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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.

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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Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely heard of the Monty Python series. The programming language that it inspired continues to remain among the most popular languages. Guess why Python has consistently topped the charts of the most popular programming languages? Because of its rich environment of libraries and tools, its easy code readability and the fact that it is so easy to pick up.  You name the domain, and you will get Python libraries available, to help you out in solving problems. Right from Artificial Intelligence, Data Science, Machine Learning, Image Processing, Speech Recognition, Computer Vision and more, Python has numerous uses. These libraries and frameworks are open source and can be easily integrated with the development environment that one has.These software frameworks, the platforms which provides necessary libraries and code components, are backbones for devloping applications. Read on to see which are the top ML frameworks and libraries in Python.1. Numpy As the name implies, this is the library which supports numerical calculations and tasks. It supports array operations and basic mathematical functions on the array and other data types of Python. The basic data type of this library is ndArray object.   Numpy has many advantagesThe base data structure is N –Dimensional array. Rich functions to handle the N-dimensional array effectively. Supports integration of C, C++ and other language code fragments. Supports many functions related to linear algebra, random numbers, transforms, statistics etc. DisadvantagesNo GPU and TPU support. Cannot automatically calculate the derivatives which is required in all ML algorithms. Numpy performance goes down when high complex calculations are required. 2. PandasThis is the most useful library for data preprocessing and preparing the data for the Machine Learning algorithms. The data from various files like CSV, Excel, Data etc. can be easily read using Pandas. The data is available in a spreadsheet like area, which makes processing easy. There are three basic data structures at the core of Pandas library: Series - One-dimensional array like object containing data and label (or index). Dataframe - Spreadsheet-like data structure containing an order collection of columns. It has both a row and column index. Panel – Collection of dataframes but rarely used data structure. AdvantagesStructured data can be read easily. Great tool for handling of data. Strong functions for manipulation and preprocessing of data. Data Exploration functions help in better understanding data. Data preprocessing capabilities help in making data ready for the application of ML algorithms. Basic Plotting functions are provided for visualization of data.  Datasets can be easily joined or merged. The functions of Pandas are optimized for large datasets. DisadvantagesGetting to know the Pandas functionalities is time consuming. The syntax is complex when multiple operations are required. Support for 3D metrics is poor. Proper documentation is not available for study. 3. MatplotlibMatplotlib is an important Python library which helps in data visualization. Understanding the data is very important for a data scientist before devising any machine learning based model. This library helps in understanding the data in a visual way. Data can be visualized using various graphical methods like line graph, bar graph, pie chart etc. This is a 2D visualization library with numerous ways of visualizing data. Image SourceAdvantagesSimple and easy to learn for beginners. Integrated with Pandas for visualization of data in effective way. Various plots are provided for better understanding of data like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot etc. Forms a base for many advanced plotting libraries. Supports storing of the various graphs as images so that they can be integrated with other applications. Can plot timeseries data (with date) very easily. DisadvantagesComplex Syntax for plotting simple graphs. The code becomes lengthy and complex for visualizations. Support for plotting of categorial data is not provided. It is a 2D visualization library. When multiple fields are required to be plotted and visualized effectively, matplotlib code can become lengthy. Managing multiple figures is difficult. 4. Seaborn Visualizations are made simpler and more advanced with the help of Seaborn library. The base for Seaborn is Matplotlib. It is a boon for programmers as statistical visualizations are simplified. Image sourceAdvantagesBest high-level interface for drawing statistical graphics. Provides support for plotting of categorial data effectively. The library provides default themes and many visualization patterns. Multiple figures are automatically created. The syntax is very simple and compact. There are many methods to integrate with Pandas dataframe, making this library most useful for visualization. DisadvantagesMemory issues due to creation of multiple figures. Less customizable and flexible as compared to Matplotlib. Scalability issues. 5. Scipy   Scipy is a Scientific Python library based on Numpy. It has functions which are best suitable for Mathematics, Science and Engineering. Many libraries are provided for Image and Signal Processing, Fourier Transform, Linear Algebra, Integration and Optimization. The functions are useful for ML algorithms and programs. AdvantagesThe base library is Numpy. Many ML related functions are provided like Linear Algebra, Optimization, Compressed Sparce Data Structure etc. Useful Linear Algebra functions are available which are required for implementation of ML related algorithms. The functions can be applied with Pandas Dataframe directly. DisadvantagesComplex functions are available and domain knowledge is needed to understand and implement these functions. There are performance issues when data size increases. Many other effective alternative libraries are available with the needed functionality. 6. Scikit-Learn Scikit-Learn is a useful open access library for use to Python developers. It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.  AdvantagesIt is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib. Simple to understand and use even for novice programmers. Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented. Popular library for doing Machine Learning related tasks. Rich in Data Preprocessing and Data Sampling functions and techniques. Plethora of evaluation measures implemented to track the performance of algorithms. Very effective for quick coding and building Machine Learning Models. DisadvantagesScikit learn, as is based on Numpy, requires additional support to run on GTP and TPU Performance is an issue with size of data. Best suitable for basic Machine Learning applications. This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning. 7. NLTK Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.AdvantagesVery simple to use for processing natural language data. Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models. It is an amazing library to play with natural language using Python. It has more than 50 trained models and lexical resources like wordnet available for use. Rich discussion forums and many examples are available to discuss how to use this library effectively. DisadvantagesIt is based on string processing, which itself has many limitations. Slower as compared to other Natural Language processing libraries like Spacy.8. Keras Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms. The API provided is simple, same as Scikit-learn. Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement. Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc. Allows to create products easily supporting multiple backends Supports multi-platform use. Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development. 9. TensorFlow TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models. This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications. Advantages Developed by Google, it is one of the best deep learning frameworks. Simple Machine Learning tasks are also supported in TensorFlow. Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow. The basic unit is Tensor which is an n-dimensional array. The basic derivatives are inherently computed which helps in developing many Machine learning Models easily. The models developed are supported on CPT, TPU and GPU. Tensorboard is the effective tool for data visualization. Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning. Disadvantages Understanding Tensor and computational graphs is tedious. Computational graphs make the code complex and sometimes face performance problems. 10. Pytorch A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks. AdvantagesThe power of Pytorch lies in construction of Deep Neural Networks. Rich functions and utilities are provided to construct and use Neural Networks. Powerful when it comes to creation of production ready models. It supports GPU operations with rich math-based library functions. Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network. Provides support for Gradient based optimization which helps in scaling up the models easily to large data. Disadvantages It is a complex framework, so learning is difficult. Documentation support for learning is not readily available. Scalability may be an issue as compared to TensorFlow. 11. Theano Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU. AdvantagesIt is a fast computation library in Python. Uses native libraries like BIAS to turn the code in faster computation. Best suited to handle computations in Deep Learning algorithms. Industry standard for Deep Learning research and development. Disadvantages It is not very popular among researchers as it is one of the older frameworks. It is not as easy to use as TensorFlow.12. CNTK CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool. AdvantagesIt is a distributed open-source deep learning framework. Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models. Provides interface with C, C++ and Java to include Machine Learning models. Can be used to build reinforcement learning models as wide functions are available. Can be used to develop GAN (Generative Adversarial Networks). Provides various ways to measure the performance of the models built. High accuracy parallel computation on Multiple GPU is provided. Disadvantages Proper documentation is not available. There is inadequate community support. ConclusionPython, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool. 
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Top 12 Python Packages for Machine Learning

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The average salary of a certified MySQL DBA or backend developer with this certification is $ 66,470 per annum.Top companies and industries hiring Certified MySQL database administrators are Fiserv, IBM, HCL, Adobe, Microsoft, Apple, Accenture, Collabera, and more.Where to take Training for Certification: KnowledgeHut has a cutting-edge curriculum for those who want to become  MySQL database administrators. It has 16+ hours of instructor-led online lectures and 80+ hours of hands-on lab. Apart from that, this self-paced course has Capstone projects.Who should take the Training (roles) for Certification: Any developer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to pursue a career as a DBA or backend developer or start his/her career in database management or backend software development can opt for this certification course. There is no other prerequisite to appear for this exam or course.Course fees for Certification: $ 255Exam fee for certification: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days after the initial attempt.7. CCA Spark and Hadoop DeveloperWith the exponential growth in data, IT firms and organizations have to manage this tremendous amount of data generated. So, many companies are actively looking for Big data and Spark developers who can optimize performance. Big Data is the term used to describe enormous volumes of data. Apache Spark supports data management as it is an open-source centralized analytics engine that handles large-scale data processing.It requires prerequisite knowledge of Scala and Python. This certification also verifies and showcases your skills through Spark and Hadoop projects. Passing this certification course gives you a logo and a license to authenticate your CCA status.It comprises of topics likeLoad data from HDFS for use in Spark applicationsWrite the results back into HDFS using SparkRead and write files in a variety of file formatsPerform standard extract, transform, load (ETL) processes on data using the Spark APIUse metastore tables as an input source or an output sink for Spark applicationsUnderstand the fundamentals of querying datasets in SparkFilter data using SparkWrite queries that calculate aggregate statisticsJoin disparate datasets using SparkProduce ranked or sorted dataSupply command-line options to change your application configuration, such as increasing available memoryDemand and Benefits: Passing the CCA Spark and Hadoop Developer Exam (CCA175) by Cloudera verifies that you have all the essential skills required to get a job as a Hadoop developer and handle Big data projects. The average salary of a certified CCA Spark and Hadoop Developer with this certification is $ 74,200 per annum.Top companies and industries hiring Certified Spark and Hadoop Developers are Primus Global, IBM, Collabera, CorroHealth, Genpact, Xerox, Accenture, and more.Where to take Training for Certification: KnowledgeHut has extensive courses for those who want to become Big Data experts and want to work as Hadoop developers. It has different courses on Big Data Analytics, Apache Storm, Hadoop Administration, Apache Spark & Scala, Big Data with Hadoop, and more.Who should take the Training (roles) for Certification: Any Big Data developer, graduate & post graduate students, Hadoop developer or computer science aspirant - who wants to make a career in Big data development or start his/her career as a Big Data or Hadoop project developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification: $ 295Application fee for certification: $ 295Exam fee for certification: $ 295Retake fee for certification: Within 30 to 60 minutes of exam completion, Cloudera will send a scorecard mail with a pass or fail status. If the candidate fails the exam, then they have to wait for 30 days for another try.  Cloudera gives additional discounts on retakes.ConclusionWhether you are starting your career as a coder or are an experienced programmer looking to grow in the industry, having a certification and proper knowledge of any popular programming language is one of the most proven ways to elevate your programming career.  We trust that this article will help you to understand your area of interest. Choose the programming language you wish to make a career in, wisely. This would also depend on your pre-existing knowledge. If you aren't sure which resource will be more informative for doing your certification as per your area of interest, KnowledgeHut (https://www.knowledgehut.com/) has all the support and expert trainers who can guide you, from start to finish—that is in clearing the exam and helping you gain sound knowledge of your preferred subject.Receiving a programming certification is an added bonus which will make you stand out from the rest. Proper training from an institute such as KnowledgeHut will help you gain skills that are relevant and in demand in the industry.
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Top-Paying Programming Certifications for 2021

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Top IT Certifications for Java Developers in 2021

Programming languages are at the heart of computer science and software development. They help developers write efficient code for developing digital solutions through applications and websites. Programming helps in automating, maintaining, assembling, and measuring the processed data.  Java is one such popular programming language. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). James Gosling is the creator of Java which was earlier named Oak. Java ranks high in the top programming languages list and is one of the most extensively used software development platforms. It is well suited to developing software solutions and other innovative projects and simulations.  Since Oracle acquired Sun Microsystems in January 2010, they have been responsible for the further development of the Java platform. All the mentioned top Java certifications verify a specific expertise level and knowledge of the Java platform highlighting particular domains. Without further due, let us now dig into the top 5 Java certifications and their details. About Oracle’s Java CertificationsOrganizations and industries consider certifications as proof of knowledge, especially when the certifications are from a recognized body or firm. Aspirants and professionals looking for possibilities in the Java development domain can avail of a plethora of benefits through the certifications mentioned in this article. There are six levels of Oracle Java Certification based on job roles, skills, and responsibilities: Oracle Certified Junior Associate (OCJA) Oracle Certified Associate (OCA) Oracle Certified Professional (OCP) Oracle Certified Specialist (OCS) Oracle Certified Expert (OCE) Oracle Certified Master (OCM) Among them, the top five Java certifications that are in demand for the year 2021 are – 1. Oracle Certified Associate Java Programmer OCAJPIt is the preliminary and most basic certification provided by Oracle for Java. It helps you gain fundamental understanding of Java programming and build a foundation in Java and other general programming concepts. There are two subcategories in this certification – OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11) OCAJP8 comprises of topics like  Creating and Using Arrays Handling Exceptions Java Basics Using Loop Constructs Using Operators and Decision Constructs Working with Inheritance Working with Java Data Types Working with Methods and Encapsulation Working with Selected classes from the Java API OCAJP11 comprises of topics like Applying Encapsulation Creating and Using Methods Creating Simple Java Programs Describing and Using Objects and Classes Handling Exceptions Java Technology and the Java Development Environment Programming Abstractly Through Interfaces Reusing Implementations Through Inheritance Understanding Modules Using Operators and Decision Constructs Working with Java Arrays Working with Java Primitive Data Types and String APIs Demand and Benefits: Having an OCAJP certification verifies that the programmer or the aspirant has all the necessary and essential skills to become an expert Java developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The entry-level salary of a junior Java developer with this certification is $ 3670 per annum; when the candidate gathers two to three years of experience, the average salary hikes to $ 5430 annually.   (Source: Glassdoor) Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., and more. Where to take Training for Certification: KnowledgeHut has a fascinating course, designed for beginners in Java programming. It offers hands-on learning with 40 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to be a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 2) Oracle Certified Professional Java Programmer OCPJPIt is a professional-level certification program provided by Oracle for Java developers. It verifies the candidates' knowledge and professional expertise. Using this certification, aspirants and other hard-core Java programmers can distinguish themselves from those Java professionals who are not certified. It comes in the second level of Oracle's Java Certification list. There are two subcategories of this certification – OCPJP Java Standard Edition 8 (OCPJP 8) and  OCPJP Java Standard Edition 11 (OCPJP 11) This certification is preferable if someone has professional experience with Java or has already worked for some years in Java technology.  OCPJP8 comprises of topics like: Advanced Class Design Building Database Applications with JDBC Concurrency Exceptions and Assertions Generics and Collections Java Class Design Java File I/O (NIO.2) Java I/O Fundamentals Java Stream API Lambda Built-in Functional Interfaces Localization Use Java SE 8 Date/Time API OCPJP11 comprises of topics like: Annotations Built-in Functional Interfaces Concurrency Database Applications with JDBC Exception Handling and Assertions Functional Interface and Lambda Expressions Generics and Collections I/O (Fundamentals and NIO.2) Java Fundamentals Java Interfaces Java Stream API Lambda Operations on Streams Localization Migration to a Modular Application Parallel Systems Secure Coding in Java SE Application Services in a Modular ApplicationDemand and Benefits: Once you are a certified Professional Java Programmer (OCPJP), you can switch to better salary slabs and organizations that hire senior Java developers. This certification also helps in getting internal promotions as Java developers in different organizations and firms. The average salary of a certified professional Java developer is $ 5300 - $ 8610 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, Morgan Stanley, Chetu, Mphasis, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java developers and professionals for learning intermediate Java topics. It has hands-on learning with 32 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any Java programmer who wants to apply for a senior Java developer's role or start his/her career as a Java programmer can opt for this professional certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Oracle Certified Expert - Web Component Developer OCEWCDIt is an intermediate-level course offered by Oracle for Java web developers. The Oracle Certified Expert Web Component Developer is for web developers who want to write web applications using Java. Through this course, they can prove their expertise in developing web apps using JSP and Servlet technologies. It verifies your expertise in Servlet 3.0 and helps in creating dynamic Web content and Web services.  It comprises of topics like Understanding Java EE Architecture Managing Persistence using JPA entities and Bean Validation Implementing business logic using EJBs Using Java Message Service API Implement SOAP Services using JAX-WS and JAXB APIs Creating Java Web Applications using Servlets and JSPs Implementing REST Services using JAX-RS API Creating Java Applications using WebSockets Developing Web Applications using JSFs Securing Java EE 7 Applications Using CDI Beans Demand and Benefits: You can opt for this course once you are a certified Professional Java Programmer (OCPJP) or certified associated Java programmer. This certification course will help you get a job in organizations having rigorous work in Servlet, Java Server Page, JSF, and web microservices. The average salary of a certified professional Java developer is $ 8,850 - $ 11,930 per annum. Top companies and industries hiring Oracle Certified Web Component Developers are Amdocs, IBM, Oracle, Capgemini, SAP, Shine, Byjus, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java web developers (. It has hands-on learning with instructor-led online lectures and live projects. Apart from this, you can get online training from Oracle University as wellWho should take the Training (roles) for Certification: Any programmer or computer science aspirant who wants to settle as a Java web developer or start his/her career as a Java web content and web service developer can opt for this certification course. As a prerequisite, you have to pass the OCPJP to opt for this certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 4. Oracle Certified Professional Java Application Developer (OCPJAD)It is an advanced-level course offered by Oracle for Java application developers. The Oracle Certified Professional Java Application Developer (OCPJAD) is for software developers who want to write different applications and automation tools using Java. Through this course, developers can prove their expertise and abilities to develop and deploy applications through Java Enterprise Edition 7. OCPJAD is ideal for desktop application developers, frontend + backend app developers, software engineers, and application architects. It comprises of topics like Creating Batch API Developing CDI Beans Concepts of Concurrency Creating Java Applications with Web-Sockets Creating Java Web Applications with JSPs Developing Java Web Applications with Servlets Developing Web Applications with JSFs Implementing Business Logic with EJBs Performing REST Services with JAX-RS API Implementing SOAP Services with JAX-WS and JAXB APIs Java EE 7 system architecture Java EE 7 Security Techniques Java Message Service API Managing Persistence with JPA Entities and Bean-ValidationDemand and Benefits: Once you pass the Certified Professional Java Application Developer (OCPJAD), you can seek employment in organizations that work on critical application development and command higher salaries. This professional certification will give you exposure to develop APIs, implementing business logic using EJBs, create message services, and apply security systems. The average salary of a certified professional application developer is $ 9,800 - $ 13,910 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, NetSuite Inc., SAP, Cognizant, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity with hands-on learning exposure and live projects. Apart from this, you can get online training from Oracle University as well. Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to become a certified Java application developer or move to the specialized sector of API development using REST, security architect or software engineer can opt for this certification course. As a prerequisite, you should have passed the OCAJP certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.5. Oracle Certified Master Java Enterprise Architect (OCMJEA)Large-scale development and service firms have different critical applications and systems to develop, manage, and maintain. Such systems require full-stack developers and specialized professionals with proven skills. Such organizations and MNCs hire only highly experienced professionals and specialists who can supervise the extensive operation, architect the defects, and define & develop systems as per requirements. The Oracle Certified Master Java Enterprise Architect (OCMJEA) is one of the most prestigious Java certifications a Java developer can achieve.  It comprises of topics like Architect Enterprise Applications through Java EE Developing Applications for the Java EE 6 Developing Applications for the Java EE 7 Developing Applications with Java EE 6 on WebLogic Server 12c Java Design Patterns Java EE 6: Develop Business Components with JMS & EJBs Java EE 6: Develop Database Applications with JPA Java EE 6: Develop Web Services with JAX-WS & JAX-RS Java EE 7: New Features Java SE 7: Develop Rich Client Applications Java SE 8: Programming Java SE 8 Fundamentals Object-Oriented Analysis and Design Using UML, etc. Demand and Benefits: Once you pass the Certified Master Java Enterprise Architect course, you get the essential skills and understanding of how to execute application development on an enterprise level. Such an experienced professional gains full-stack Java development skills. They get hired with the responsibility of undertaking Java projects from the very start to their final delivery. Many Certified Master Java Enterprise Architects work as managers or senior managerial roles in industries and firms. The average salary of a certified professional application developer is $ 14,000 - $ 19,210 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are IBM, Oracle, Microsoft, HCL, Capgemini, NetSuite Inc., SAP, Cognizant, Atlassian, etc. Where to take Training for Certification: KnowledgeHut has a fascinating Java course  with hands-on learning exposure and a live project. Apart from that, a professional can train himself through ILT (Instructor-Led-in-Class), Learning Subscription, TOD (Training on Demand), LVC (Live Virtual Class), or classes delivered by Oracle Authorized Education Center . Other Oracle Authorized Partner Oracle Academy, Oracle University Training Center, or Oracle Workforce Development Program can also benefit and train you in this course.  Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to move to a senior role in the enterprise-level or want to become a manager or team lead can opt for this certification course. As a prerequisite, you need to have passed the OCPJP certification.  Course fees for Certification:  $248 Application fee for certification: $ 248 Exam fee for certification: $ 248 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. Java is an evergreen programming language and is here to stay, at least for the next couple of decades. A vast community of professionals and entry-level aspirants enjoy the benefit of this pure object-oriented, class-based, multi-paradigm, high-level programming language. Java Certification requires proper training.KnowledgeHut has the required infrastructure and quality education faculty, both online and offline, to train aspirants for these Oracle Certifications. It caters to well-structured, industry-oriented Java certification training, explicitly designed to serve the candidates according to the latest industry needs. Getting proper training from KnowledgeHut will help aspirants master core knowledge of Java plus equip themselves with the industry standards to manage large projects. 
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Top IT Certifications for Java Developers in 2021

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