this is my PDF file "https://drive.google.com/open?id=1M9k1AO17ZSwT6HTrTrB-uz85ps3WL1wS"
Help me someone to extract this , as i search on SO getting some clue to extract text using these libries PyPDF2, PyPDF2.pdf , PageObject, u_, ContentStream, b_, TextStringObject ,but not getting how to use it.
someone please help me to extract this with some explanation, so i can understand the code and tell me how to read DOM of PDF file.
you need to install some libaries:
pip install PyPDF2
pip install textract
pip install nltk
This will download the libraries you require t0 parsePDF documents and extract keywords. In order to do this, make sure your PDF file is stored within the folder where you’re writing your script.
Startup your favourite editor and type:
Note: All lines starting with # are comments.
Step 1: Import all libraries:
import PyPDF2
import textract
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
Step 2: Read PDF File
#write a for-loop to open many files -- leave a comment if you'd #like to learn how
filename = 'enter the name of the file here'
#open allows you to read the file
pdfFileObj = open(filename,'rb')
#The pdfReader variable is a readable object that will be parsed
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
#discerning the number of pages will allow us to parse through all #the pages
num_pages = pdfReader.numPages
count = 0
text = ""
#The while loop will read each page
while count < num_pages:
pageObj = pdfReader.getPage(count)
count +=1
text += pageObj.extractText()
#This if statement exists to check if the above library returned #words. It's done because PyPDF2 cannot read scanned files.
if text != "":
text = text
#If the above returns as False, we run the OCR library textract to #convert scanned/image based PDF files into text
else:
text = textract.process(fileurl, method='tesseract', language='eng')
# Now we have a text variable which contains all the text derived #from our PDF file. Type print(text) to see what it contains. It #likely contains a lot of spaces, possibly junk such as '\n' etc.
# Now, we will clean our text variable, and return it as a list of keywords.
Step 3: Convert text into keywords
#The word_tokenize() function will break our text phrases into #individual words
tokens = word_tokenize(text)
#we'll create a new list which contains punctuation we wish to clean
punctuations = ['(',')',';',':','[',']',',']
#We initialize the stopwords variable which is a list of words like #"The", "I", "and", etc. that don't hold much value as keywords
stop_words = stopwords.words('english')
#We create a list comprehension which only returns a list of words #that are NOT IN stop_words and NOT IN punctuations.
keywords = [word for word in tokens if not word in stop_words and not word in punctuations]
Now you have keywords for your file stored as a list. You can do whatever you want with it. Store it in a spreadsheet if you want to make the PDF searchable, or parse a lot of files and conduct a cluster analysis. You can also use it to create a recommender system for resumes for jobs ;)
Related
So first off what im trying to do: create a pdf parser that will take ONLY tables out of any given pdf. I currently have some pdfs that are for parts manuals which contain an image of the part and then a table for details of the parts and I want to scrape and parse the table data from the pdf into a csv or similar excel style file(csv, xls etc)
What ive tried/trying: I am currently using python3 and tabula(i have no preference for either of these and open to other options) in which I have a py program that is able to scrape all the data of any pdf or directory of pdfs however it takes EVERYTHING including the image file code that has a bunch of 0 1 NaN(adding examples at the bottom). I was thinking of writing a filter function that removes these however that feels like overkill and was wondering/hoping there is a way to filter out the images with tabula or another library? (side note ive also attempted camelot however the module is not importing correctly even when it is in my pip freeze and this happens on both my mac m1 and mac m2 so assuming there is no arm support)
If anyone could help me or help guide me in a direction of a library or method of being able to iterate through all pages in a pdf and JUST grab the tables for export t csv that would be AMAZING!
current main file:
from tabula.io import read_pdf;
from traceback import print_tb;
import pandas as pd;
from tabulate import tabulate;
import os
def parser(fileName, count):
print("\nFile Number: ",count, "\nNow parsing file: ", fileName)
df = read_pdf(fileName, pages="all") #address of pdf file
for i in range(len(df)):
df[i].to_excel("./output/test"+str(i)+".xlsx")
print(tabulate(df))
print_tb(df)
def reader(type):
filecount = 1
if(type == 'f'):
file = input("\nFile(f) type selected\nplease enter full file name with path (ex. Users/Name/directory1/filename.pdf: ")
parser(file, filecount)
elif(type == 'd'):
#directory selected
location = input("\nPlease enter diectory path, if in the same folder just enter a period(.)")
print("Opening directory: ", location)
#loop through and parse directory
for filename in os.listdir(location):
f = os.path.join(location, filename)
# checking if it is a file
if os.path.isfile(f):
parser(f, filecount)
filecount + 1
else:
print('\n\n ERROR, path given does not contain a file or is not a directory type..')
else:
print("Error: please select directory(d) or file(f)")
fileType = input("\n-----> Hello!\n----> Would you like to parse a directory(d) or file(f)?").lower()
reader(fileType)
How could I read a word document by pages (I want to create a dictionary, where the keys would be the number of pages and their respective values would be the strings corresponding to the pages: {"1": "content 1", "2": "content 2 ", ...}) with docx2python? If it is not possible with this package, with what package could I do it?
This is my code so far, it returns a whole word document as a string. Thank you.
!pip install docx2python
from docx2python import docx2python
def read_word(file_path):
"""
Function that reads a Word file and returns a string
"""
# Extract docx content, ignore images
doc = docx2python(file_path, extract_image = False)
# Get all text in a single string
output = doc.text
return output
I had a huge document with many repeated sentences such as (footer text, hyperlinks with alphanumeric chars), I need to get rid of those repeated hyperlinks or Footer text. I have tried with the below code but unfortunately couldn't succeed. Please review and help.
corpus = "We use file handling methods in python to remove duplicate lines in python text file or function. The text file or function has to be in the same directory as the python program file. Following code is one way of removing duplicates in a text file bar.txt and the output is stored in foo.txt. These files should be in the same directory as the python script file, else it won’t work.Now, we should crop our big image to extract small images with amounts.In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams).We use file handling methods in python to remove duplicate lines in python text file or function.As an example I will use some image of a bill, saved in the pdf format. From this bill I want to extract some amounts.All our wrappers, except of textract, can’t work with the pdf format, so we should transform our pdf file to the image (jpg). We will use wand for this.Now, we should crop our big image to extract small images with amounts."
from nltk.tokenize import sent_tokenize
sentences_with_dups = []
for sentence in corpus:
words = sentence.sent_tokenize(corpus)
if len(set(words)) != len(words):
sentences_with_dups.append(sentence)
print(sentences_with_dups)
else:
print('No duplciates found')
Error message for the above code :
AttributeError: 'str' object has no attribute 'sent_tokenize'
Desired Output :
Duplicates = ['We use file handling methods in python to remove duplicate lines in python text file or function.','Now, we should crop our big image to extract small images with amounts.']
Cleaned_corpus = {removed duplicates from corpus}
First of all, the example you provided is messed up with spaces between the last period and next sentence, there are a lot of space missing in between them, so I cleaned up.
Then you can do:
corpus = "......"
sentences = sent_tokenize(corpus)
duplicates = list(set([s for s in sentences if sentences.count(s) > 1]))
cleaned = list(set(sentences))
Above will mess the order. If you care about the order, you can do the following to preserve:
duplicates = []
cleaned = []
for s in sentences:
if s in cleaned:
if s in duplicates:
continue
else:
duplicates.append(s)
else:
cleaned.append(s)
I am trying to extract a set of alpha numeric characters from a text file.
below would be some lines in the file. I want to extract the '#' as well as anything that follows.
im trying to pull #bob from a file.
this is a #line in the #file
#bob is a wierdo
the below code is what I have so far.
def getAllPeople(fileName):
#give empty list
allPeople=[]
#open TweetsFile.txt
with open(fileName, 'r') as f1:
lines=f1.readlines()
#split all words into strings
for word in lines:
char = word.split("#")
print(char)
#close the file
f1.close()
What I am trying to get is;
['#bob','#line','#file', '#bob']
If you do not want to use re, take Andrew's suggestion
mentions = list(filter(lambda x: x.startswith('#'), tweet.split()))
otherwise, see the marked duplicate.
mentions = [w for w in tweet.split() if w.startswith('#')]
since you apparently can not use filter or lambda.
Im writing a script that takes a website url and downloads it using beautiful soup. It then uses gensim.summarization to summarize the text but I keep getting ValueError("input must have more than one sentence") even thought the text has more than one sentence. The first section of the script works that downloads the text but I cant get the second part to summarize the text.
import bs4 as bs
import urllib.request
from gensim.summarization import summarize
from gensim.summarization.textcleaner import split_sentences
#===========================================
print("(Insert URL)")
url = input()
sauce = urllib.request.urlopen(url).read()
soup = bs.BeautifulSoup(sauce,'lxml')
#===========================================
print(soup.title.string)
with open (soup.title.string + '.txt', 'wb') as file:
for paragraph in soup.find_all('p'):
text = paragraph.text.replace('.', '.\n')
text = split_sentences(text)
text = summarize(str(text))
text = text.encode('utf-8', 'ignore')
#===========================================
file.write(text+'\n\n'.encode('utf-8'))
It should create a .txt file with the summarized text in it after the script is run in whatever folder the .py file is located
You should not use split_sentences() before passing the text to summarize() since summarize() takes a string (with multiple sentences) as input.
In your code you are first turning your text into a list of sentences (using split_sentences()) and then converting that back to a string (with str()). The result of this is a string like "['First sentence', 'Second sentence']". It doesn't make sense to pass this on to summarize().
Instead you should simply pass your raw text as input:
text = summarize(text)