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What are Python KeyError Exceptions and How to Handle Them

There are times when you have written your code but while you execute, it might not run. These types of situations occur when the input is inappropriate or you try to open a file with a wrong path or try to divide a number by zero. Due to some errors or incorrect command the output will not be displayed. This is because of errors and exceptions which are a part of the Python programming language. Learn about such concepts and gain further knowledge by joining Python Programming Course.What is Exception Handling?Python raises exceptions when it encounters errors during execution. A Python Exception is basically a construct that signals any important event, such as a run-time error.Exception Handling is the process of responding to executions during computations, which often interrupts the usual flow of executing a program. It can be performed both at the software level as part of the program and also at hardware level using built-in CPU mechanisms.Why is Exception Handling Important?Although exceptions might be irritating when they occur, they play an essential role in high level languages by acting as a friend to the user.An error at the time of execution might lead to two things— either your program will die or will display a blue screen of death. On the other hand, exceptions act as communication tools. It allows the program to answer the questions — what, why and how something goes wrong and then terminates the program in a delicate manner.In simple words, exception handling protects against uncontrollable program failures and increases the potency and efficiency of your code. If you want to master yourself in programming, the knowledge of exceptions and how to handle them is very crucial, especially in Python.What are the Errors and Exceptions in Python?Python doesn’t like errors and exceptions and displays its dissatisfaction by terminating the program abruptly.There are basically two types of errors in the Python language-Syntax Error.Errors occuring at run-time or Exceptions.Syntax ErrorsSyntax Errors, also known as parsing errors, occur when the parser identifies an incorrect statement. In simple words, syntax error occurs when the proper structure or syntax of the programming language is not followed.An example of a syntax error:>>> print( 1 / 0 )) File "<stdin>", line 1 print( 1 / 0 ))   ^SyntaxError: invalid syntaxExceptionsExceptions occur during run-time. Python raises an exception when your code has a correct syntax but it encounters a run-time issue which it is not able to handle.There are a number of defined built-in exceptions in Python which are used in specific situations. Some of the built-in exceptions are:ExceptionCause Of ErrorArithmeticErrorRaised when numerical computation fails.FloatingPointErrorRaised when floating point calculation fails.AssertionErrorRaised in case of failure of the Assert statement.ZeroDivisionErrorRaised when division or modulo by zero takes place for all numerical values.OverflowErrorRaised when result of an arithmetic operation is very large to be represented.IndexErrorRaised when an index is not found in a sequence.ImportErrorRaised when the imported module is not found.IndentationErrorRaised when indentation is not specified properly.KeyboardInterruptRaised when the user hits interrupt key.RuntimeErrorRaised when a generated error does not fall into any category.SyntaxErrorRaised when there is an error in Python syntax.IOErrorRaised when Python cannot access a file correctly on disk.KeyErrorRaised when a key is not found in a dictionary.ValueErrorRaised when an argument to a function is the right type but not in the right domain.NameErrorRaised when an identifier is not found in the local or global namespace.TypeErrorRaised when an argument to a function is not in the right type.There are another type of built-in exceptions called warnings. They are usually issued in situations where the user is alerted of some conditions. The condition does not raise an exception; rather it  terminates the program.What is a Python KeyError?Before getting into KeyError, you must know the meaning of dictionary and mapping in Python. Dictionary (dict) is an unordered collection of objects which deals with data type key. They are Python’s implementation of data structures and are also known as associative arrays. They comprise key-value pairs, in which each pair maps the key to its associated value.Dictionary is basically a data structure that maps one set of values into another and is the most common mapping in Python.Exception hierarchy of KeyError:->BaseException              ->Exception                         ->LookupError                                       ->KeyErrorA Python KeyError is raised when you try to access an invalid key in a dictionary. In simple terms, when you see a KeyError, it denotes that the key you were looking for could not be found.An example of KeyError:>>> prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} >>> prices['Eraser'] Traceback (most recent call last): File "<pyshell#1>", line 1, in <module> prices['Eraser'] KeyError: 'Eraser'Here, dictionary prices is declared with the prices of three items. The KeyError is raised when the item ‘Eraser’ is being accessed which is not present in prices.Whenever an exception is raised in Python, it is done using traceback, as you can see in the example code above. It tells why an exception is raised and what caused it.Let’s execute the same Python code from a file. This time, you will be asked to give the name of the item whose price you want to know:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') print(f'The price of {item} is {prices[item]}')You will get a traceback again but you’ll also get the information about the line from which the KeyError is raised:Get price of: Eraser Traceback (most recent call last): File "prices.py", line 5, in <module> print(f'The price of {item} is {prices[item]}') KeyError: 'Eraser'The traceback in the example above provides the following information:A KeyError was raised.The key ‘Eraser’ was not found.The line number which raised the exception along with that line.Where else will you find a Python KeyError?Although most of the time, a KeyError is raised because of an invalid key in a Python dictionary or a dictionary subclass, you may also find it in other places in the Python Standard Library, such as in a zipfile. However, it denotes the same semantic meaning of the Python KeyError, which is not finding the requested key.An example of such:>>> from zipfile import ZipFile >>> my_zip_file = ZipFile('Avengers.zip') >>> my_zip_file.getinfo('Batman')Traceback (most recent call last): File "<pyshell#1>", line 1, in <module> File "myzip.py", line 1119, in getinfo 'There is no item named %r in the archive' % name) KeyError: "There is no item named 'Batman' in the archive"In this example, the zipfile.ZipFile class is used to derive information about a ZIP archive ‘Batman’ using the getinfo() function. Here, the traceback indicates that the problem is not in your code but in the zipfile code, by showing the line which caused the problem. The exception raised here is not because of a LookUpError but rather due to the zipfile.ZipFile.getinfo()function call.When do you need to raise a Python KeyError?In Python Programming, it might be sensible at times to forcefully raise exceptions in your own code. You can usually raise an exception using the raise keyword and by calling the KeyError exception:>>> raise KeyError('Batman')Here, ‘Batman’ acts as the missing key. However, in most cases, you should provide more information about the missing key so that your next developer has a clear understanding of the problem.Conditions to raise a Python KeyError in your code:It should match the generic meaning behind the exception.A message should be displayed about the missing key along with the missing key which needs to be accessed.How to Handle a Python KeyError?The main motive of handling a Python KeyError is to stop unexpected KeyError exceptions to be raised. There are a number of number of ways of handling a KeyError exception.Using get()The get()is useful in cases where the exception is raised due to a failed dictionary LookupError. It returns either the specified key value or a default value.# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') price = prices.get(item) if price:   print(f'The price of {item} is {prices[item]}')   else:   print(f'The price of {item} is not known')This time, you’ll not get a KeyError because the get() uses a better and safer method to retrieve the price and if not found, the default value is displayed:Get price of: EraserThe price of Eraser is not knownIn this example, the variable price will either have the price of the item in the dictionary or the default value ( which is None by default ).In the example above, when the key ‘Eraser’ is not found in the dictionary, the get() returns  None by default rather than raising a KeyError. You can also give another default value as a second argument by calling get():price = prices.get(item,0)If the key is not found, it will return 0 instead of None.Checking for KeysIn some situations, the get() might not provide the correct information. If it returns None, it will mean that the key was not found or the value of the key in Python Dictionary is actually None, which might not be true in some cases. In such situations, you need to determine the existence of a key in the dictionary. You can use the if and in operator to handle such cases. It checks whether a key is present in the mapping or not by returning a boolean (True or False) value:dict = dictionary() for i in range(50):   key = i % 10     if key in dict: dict[key] += 1 else: dict[key] = 1In this case, we do not check what the value of the missing key is but rather we check whether the key is in the dictionary or not. This is a special way of handling an exception which is used rarely.This technique of handling exceptions is known as Look Before You Leap(LBYL).Using try-exceptThe try-except block is one of the best possible ways to handle the KeyError exceptions. It is also useful where the get() and the if and in operators are not supported.Let’s apply the try-except block on our earlier retrieval of prices code:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') try: print(f'The price of {item} is {prices[item]}') except KeyError: print(f'The price of {item} is not known')Here, in this example there are two cases— normal case and a backup case. try block corresponds to the normal case and except block to the backup case. If the normal case doesn’t print the name of the item and the price and raises a KeyError, the backup case prints a different statement or a message.Using try-except-elseThis is another way of handling exceptions. The try-except-else  has three blocks— try block, except block and else block.The else condition in a try-except statement is useful when the try condition doesn’t raise an exception. However, it must follow all the except conditions.Let us take our previous price retrieval code to illustrate try-except-else:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of:') try: print(f'The price of {item} is {prices[item]}') except KeyError: print(f'The price of {item} is not known') else: print(f'There is no error in the statement')First, we access an existing key in the try-except block. If the Keyerror is not raised, there are no errors. Then the else condition is executed and the statement is displayed on the screen.Using finallyThe try statement in Python can have an optional finally condition. It is used to define clean-up actions and is always executed irrespective of anything. It is generally used to release external sources.An example to show finally:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') try: print(f'The price of {item} is {prices[item]}') except KeyError: print(f'The price of {item} is not known') finally: print(f'The finally statement is executed')Remember, the finally statement will always be executed whether an exception has occurred or not.How to raise Custom Exceptions in Python?Python comprises of a number of built-in exceptions which you can use in your program. However, when you’re developing your own packages, you might need to create your own custom exceptions to increase the flexibility of your program.You can create a custom Python exception using the pre-defined class Exception:def square(x): if x<=0 or y<=0: raise Exception('x should be positive') return x * xHere, the function square calculates the square of a number. We raise an Exception if either the input number is negative or not.Disadvantages of Exception HandlingThough exception handling is very useful in catching and handling exceptions in Python, it also has several disadvantages. Some of which are as follows—It can trap only run-time errors.When you use try-except, the program will lose some performance and slow down a bit.The size of the code increases when you use multiple try, except, else and finally blocks.The concept of try-catch might be a little difficult to understand for beginners. It is useful only in exceptional error cases.Other than these disadvantages, understanding the concept of Exception Handling can ease your career as a programmer in the world of Python.ConclusionSince you have now become quite an expert in handling KeyError exceptions, you can easily debug actual errors and reduce the number of bugs in your code. Let us sum up what we’ve learnt in the article so far:Exception Handling and its importance.Different types of exceptions.Python KeyError Finding and raising a Python KeyError.Handling Python KeyError.Custom Exceptions.Demerits of Exception Handling.Exceptions are considered as the tools of communication that guard you from potential damage. If you’re clear in the understanding of exceptions, they will act as a guide to your solutions.So next time when you see a Python KeyError raised, you’ll find all the information about the location of your error and how to handle that. You will easily know how to access the key using the safer get() or the more general try-except-else blocks to control your program’s flow more efficiently and predictably.However, if you wish to know more about errors and exceptions, you can look into the full documentation of Python Standard Library’s Errors and Exceptions and Exception Handling or register for the Python Certification Course at KnowledgeHut. You can also learn more about Python Programming in this complete Python Tutorial.
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What are Python KeyError Exceptions and How to Handle Them

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What are Python KeyError Exceptions and How to Handle Them

There are times when you have written your code but while you execute, it might not run. These types of situations occur when the input is inappropriate or you try to open a file with a wrong path or try to divide a number by zero. Due to some errors or incorrect command the output will not be displayed. This is because of errors and exceptions which are a part of the Python programming language. Learn about such concepts and gain further knowledge by joining Python Programming Course.

What is Exception Handling?

Python raises exceptions when it encounters errors during execution. A Python Exception is basically a construct that signals any important event, such as a run-time error.

Exception Handling is the process of responding to executions during computations, which often interrupts the usual flow of executing a program. It can be performed both at the software level as part of the program and also at hardware level using built-in CPU mechanisms.

Why is Exception Handling Important?

Although exceptions might be irritating when they occur, they play an essential role in high level languages by acting as a friend to the user.

An error at the time of execution might lead to two things— either your program will die or will display a blue screen of death. On the other hand, exceptions act as communication tools. It allows the program to answer the questions — what, why and how something goes wrong and then terminates the program in a delicate manner.

In simple words, exception handling protects against uncontrollable program failures and increases the potency and efficiency of your code. If you want to master yourself in programming, the knowledge of exceptions and how to handle them is very crucial, especially in Python.

What are the Errors and Exceptions in Python?

Python doesn’t like errors and exceptions and displays its dissatisfaction by terminating the program abruptly.

There are basically two types of errors in the Python language-

Type of errors in python

  • Syntax Error.
  • Errors occuring at run-time or Exceptions.

Syntax Errors

Syntax Errors, also known as parsing errors, occur when the parser identifies an incorrect statement. In simple words, syntax error occurs when the proper structure or syntax of the programming language is not followed.

An example of a syntax error:

>>> print( 1 / 0 ))
File "<stdin>", line 1
print( 1 / 0 ))  
    ^

SyntaxError: invalid syntax

Exceptions

Exceptions occur during run-time. Python raises an exception when your code has a correct syntax but it encounters a run-time issue which it is not able to handle.

There are a number of defined built-in exceptions in Python which are used in specific situations. Some of the built-in exceptions are:

ExceptionCause Of Error
ArithmeticErrorRaised when numerical computation fails.
FloatingPointErrorRaised when floating point calculation fails.
AssertionErrorRaised in case of failure of the Assert statement.
ZeroDivisionErrorRaised when division or modulo by zero takes place for all numerical values.
OverflowErrorRaised when result of an arithmetic operation is very large to be represented.
IndexErrorRaised when an index is not found in a sequence.
ImportErrorRaised when the imported module is not found.
IndentationErrorRaised when indentation is not specified properly.
KeyboardInterruptRaised when the user hits interrupt key.
RuntimeErrorRaised when a generated error does not fall into any category.
SyntaxErrorRaised when there is an error in Python syntax.
IOErrorRaised when Python cannot access a file correctly on disk.
KeyErrorRaised when a key is not found in a dictionary.
ValueErrorRaised when an argument to a function is the right type but not in the right domain.
NameErrorRaised when an identifier is not found in the local or global namespace.
TypeErrorRaised when an argument to a function is not in the right type.

There are another type of built-in exceptions called warnings. They are usually issued in situations where the user is alerted of some conditions. The condition does not raise an exception; rather it  terminates the program.

What is a Python KeyError?

Before getting into KeyError, you must know the meaning of dictionary and mapping in Python. 

Dictionary (dict) is an unordered collection of objects which deals with data type key. They are Python’s implementation of data structures and are also known as associative arrays. They comprise key-value pairs, in which each pair maps the key to its associated value.

Dictionary is basically a data structure that maps one set of values into another and is the most common mapping in Python.

Exception hierarchy of KeyError:

->BaseException

              ->Exception

                         ->LookupError

                                       ->KeyError

A Python KeyError is raised when you try to access an invalid key in a dictionary. In simple terms, when you see a KeyError, it denotes that the key you were looking for could not be found.

An example of KeyError:

>>> prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25}
>>> prices['Eraser']
Traceback (most recent call last):
File "<pyshell#1>", line 1, in <module>
prices['Eraser']
KeyError: 'Eraser'

Here, dictionary prices is declared with the prices of three items. The KeyError is raised when the item ‘Eraser’ is being accessed which is not present in prices.

Whenever an exception is raised in Python, it is done using traceback, as you can see in the example code above. It tells why an exception is raised and what caused it.

Let’s execute the same Python code from a file. This time, you will be asked to give the name of the item whose price you want to know:

# prices.py
prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25}
item = input('Get price of: ')
print(f'The price of {item} is {prices[item]}')

You will get a traceback again but you’ll also get the information about the line from which the KeyError is raised:

Get price of: Eraser
Traceback (most recent call last):
 File "prices.py", line 5, in <module>
  print(f'The price of {item} is {prices[item]}')
KeyError: 'Eraser'

The traceback in the example above provides the following information:

  1. A KeyError was raised.
  2. The key ‘Eraser’ was not found.
  3. The line number which raised the exception along with that line.

Where else will you find a Python KeyError?

Although most of the time, a KeyError is raised because of an invalid key in a Python dictionary or a dictionary subclass, you may also find it in other places in the Python Standard Library, such as in a zipfile. However, it denotes the same semantic meaning of the Python KeyError, which is not finding the requested key.

An example of such:

>>> from zipfile import ZipFile
>>> my_zip_file = ZipFile('Avengers.zip')
>>> my_zip_file.getinfo('Batman')
Traceback (most recent call last):
File "<pyshell#1>", line 1, in <module>
File "myzip.py", line 1119, in getinfo
'There is no item named %r in the archive' % name)
KeyError: "There is no item named 'Batman' in the archive"

In this example, the zipfile.ZipFile class is used to derive information about a ZIP archive ‘Batman’ using the getinfo() function. 

Here, the traceback indicates that the problem is not in your code but in the zipfile code, by showing the line which caused the problem. The exception raised here is not because of a LookUpError but rather due to the zipfile.ZipFile.getinfo()function call.

When do you need to raise a Python KeyError?

In Python Programming, it might be sensible at times to forcefully raise exceptions in your own code. You can usually raise an exception using the raise keyword and by calling the KeyError exception:

>>> raise KeyError('Batman')

Here, ‘Batman’ acts as the missing key. However, in most cases, you should provide more information about the missing key so that your next developer has a clear understanding of the problem.

Conditions to raise a Python KeyError in your code:

  • It should match the generic meaning behind the exception.
  • A message should be displayed about the missing key along with the missing key which needs to be accessed.

How to Handle a Python KeyError?

The main motive of handling a Python KeyError is to stop unexpected KeyError exceptions to be raised. There are a number of number of ways of handling a KeyError exception.

Using get()

The get()is useful in cases where the exception is raised due to a failed dictionary LookupError. It returns either the specified key value or a default value.

# prices.py
prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25}
item = input('Get price of: ')
price = prices.get(item)
if price:
  print(f'The price of {item} is {prices[item]}')
  else:
  print(f'The price of {item} is not known')

This time, you’ll not get a KeyError because the get() uses a better and safer method to retrieve the price and if not found, the default value is displayed:

Get price of: Eraser

The price of Eraser is not known

In this example, the variable price will either have the price of the item in the dictionary or the default value ( which is None by default ).

In the example above, when the key ‘Eraser’ is not found in the dictionary, the get() returns  None by default rather than raising a KeyError. You can also give another default value as a second argument by calling get():

price = prices.get(item,0)

If the key is not found, it will return 0 instead of None.

Checking for Keys

In some situations, the get() might not provide the correct information. If it returns None, it will mean that the key was not found or the value of the key in Python Dictionary is actually None, which might not be true in some cases. In such situations, you need to determine the existence of a key in the dictionary. 

You can use the if and in operator to handle such cases. It checks whether a key is present in the mapping or not by returning a boolean (True or False) value:

dict = dictionary()
for i in range(50):
  key = i % 10
    if key in dict:
dict[key] += 1
else:
dict[key] = 1

In this case, we do not check what the value of the missing key is but rather we check whether the key is in the dictionary or not. This is a special way of handling an exception which is used rarely.

This technique of handling exceptions is known as Look Before You Leap(LBYL).

Using try-except

The try-except block is one of the best possible ways to handle the KeyError exceptions. It is also useful where the get() and the if and in operators are not supported.

Let’s apply the try-except block on our earlier retrieval of prices code:

# prices.py
prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25}
item = input('Get price of: ')

try:
print(f'The price of {item} is {prices[item]}')
except KeyError:
print(f'The price of {item} is not known')

Here, in this example there are two cases— normal case and a backup case. try block corresponds to the normal case and except block to the backup case. If the normal case doesn’t print the name of the item and the price and raises a KeyError, the backup case prints a different statement or a message.

Using try-except-else

This is another way of handling exceptions. The try-except-else  has three blocks— try block, except block and else block.

The else condition in a try-except statement is useful when the try condition doesn’t raise an exception. However, it must follow all the except conditions.

Let us take our previous price retrieval code to illustrate try-except-else:

# prices.py
prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25}
item = input('Get price of:')
try:
print(f'The price of {item} is {prices[item]}')
except KeyError:
print(f'The price of {item} is not known')
else:
print(f'There is no error in the statement')

First, we access an existing key in the try-except block. If the Keyerror is not raised, there are no errors. Then the else condition is executed and the statement is displayed on the screen.

Using finally

The try statement in Python can have an optional finally condition. It is used to define clean-up actions and is always executed irrespective of anything. It is generally used to release external sources.

An example to show finally:

# prices.py
prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25}
item = input('Get price of: ')
try:
print(f'The price of {item} is {prices[item]}')
except KeyError:
print(f'The price of {item} is not known')
finally:
print(f'The finally statement is executed')

Remember, the finally statement will always be executed whether an exception has occurred or not.

How to raise Custom Exceptions in Python?

Python comprises of a number of built-in exceptions which you can use in your program. However, when you’re developing your own packages, you might need to create your own custom exceptions to increase the flexibility of your program.

You can create a custom Python exception using the pre-defined class Exception:

def square(x):
if x<=0 or y<=0:
raise Exception('x should be positive')
return x * x

Here, the function square calculates the square of a number. We raise an Exception if either the input number is negative or not.

Disadvantages of Exception Handling

Though exception handling is very useful in catching and handling exceptions in Python, it also has several disadvantages. Some of which are as follows—

  • It can trap only run-time errors.
  • When you use try-except, the program will lose some performance and slow down a bit.
  • The size of the code increases when you use multiple try, except, else and finally blocks.
  • The concept of try-catch might be a little difficult to understand for beginners. 
  • It is useful only in exceptional error cases.

Other than these disadvantages, understanding the concept of Exception Handling can ease your career as a programmer in the world of Python.

Conclusion

Since you have now become quite an expert in handling KeyError exceptions, you can easily debug actual errors and reduce the number of bugs in your code. 

Let us sum up what we’ve learnt in the article so far:

  • Exception Handling and its importance.
  • Different types of exceptions.
  • Python KeyError 
  • Finding and raising a Python KeyError.
  • Handling Python KeyError.
  • Custom Exceptions.
  • Demerits of Exception Handling.

Exceptions are considered as the tools of communication that guard you from potential damage. If you’re clear in the understanding of exceptions, they will act as a guide to your solutions.

So next time when you see a Python KeyError raised, you’ll find all the information about the location of your error and how to handle that. You will easily know how to access the key using the safer get() or the more general try-except-else blocks to control your program’s flow more efficiently and predictably.

However, if you wish to know more about errors and exceptions, you can look into the full documentation of Python Standard Library’s Errors and Exceptions and Exception Handling or register for the Python Certification Course at KnowledgeHut. You can also learn more about Python Programming in this complete Python Tutorial.

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

abhinandan 05 Aug 2019

Thanks for providing such a useful article priyankur, I love the time that I have spent on this article.

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In this article we will see:Why is it important to know the ways to round numbersHow to use various strategies to round numbersHow data is affected by rounding itHow to use NumPy arrays and Pandas DataFrames to round numbersLet us first learn about Python’s built-in rounding process.About Python’s Built-in round() FunctionPython Programming offers a built-in round() function which rounds off a number to the given number of digits and makes rounding of numbers easier. The function round() accepts two numeric arguments, n and n digits and then returns the number n after rounding it to ndigits. If the number of digits are not provided for round off, the function rounds off the number n to the nearest integer.Suppose, you want to round off a number, say 4.5. It will be rounded to the nearest whole number which is 5. However, the number 4.74 will be rounded to one decimal place to give 4.7.It is important to quickly and readily round numbers while you are working with floats which have many decimal places. The inbuilt Python function round() makes it simple and easy.Syntaxround(number, number of digits)The parameters in the round() function are:number - number to be roundednumber of digits (Optional) - number of digits up to which the given number is to be rounded.The second parameter is optional. In case, if it is missing then round() function returns:For an integer, 12, it rounds off to 12For a decimal number, if the last digit after the decimal point is >=5 it will round off to the next whole number, and if =5 print(round(5.476, 2))     # when the (ndigit+1)th digit is  1 print(round("x", 2)) TypeError: type str doesn't define __round__ methodAnother example,print(round(1.5)) print(round(2)) print(round(2.5))The output will be:2 2 2The function round() rounds 1.5 up to 2, and 2.5 down to 2. This is not a bug, the round() function behaves this way. In this article you will learn a few other ways to round a number. Let us look at the variety of methods to round a number.Diverse Methods for RoundingThere are many ways to round a number with its own advantages and disadvantages. Here we will learn some of the techniques to rounding a number.TruncationTruncation, as the name means to shorten things. It is one of the simplest methods to round a number which involves truncating a number to a given number of digits. In this method, each digit after a given position is replaced with 0. Let us look into some examples.ValueTruncated ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34The truncate() function can be used for positive as well as negative numbers:>>> truncate(19.5) 19.0 >>> truncate(-2.852, 1) -2.8 >>> truncate(2.825, 2) 2.82The truncate() function can also be used to truncate digits towards the left of the decimal point by passing a negative number.>>> truncate(235.7, -1) 230.0 >>> truncate(-1936.37, -3) -1000.0When a positive number is truncated, we are basically rounding it down. Similarly, when we truncate a negative number, the number is rounded up. Let us look at the various rounding methods.Rounding UpThere is another strategy called “rounding up” where a number is rounded up to a specified number of digits. For example:ValueRound Up ToResult12.345Tens place2018.345Ones place1918.345Tenths place18.418.345Hundredths place18.35The term ceiling is used in mathematics to explain the nearest integer which is greater than or equal to a particular given number. In Python, for “rounding up” we use two functions namely,ceil() function, andmath() functionA non-integer number lies between two consecutive integers. For example, considering a number 5.2, this will lie between 4 and 5. Here, ceiling is the higher endpoint of the interval, whereas floor is the lower one. Therefore, ceiling of 5.2 is 5, and floor of 5.2 is 4. However, the ceiling of 5 is 5.In Python, the function to implement the ceiling function is the math.ceil() function. It always returns the closest integer which is greater than or equal to its input.>>> import math >>> math.ceil(5.2) 6 >>> math.ceil(5) 5 >>> math.ceil(-0.5) 0If you notice you will see that the ceiling of -0.5 is 0, and not -1.Let us look into a short code to implement the “rounding up” strategy using round_up() function:def round_up(n, decimals=0):     multiplier = 10 ** decimals     return math.ceil(n * multiplier) / multiplierLet’s look at how round_up() function works with various inputs:>>> round_up(3.1) 4.0 >>> round_up(3.23, 1) 3.3 >>> round_up(3.543, 2) 3.55You can pass negative values  to decimals, just like we did in truncation.>>> round_up(32.45, -1) 40.0 >>> round_up(3352, -2) 3400You can follow the diagram below to understand round up and round down. Round up to the right and down to the left.Rounding up always rounds a number to the right on the number line, and rounding down always rounds a number to the left on the number line.Rounding DownSimilar to rounding up we have another strategy called rounding down whereValueRounded Down ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34In Python, rounding down can be implemented using a similar algorithm as we truncate or round up. Firstly you will have to shift the decimal point and then round an integer. Lastly shift the decimal point back.math.ceil() is used to round up to the ceiling of the number once the decimal point is shifted. For “rounding down” we first need to round the floor of the number once the decimal point is shifted.>>> math.floor(1.2) 1 >>> math.floor(-0.5) -1Here’s the definition of round_down():def round_down(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n * multiplier) / multiplierThis is quite similar to round_up() function. Here we are using math.floor() instead of math.ceil().>>> round_down(1.5) 1 >>> round_down(1.48, 1) 1.4 >>> round_down(-0.5) -1Rounding a number up or down has extreme effects in a large dataset. After rounding up or down, you can actually remove a lot of precision as well as alter computations.Rounding Half UpThe “rounding half up” strategy rounds every number to the nearest number with the specified precision, and breaks ties by rounding up. Here are some examples:ValueRound Half Up ToResult19.825Tens place1019.825Ones place2019.825Tenths place19.819.825Hundredths place19.83In Python, rounding half up strategy can be implemented by shifting the decimal point to the right by the desired number of places. In this case you will have to determine whether the digit after the shifted decimal point is less than or greater than equal to 5.You can add 0.5 to the value which is shifted and then round it down with the math.floor() function.def round_half_up(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n*multiplier + 0.5) / multiplierIf you notice you might see that round_half_up() looks similar to round_down. The only difference is to add 0.5 after shifting the decimal point so that the result of rounding down matches with the expected value.>>> round_half_up(19.23, 1) 19.2 >>> round_half_up(19.28, 1) 19.3 >>> round_half_up(19.25, 1) 19.3Rounding Half DownIn this method of rounding, it rounds to the nearest number similarly like “rounding half up” method, the difference is that it breaks ties by rounding to the lesser of the two numbers. Here are some examples:ValueRound Half Down ToResult16.825Tens place1716.825Ones place1716.825Tenths place16.816.825Hundredths place16.82In Python, “rounding half down” strategy can be implemented by replacing math.floor() in the round_half_up() function with math.ceil() and then by subtracting 0.5 instead of adding:def round_half_down(n, decimals=0):     multiplier = 10 ** decimals return math.ceil(n*multiplier - 0.5) / multiplierLet us look into some test cases.>>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0 >>> round_half_down(2.25, 1) 2.2In general there are no bias for both round_half_up() and round_half_down(). However, rounding of data with more number of ties results in bias. Let us consider an example to understand better.>>> data = [-2.15, 1.45, 4.35, -12.75]Let us compute the mean of these numbers:>>> statistics.mean(data) -2.275Now let us compute the mean on the data after rounding to one decimal place with round_half_up() and round_half_down():>>> rhu_data = [round_half_up(n, 1) for n in data] >>> statistics.mean(rhu_data) -2.2249999999999996 >>> rhd_data = [round_half_down(n, 1) for n in data] >>> statistics.mean(rhd_data) -2.325The round_half_up() function results in a round towards positive infinity bias, and round_half_down() results in a round towards negative infinity bias.Rounding Half Away From ZeroIf you have noticed carefully while going through round_half_up() and round_half_down(), neither of the two is symmetric around zero:>>> round_half_up(1.5) 2.0 >>> round_half_up(-1.5) -1.0 >>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0In order to introduce symmetry, you can always round a tie away from zero. The table mentioned below illustrates it clearly:ValueRound Half Away From Zero ToResult16.25Tens place2016.25Ones place1616.25Tenths place16.3-16.25Tens place-20-16.25Ones place-16-16.25Tenths place-16.3The implementation of “rounding half away from zero” strategy on a number n is very simple. All you need to do is start as usual by shifting the decimal point to the right a given number of places and then notice the digit d immediately to the right of the decimal place in this new number. Here, there are four cases to consider:If n is positive and d >= 5, round upIf n is positive and d < 5, round downIf n is negative and d >= 5, round downIf n is negative and d < 5, round upAfter rounding as per the rules mentioned above, you can shift the decimal place back to the left.There is a question which might come to your mind - How do you handle situations where the number of positive and negative ties are drastically different? The answer to this question brings us full circle to the function that deceived us at the beginning of this article: Python’s built-in  round() function.Rounding Half To EvenThere is a way to mitigate rounding bias while you are rounding values in a dataset. You can simply round ties to the nearest even number at the desired precision. Let us look at some examples:ValueRound Half To Even ToResult16.255Tens place2016.255Ones place1616.255Tenths place16.216.255Hundredths place16.26To prove that round() really does round to even, let us try on a few different values:>>> round(4.5) 4 >>> round(3.5) 4 >>> round(1.75, 1) 1.8 >>> round(1.65, 1) 1.6The Decimal ClassThe  decimal module in Python is one of those features of the language which you might not be aware of if you have just started learning Python. Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – except from the decimal arithmetic specification. Some of the benefits of the decimal module are mentioned below -Exact decimal representation: 0.1 is actually 0.1, and 0.1 + 0.1 + 0.1 - 0.3 returns 0, as expected.Preservation of significant digits: When you add 1.50 and 2.30, the result is 3.80 with the trailing zero maintained to indicate significance.User-alterable precision: The default precision of the decimal module is twenty-eight digits, but this value can be altered by the user to match the problem at hand.Let us see how rounding works in the decimal module.>>> import decimal >>> decimal.getcontext() Context(     prec=28,     rounding=ROUND_HALF_EVEN,     Emin=-999999,     Emax=999999,     capitals=1,     clamp=0,     flags=[],     traps=[         InvalidOperation,         DivisionByZero,         Overflow     ] )The function decimal.getcontext() returns a context object which represents the default context of the decimal module. It also includes the default precision and the default rounding strategy.In the above example, you will see that the default rounding strategy for the decimal module is ROUND_HALF_EVEN. It allows to align with the built-in round() functionLet us create a new Decimal instance by passing a string containing the desired value and declare a number using the decimal module’s Decimal class.>>> from decimal import Decimal >>> Decimal("0.1") Decimal('0.1')You may create a Decimal instance from a floating-point number but in that case, a floating-point representation error will be introduced. For example, this is what happens when you create a Decimal instance from the floating-point number 0.1>>> Decimal(0.1) Decimal('0.1000000000000000055511151231257827021181583404541015625')You may create Decimal instances from strings containing the decimal numbers you need in order to maintain exact precision.Rounding a Decimal using the .quantize() method:>>> Decimal("1.85").quantize(Decimal("1.0")) Decimal('1.8')The Decimal("1.0") argument in .quantize() allows to determine the number of decimal places in order to round the number. As 1.0 has one decimal place, the number 1.85 rounds to a single decimal place. Rounding half to even is the default strategy, hence the result is 1.8.Decimal class:>>> Decimal("2.775").quantize(Decimal("1.00")) Decimal('2.78')Decimal module provides another benefit. After performing arithmetic the rounding is taken care of automatically and also the significant digits are preserved.>>> decimal.getcontext().prec = 2 >>> Decimal("2.23") + Decimal("1.12") Decimal('3.4')To change the default rounding strategy, you can set the decimal.getcontect().rounding property to any one of several  flags. The following table summarizes these flags and which rounding strategy they implement:FlagRounding Strategydecimal.ROUND_CEILINGRounding updecimal.ROUND_FLOORRounding downdecimal.ROUND_DOWNTruncationdecimal.ROUND_UPRounding away from zerodecimal.ROUND_HALF_UPRounding half away from zerodecimal.ROUND_HALF_DOWNRounding half towards zerodecimal.ROUND_HALF_EVENRounding half to evendecimal.ROUND_05UPRounding up and rounding towards zeroRounding NumPy ArraysIn Data Science and scientific computation, most of the times we store data as a  NumPy array. One of the most powerful features of NumPy is the use of  vectorization and broadcasting to apply operations to an entire array at once instead of one element at a time.Let’s generate some data by creating a 3×4 NumPy array of pseudo-random numbers:>>> import numpy as np >>> np.random.seed(444) >>> data = np.random.randn(3, 4) >>> data array([[ 0.35743992,  0.3775384 ,  1.38233789,  1.17554883],        [-0.9392757 , -1.14315015, -0.54243951, -0.54870808], [ 0.20851975, 0.21268956, 1.26802054, -0.80730293]])Here, first we seed the np.random module to reproduce the output easily. Then a 3×4 NumPy array of floating-point numbers is created with np.random.randn().Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.To round all of the values in the data array, pass data as the argument to the  np.around() function. The desired number of decimal places is set with the decimals keyword argument. In this case, round half to even strategy is used similar to Python’s built-in round() function.To round the data in your array to integers, NumPy offers several options which are mentioned below:numpy.ceil()numpy.floor()numpy.trunc()numpy.rint()The np.ceil() function rounds every value in the array to the nearest integer greater than or equal to the original value:>>> np.ceil(data) array([[ 1.,  1.,  2.,  2.],        [-0., -1., -0., -0.], [ 1., 1., 2., -0.]])Look at the code carefully, we have a new number! Negative zero! Let us now take a look at Pandas library, widely used in Data Science with Python.Rounding Pandas Series and DataFramePandas has been a game-changer for data analytics and data science. The two main data structures in Pandas are Dataframe and Series. Dataframe works like an Excel spreadsheet whereas you can consider Series to be columns in a spreadsheet. Series.round() and DataFrame.round() methods. Let us look at an example.Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.>>> import pandas as pd >>> # Re-seed np.random if you closed your REPL since the last example >>> np.random.seed(444) >>> series = pd.Series(np.random.randn(4)) >>> series 0    0.357440 1    0.377538 2    1.382338 3    1.175549 dtype: float64 >>> series.round(2) 0    0.36 1    0.38 2    1.38 3    1.18 dtype: float64 >>> df = pd.DataFrame(np.random.randn(3, 3), columns=["A", "B", "C"]) >>> df           A         B         C 0 -0.939276 -1.143150 -0.542440 1 -0.548708  0.208520  0.212690 2  1.268021 -0.807303 -3.303072 >>> df.round(3)        A      B      C 0 -0.939 -1.143 -0.542 1 -0.549  0.209  0.213 2  1.268 -0.807 -3.303 The DataFrame.round() method can also accept a dictionary or a Series, to specify a different precision for each column. For instance, the following examples show how to round the first column of df to one decimal place, the second to two, and the third to three decimal places: >>> # Specify column-by-column precision with a dictionary >>> df.round({"A": 1, "B": 2, "C": 3})      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 >>> # Specify column-by-column precision with a Series >>> decimals = pd.Series([1, 2, 3], index=["A", "B", "C"]) >>> df.round(decimals)      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 If you need more rounding flexibility, you can apply NumPy's floor(), ceil(), and print() functions to Pandas Series and DataFrame objects: >>> np.floor(df)      A    B    C 0 -1.0 -2.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -4.0 >>> np.ceil(df)      A    B    C 0 -0.0 -1.0 -0.0 1 -0.0  1.0  1.0 2  2.0 -0.0 -3.0 >>> np.rint(df)      A    B    C 0 -1.0 -1.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -3.0 The modified round_half_up() function from the previous section will also work here: >>> round_half_up(df, decimals=2)       A     B     C 0 -0.94 -1.14 -0.54 1 -0.55  0.21  0.21 2 1.27 -0.81 -3.30Best Practices and ApplicationsNow that you have come across most of the rounding techniques, let us learn some of the best practices to make sure we round numbers in the correct way.Generate More Data and Round LaterSuppose you are dealing with a large set of data, storage can be a problem at times. For example, in an industrial oven you would want to measure the temperature every ten seconds accurate to eight decimal places, using a temperature sensor. These readings will help to avoid large fluctuations which may lead to failure of any heating element or components. We can write a Python script to compare the readings and check for large fluctuations.There will be a large number of readings as they are being recorded each and everyday. You may consider to maintain three decimal places of precision. But again, removing too much precision may result in a change in the calculation. However, if you have enough space, you can easily store the entire data at full precision. With less storage, it is always better to store at least two or three decimal places of precision which are required for calculation.In the end, once you are done computing the daily average of the temperature, you may calculate it to the maximum precision available and finally round the result.Currency Exchange and RegulationsWhenever we purchase an item from a particular place, the tax amount paid against the amount of the item depends largely on geographical factors. An item which costs you $2 may cost you less (say $1.8)  if you buy the same item from a different state. It is due to regulations set forth by the local government.In another case, when the minimum unit of currency at the accounting level in a country is smaller than the lowest unit of physical currency, Swedish rounding is done. You can find a list of such rounding methods used by various countries if you look up on the internet.If you want to design any such software for calculating currencies, keep in mind to check the local laws and regulations applicable in your present location.Reduce errorAs you are rounding numbers in a large datasets used in complex computations, your primary concern should be to limit the growth of the error due to rounding.SummaryIn this article we have seen a few methods to round numbers, out of those “rounding half to even” strategy minimizes rounding bias the best. We are lucky to have Python, NumPy, and Pandas already have built-in rounding functions to use this strategy. Here, we have learned about -Several rounding strategies, and how to implement in pure Python.Every rounding strategy inherently introduces a rounding bias, and the “rounding half to even” strategy mitigates this bias well, most of the time.You can round NumPy arrays and Pandas Series and DataFrame objects.If you enjoyed reading this article and found it to be interesting, leave a comment. To learn more about rounding numbers and other features of Python, join our Python certification course.
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How to Round Numbers 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, 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 use Split in Python

The split function is a string manipulation tool in Python.  A string is a collection or array of characters in a sequence that is written inside single quotes, double quotes, or triple quotes; a character ‘a’ in Python is also considered a string value with length 1. The split function is used when we need to break down a large string into smaller strings.Strings represent Unicode character values and are mutable in nature which means the value of a string cannot be altered after it has been declared.  An example of declaring and displaying a string in Python: name = “Python”  print(name[0]) Although we cannot change a string after the declaration, we can split a string into different strings using a variety of different ways in Python.  In this article, we will take a deeper dive and understand how to use Split is in Python. We will begin by understanding what the Split function does, what the need is for such a function and how we work with this function. We will then take a look at Split parameters in Python and the different ways of using the Split function. What is Split in Python? If you have worked on the concatenation of strings that are used to merge or combine different strings into one, the split function performs just the opposite of it. The function scans through a string and separates it when it encounters a separator which has been specified before.  However, if the function does not find any defined separator, it uses white space by default.  The syntax of the Split function is as follows: string.split(separator,max) The separator is a character that has been pre-defined and it gets placed between each variable in the output. The split function depends on the value of the separator variable. What is the need for Split function? The Split function returns a list of words after separating the string or line with the help of a delimiter string such as the comma ( , ) character. Some of the merits of using Split function in Python are listed as follows: It is useful in situations where you need to break down a large string into smaller strings. If the separator is not present within the split function, the white spaces are considered as separators. The split function helps to analyze and deduce conclusions easily. It is also useful in decoding strings encrypted in some manner.  How to work with Split function? Strings variables in Python contain numeric and alphanumeric data which are used to store data directories or display different messages. They are very useful tools for programmers working in Python.  The .split() method is a beneficial tool for manipulating strings. It returns a list of strings after the main string is separated by a delimiter. The method returns one or more new strings and the substrings also get returned in the list datatype.  A simple example of the split function is as follows: x = ‘red,orange,yellow’  x.split(“,”)  [‘red’, ‘orange’, ‘yellow’] Here, we have declared a string variable x with three strings. When the split function is implemented with a comma ( , ) as a separator, the strings get separated with commas in between them.  What are Split parameters in Python? The Split function analyses through a string and separates it whenever the program comes across a pre-defined separator. It depends on mainly three different parameters to optimize the execution of the program: Separator - It instructs Python where to break the string. It works as a delimiter and the string is separated depending upon the pre-defined separator. It is optional which means if the separator is not specified in split, the function uses white space as the default separator. However, if the separator is specified as a string value, the output will be an empty string. Maxsplit-  It specifies the number of times the string can be broken up. It is also optional and it’s default value is -1 which denotes that there are no limits on the number of times a string can be split. If the maxsplit is not defined in the split function, the entire string is scanned and Python separates it whenever a delimiter is encountered.    Return - It returns a list of strings after the split function breaks the string by the specified separator. What are the different ways of using the Split function? Python consists of several different ways by which we can implement the Split function. The different techniques are explained below: Python consists of several different ways by which we can implement the Split function. The different techniques are explained below: Splitting String by Space The split() method in Python splits the string on whitespace if no argument is specified in the function. An example of splitting a string without an argument is shown below: str = “Python is cool”  print(str.split()) The output of the above code is as follows: [‘Python’, ‘is’, ‘cool’] In the example above, we have declared variable str with a string value. You can see that we have not defined any arguments in the Split function, so the string gets split with whitespaces.  Splitting String on first occurrence When we split a string based on the first occurrence of a character, it results in two substrings – the first substring contains the characters before the separator and the second substring contains the character after the separator.  An example of splitting a string on the first occurrence of a character is shown below: str = “abcabc”  print(str.split(c)) The output of the above code is as follows: [‘ab’, ‘abc’] Here, we have declared str with a string value “abcabc”. The split function is implemented with separator as “c” and maxsplit value is taken as 1. Whenever the program encounters “c” in the string, it separates the string into two substrings  – the first string contains characters before “c” and the second one contains characters after “c”.  Splitting a file into a list When you want to split a file into a list, the result turns out to be another list wherein each of the elements is a line of your file. Consider you have a file that contains two lines “First line\nSecond Line”. The resulting output of the split function will be [ “First Line”, “Second line”]. You can perform a file split using the Python in-built function splitlines(). Consider you have a file named “sample.txt” which contains two lines with two strings in each line respectively – “Hi there”, “You are learning Python”. An example of splitting “sample.txt” into a list is shown below: f = open(“sample.txt”, “r”)  info = f.read()  print(info.splitlines())  f.close() The output of the above code is as follows: [‘Hi there’, ‘You are learning Python’] We have a file “sample.txt” which is opened in read (“r”) mode using the open() function. Then, we have called f.read() which returns the entire file as a string. The splitlines() function is implemented and it splits the file into two different substrings which are the two lines contained in “sample.txt”. Splitting a String by newline character (\n) You can split a string using the newline character (\n) in Python. We will take a string which will be separated by the newline character and then split the string. The newline character will act as the separator in the Split function.  An example of splitting a string by newline character is shown below: str = “Welcome\nto\nPython\nSplit”  print(str.split(‘\n’)) The output of the above code is as follows: [‘Welcome’, ‘to’, ‘Python’, ‘Split’] Here, we have declared a variable str with a string that contains newline characters (\n) in between the original string.The Split function is implemented with “\n”  as the separator. Whenever the function sees a newline character, it separates the string into substrings.  You can also perform split by newline character with the help of the splitlines() function. Splitting a String by tab (\t) Tabs are considered as escape characters “\t” in text (.txt) files. When we split a string by tabs, the Split function separates the string at each tab and the result is a list of substrings. The escape character “\t” is used as the separator in the Split function. An example of splitting a string by tab is shown below: str = “Python\tis\ta\tscripting\tlanguage”  print(str.split(“\t”)) The output of the above code is as follows: ['Python', 'is', 'a', 'scripting', 'language'] Here, the variable str is declared with a string with tabs (“\t”). The Split function is executed with “\t” as the separator. Whenever the function finds an escape character, it splits the string and the output comes out to be a list of substrings. Splitting a String by comma (,) We can also split a string by commas (“,”) where commas act as the delimiter in the Split function. The result is a list of strings that are contained in between the commas in the original string.  An example of splitting a string by commas is shown below: str = “Python,was,released,in,1991”  print(str.split(“,”)) The output of the above code is as follows: ['Python', 'was', 'released', 'in', '1991'] Here, the variable str is declared with a string with commas (“,”)  in between them. The Split function is implemented with “,”  as the separator. Whenever the function sees a comma character, it separates the string and the output is a list of substrings between the commas in str. Splitting a String with multiple delimiters You can split a string using multiple delimiters by putting different characters as separator in the Split function. A delimiter is one or more characters in a sequence that are used to denote the bounds between regions in a text. A comma character (“,”) or a colon (“:”) is an example of a delimiter. A string with multiple delimiters can be split using the re.split() function. An example of splitting a string with multiple delimiters is shown below: import re  str = 'Python\nis; an*easy\nlanguage'  print(re.split('; |, |\*|\n',str)) The output of the above code is as follows: ['Python', 'is', 'an', 'easy', 'language'] In the example above, we import the built-in module re which imports the libraries and functions of Regular Expressions. The variable str is declared with a string with multiple delimiters like newline (\n), semicolon (;), or an asterisk (*). There.split() function is implemented with different delimiters as separator and the output is a list of strings excluding the delimiters.  Splitting a String into a list When you split a string into a list around a delimiter, the output comes out to be a partitioned list of substrings. You can take any delimiter as a separator in the Split function to separate the string into a list. An example of splitting a string into a list is shown below: str = “New York-Texas-Colombia”  print(str.split(“-”)) The output of the above code is as follows: ['New York', 'Texas', 'Colombia'] The variable str is declared with a string with dash characters( - ) in between and the Split function is executed with a dash ( - )  as the separator. The function splits the string whenever it encounters a dash and the result is a list of substrings. Splitting a String by hash (#) You can also split any string with a hash character (#) as the delimiter. The Split function takes a hash (#) as the separator and then splits the string at the point where a hash is found. The result is a list of substrings.  An example of splitting a string using a hash is shown below: str = “Python#isa#multi-purpose#language”  print(str.split(“#”)) The output of the above code is as follows: ['Python', 'is a', 'multi-purpose', 'language'] The variable str is declared with a string with hash characters( # ) in between them. The Split function is executed with a hash as the separator. The function splits the string wherever it finds a hash  ( # ) and the result is a list of substrings excluding the hash character. Splitting a String using maxsplit parameter The maxsplit parameter defines the maximum number of splits the function can do. You can perform split by defining a value to the maxsplit parameter. If you put whitespaces as separator and the maxsplit value to be 2, the Split function splits the string into a list with maximum two items.  An example of splitting a string using the maxsplit parameter is shown below: subjects = “Maths Science English History Geography”  print(subjects.split(“ ”,2)) The output of the above code is as follows: ['Maths', 'Science', 'English History Geography']  Here, you can see the variable str is declared with a string of different subject names. The Split function takes whitespace (“ ”) as a separator and the maximum number of splits or maxsplit is 2. The first two strings “Maths” and “Science” are split and the rest of them are in a single string. Splitting a String into an array of characters You can separate a string into an array of characters with the help of the list() function. The result is a list where each of the element is a specific character.  An example of splitting a string into an array of characters  is shown below: str = “PYTHON”  print(list(str)) The output of the above code is as follows: ['P', 'Y', 'T', 'H', 'O', 'N'] Here, the variable str is a string. The string is separated into individual characters using the list() function and the result is a list of elements with each character of the string. Splitting a String using substring You can obtain a string after or before a specific substring with the split() function. A specific string is given as the separator in the Split function and the result comes out to be the strings before and after that particular string.   An example of splitting a string using substring  is shown below: fruits = “Orange Banana Mango Apple Cherry”  print(fruits.split(“Mango”)) The output of the above code is as follows: ['Orange Banana ', ' Apple Cherry'] Here, the variable fruits is a string with names of different fruits. We take the string “Mango” as the separator in the Split function. Whenever the function finds the string “Mango”, it splits the whole string into two substrings – one substring before “Mango” and another substring after “Mango”.  Miscellaneous tips on Split function Since we have now reached at the end of the article, let me give you some useful tips on the Split function: If the maxsplit is not defined in the function and there are enough delimiters in the string, the result will have a length of maxsplit +1.If you want to recombine a string that has been already split in Python, you can perform the concatenation of strings.The Python Split function only works on string variables. If you come across any problem with working with split, you can force the compiler to treat the variable as a string with str(x).Conclusion The .split() function in Python is a very useful tool to split strings into chunks depending upon a delimiter which could be anything starting from characters or numbers or even text. You can also specify the number of splits you want the function to perform using maxsplit, which is used to extract a specific value or text from any given string using list or Arrays. Here are the key areas you should have gained a good understanding on by reading this article: What is a String. What is Split and why is it needed. How does a Python Split function work. What are the Split parameters. What are the many different ways of Splitting strings in Python Important tips on Split You have learned about the Python split function and the different ways to implement in your program. With this, you can begin to work on any project which requires the use of the Split.  If you wish to extend your knowledge about Strings and Split function in Python, you can refer to the official documentation of Python. Also, don’t forget to check out the remaining tutorials made freely available to you. 
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How to use Split in Python

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