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How to Work With a PDF in Python

Whether it is an ebook, digitally signed agreements, password protected documents, or scanned documents such as passports, the most preferred file format is PDF or Portable Document Format. It was originally developed by Adobe and is a file format used to present and transfer documents easily and reliably. It uses the file extension .pdf. In fact, PDF being the most widely used digital media, is now considered as an open standard which is maintained by the International Standards Organization (ISO). Python has relatively easy syntax which makes it even easier for the ones who are in their initial stage of learning the language. The popular Python libraries are well suited and integrated which allows to easily extract documents from a PDF, rotate pages if required, split pdf to make separate documents, or add watermarks in them.Now an important question rises, why do we need Python to process PDFs? Well, processing a PDF falls under the category of text analytics. There are several libraries and frameworks available which are designed in Python exclusively for text analytics. This makes it easier to play with a PDF in Python. You can also extract information from PDF and use into Natural Language Processing or any other Machine Learning models. Get certified and learn more about Python Programming and apply those skills and knowledge in the real world.History of  pyPDF, PyPDF2, pyPDF4The first PyPDF package was released in 2005 and the last official release in 2010. After a year or so, a  company named Phasit sponsored a branch of the PyPDF called PyPDF2 which was consistent with the original package and worked pretty well for several years.A series of packages were released later on with the name of PyPDF3 and later renamed as PyPDF4. The biggest difference between PyPDF and the other versions was that the later versions supported Python3. PyPDF2 has been discarded recently. But since PyPDF4 is not fully backward compatible with the PyPDf2, it is suggested to use PyPDF2. You can also use a substitute package - pdfrw. Pdfrw was created by Patrick Maupin and allows you to perform all functions which PyPDF2 is capable of except a few such as encryption, decryption, and types of decompression.Some common libraries in PythonLet us look into some of the libraries Python offers to handle PDFs:PdfMiner It is a tool used to extract information from PDF documents. PDFMiner allows the user to analyze text data and obtain the definite location of a text. It provides information such as fonts and lines. We can also use it as a PDF transformer and a PDF parser.PyPDF2PyPDF2 is purely a Python library which allows users to split, merge, crop, encrypt, and transform PDFs. You can also add customized data, view options, and passwords to the documents. Tabula-pyIt is a Python wrapper of tabula-java which can read tables from PDF files and convert into Pandas Dataframe or into CSV/TSV/JSON file formats.SlateIt is a Python package which facilitates the extraction of information and is dependent on the PdfMiner package.PDFQueryA light Python wrapper which uses minimum code to extract data from PDFs.xPDFIt is an open source viewer of PDF which also includes an extractor, converter and other utilities. Out of all the libraries mentioned above, PyPDF2 is the most used to perform operations like extraction, merging, splitting and so on.Installing PyPDF2If you're using Anaconda, you can install PyPDF2 using pip or conda. To install PyPDF2 using pip, run the following command in the command line:pip install PyPDF2The module is case-sensitive. So you need to make sure that proper syntax is followed. The installation is really quick since PyPDF2 is free of dependencies.Extracting Document Information from a PDF in PythonPyPDF2 can be used to extract metadata and all sorts of texts from PDF when you are performing operations on preexisting PDF files. The types of data you can extract are:AuthorCreatorProducerSubjectTitleNumber of PagesTo understand it better, let us use an existing PDF in your system or you can go to Leanpub and download a book sample.The code for extracting the document information from the PDF—# get_doc_info.py from PyPDF2 import PdfFileReader def getinfo(path):     with open(path, 'rb') as f:         PDF = PdfFileReader(f)         information = PDF.getDocumentInfo()         numberofpages = PDF.getNumPages()     print(information)     author = information.author     creator = information.creator     producer =information .producer     subject = information.subject     title = information.title if __name__ == '__main__':     path = 'reportlab-sample.pdf'     getinfo(path)The output of the program above will look like—Here, we have firstly imported PdfFileReader from the PyPDF2 package. The class PdfFileReader is used to interact with PDF files like reading and extracting information using accessor methods. Then, we have created our own function getinfo with a PDF file as an argument and then called the getdocumentinfo(). This returned an instance of DocumentInformation. And finally we got extract information like the author, creator, subject or title, etc.getNumPages() is used to count the number of pages in the document. PdfMiner can be used when you want to extract text from a PDF file. It is potent and particularly designed for extracting text from PDF.We have learned to extract information from PDF. Now let’s learn how to rotate a PDF. Rotating pages in PDFA lot of times we receive PDFs which contain pages in landscape orientation instead of portrait. You may also find certain documents to be upside down, which happens while scanning a document or mailing. However, we can rotate the pages clockwise or counterclockwise according to our choice using Python with PyPDF2.The code for rotating the article is as follows—# rotate_pages.py from PyPDF2 import PdfFileReader, PdfFileWriter def rotate(pdf_path):     pdf_write = PdfFileWriter()     pdf_read = PdfFileReader(path)     # Rotate page 90 degrees to the right     page1 = pdf_read.getPage(0).rotateClockwise(90)     pdf_write.addPage(page1)     # Rotate page 90 degrees to the left     page2 = pdf_read.getPage(1).rotateCounterClockwise(90)     pdf_write.addPage(page2)     # Add a page in normal orientation     pdf_write.addPage(pdf_read.getPage(2))     with open('rotate_pages.pdf', 'wb') as fh:         pdf_write.write(fh) if __name__ == '__main__':     path = 'mldocument.pdf'     rotate(path)The output of the code will be as follows—Here firstly we imported the PdfFileReader and the PdfFileWriter so that we can write out a new PDF file. Then we declared a function rotate with a path to the PDF that is to be modified. Within the function, we created a read object pdf_read and write object pdf_write.Then, we used the getPage() to grab the pages. Two pages page1 and page2 are taken and rotated to 90 degrees clockwise and 90 degrees counterclockwise respectively using rotateClockwise() and rotateCounterClockwise().We used addPage() function after each rotation method calls. This adds the rotated page to the write object. The last page we add is page3 without any rotation.Lastly, we have used write() with a file-like parameter to write out the new PDF. The final PDF contains three pages, the first two will be in the landscape mode and rotated in reversed direction and the third page will be in normal orientation.Now we will learn to merge different PDFs into one.Merging PDFsIn many cases, we need to merge two PDFs into a single one. For example, suppose you are working on a project report and you need to print it and bind it into a book. It contains a cover page followed by the project report. So you have two different PDFs and you want to merge them into one PDF. You can simply use Python to do so. Let us see how can we merge PDFs into one.The code for merging two PDF documents using PyPDF in mentioned below:# pdf_merging.py from PyPDF2 import PdfFileReader, PdfFileWriter def pdfmerger(paths, output):     pdfwrite = PdfFileWriter()     for path in paths:         pdfread = PdfFileReader(path)         for page in range(pdfread.getNumPages()):             # Add each page to the writer object             pdfwrite.addPage(pdfread.getPage(page))     # Write out the merged PDF     with open(output, 'wb') as out:         pdfwrite.write(out) if __name__ == '__main__':     paths = ['document-1.pdf', 'document-2.pdf']     pdfmerger(paths, output='merged.pdf')Here we have created a function pdfmerger() which takes a number of inputs and a single output. Then we created a PdfFileReader() object for each PDF path and looped over the pages, added each page to the write object. Finally, using the write() function the object’s contents are written to the disk.PyPDF2 makes the process of merging simpler by creating the PdfFileMerger class.Code for merging two documents using PyPDF2—# pdf_merger2.py import glob from PyPDF2 import PdfFileMerger def merger(output_path, input_paths):     pdfmerge = PdfFileMerger()     file_handles = []     for path in input_paths:         pdfmerge.append(path)     with open(output_path, 'wb') as fileobj:         pdfmerge.write(fileobj) if __name__ == '__main__':     paths = glob.glob('d-1.pdf')     paths.sort()     merger('d-2.pdf', paths)The PyPDF2 makes it simpler in the way that we don’t need to loop the pages of each document ourselves.  Here, we created the object pdfmerge and looped through the PDF paths. The PyPDF2 automatically appends the whole document. Finally, we write it out.Let’s perform the opposite of merging now!Splitting PDFsThe PyPDF2 package has the ability to split up a single PDF into multiple PDFs. It allows us to split pages into different PDFs. Suppose we have a set of scanned documents in a single PDF and we need to separate the pages into different PDFs as per requirement, we can simply use Python to select pages we want to split and get the work done.Code for splitting a single PDF into multiple PDFs—# pdf_splitter.py import os from PyPDF2 import PdfFileReader, PdfFileWriter def splitpdf(path):     fname = os.path.splitext(os.path.basename(path))[0]     pdf = PdfFileReader(path)     for page in range(pdf.getNumPages()):         pdfwrite = PdfFileWriter()         pdfwrite.addPage(pdf.getPage(page))         outputfilename = '{}_page_{}.pdf'.format(             fname, page+1)         with open(outputfilename, 'wb') as out:             pdfwrite.write(out)         print('Created: {}'.format(outputfilename)) if __name__ == '__main__':     path = 'document-1.pdf'     splitpdf(path)Here we have imported the PdfFileReader and PdfFileWriter from PyPDF2. Then we created a function called splitpdf() which accepts the path of PDF we want to split. The first line of the function takes the name of the input file. Then we open the PDF and create a read object. Using the read object’s getNumPages(), we loop over all the pages.In the next step, we created an instance of PdfFileWriter inside the for loop. Then, we created a PDF write instance and added each page to it for each of the pages in the PDF input. We also created a unique filename using the original filename + the word ‘page’ + the page number + 1.Once we are done with running the script, we will have each of the pages of the input PDF split into multiple PDFs. Now let us learn how to add a watermark to a PDF and keep it secured.Adding Overlays/WatermarksAn image or superimposed text on selected pages in a PDF document is referred to as a Watermark. The Watermark adds security features and protects our rational property like images and PDFs. Watermarks are also called overlays.The PyPDF2 allows us to watermark documents. We just need to have a PDF which will consist of our watermark text, image or signature.Code for adding a watermark in a PDF—# watermarker.py from PyPDF2 import PdfFileWriter, PdfFileReader def watermark(inputpdf, outputpdf, watermarkpdf):     watermark = PdfFileReader(watermarkpdf)     watermarkpage = watermark.getPage(0)     pdf = PdfFileReader(inputpdf)     pdfwrite = PdfFileWriter()     for page in range(pdf.getNumPages()):         pdfpage = pdf.getPage(page)         pdfpage.mergePage(watermarkpage)         pdfwrite.addPage(pdfpage)     with open(outputpdf, 'wb') as fh:         pdfwrite.write(fh) if __name__ == '__main__':     watermark(inputpdf='document-1.pdf',               outputpdf='watermarked_w9.pdf',               watermarkpdf='watermark.pdf')The output of the code will look like— There are three arguments of the function watermark(): inputpdf: The path of the PDF that is to be watermarked. outputpdf: The path where the watermarked PDF will be saved. watermarkpdf: The PDF which contains the watermark.Firstly, we extract the PDF page which contains the watermark image or text and then open that PDF page where we want to give the desired watermark.Using the inputpdf, we create a read object and using the pdfwrite, we create a write object to write out the watermarked PDF and then iterate over the pages.Next, we call the page object’s mergePage and apply the watermark and add that to the write object pdfwrite.When the loop terminates, the watermarked PDF is written out to the disk and it’s done!Encrypting a PDFIn the PDF world, the PyPDF2 package allows an owner password which gives the user the advantage to work as an administrator. The package also provides the user password which allows us to open the document upon entering the password.The PyPDF2 basically doesn’t permit any allowances on any PDF file yet it allows the user to set the owner password and user password.Code to add a password and add encryption to a PDF—# pdf_encrypt.py from PyPDF2 import PdfFileWriter, PdfFileReader def encryption(inputpdf, outputpdf, password):     pdfwrite = PdfFileWriter()     pdfread = PdfFileReader(inputpdf)     for page in range(pdfread.getNumPages()):         pdfwrite.addPage(pdfread.getPage(page))     pdfwrite.encrypt(user_pwd=password, owner_pwd=None,                       use_128bit=True)     with open(outputpdf, 'wb') as fh:         pdfwrite.write(fh) if __name__ == '__main__':     encryption(inputpdf='document-1.pdf',                   outputpdf='document-1-encrypted.pdf',                   password='twofish')We declare a  function named encryption() with three arguments—the input PDF path, the output PDF path and the password that we want to keep. Then we create one read object pdfread and one write object pdfwrite. Now we loop over all the pages and add them to the write object since we need to encrypt the entire document.Finally, we call the encrypt() function which accepts three parameters—the user password, the owner password and the whether or not to use 128-bit encryption. The PDF  will be encrypted to 40-bit encryption if the argument use128bit is set to false. Also if the owner password is set to none, then it will be set to user password automatically.Reading the Table data from PDFSuppose you want to work with the Table data in Pdf, you can use tabula-py to read tables in a PDF. To install tabula-py, run:pip install tabula-pyCode to extract simple Text from pdf using PyPDF2:import tabula # readinf the PDF file that contain Table Data # you can find the pdf file with complete code in below # read_pdf will save the pdf table into Pandas Dataframe df = tabula.read_pdf("document.pdf") # in order to print first 5 lines of Table df.head()If you PDF file contains Multiple Tabledf = tabula.read_pdf("document.pdf",multiple_tables=True)If you want to extract Information from the specific part of any specific page of PDFtabula.read_pdf("document.pdf", area=(126,149,212,462), pages=1)If you want the output into JSON Formattabula.read_pdf("offense.pdf", output_format="json")Exporting PDF into ExcelSuppose you want to export a PDF into Excel, you can do so by writing the following code and convert the PDF Data into Excel or CSV.tabula.convert_into("document.pdf", "document_testing.xlsx", output_format="xlsx")Let us sum up what we have learned in the article:Extraction of data from a PDFRotate pages in a PDFMerge PDFs into one PDFSplit a PDF into many PDFsAdd watermarks or overlays in a PDFAdd password or encryption to a PDFReading table from PDFExporting PDF into Excel or CSVAs you have seen, PyPDF2 is one of the most useful tools available in Python. The features of PyPDF2 makes life easier whether you are working on a large project or even when you quickly want to make some changes to your PDF documents. Learn more about such libraries and frameworks as KnowledgeHut offers Python Certification Course for Programmers, Developers, Jr./Sr Software Engineers/Developers and anybody who wants to learn Python.
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How to Work With a PDF in Python

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How to Work With a PDF in Python

Whether it is an ebook, digitally signed agreements, password protected documents, or scanned documents such as passports, the most preferred file format is PDF or Portable Document Format. It was originally developed by Adobe and is a file format used to present and transfer documents easily and reliably. It uses the file extension .pdf. In fact, PDF being the most widely used digital media, is now considered as an open standard which is maintained by the International Standards Organization (ISO). 

Python has relatively easy syntax which makes it even easier for the ones who are in their initial stage of learning the language. The popular Python libraries are well suited and integrated which allows to easily extract documents from a PDF, rotate pages if required, split pdf to make separate documents, or add watermarks in them.

Now an important question rises, why do we need Python to process PDFs? Well, processing a PDF falls under the category of text analytics. There are several libraries and frameworks available which are designed in Python exclusively for text analytics. This makes it easier to play with a PDF in Python. You can also extract information from PDF and use into Natural Language Processing or any other Machine Learning models. Get certified and learn more about Python Programming and apply those skills and knowledge in the real world.

History of  pyPDF, PyPDF2, pyPDF4

The first PyPDF package was released in 2005 and the last official release in 2010. After a year or so, a  company named Phasit sponsored a branch of the PyPDF called PyPDF2 which was consistent with the original package and worked pretty well for several years.

A series of packages were released later on with the name of PyPDF3 and later renamed as PyPDF4. The biggest difference between PyPDF and the other versions was that the later versions supported Python3. 

PyPDF2 has been discarded recently. But since PyPDF4 is not fully backward compatible with the PyPDf2, it is suggested to use PyPDF2. You can also use a substitute package - pdfrw. Pdfrw was created by Patrick Maupin and allows you to perform all functions which PyPDF2 is capable of except a few such as encryption, decryption, and types of decompression.

Some common libraries in Python

Let us look into some of the libraries Python offers to handle PDFs:

PdfMiner 

It is a tool used to extract information from PDF documents. PDFMiner allows the user to analyze text data and obtain the definite location of a text. It provides information such as fonts and lines. We can also use it as a PDF transformer and a PDF parser.

PyPDF2

PyPDF2 is purely a Python library which allows users to split, merge, crop, encrypt, and transform PDFs. You can also add customized data, view options, and passwords to the documents. 

Tabula-py

It is a Python wrapper of tabula-java which can read tables from PDF files and convert into Pandas Dataframe or into CSV/TSV/JSON file formats.

Slate

It is a Python package which facilitates the extraction of information and is dependent on the PdfMiner package.

PDFQuery

A light Python wrapper which uses minimum code to extract data from PDFs.

xPDF

It is an open source viewer of PDF which also includes an extractor, converter and other utilities. 

Out of all the libraries mentioned above, PyPDF2 is the most used to perform operations like extraction, merging, splitting and so on.

Installing PyPDF2

If you're using Anaconda, you can install PyPDF2 using pip or conda. To install PyPDF2 using pip, run the following command in the command line:

pip install PyPDF2

The module is case-sensitive. So you need to make sure that proper syntax is followed. The installation is really quick since PyPDF2 is free of dependencies.

Extracting Document Information from a PDF in Python

PyPDF2 can be used to extract metadata and all sorts of texts from PDF when you are performing operations on preexisting PDF files. The types of data you can extract are:

  • Author
  • Creator
  • Producer
  • Subject
  • Title
  • Number of Pages

To understand it better, let us use an existing PDF in your system or you can go to Leanpub and download a book sample.

The code for extracting the document information from the PDF—

# get_doc_info.py
from PyPDF2 import PdfFileReader
def getinfo(path):
    with open(path, 'rb') as f:
        PDF = PdfFileReader(f)
        information = PDF.getDocumentInfo()
        numberofpages = PDF.getNumPages()
    print(information)
    author = information.author
    creator = information.creator
    producer =information .producer
    subject = information.subject
    title = information.title
if __name__ == '__main__':
    path = 'reportlab-sample.pdf'
    getinfo(path)

The output of the program above will look like—

Here, we have firstly imported PdfFileReader from the PyPDF2 package. The class PdfFileReader is used to interact with PDF files like reading and extracting information using accessor methods. 

Then, we have created our own function getinfo with a PDF file as an argument and then called the getdocumentinfo()This returned an instance of DocumentInformation. And finally we got extract information like the author, creator, subject or title, etc.

getNumPages() is used to count the number of pages in the document. 

PdfMiner can be used when you want to extract text from a PDF file. It is potent and particularly designed for extracting text from PDF.

We have learned to extract information from PDF. Now let’s learn how to rotate a PDF. 

Rotating pages in PDF

A lot of times we receive PDFs which contain pages in landscape orientation instead of portrait. You may also find certain documents to be upside down, which happens while scanning a document or mailing. However, we can rotate the pages clockwise or counterclockwise according to our choice using Python with PyPDF2.

The code for rotating the article is as follows—

# rotate_pages.py
from PyPDF2 import PdfFileReader, PdfFileWriter
def rotate(pdf_path):
    pdf_write = PdfFileWriter()
    pdf_read = PdfFileReader(path)
    # Rotate page 90 degrees to the right
    page1 = pdf_read.getPage(0).rotateClockwise(90)
    pdf_write.addPage(page1)
    # Rotate page 90 degrees to the left
    page2 = pdf_read.getPage(1).rotateCounterClockwise(90)
    pdf_write.addPage(page2)
    # Add a page in normal orientation
    pdf_write.addPage(pdf_read.getPage(2))
    with open('rotate_pages.pdf', 'wb') as fh:
        pdf_write.write(fh)
if __name__ == '__main__':
    path = 'mldocument.pdf'
    rotate(path)

The output of the code will be as follows—

Rotating pages Output in Python

Here firstly we imported the PdfFileReader and the PdfFileWriter so that we can write out a new PDF file. Then we declared a function rotate with a path to the PDF that is to be modified. Within the function, we created a read object pdf_read and write object pdf_write.

Then, we used the getPage() to grab the pages. Two pages page1 and page2 are taken and rotated to 90 degrees clockwise and 90 degrees counterclockwise respectively using rotateClockwise() and rotateCounterClockwise().

We used addPage() function after each rotation method calls. This adds the rotated page to the write object. The last page we add is page3 without any rotation.

Lastly, we have used write() with a file-like parameter to write out the new PDF. The final PDF contains three pages, the first two will be in the landscape mode and rotated in reversed direction and the third page will be in normal orientation.

Now we will learn to merge different PDFs into one.

Merging PDFs

In many cases, we need to merge two PDFs into a single one. For example, suppose you are working on a project report and you need to print it and bind it into a book. It contains a cover page followed by the project report. So you have two different PDFs and you want to merge them into one PDF. You can simply use Python to do so. Let us see how can we merge PDFs into one.

The code for merging two PDF documents using PyPDF in mentioned below:

# pdf_merging.py
from PyPDF2 import PdfFileReader, PdfFileWriter
def pdfmerger(paths, output):
    pdfwrite = PdfFileWriter()
    for path in paths:
        pdfread = PdfFileReader(path)
        for page in range(pdfread.getNumPages()):
            # Add each page to the writer object
            pdfwrite.addPage(pdfread.getPage(page))
    # Write out the merged PDF
    with open(output, 'wb') as out:
        pdfwrite.write(out)
if __name__ == '__main__':
    paths = ['document-1.pdf', 'document-2.pdf']
    pdfmerger(paths, output='merged.pdf')

Here we have created a function pdfmerger() which takes a number of inputs and a single output. Then we created a PdfFileReader() object for each PDF path and looped over the pages, added each page to the write object. Finally, using the write() function the object’s contents are written to the disk.

PyPDF2 makes the process of merging simpler by creating the PdfFileMerger class.

Code for merging two documents using PyPDF2—

# pdf_merger2.py

import glob
from PyPDF2 import PdfFileMerger

def merger(output_path, input_paths):
    pdfmerge = PdfFileMerger()
    file_handles = []

    for path in input_paths:
        pdfmerge.append(path)

    with open(output_path, 'wb') as fileobj:
        pdfmerge.write(fileobj)

if __name__ == '__main__':
    paths = glob.glob('d-1.pdf')
    paths.sort()
    merger('d-2.pdf', paths)

The PyPDF2 makes it simpler in the way that we don’t need to loop the pages of each document ourselves.  Here, we created the object pdfmerge and looped through the PDF paths. The PyPDF2 automatically appends the whole document. Finally, we write it out.

Let’s perform the opposite of merging now!

Splitting PDFs

The PyPDF2 package has the ability to split up a single PDF into multiple PDFs. It allows us to split pages into different PDFs. Suppose we have a set of scanned documents in a single PDF and we need to separate the pages into different PDFs as per requirement, we can simply use Python to select pages we want to split and get the work done.

Code for splitting a single PDF into multiple PDFs—

# pdf_splitter.py
import os
from PyPDF2 import PdfFileReader, PdfFileWriter
def splitpdf(path):
    fname = os.path.splitext(os.path.basename(path))[0]
    pdf = PdfFileReader(path)
    for page in range(pdf.getNumPages()):
        pdfwrite = PdfFileWriter()
        pdfwrite.addPage(pdf.getPage(page))
        outputfilename = '{}_page_{}.pdf'.format(
            fname, page+1)
        with open(outputfilename, 'wb') as out:
            pdfwrite.write(out)
        print('Created: {}'.format(outputfilename))
if __name__ == '__main__':
    path = 'document-1.pdf'
    splitpdf(path)

Here we have imported the PdfFileReader and PdfFileWriter from PyPDF2. Then we created a function called splitpdf() which accepts the path of PDF we want to split. 

The first line of the function takes the name of the input file. Then we open the PDF and create a read object. Using the read object’s getNumPages(), we loop over all the pages.

In the next step, we created an instance of PdfFileWriter inside the for loop. Then, we created a PDF write instance and added each page to it for each of the pages in the PDF input. We also created a unique filename using the original filename + the word ‘page’ + the page number + 1.

Once we are done with running the script, we will have each of the pages of the input PDF split into multiple PDFs. 

Now let us learn how to add a watermark to a PDF and keep it secured.

Adding Overlays/Watermarks

An image or superimposed text on selected pages in a PDF document is referred to as a Watermark. The Watermark adds security features and protects our rational property like images and PDFs. Watermarks are also called overlays.

The PyPDF2 allows us to watermark documents. We just need to have a PDF which will consist of our watermark text, image or signature.

Code for adding a watermark in a PDF—

# watermarker.py
from PyPDF2 import PdfFileWriter, PdfFileReader
def watermark(inputpdf, outputpdf, watermarkpdf):
    watermark = PdfFileReader(watermarkpdf)
    watermarkpage = watermark.getPage(0)
    pdf = PdfFileReader(inputpdf)
    pdfwrite = PdfFileWriter()
    for page in range(pdf.getNumPages()):
        pdfpage = pdf.getPage(page)
        pdfpage.mergePage(watermarkpage)
        pdfwrite.addPage(pdfpage)
    with open(outputpdf, 'wb') as fh:
        pdfwrite.write(fh)
if __name__ == '__main__':
    watermark(inputpdf='document-1.pdf',
              outputpdf='watermarked_w9.pdf',
              watermarkpdf='watermark.pdf')

The output of the code will look like— 

Adding Overlays/Watermarks Outputs in Python

There are three arguments of the function watermark():

  1.  inputpdf: The path of the PDF that is to be watermarked.
  2.  outputpdf: The path where the watermarked PDF will be saved.
  3.  watermarkpdf: The PDF which contains the watermark.

Firstly, we extract the PDF page which contains the watermark image or text and then open that PDF page where we want to give the desired watermark.

Using the inputpdf, we create a read object and using the pdfwrite, we create a write object to write out the watermarked PDF and then iterate over the pages.

Next, we call the page object’s mergePage and apply the watermark and add that to the write object pdfwrite.

When the loop terminates, the watermarked PDF is written out to the disk and it’s done!

Encrypting a PDF

In the PDF world, the PyPDF2 package allows an owner password which gives the user the advantage to work as an administrator. The package also provides the user password which allows us to open the document upon entering the password.

The PyPDF2 basically doesn’t permit any allowances on any PDF file yet it allows the user to set the owner password and user password.

Code to add a password and add encryption to a PDF—

# pdf_encrypt.py
from PyPDF2 import PdfFileWriter, PdfFileReader
def encryption(inputpdf, outputpdf, password):
    pdfwrite = PdfFileWriter()
    pdfread = PdfFileReader(inputpdf)
    for page in range(pdfread.getNumPages()):
        pdfwrite.addPage(pdfread.getPage(page))
    pdfwrite.encrypt(user_pwd=password, owner_pwd=None,
                      use_128bit=True)
    with open(outputpdf, 'wb') as fh:
        pdfwrite.write(fh)
if __name__ == '__main__':
    encryption(inputpdf='document-1.pdf',
                  outputpdf='document-1-encrypted.pdf',
                  password='twofish')

We declare a  function named encryption() with three arguments—the input PDF path, the output PDF path and the password that we want to keep. 

Then we create one read object pdfread and one write object pdfwrite. Now we loop over all the pages and add them to the write object since we need to encrypt the entire document.

Finally, we call the encrypt() function which accepts three parameters—the user password, the owner password and the whether or not to use 128-bit encryption. The PDF  will be encrypted to 40-bit encryption if the argument use128bit is set to false. Also if the owner password is set to none, then it will be set to user password automatically.

Reading the Table data from PDF

Suppose you want to work with the Table data in Pdf, you can use tabula-py to read tables in a PDF. To install tabula-py, run:

pip install tabula-py

Code to extract simple Text from pdf using PyPDF2:

import tabula
# readinf the PDF file that contain Table Data
# you can find the pdf file with complete code in below
# read_pdf will save the pdf table into Pandas Dataframe

df = tabula.read_pdf("document.pdf")
# in order to print first 5 lines of Table

df.head()

If you PDF file contains Multiple Table

df = tabula.read_pdf("document.pdf",multiple_tables=True)

If you want to extract Information from the specific part of any specific page of PDF

tabula.read_pdf("document.pdf", area=(126,149,212,462), pages=1)

If you want the output into JSON Format

tabula.read_pdf("offense.pdf", output_format="json")

Exporting PDF into Excel

Suppose you want to export a PDF into Excel, you can do so by writing the following code and convert the PDF Data into Excel or CSV.

tabula.convert_into("document.pdf", "document_testing.xlsx", output_format="xlsx")

Let us sum up what we have learned in the article:

  • Extraction of data from a PDF
  • Rotate pages in a PDF
  • Merge PDFs into one PDF
  • Split a PDF into many PDFs
  • Add watermarks or overlays in a PDF
  • Add password or encryption to a PDF
  • Reading table from PDF
  • Exporting PDF into Excel or CSV

As you have seen, PyPDF2 is one of the most useful tools available in Python. The features of PyPDF2 makes life easier whether you are working on a large project or even when you quickly want to make some changes to your PDF documents. Learn more about such libraries and frameworks as KnowledgeHut offers Python Certification Course for Programmers, Developers, Jr./Sr Software Engineers/Developers and anybody who wants to learn Python.

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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1 comments

Rithvik sharma 11 Jul 2019 1 likes

Nice understanding and easy to read

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Scala Vs Python Vs R Vs Java - Which language is better for Spark & Why?

One of the most important decisions for the Big data learners or beginners is choosing the best programming language for big data manipulation and analysis. Just understanding business problems and choosing the right model is not enough but implementing them perfectly is equally important and choosing the right language (or languages) for solving the problem goes a long way. If you search top and highly effective programming languages for Big Data on Google, you will find the following top 4 programming languages: JavaScalaPythonRJavaJava is one of the oldest languages of all 4 programming languages listed here. Traditional Frameworks of Big data like Apache Hadoop and all the tools within its ecosystem are Java-based and hence using java opens up the possibility of utilizing large ecosystem of tools in the big data world.  ScalaA beautiful crossover between object-oriented and functional programming language is Scala. Scala is a highly Scalable Language. Scala was invented by the German Computer Scientist, Martin Odersky and the first version was launched in the year 2003.PythonPython was originally conceptualized by Guido van Rossum in the late 1980s. Initially, it was designed as a response to the ABC programming language and later gained its popularity as a functional language in a big data world. Python has been declared as one of the fastest-growing programming languages in 2018 as per the recently held Stack Overflow Developer Survey. Many data analysis, manipulation, machine learning, deep learning libraries are written in Python and hence it has gained its popularity in the big data ecosystem. It’s a very user-friendly language and it is its biggest advantage.  Fun factPython is not named after the snake. It’s named after the British TV show Monty Python.RR is the language of statistics. R is a language and environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is named partly after the first names of the first two R authors and partly as a play on the name of S*. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.*SS is a statistical programming language developed primarily by John Chambers and R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme.Every framework is implemented in the underlying programming language for its implementation. Ex Zend uses PHP, Panda Framework uses python similarly Hadoop framework uses Java and Spark uses Scala.However, Spark officially supports Java, Scala, Python and R, all 4 languages. If one browses through Apache Spark’s official website documentation, he/she would find many other languages utilized by the open-source community for Spark implementation.    When any developer wants to start learning Spark, the first question he stumbles upon is, out of these pools of languages, which one to use and which one to master? Solution Architects would have a tough time choosing the right language for spark framework and Organizations will always be wondering, which skill sets are relevant for my problem if one doesn’t have the right knowledge about these languages in the context of Spark.    This article will try to answer all these queries.so let’s start-JavaOldest of all and popular, widely adopted programming language of all. There is a number offeatures/advantages due to which Java is favorite for Big data developers and tool creators:Java is platform-agnostic language and hence it can run on almost any system. Java is portable due to something called Java Virtual Machine – JVM. JVM is a foundation of Hadoop ecosystem tools like Map Reduce, Storm, Spark, etc. These tools are written in Java and run on JVM.Java provides various communities support like GitHub and stack overflow etc.Java is scalable, backward compatible, stable and production-ready language. Also, supports a large variety of tried and tested libraries.It is statically typed language (We would see details of this functionality in later sections, in comparison with others)Java is mostly the choice for most of the big data projects but for the Spark framework, one has to ponder upon, whether Java would be the best fit.One major drawback of Java is its verbosity. One has to write long code (number of lines of code) to achieve simple functionality in Java.Java does not support Read-Evaluate-Print-Loop (REPL) which is a major deal-breaker when choosing a programming language for big data processing.ScalaScala is comparatively new to the programming scene but has become popular very quickly. Above are a few quotes from bigger names in the industry for Scala. From the Spark context, many experts prefer Scala over other programming languages as Spark is written in Scala. Scala is the native language of Spark. It means any new API always first be available in Scala.Scala is a hybrid functional programming language because It has both the features of object-oriented programming and functional programming. As an OO Programming Language, it considers every value as an object and all OOPS concepts apply. As a functional programming language, it defines and supports functions. All operations are done as functions. No variable stands by itself. Scala is a machine-compiled language.Scala and Java are popular programming languages that run over JVM. JVM makes these languages framework friendly. One can say, Scala is an advanced level of Java.Features/Advantages of Scala:It’s general-purpose object-oriented language with functional language properties too. It’s less verbose than Java.It can work with JVM and hence is portable.It can support Java APIs comfortably.It's fast and robust in Spark context as its Spark native.It is a statically typed language.Scala supports Read-Evaluate-Print-Loop (REPL)Drawbacks / Downsides of Scala:Scala is complex to learn due to the functional nature of language.Steep learning curve.Lack of matured machine learning languages.PythonPython is one of the de-facto languages of Data Science. It is a simple, open-source, general-purpose language and is very easy to learn. It has a rich set of libraries, utilities, ready-to-use features and support to a number of mature machine learning, big data processing, visualization libraries.Advantages of Python:It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a command-line interpreter and it responds immediately. Java lacks this feature.Easy to learn, easy debugging, fewer lines of code.It is dynamically typed. i.e. can dynamically defined variable types. i.e. Python as a language is type-safe.Python is platform agnostic and scalable.Drawbacks/Disadvantages:Python is slow. Big data professionals find projects built in Java / Scala are faster and robust than the once with python.Whilst using user-defined functions or third party libraries in Python with Spark, processing would be slower as increased processing is involved as Python does not have equivalent Java/Scala native language API for these functionalities.Python does not support heavy weight processing fork() using uWSGI but it does not support true multithreading.R LanguageR is the favourite language of statisticians. R is fondly called a language of statisticians.  It’s popular for research, plotting, and data analysis. Together with RStudio, it makes a killer statistic, plotting, and data analytics application.R is majorly used for building data models to be used for data analysis.Advantages/Features of R:Strong statistical modeling and visualization capabilities.Support for ‘data science’ related work.It can be integrated with Apache Hadoop and Spark easily.Drawbacks/Disadvantages of R:R is not a general-purpose language.The code written in R cannot be directly deployed into production. It needs conversion into Java or Python.Not as fast as Java / Scala.Comparison of four languages for Apache SparkWith the introduction of these 4 languages, let’s now compare these languages for the Spark framework:These languages can be categorized into 2 buckets basis high-level spark architecture support, broadly:JVM Languages: Java and ScalaNon-JVM Languages: Python and RDue to these categorizations, performance may vary. Let’s understand architecture in little depth to understand the performance implications of using these languages. This would also help us to understand the question of when to use which language.Spark Framework High-level architecture An application written in any one of the languages is submitted on the driver node and further driver node distributes the workload by dividing the execution on multiple worker nodes.JVM compatible Application Execution Flow Consider the applications written are JVM compatible (Java/Scala). Now, Spark is also written in native JVM compatible Scala language, hence there is no explicit conversion required at any point of time to execute JVM compatible applications on Spark. Also, this makes the native language applications faster to perform on the Spark framework.There are multiple scenarios for Python/R written applications:Python/R driver talk to JVM driver by socket-based API. On the driver node, both the driver processes are invoked when the application language is non-JVM language.Scenario 1: Applications for which Equivalent Java/Scala Driver API exists - This scenario executes the same way as JVM compatible applications by invoking Java API on the driver node itself. The cost for inter-process communication through sockets is negligible and hence performance is comparable. This is with the assumption that processed data over worker nodes are not to be sent back to the Driver again.Scenario 1(b): If the assumption taken is void in scenario 1 i.e. processed data on worker nodes is to be sent back to driver then there is significant overhead and serialization required. This adds to processing time and hence performance in this scenario deteriorates.Scenario 2: Applications for which Equivalent Java/Scala Driver API do not exist – Ex. UDF (User-defined functions) / Third party python libraries. In such cases equivalent Java API doesn’t exist and hence, additional executor sessions are initiated on worker node and python API is serialized on worker node and executed. This python worker processes in addition to JVM and coordination between them is overhead. Processes also compete for resources which adds to memory contention.In addition, if the data is to send back to the driver node then processing takes a lot of time and problem scales up as volume increases and hence performance is bigger problem.As we have seen a performance, Let’s see the tabular comparison between these languages.Comparison PointsJavaScalaPythonRPerformanceFasterFaster (about 10x faster than Python)SlowerSlowerLearning CurveEasier than JavaTougher than PythonSteep learning curve than Java & PythonEasiestModerateUser GroupsWeb/Hadoop programmersBig Data ProgrammersBeginners & Data EngineersData Scientists/ StatisticiansUsageWeb development and Hadoop NativeSpark NativeData Engineering/ Machine Learning/ Data VisualizationVisualization/ Data Analysis/ Statistics use casesType of LanguageObject-Oriented, General PurposeObject-Oriented & Functional General PurposeGeneral PurposeSpecifically for Data Scientists.Needs conversion into Scala/Python before productizingConcurrencySupport ConcurrencySupport ConcurrencyDoes not Support ConcurrencyNAEase of UseVerboseLesser Verbose than ScalaLeast VerboseNAType SafetyStatically typedStatically typed (except for Spark 2.0 Data frames)Dynamically TypedDynamically TypedInterpreted Language (REPL)NoNoYesYesMaturated machine learning libraries availability/ SupportLimitedLimitedExcellentExcellentVisualization LibrariesLimitedLimitedExcellentExcellentWeb Notebooks SupportIjava Kernel in Jupyter NotebookApache Zeppelin Notebook SupportJupyter Notebook SupportR NotebookWhich language is better for Spark and Why?With the info we gathered for the languages, let's move to the main question i.e. which language to choose for Spark? My answer is not a straightforward single language for this question. I will state my point of view for choosing the proper language: If you are a beginner and want to choose a language from learning Spark perspective. If you are organization/ self employed or looking to answer a question for solutioning a project perspective. I. If you are beginner:If you are a beginner and have no prior education of programming language then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking for getting into roles like ‘data engineering’, knowledge of Python along with supported libraries will go a long way. If you are a beginner but have education in programming languages, then you may find Java very familiar and easy to build upon prior knowledge. After all, it grapevine of all the languages.  If you are a hardcore bigdata programmer and love exploring complexities, Scala is the choice for you. It’s complex but experts say if once you love Scala, you will prefer it over other languages anytime.If you are a data scientist, statistician and looking to work with Spark, R is the language for you. R is more science oriented than Python. II. If you are organization/looking for choice of language for implementations:You need to answer the following important questions before choosing the language:Skills and Proficiency: Which skill-sets and proficiency over language, you already have with you/in your team?Design goals and availability of features/ Capability of language: Which libraries give you better support for the type of problem(s) you are trying to solve.Performance implications Details of these explained below: 1. Skillset: This is very straightforward. Whichever is available skill set within a team, go with that to solve your problem, after evaluating answers of other two questions. If you are self-employed, the one you have proficiency is the most likely suitable choice of language.  2. Library Support:  Following gives high-level capabilities of languages:R: Good for research, plotting, and data analysis.Python: Good for small- or medium-scale projects to build models and analyse data, especially for fast start-ups or small teams.Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance.In my opinion, Scala/Java can be used for larger robust projects to ease maintenance. Also, If one wants the app to scale quickly and needs it to be robust, Scala is the choice.Python and R: Python is more universal language than R, but R is more science oriented. Broadly, one can say Python can be implemented for Data engineering use cases and R for Data science-oriented use cases. On the other hand, if you discover these two languages have about the same library support you need, then pick the one whose syntax you prefer. You may find that you need both depending on the situation. 3. Performance: As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc) they can perform equally well.ConclusionFor learning, depending upon your prior knowledge, Python is the easiest of all to pick up. For implementations, Choice is in your hands which language to choose for implementations but let me tell you one secret or a tip, you don’t have to stick to one language until you finish your project. You can divide your problem in small buckets and utilize the best language to solve the problem. This way, you can achieve balance between optimum performance, availability, proficiency in a skill, and sub-problem at hand.  Do let us know how your experience was in learning the language comparisons and the language you think is better for Spark. Moreover, which one you think is “the one for you”, through comments below.
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What is Context in React? How to use Context in React?

What the hack is Context?Have you ever wondered about passing data or using states in between different components without using Props? Or passing a state from Parent to Child component without manually passing a prop at every level?  Let’s understand with an example below:Here we have a parent component app.js where we have defined our states. We want to access the data of state in the last child which is “Child 1.2” in the below chart.app.js Parent ComponentThe ideal or older approach in React is to pass the data from the root component to the last component is via Props. We have to pass props in each intermediary level so as to send in the last level. While this approach also works, the real problems begin if data is needed on a different branch i.e Child 2.1 or Child 2.2 in above chart…In order to solve this problem, we need to pass data from the root/top level of the application through all the intermediate components to the one where we want to pass the data, even though some intermediate components don't even need it.  This mind-numbing process is known as prop drilling,  Prop Drillingwhere you’re passing the state from your root component to the bottom and you end up passing the data via props through components that do not even necessarily need themOne really good solution to solve the above problem is using Context According to the React documentation:  “Context provides a way to pass data through the component tree without having to pass props down manually at every level”Ordinarily, we’d have used any state management library like Redux or have used HOC’s to pass the data in a tedious manner. But what if we don’t want to use it all? Here comes the role of new Context API!In layman words, it gives an approach to make specific data available to all components throughout the React component tree regardless of how deeply nested those components are.Context is just like a global object to the subtree of the React component.When to use the Context APIThe Context API is convenient for sharing data that is either global, such as setting the header and footer theme of a website or logic of user authentication and many more. In cases like these, we can use the Context API without using any extra library or external modules. It can also be used in a multilingual application where we want to implement multiple languages that can be translated into the required text with the help of ContextAPI. It will save prop-drilling   In fact, in any situation where we have to pass a prop through a component so it reaches another component, inside down the tree is where we can use the Context API.Introducing The Context APIThe context API is a way to pass data from top component to bottom ones, without manually passing it to via props. Context is fundamentally utilized when some data needs to be accessible by numerous components at different nesting levels. To create a new Context, we can use the React createContext function like below: const MyContext = React.createContext(defaultValue);In React, data is often passed from a parent to its child component as a property. Here, we can also omit the default value which we have passed to the context, if needed.React data passing from parent to its child Let’s Get Started With ContextThree things are needed to tap into the power of context: 1. The context itselfTo create a context we can use React.createContext method which creates a context object. This is used to ensure that the components at different level can use the same context to fetch the data.   In React.createContext, we can pass an input parameter as an argument which could be anything or it can be null as well.import React from `react';  const ThemeContext = React.createContext('dark');  // Create our context        export default ThemeContext;In this example, a string is passed for the current Context which is “dark”. So we can say, the current theme required for a specific component is Dark.   Also, we have exported the object so that we can use it in other places. In one app, React also allows you to create multiple contexts. We should always try to separate context for different purposes, so as to maintain the code structure and better readability. We will see that later in our reading.   What next?? Now, to utilize the power of Context in our example, we want to provide this type of theme to all the components.  Context exposes a pair of elements which is a Provider Component and a Consumer Component.2. A context providerOkay, so now we have our Context object. And to make the context available to all our components we have to use a Provider.   But, what is Provider? According to the React documentation:"every context object comes with a Provider React component that allows consuming components to subscribe to context changes"In other words, Provider accepts a prop (value) and the data in this prop can be used in all the other child components. This value could be anything from the component state.// myProvider.js import React from 'react'; import Theme from './theme'; const myProvider = () => ( ...   ); export default myProvider;We can say that a provider acts just like a delivery service.prop finding context and deliverling it to consumerWhen a consumer asks for something, it finds it in the context and delivers it to where it's needed.But wait, who or what is the consumer???3.  A context consumer What is Consumer? A consumer is a place to keep the stored information. It can request for the data using the provider and can even manipulate the global store if the provider allows it. In our previous example, let’s grab the theme value and use it in our Header component. // Header.js   import React from 'react'; import Theme from './theme';   const Header = () => (                        {theme => Selected theme is {theme}}             );   export default Header;Dynamic Context:   We can also change the value of the provider by simply providing a dynamic context. One way of achieving it is by placing the Provider inside the component itself and grabbing the value from component state as below:// Footer.js   import React from 'react';   class Footer extends React.Component {    state = {        theme: 'dark'    };      render() {        return (                                                );    } }Simple, no? We can easily change the value of  the Provider to any Consumer.Consuming Context With Class-based ComponentsWe all pretty know that there are two methods to write components in React, which is Class based components and Function based components. We have already seen a demo of how we can use the power of Context in class based components.  One is to use the context from Consumer like “ThemeContext.Consumer” and the other method is by assigning context object from current Context to contextType property of our class.import React, { Component } from "react"; import MyThemeContext from "../Context/MyThemeContext"; import GlobalTheme from "../theme";   class Main extends Component {    constructor() {        super();    }    static contextType = MyThemeContext;  //assign context to component    render() {        const currentTheme = GlobalTheme[this.context];        return (            ...        );    }   }There is always a difference in how we want to use the Context. We can either provide it outside the render() method or use the Context Consumer as a component itself.  Here in the above example, we have used a static property named as contextType which is used to access the context data. It can be utilized by using this.context. This method however, limits you consuming, only one context at a time.Consuming Context With Functional ComponentsContext with Functional based components is quite easy and less tedious. In this we can access the context value through props with the help of useContext method in React. This hook (useContext) can be passed in as the argument along with our Context to consume the data in the functional component.const value = useContext(MyContext);It accepts a context object and returns the current context value. To read more about hooks, read here.  Our previous example looks like:import React, { useContext } from 'react' import MyThemeContext from './theme-context'   const User = props => {    const context = useContext(MyThemeContext)    return ...Now, instead of wrapping our content in a Consumer component we have access to the theme context state through the ‘context’ variable.But we should avoid using context for keeping the states locally. Instead of  conext, we can use local state there.Use of Multiple ContextsIt may be possible that we want to add multiple contexts in our application. Like holding a theme for the entire app, changing the language based on the location, performing some A/B testing, using global parameters for login or user Profile… For instance, let’s say there is a requirement to keep both Theme context and userInfo Context, the code will look like as:       ...   It’s quite possible in React to hold multiple Contexts, but this definitely hampers rendering, serving ‘n’ number of contexts in ‘m’ component and holding the updated value in each rendered component.To avoid this and to make re-rendering faster, it is suggested to make each context consumer in the tree as a separate node or into different contexts.                 And we can perform the nesting in context as:    {theme => (                    {colour => (                Theme: {theme} and colour: {colour}            )}            )} It’s worth noting that when a value of a context changes in the parent component, the child components or the components’ holding that value should be rerendered or changed. Hence, whenever there is a change in the value of provider, it will cause its consumers to re-render.ConclusionDon’t you think this concept is just amazing?? Writing a global context like theme or language or userProfile and using the data of them directly in the child or other components? Implementing these stateful logic by global preferences was never so easy, but Context made this transportation job a lot simple and achievable! Hope you find this article useful. Happy Coding!Having challenge learning to code? Let our experts help you with customized courses!
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How to use sys.argv in Python

The sys module is one of the common and frequently used modules in Python. In this article, we will walk you through how to use the sys module. We will learn about what argv[0] and sys.argv[1] are and how they work. We will then go into how to parse Command Line options and arguments, the various ways to use argv and how to pass command line arguments in Python 3.x In simple terms,Command Line arguments are a way of managing the script or program externally by providing the script name and the input parameters from command line options while executing the script. Command line arguments are not specific just to Python. These can be found in other programming languages like C, C# , C++, PHP, Java, Perl, Ruby and Shell scripting. Understanding sys.argv with examples  sys.argv is a list in Python that contains all the command-line arguments passed to the script. It is essential in Python while working with Command Line arguments. Let us take a closer look with a few examples. With the len(sys.argv) function, you can count the number of arguments. import sys print ("Number of arguments:", len(sys.argv), "arguments") print ("Argument List:", str(sys.argv)) $ python test.py arg1 arg2 arg3 Number of arguments: 4 arguments. Argument List: ['test.py', 'arg1', 'arg2', 'arg3']Module name to be used while using sys.argv To use sys.argv, you will first need to the sys module. What is argv[0]? Remember that sys.argv[0] is the name of the script. Here – Script name is sysargv.py import sys print ("This is the name of the script: ", sys.argv[0]) print ("Number of arguments: ", len(sys.argv)) print ("The arguments are: " , str(sys.argv))Output:This is the name of the script:  sysargv.py                                                                               Number of arguments:  1                                                                                                 The arguments are:  ['sysargv.py']What is "sys. argv [1]"? How does it work? When a python script is executed with arguments, it is captured by Python and stored in a list called sys.argv. So, if the below script is executed: python sample.py Hello Python Then inside sample.py, arguments are stored as: sys.argv[0] == ‘sample.py’ sys.argv[1] == ‘Hello’ sys.argv[2] == ‘Python’Here,sys.argv[0] is always the filename/script executed and sys.argv[1] is the first command line argument passed to the script . Parsing Command Line options and arguments  Python provides a module named as getopt which helps to parse command line options and arguments. Itprovides a function – getopt, whichis used for parsing the argument sequence:sys.argv. Below is the syntax: getopt.getopt(argv, shortopts, longopts=[]) argv: argument list to be passed.shortopts: String of short options as list . Options in the arguments should be followed by a colon (:).longopts: String of long options as list. Options in the arguments should be followed by an equal sign (=). import getopt import sys   first ="" last ="" argv = sys.argv[1:] try:     options, args = getopt.getopt(argv, "f:l:",                                ["first =",                                 "last ="]) except:     print("Error Message ")   for name, value in options:     if name in ['-f', '--first']:         first = value     elif name in ['-l', '--last']:         last = value   print(first + " " + last)Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python getopt_ex.py -f Knowledge -l Hut Knowledge Hut (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python getopt_ex.py --first Knowledge –last Hut Knowledge HutWhat are command line arguments? Why do we use them? Command line arguments are parameters passed to a program/script at runtime. They provide additional information to the program so that it can execute. It allows us to provide different inputs at the runtime without changing the code. Here is a script named as argparse_ex.py: import argparse parser = argparse.ArgumentParser() parser.add_argument("-n", "--name", required=True) args = parser.parse_args() print(f'Hi {args.name} , Welcome ')Here we need to import argparse package Then we need to instantiate the ArgumentParser object as parser. Then in the next line , we add the only argument, --name . We must specify either shorthand (-n) or longhand versions (--name)  where either flag could be used in the command line as shown above . This is a required argument as mentioned by required=True Output:  (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py --name Nandan  Hi Nandan , Welcome  (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py -n Nandan  Hi Nandan , Welcome The example above must have the --name or –n option, or else it will fail.(venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py --name   usage: argparse_ex.py [-h] --name NAME argparse_ex.py: error: the following arguments are required: --namePassing command line arguments in Python 3.x argv represents an array having the command line arguments of thescript . Remember that here, counting starts fromzero [0], not one (1). To use it, we first need to import sys module (import sys). The first argument, sys.argv[0], is always the name of the script and sys.argv[1] is the first argument passed to the script. Here, we need to slice the list to access all the actual command line arguments. import sys if __name__ == '__main__':     for idx, arg in enumerate(sys.argv):        print("Argument #{} is {}".format(idx, arg))     print ("No. of arguments passed is ", len(sys.argv))Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv\Scripts>python argv_count.py Knowledge Hut 21 Argument #0 is argv_count.py Argument #1 is Knowledge Argument #2 is Hut Argument #3 is 21 No. of arguments passed is  4Below script - password_gen.py is used to generate a secret password by taking password length as command line argument.import secrets , sys, os , string ''' This script generates a secret password using possible key combinations''' ''' Length of the password is passed as Command line argument as sys.argv[1]''' char = string.ascii_letters+string.punctuation+string.digits length_pwd = int(sys.argv[1])   result = "" for i in range(length_pwd):     next= secrets.SystemRandom().randrange(len(char))     result = result + char[next] print("Secret Password ==" ,result,"\n")Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv\Scripts>python password_gen.py 12 Secret Password == E!MV|,M][i*[Key takeaways Let us summarize what we've learnt so far. We have seen how to use the sys module in Python, we have looked at what areargv[0] and sys.argv[1] are and how they work, what Command Line arguments are and why we use them and how to parse Command Line options and arguments. We also dived into multiple ways to use argv and how to pass command line arguments in Python 3.xHope this mini tutorial has been helpful in explaining the usage of sys.argv and how it works in Python. Be sure to check out the rest of the tutorials on KnowledgeHut’s website and don't forget to practice with your code! 
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How to use sys.argv in Python

The sys module is one of the common and frequently... Read More

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