Search

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. 
Rated 4.0/5 based on 12 customer reviews

How to use Split in Python

7K
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 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 themThe 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: 

  1. 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.
  2. If you want to recombine a string that has been already split in Python, you can perform the concatenation of strings.
  3. 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. 

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.

Join the Discussion

Your email address will not be published. Required fields are marked *

Suggested Blogs

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

One of the most important decisions for the Big data learners or beginners is choosing the best programming language for big data manipulation and analysis. Just understanding business problems and choosing the right model is not enough but implementing them perfectly is equally important and choosing the right language (or languages) for solving the problem goes a long way. If you search top and highly effective programming languages for Big Data on Google, you will find the following top 4 programming languages: JavaScalaPythonRJavaJava is one of the oldest languages of all 4 programming languages listed here. Traditional Frameworks of Big data like Apache Hadoop and all the tools within its ecosystem are Java-based and hence using java opens up the possibility of utilizing large ecosystem of tools in the big data world.  ScalaA beautiful crossover between object-oriented and functional programming language is Scala. Scala is a highly Scalable Language. Scala was invented by the German Computer Scientist, Martin Odersky and the first version was launched in the year 2003.PythonPython was originally conceptualized by Guido van Rossum in the late 1980s. Initially, it was designed as a response to the ABC programming language and later gained its popularity as a functional language in a big data world. Python has been declared as one of the fastest-growing programming languages in 2018 as per the recently held Stack Overflow Developer Survey. Many data analysis, manipulation, machine learning, deep learning libraries are written in Python and hence it has gained its popularity in the big data ecosystem. It’s a very user-friendly language and it is its biggest advantage.  Fun factPython is not named after the snake. It’s named after the British TV show Monty Python.RR is the language of statistics. R is a language and environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is named partly after the first names of the first two R authors and partly as a play on the name of S*. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.*SS is a statistical programming language developed primarily by John Chambers and R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme.Every framework is implemented in the underlying programming language for its implementation. Ex Zend uses PHP, Panda Framework uses python similarly Hadoop framework uses Java and Spark uses Scala.However, Spark officially supports Java, Scala, Python and R, all 4 languages. If one browses through Apache Spark’s official website documentation, he/she would find many other languages utilized by the open-source community for Spark implementation.    When any developer wants to start learning Spark, the first question he stumbles upon is, out of these pools of languages, which one to use and which one to master? Solution Architects would have a tough time choosing the right language for spark framework and Organizations will always be wondering, which skill sets are relevant for my problem if one doesn’t have the right knowledge about these languages in the context of Spark.    This article will try to answer all these queries.so let’s start-JavaOldest of all and popular, widely adopted programming language of all. There is a number offeatures/advantages due to which Java is favorite for Big data developers and tool creators:Java is platform-agnostic language and hence it can run on almost any system. Java is portable due to something called Java Virtual Machine – JVM. JVM is a foundation of Hadoop ecosystem tools like Map Reduce, Storm, Spark, etc. These tools are written in Java and run on JVM.Java provides various communities support like GitHub and stack overflow etc.Java is scalable, backward compatible, stable and production-ready language. Also, supports a large variety of tried and tested libraries.It is statically typed language (We would see details of this functionality in later sections, in comparison with others)Java is mostly the choice for most of the big data projects but for the Spark framework, one has to ponder upon, whether Java would be the best fit.One major drawback of Java is its verbosity. One has to write long code (number of lines of code) to achieve simple functionality in Java.Java does not support Read-Evaluate-Print-Loop (REPL) which is a major deal-breaker when choosing a programming language for big data processing.ScalaScala is comparatively new to the programming scene but has become popular very quickly. Above are a few quotes from bigger names in the industry for Scala. From the Spark context, many experts prefer Scala over other programming languages as Spark is written in Scala. Scala is the native language of Spark. It means any new API always first be available in Scala.Scala is a hybrid functional programming language because It has both the features of object-oriented programming and functional programming. As an OO Programming Language, it considers every value as an object and all OOPS concepts apply. As a functional programming language, it defines and supports functions. All operations are done as functions. No variable stands by itself. Scala is a machine-compiled language.Scala and Java are popular programming languages that run over JVM. JVM makes these languages framework friendly. One can say, Scala is an advanced level of Java.Features/Advantages of Scala:It’s general-purpose object-oriented language with functional language properties too. It’s less verbose than Java.It can work with JVM and hence is portable.It can support Java APIs comfortably.It's fast and robust in Spark context as its Spark native.It is a statically typed language.Scala supports Read-Evaluate-Print-Loop (REPL)Drawbacks / Downsides of Scala:Scala is complex to learn due to the functional nature of language.Steep learning curve.Lack of matured machine learning languages.PythonPython is one of the de-facto languages of Data Science. It is a simple, open-source, general-purpose language and is very easy to learn. It has a rich set of libraries, utilities, ready-to-use features and support to a number of mature machine learning, big data processing, visualization libraries.Advantages of Python:It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a command-line interpreter and it responds immediately. Java lacks this feature.Easy to learn, easy debugging, fewer lines of code.It is dynamically typed. i.e. can dynamically defined variable types. i.e. Python as a language is type-safe.Python is platform agnostic and scalable.Drawbacks/Disadvantages:Python is slow. Big data professionals find projects built in Java / Scala are faster and robust than the once with python.Whilst using user-defined functions or third party libraries in Python with Spark, processing would be slower as increased processing is involved as Python does not have equivalent Java/Scala native language API for these functionalities.Python does not support heavy weight processing fork() using uWSGI but it does not support true multithreading.R LanguageR is the favourite language of statisticians. R is fondly called a language of statisticians.  It’s popular for research, plotting, and data analysis. Together with RStudio, it makes a killer statistic, plotting, and data analytics application.R is majorly used for building data models to be used for data analysis.Advantages/Features of R:Strong statistical modeling and visualization capabilities.Support for ‘data science’ related work.It can be integrated with Apache Hadoop and Spark easily.Drawbacks/Disadvantages of R:R is not a general-purpose language.The code written in R cannot be directly deployed into production. It needs conversion into Java or Python.Not as fast as Java / Scala.Comparison of four languages for Apache SparkWith the introduction of these 4 languages, let’s now compare these languages for the Spark framework:These languages can be categorized into 2 buckets basis high-level spark architecture support, broadly:JVM Languages: Java and ScalaNon-JVM Languages: Python and RDue to these categorizations, performance may vary. Let’s understand architecture in little depth to understand the performance implications of using these languages. This would also help us to understand the question of when to use which language.Spark Framework High-level architecture An application written in any one of the languages is submitted on the driver node and further driver node distributes the workload by dividing the execution on multiple worker nodes.JVM compatible Application Execution Flow Consider the applications written are JVM compatible (Java/Scala). Now, Spark is also written in native JVM compatible Scala language, hence there is no explicit conversion required at any point of time to execute JVM compatible applications on Spark. Also, this makes the native language applications faster to perform on the Spark framework.There are multiple scenarios for Python/R written applications:Python/R driver talk to JVM driver by socket-based API. On the driver node, both the driver processes are invoked when the application language is non-JVM language.Scenario 1: Applications for which Equivalent Java/Scala Driver API exists - This scenario executes the same way as JVM compatible applications by invoking Java API on the driver node itself. The cost for inter-process communication through sockets is negligible and hence performance is comparable. This is with the assumption that processed data over worker nodes are not to be sent back to the Driver again.Scenario 1(b): If the assumption taken is void in scenario 1 i.e. processed data on worker nodes is to be sent back to driver then there is significant overhead and serialization required. This adds to processing time and hence performance in this scenario deteriorates.Scenario 2: Applications for which Equivalent Java/Scala Driver API do not exist – Ex. UDF (User-defined functions) / Third party python libraries. In such cases equivalent Java API doesn’t exist and hence, additional executor sessions are initiated on worker node and python API is serialized on worker node and executed. This python worker processes in addition to JVM and coordination between them is overhead. Processes also compete for resources which adds to memory contention.In addition, if the data is to send back to the driver node then processing takes a lot of time and problem scales up as volume increases and hence performance is bigger problem.As we have seen a performance, Let’s see the tabular comparison between these languages.Comparison PointsJavaScalaPythonRPerformanceFasterFaster (about 10x faster than Python)SlowerSlowerLearning CurveEasier than JavaTougher than PythonSteep learning curve than Java & PythonEasiestModerateUser GroupsWeb/Hadoop programmersBig Data ProgrammersBeginners & Data EngineersData Scientists/ StatisticiansUsageWeb development and Hadoop NativeSpark NativeData Engineering/ Machine Learning/ Data VisualizationVisualization/ Data Analysis/ Statistics use casesType of LanguageObject-Oriented, General PurposeObject-Oriented & Functional General PurposeGeneral PurposeSpecifically for Data Scientists.Needs conversion into Scala/Python before productizingConcurrencySupport ConcurrencySupport ConcurrencyDoes not Support ConcurrencyNAEase of UseVerboseLesser Verbose than ScalaLeast VerboseNAType SafetyStatically typedStatically typed (except for Spark 2.0 Data frames)Dynamically TypedDynamically TypedInterpreted Language (REPL)NoNoYesYesMaturated machine learning libraries availability/ SupportLimitedLimitedExcellentExcellentVisualization LibrariesLimitedLimitedExcellentExcellentWeb Notebooks SupportIjava Kernel in Jupyter NotebookApache Zeppelin Notebook SupportJupyter Notebook SupportR NotebookWhich language is better for Spark and Why?With the info we gathered for the languages, let's move to the main question i.e. which language to choose for Spark? My answer is not a straightforward single language for this question. I will state my point of view for choosing the proper language: If you are a beginner and want to choose a language from learning Spark perspective. If you are organization/ self employed or looking to answer a question for solutioning a project perspective. I. If you are beginner:If you are a beginner and have no prior education of programming language then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking for getting into roles like ‘data engineering’, knowledge of Python along with supported libraries will go a long way. If you are a beginner but have education in programming languages, then you may find Java very familiar and easy to build upon prior knowledge. After all, it grapevine of all the languages.  If you are a hardcore bigdata programmer and love exploring complexities, Scala is the choice for you. It’s complex but experts say if once you love Scala, you will prefer it over other languages anytime.If you are a data scientist, statistician and looking to work with Spark, R is the language for you. R is more science oriented than Python. II. If you are organization/looking for choice of language for implementations:You need to answer the following important questions before choosing the language:Skills and Proficiency: Which skill-sets and proficiency over language, you already have with you/in your team?Design goals and availability of features/ Capability of language: Which libraries give you better support for the type of problem(s) you are trying to solve.Performance implications Details of these explained below: 1. Skillset: This is very straightforward. Whichever is available skill set within a team, go with that to solve your problem, after evaluating answers of other two questions. If you are self-employed, the one you have proficiency is the most likely suitable choice of language.  2. Library Support:  Following gives high-level capabilities of languages:R: Good for research, plotting, and data analysis.Python: Good for small- or medium-scale projects to build models and analyse data, especially for fast start-ups or small teams.Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance.In my opinion, Scala/Java can be used for larger robust projects to ease maintenance. Also, If one wants the app to scale quickly and needs it to be robust, Scala is the choice.Python and R: Python is more universal language than R, but R is more science oriented. Broadly, one can say Python can be implemented for Data engineering use cases and R for Data science-oriented use cases. On the other hand, if you discover these two languages have about the same library support you need, then pick the one whose syntax you prefer. You may find that you need both depending on the situation. 3. Performance: As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc) they can perform equally well.ConclusionFor learning, depending upon your prior knowledge, Python is the easiest of all to pick up. For implementations, Choice is in your hands which language to choose for implementations but let me tell you one secret or a tip, you don’t have to stick to one language until you finish your project. You can divide your problem in small buckets and utilize the best language to solve the problem. This way, you can achieve balance between optimum performance, availability, proficiency in a skill, and sub-problem at hand.  Do let us know how your experience was in learning the language comparisons and the language you think is better for Spark. Moreover, which one you think is “the one for you”, through comments below.
Rated 4.5/5 based on 1 customer reviews
8243
Scala Vs Python Vs R Vs Java - Which language is b...

One of the most important decisions for the Big da... Read More

What is Context in React? How to use Context in React?

What the hack is Context?Have you ever wondered about passing data or using states in between different components without using Props? Or passing a state from Parent to Child component without manually passing a prop at every level?  Let’s understand with an example below:Here we have a parent component app.js where we have defined our states. We want to access the data of state in the last child which is “Child 1.2” in the below chart.app.js Parent ComponentThe ideal or older approach in React is to pass the data from the root component to the last component is via Props. We have to pass props in each intermediary level so as to send in the last level. While this approach also works, the real problems begin if data is needed on a different branch i.e Child 2.1 or Child 2.2 in above chart…In order to solve this problem, we need to pass data from the root/top level of the application through all the intermediate components to the one where we want to pass the data, even though some intermediate components don't even need it.  This mind-numbing process is known as prop drilling,  Prop Drillingwhere you’re passing the state from your root component to the bottom and you end up passing the data via props through components that do not even necessarily need themOne really good solution to solve the above problem is using Context According to the React documentation:  “Context provides a way to pass data through the component tree without having to pass props down manually at every level”Ordinarily, we’d have used any state management library like Redux or have used HOC’s to pass the data in a tedious manner. But what if we don’t want to use it all? Here comes the role of new Context API!In layman words, it gives an approach to make specific data available to all components throughout the React component tree regardless of how deeply nested those components are.Context is just like a global object to the subtree of the React component.When to use the Context APIThe Context API is convenient for sharing data that is either global, such as setting the header and footer theme of a website or logic of user authentication and many more. In cases like these, we can use the Context API without using any extra library or external modules. It can also be used in a multilingual application where we want to implement multiple languages that can be translated into the required text with the help of ContextAPI. It will save prop-drilling   In fact, in any situation where we have to pass a prop through a component so it reaches another component, inside down the tree is where we can use the Context API.Introducing The Context APIThe context API is a way to pass data from top component to bottom ones, without manually passing it to via props. Context is fundamentally utilized when some data needs to be accessible by numerous components at different nesting levels. To create a new Context, we can use the React createContext function like below: const MyContext = React.createContext(defaultValue);In React, data is often passed from a parent to its child component as a property. Here, we can also omit the default value which we have passed to the context, if needed.React data passing from parent to its child Let’s Get Started With ContextThree things are needed to tap into the power of context: 1. The context itselfTo create a context we can use React.createContext method which creates a context object. This is used to ensure that the components at different level can use the same context to fetch the data.   In React.createContext, we can pass an input parameter as an argument which could be anything or it can be null as well.import React from `react';  const ThemeContext = React.createContext('dark');  // Create our context        export default ThemeContext;In this example, a string is passed for the current Context which is “dark”. So we can say, the current theme required for a specific component is Dark.   Also, we have exported the object so that we can use it in other places. In one app, React also allows you to create multiple contexts. We should always try to separate context for different purposes, so as to maintain the code structure and better readability. We will see that later in our reading.   What next?? Now, to utilize the power of Context in our example, we want to provide this type of theme to all the components.  Context exposes a pair of elements which is a Provider Component and a Consumer Component.2. A context providerOkay, so now we have our Context object. And to make the context available to all our components we have to use a Provider.   But, what is Provider? According to the React documentation:"every context object comes with a Provider React component that allows consuming components to subscribe to context changes"In other words, Provider accepts a prop (value) and the data in this prop can be used in all the other child components. This value could be anything from the component state.// myProvider.js import React from 'react'; import Theme from './theme'; const myProvider = () => ( ...   ); export default myProvider;We can say that a provider acts just like a delivery service.prop finding context and deliverling it to consumerWhen a consumer asks for something, it finds it in the context and delivers it to where it's needed.But wait, who or what is the consumer???3.  A context consumer What is Consumer? A consumer is a place to keep the stored information. It can request for the data using the provider and can even manipulate the global store if the provider allows it. In our previous example, let’s grab the theme value and use it in our Header component. // Header.js   import React from 'react'; import Theme from './theme';   const Header = () => (                        {theme => Selected theme is {theme}}             );   export default Header;Dynamic Context:   We can also change the value of the provider by simply providing a dynamic context. One way of achieving it is by placing the Provider inside the component itself and grabbing the value from component state as below:// Footer.js   import React from 'react';   class Footer extends React.Component {    state = {        theme: 'dark'    };      render() {        return (                                                );    } }Simple, no? We can easily change the value of  the Provider to any Consumer.Consuming Context With Class-based ComponentsWe all pretty know that there are two methods to write components in React, which is Class based components and Function based components. We have already seen a demo of how we can use the power of Context in class based components.  One is to use the context from Consumer like “ThemeContext.Consumer” and the other method is by assigning context object from current Context to contextType property of our class.import React, { Component } from "react"; import MyThemeContext from "../Context/MyThemeContext"; import GlobalTheme from "../theme";   class Main extends Component {    constructor() {        super();    }    static contextType = MyThemeContext;  //assign context to component    render() {        const currentTheme = GlobalTheme[this.context];        return (            ...        );    }   }There is always a difference in how we want to use the Context. We can either provide it outside the render() method or use the Context Consumer as a component itself.  Here in the above example, we have used a static property named as contextType which is used to access the context data. It can be utilized by using this.context. This method however, limits you consuming, only one context at a time.Consuming Context With Functional ComponentsContext with Functional based components is quite easy and less tedious. In this we can access the context value through props with the help of useContext method in React. This hook (useContext) can be passed in as the argument along with our Context to consume the data in the functional component.const value = useContext(MyContext);It accepts a context object and returns the current context value. To read more about hooks, read here.  Our previous example looks like:import React, { useContext } from 'react' import MyThemeContext from './theme-context'   const User = props => {    const context = useContext(MyThemeContext)    return ...Now, instead of wrapping our content in a Consumer component we have access to the theme context state through the ‘context’ variable.But we should avoid using context for keeping the states locally. Instead of  conext, we can use local state there.Use of Multiple ContextsIt may be possible that we want to add multiple contexts in our application. Like holding a theme for the entire app, changing the language based on the location, performing some A/B testing, using global parameters for login or user Profile… For instance, let’s say there is a requirement to keep both Theme context and userInfo Context, the code will look like as:       ...   It’s quite possible in React to hold multiple Contexts, but this definitely hampers rendering, serving ‘n’ number of contexts in ‘m’ component and holding the updated value in each rendered component.To avoid this and to make re-rendering faster, it is suggested to make each context consumer in the tree as a separate node or into different contexts.                 And we can perform the nesting in context as:    {theme => (                    {colour => (                Theme: {theme} and colour: {colour}            )}            )} It’s worth noting that when a value of a context changes in the parent component, the child components or the components’ holding that value should be rerendered or changed. Hence, whenever there is a change in the value of provider, it will cause its consumers to re-render.ConclusionDon’t you think this concept is just amazing?? Writing a global context like theme or language or userProfile and using the data of them directly in the child or other components? Implementing these stateful logic by global preferences was never so easy, but Context made this transportation job a lot simple and achievable! Hope you find this article useful. Happy Coding!Having challenge learning to code? Let our experts help you with customized courses!
Rated 4.0/5 based on 1 customer reviews
7930
What is Context in React? How to use Context in Re...

What the hack is Context?Have you ever wondered ab... Read More

How to use sys.argv in Python

The sys module is one of the common and frequently used modules in Python. In this article, we will walk you through how to use the sys module. We will learn about what argv[0] and sys.argv[1] are and how they work. We will then go into how to parse Command Line options and arguments, the various ways to use argv and how to pass command line arguments in Python 3.x In simple terms,Command Line arguments are a way of managing the script or program externally by providing the script name and the input parameters from command line options while executing the script. Command line arguments are not specific just to Python. These can be found in other programming languages like C, C# , C++, PHP, Java, Perl, Ruby and Shell scripting. Understanding sys.argv with examples  sys.argv is a list in Python that contains all the command-line arguments passed to the script. It is essential in Python while working with Command Line arguments. Let us take a closer look with a few examples. With the len(sys.argv) function, you can count the number of arguments. import sys print ("Number of arguments:", len(sys.argv), "arguments") print ("Argument List:", str(sys.argv)) $ python test.py arg1 arg2 arg3 Number of arguments: 4 arguments. Argument List: ['test.py', 'arg1', 'arg2', 'arg3']Module name to be used while using sys.argv To use sys.argv, you will first need to the sys module. What is argv[0]? Remember that sys.argv[0] is the name of the script. Here – Script name is sysargv.py import sys print ("This is the name of the script: ", sys.argv[0]) print ("Number of arguments: ", len(sys.argv)) print ("The arguments are: " , str(sys.argv))Output:This is the name of the script:  sysargv.py                                                                               Number of arguments:  1                                                                                                 The arguments are:  ['sysargv.py']What is "sys. argv [1]"? How does it work? When a python script is executed with arguments, it is captured by Python and stored in a list called sys.argv. So, if the below script is executed: python sample.py Hello Python Then inside sample.py, arguments are stored as: sys.argv[0] == ‘sample.py’ sys.argv[1] == ‘Hello’ sys.argv[2] == ‘Python’Here,sys.argv[0] is always the filename/script executed and sys.argv[1] is the first command line argument passed to the script . Parsing Command Line options and arguments  Python provides a module named as getopt which helps to parse command line options and arguments. Itprovides a function – getopt, whichis used for parsing the argument sequence:sys.argv. Below is the syntax: getopt.getopt(argv, shortopts, longopts=[]) argv: argument list to be passed.shortopts: String of short options as list . Options in the arguments should be followed by a colon (:).longopts: String of long options as list. Options in the arguments should be followed by an equal sign (=). import getopt import sys   first ="" last ="" argv = sys.argv[1:] try:     options, args = getopt.getopt(argv, "f:l:",                                ["first =",                                 "last ="]) except:     print("Error Message ")   for name, value in options:     if name in ['-f', '--first']:         first = value     elif name in ['-l', '--last']:         last = value   print(first + " " + last)Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python getopt_ex.py -f Knowledge -l Hut Knowledge Hut (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python getopt_ex.py --first Knowledge –last Hut Knowledge HutWhat are command line arguments? Why do we use them? Command line arguments are parameters passed to a program/script at runtime. They provide additional information to the program so that it can execute. It allows us to provide different inputs at the runtime without changing the code. Here is a script named as argparse_ex.py: import argparse parser = argparse.ArgumentParser() parser.add_argument("-n", "--name", required=True) args = parser.parse_args() print(f'Hi {args.name} , Welcome ')Here we need to import argparse package Then we need to instantiate the ArgumentParser object as parser. Then in the next line , we add the only argument, --name . We must specify either shorthand (-n) or longhand versions (--name)  where either flag could be used in the command line as shown above . This is a required argument as mentioned by required=True Output:  (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py --name Nandan  Hi Nandan , Welcome  (venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py -n Nandan  Hi Nandan , Welcome The example above must have the --name or –n option, or else it will fail.(venv) C:\Users\Nandank\PycharmProjects\DSA\venv>python argparse_ex.py --name   usage: argparse_ex.py [-h] --name NAME argparse_ex.py: error: the following arguments are required: --namePassing command line arguments in Python 3.x argv represents an array having the command line arguments of thescript . Remember that here, counting starts fromzero [0], not one (1). To use it, we first need to import sys module (import sys). The first argument, sys.argv[0], is always the name of the script and sys.argv[1] is the first argument passed to the script. Here, we need to slice the list to access all the actual command line arguments. import sys if __name__ == '__main__':     for idx, arg in enumerate(sys.argv):        print("Argument #{} is {}".format(idx, arg))     print ("No. of arguments passed is ", len(sys.argv))Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv\Scripts>python argv_count.py Knowledge Hut 21 Argument #0 is argv_count.py Argument #1 is Knowledge Argument #2 is Hut Argument #3 is 21 No. of arguments passed is  4Below script - password_gen.py is used to generate a secret password by taking password length as command line argument.import secrets , sys, os , string ''' This script generates a secret password using possible key combinations''' ''' Length of the password is passed as Command line argument as sys.argv[1]''' char = string.ascii_letters+string.punctuation+string.digits length_pwd = int(sys.argv[1])   result = "" for i in range(length_pwd):     next= secrets.SystemRandom().randrange(len(char))     result = result + char[next] print("Secret Password ==" ,result,"\n")Output:(venv) C:\Users\Nandank\PycharmProjects\DSA\venv\Scripts>python password_gen.py 12 Secret Password == E!MV|,M][i*[Key takeaways Let us summarize what we've learnt so far. We have seen how to use the sys module in Python, we have looked at what areargv[0] and sys.argv[1] are and how they work, what Command Line arguments are and why we use them and how to parse Command Line options and arguments. We also dived into multiple ways to use argv and how to pass command line arguments in Python 3.xHope this mini tutorial has been helpful in explaining the usage of sys.argv and how it works in Python. Be sure to check out the rest of the tutorials on KnowledgeHut’s website and don't forget to practice with your code! 
Rated 4.0/5 based on 14 customer reviews
5989
How to use sys.argv in Python

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

20% Discount