbox(bounding_box, relative=True).extract_text()
inputpdf = PdfFileReader(open(file, "rb"))
output = PdfFileWriter()
for x, page in enumerate(pdf.pages):
st2 = page.within_bbox(bounding_box, relative=True).extract_text()
if st2 != previous_page_text:
output.addPage(inputpdf.getPage(i))
if st2 == scene_text:
if st2 == pdf.pages[x+1].within_bbox(bounding_box, relative=True).extract_text():
previous_page_text = st2
with open("page_export/" + scene_text + ".pdf", "wb") as output_stream:
output.write(output_stream)

Open, save and extract text PDFs from links in python dataframe

I would like to iterate through PDF links saved in python dataframe. The goal is to open the PDF links, save the PDFs and extract text from them, then save the text from each corresponding link in a new column.
Dataframe looks like this:
URL
0 https://westafricatradehub.com/wp-content/uploads/2021/07/RFA-WATIH-1295_Senegal-RMNCAH-Activity_English-Version.pdf
1 https://westafricatradehub.com/wp-content/uploads/2021/07/RFA-WATIH-1295_Activit%C3%A9-RMNCAH-S%C3%A9n%C3%A9gal_Version-Fran%C3%A7aise.pdf
2 https://westafricatradehub.com/wp-content/uploads/2021/07/Attachment-2_Full-Application-Template_Senegal-RMNCAH-Activity_English-Version.docx
3 https://westafricatradehub.com/wp-content/uploads/2021/07/Pi%C3%A8ce-Jointe-2_Mod%C3%A8le-de-Demande-Complet_Activit%C3%A9-RMNCAH-S%C3%A9n%C3%A9gal_Version-Fran%C3%A7aise.docx
4 https://westafricatradehub.com/wp-content/uploads/2021/07/Attachment-3_Trade-Hub-Performance-Indicators-Table.xlsx
5 https://westafricatradehub.com/wp-content/uploads/2021/07/Attachment-10_Project-Budget-Template-RMNCAH.xlsx
6 https://westafricatradehub.com/wp-content/uploads/2021/08/Senegal-Health-RFA-Webinar-QA.pdf
7 https://westafricatradehub.com/wp-content/uploads/2021/02/APS-WATIH-1021_Catalytic-Business-Concepts-Round-2.pdf
8 https://westafricatradehub.com/wp-content/uploads/2021/02/APS-WATIH-1021_Concepts-d%E2%80%99Affaires-Catalytiques-2ieme-Tour.pdf
9 https://westafricatradehub.com/wp-content/uploads/2021/06/APS-WATIH-1247_Research-Development-Round-2.pdf
I was able to do that for one link but not for the whole dataframe
import urllib.request
pdf_link = "https://westafricatradehub.com/wp-content/uploads/2021/07/RFA-WATIH-1295_Senegal-RMNCAH-Activity_English-Version.pdf"
def download_file(download_url, filename):
response = urllib.request.urlopen(download_url)
file = open(filename + ".pdf", 'wb')
file.write(response.read())
file.close()
download_file(pdf_link, "Test")
#Code to extract text from PDF
import textract
text = textract.process("/Users/fze/Dropbox (LCG Team)/LCG Folder (1)/BD Scan Automation/Python codes/Test.PDF")
print(text)
Thank you!

Here you go:
import urllib.request
import textract
def download_file(download_url, filename):
response = urllib.request.urlopen(download_url)
file = open(filename + ".pdf", 'wb')
file.write(response.read())
file.close()
df['Text']=''
for i in range(df.shape[0]):
pdf_link=df.iloc[i,0]
download_file(pdf_link, f"pdf_{i}")
text = textract.process(f"/Users/fze/Dropbox (LCG Team)/LCG Folder (1)/BD Scan Automation/Python codes/pdf_{i}.PDF")
df['Text'][i]=text

BeautifulSoup - scraping a forum page

I'm trying to scrape a forum discussion and export it as a csv file, with rows such as "thread title", "user", and "post", where the latter is the actual forum post from each individual.
I'm a complete beginner with Python and BeautifulSoup so I'm having a really hard time with this!
My current problem is that all the text is split into one character per row in the csv file. Is there anyone out there who can help me out? It would be fantastic if someone could give me a hand!
Here's the code I've been using:
from bs4 import BeautifulSoup
import csv
import urllib2
f = urllib2.urlopen("https://silkroad5v7dywlc.onion.to/index.php?action=printpage;topic=28536.0")
soup = BeautifulSoup(f)
b = soup.get_text().encode("utf-8").strip() #the posts contain non-ascii words, so I had to do this
writer = csv.writer(open('silkroad.csv', 'w'))
writer.writerows(b)

Ok here we go. Not quite sure what I'm helping you do here, but hopefully you have a good reason to be analyzing silk road posts.
You have a few issues here, the big one is that you aren't parsing the data at all. What you're essentially doing with .get_text() is going to the page, highlighting the whole thing, and then copying and pasting the whole thing to a csv file.
So here is what you should be trying to do:
Read the page source
Use soup to break it into sections you want
Save sections in parallel arrays for author, date, time, post, etc
Write data to csv file row by row
I wrote some code to show you what that looks like, it should do the job:
from bs4 import BeautifulSoup
import csv
import urllib2
# get page source and create a BeautifulSoup object based on it
print "Reading page..."
page = urllib2.urlopen("https://silkroad5v7dywlc.onion.to/index.php?action=printpage;topic=28536.0")
soup = BeautifulSoup(page)
# if you look at the HTML all the titles, dates,
# and authors are stored inside of <dt ...> tags
metaData = soup.find_all("dt")
# likewise the post data is stored
# under <dd ...>
postData = soup.find_all("dd")
# define where we will store info
titles = []
authors = []
times = []
posts = []
# now we iterate through the metaData and parse it
# into titles, authors, and dates
print "Parsing data..."
for html in metaData:
text = BeautifulSoup(str(html).strip()).get_text().encode("utf-8").replace("\n", "") # convert the html to text
titles.append(text.split("Title:")[1].split("Post by:")[0].strip()) # get Title:
authors.append(text.split("Post by:")[1].split(" on ")[0].strip()) # get Post by:
times.append(text.split(" on ")[1].strip()) # get date
# now we go through the actual post data and extract it
for post in postData:
posts.append(BeautifulSoup(str(post)).get_text().encode("utf-8").strip())
# now we write data to csv file
# ***csv files MUST be opened with the 'b' flag***
csvfile = open('silkroad.csv', 'wb')
writer = csv.writer(csvfile)
# create template
writer.writerow(["Time", "Author", "Title", "Post"])
# iterate through and write all the data
for time, author, title, post in zip(times, authors, titles, posts):
writer.writerow([time, author, title, post])
# close file
csvfile.close()
# done
print "Operation completed successfully."
EDIT: Included solution that can read files from directory and use data from that
Okay, so you have your HTML files in a directory. You need to get a list of files in the directory, iterate through them, and append to your csv file for each file in the directory.
This is the basic logic of our new program.
If we had a function called processData() that took a file path as an argument and appended data from the file to your csv file here is what it would look like:
# the directory where we have all our HTML files
dir = "myDir"
# our csv file
csvFile = "silkroad.csv"
# insert the column titles to csv
csvfile = open(csvFile, 'wb')
writer = csv.writer(csvfile)
writer.writerow(["Time", "Author", "Title", "Post"])
csvfile.close()
# get a list of files in the directory
fileList = os.listdir(dir)
# define variables we need for status text
totalLen = len(fileList)
count = 1
# iterate through files and read all of them into the csv file
for htmlFile in fileList:
path = os.path.join(dir, htmlFile) # get the file path
processData(path) # process the data in the file
print "Processed '" + path + "'(" + str(count) + "/" + str(totalLen) + ")..." # display status
count = count + 1 # increment counter
As it happens our processData() function is more or less what we did before, with a few changes.
So this is very similar to our last program, with a few small changes:
We write the column headers first thing
Following that we open the csv with the 'ab' flag to append
We import os to get a list of files
Here's what that looks like:
from bs4 import BeautifulSoup
import csv
import urllib2
import os # added this import to process files/dirs
# ** define our data processing function
def processData( pageFile ):
''' take the data from an html file and append to our csv file '''
f = open(pageFile, "r")
page = f.read()
f.close()
soup = BeautifulSoup(page)
# if you look at the HTML all the titles, dates,
# and authors are stored inside of <dt ...> tags
metaData = soup.find_all("dt")
# likewise the post data is stored
# under <dd ...>
postData = soup.find_all("dd")
# define where we will store info
titles = []
authors = []
times = []
posts = []
# now we iterate through the metaData and parse it
# into titles, authors, and dates
for html in metaData:
text = BeautifulSoup(str(html).strip()).get_text().encode("utf-8").replace("\n", "") # convert the html to text
titles.append(text.split("Title:")[1].split("Post by:")[0].strip()) # get Title:
authors.append(text.split("Post by:")[1].split(" on ")[0].strip()) # get Post by:
times.append(text.split(" on ")[1].strip()) # get date
# now we go through the actual post data and extract it
for post in postData:
posts.append(BeautifulSoup(str(post)).get_text().encode("utf-8").strip())
# now we write data to csv file
# ***csv files MUST be opened with the 'b' flag***
csvfile = open('silkroad.csv', 'ab')
writer = csv.writer(csvfile)
# iterate through and write all the data
for time, author, title, post in zip(times, authors, titles, posts):
writer.writerow([time, author, title, post])
# close file
csvfile.close()
# ** start our process of going through files
# the directory where we have all our HTML files
dir = "myDir"
# our csv file
csvFile = "silkroad.csv"
# insert the column titles to csv
csvfile = open(csvFile, 'wb')
writer = csv.writer(csvfile)
writer.writerow(["Time", "Author", "Title", "Post"])
csvfile.close()
# get a list of files in the directory
fileList = os.listdir(dir)
# define variables we need for status text
totalLen = len(fileList)
count = 1
# iterate through files and read all of them into the csv file
for htmlFile in fileList:
path = os.path.join(dir, htmlFile) # get the file path
processData(path) # process the data in the file
print "Processed '" + path + "'(" + str(count) + "/" + str(totalLen) + ")..." # display status
count = count + 1 # incriment counter

finding on which page a search string is located in a pdf document using python

Which python packages can I use to find out out on which page a specific “search string” is located ?
I looked into several python pdf packages but couldn't figure out which one I should use.
PyPDF does not seem to have this functionality and PDFMiner seems to be an overkill for such simple task.
Any advice ?
More precise:
I have several PDF documents and I would like to extract pages which are between a string “Begin” and a string “End” .

I finally figured out that pyPDF can help. I am posting it in case it can help somebody else.
(1) a function to locate the string
def fnPDF_FindText(xFile, xString):
# xfile : the PDF file in which to look
# xString : the string to look for
import pyPdf, re
PageFound = -1
pdfDoc = pyPdf.PdfFileReader(file(xFile, "rb"))
for i in range(0, pdfDoc.getNumPages()):
content = ""
content += pdfDoc.getPage(i).extractText() + "\n"
content1 = content.encode('ascii', 'ignore').lower()
ResSearch = re.search(xString, content1)
if ResSearch is not None:
PageFound = i
break
return PageFound
(2) a function to extract the pages of interest
def fnPDF_ExtractPages(xFileNameOriginal, xFileNameOutput, xPageStart, xPageEnd):
from pyPdf import PdfFileReader, PdfFileWriter
output = PdfFileWriter()
pdfOne = PdfFileReader(file(xFileNameOriginal, "rb"))
for i in range(xPageStart, xPageEnd):
output.addPage(pdfOne.getPage(i))
outputStream = file(xFileNameOutput, "wb")
output.write(outputStream)
outputStream.close()
I hope this will be helpful to somebody else

I was able to successfully get the output using the code below.
Code:
import PyPDF2
import re
# Open the pdf file
object = PyPDF2.PdfFileReader(r"C:\TEST.pdf")
# Get number of pages
NumPages = object.getNumPages()
# Enter code here
String = "Enter_the_text_to_Search_here"
# Extract text and do the search
for i in range(0, NumPages):
PageObj = object.getPage(i)
Text = PageObj.extractText()
if re.search(String,Text):
print("Pattern Found on Page: " + str(i))
Sample Output:
Pattern Found on Page: 7

Finding on which page a search string is located in a pdf document using python
PyPDF2
# import packages
import PyPDF2
import re
# open the pdf file
object = PyPDF2.PdfFileReader(r"source_file_path")
# get number of pages
NumPages = object.getNumPages()
# define keyterms
String = "P4F-21B"
# extract text and do the search
for i in range(0, NumPages):
PageObj = object.getPage(i)
Text = PageObj.extractText()
ResSearch = re.search(String, Text)
if ResSearch != None:
print(ResSearch)
print("Page Number" + str(i+1))
Output:
<re.Match object; span=(57, 64), match='P4F-21B'>
Page Number1
PyMuPDF
import fitz
import re
# load document
doc = fitz.open(r"C:\Users\shraddha.shetty\Desktop\OCR-pages-deleted.pdf")
# define keyterms
String = "P4F-21B"
# get text, search for string and print count on page.
for page in doc:
text = ''
text += page.get_text()
if len(re.findall(String, text)) > 0:
print(f'count on page {page.number + 1} is: {len(re.findall(String, text))}')

In addition to what #user1043144 mentioned,
To use with python 3.x
Use PyPDF2
import PyPDF2
Use open instead of file
PdfFileReader(open(xFile, 'rb'))

updated answer with PYDF2
import re
import PyPDF2
def pdf_find_text(xfile_pdf, xsearch_string, ignore_case = False):
'''
find page(s) on which a given text is located in a pdf
input: pdf file and the string to search
(string to search can be in a regex like 'references\n')
N.B:
results need to be checked
in case of pdf whose page numbers are not zero indexed ,
the results seems off (by one page)
'''
xlst_res = []
xreader = PyPDF2.PdfFileReader(xfile_pdf)
for xpage_nr, xpage in enumerate(xreader.pages):
xpage_text = xpage.extractText()
xhits = None
if ignore_case == False:
xhits = re.search(xsearch_string, xpage_text.lower())
else:
xhits = re.search(xsearch_string, xpage_text.lower(), re.IGNORECASE)
if xhits:
xlst_res.append(xpage_nr)
return {'num_pages': xreader.numPages, 'page_hits': xlst_res}
def pdf_extract_pages(xpdf_original, xpdf_new , xpage_start, xpage_end):
'''
given a pdf extract a page range and save it in a new pdf file
'''
with open(xpdf_original, 'rb') as xfile_1, open(xpdf_new , 'wb') as xfile_2:
xreader = PyPDF2.PdfFileReader(xfile_1)
xwriter = PyPDF2.PdfFileWriter()
for xpage_nr in range(xpage_start, xpage_end ):
xwriter.addPage(xreader.getPage(xpage_nr))
xwriter.write(xfile_2)