Search

How to concatenate strings using Python

The string data type in Python is a very important building block of programming. It is basically a sequence of one or more characters that represent Unicode characters. The characters could be letters, numbers, or symbols. The strings are immutable in nature, which means they are unchanging.  You can implement string formatting techniques in Python like merging or splitting strings in Python. When you merge or combine two or more strings in Python, it is called string concatenation. In this article, we will understand what concatenation is and its importance. We will delve into different ways of concatenating strings including the + operator, * operator and % operator and take you through various concatenation methods including the join() method, format() function, the f-string and StringIO methods. What is concatenation in Python? String Concatenation is the operation of joining character strings end-to-end. If you have just started working on Python, you might come through a time when you will need to merge or combine the contents of two or more strings together. In technical terms, this merging or combining of strings together into a single string is called String concatenation. The simplest way to explain concatenation in Python is when you take two separate strings stored in the Interpreter and combine them so that they become one single string. For example, if you take one string as “foot” and another string as “ball” and then merge them into using concatenation technique it comes out to be a single string “football”. There are several ways in which you can perform string concatenation in Python. However, the simplest method is using the “+” operator. What is the need for String formatting in Python? String formatting in Python is a robust and important part of the toolkit of any Python programmer. String formatting techniques have greatly evolved since the time Python was developed. Almost every piece of production software created has its advantage in some way or the other.  Formatted strings in Python are evaluated at run time which acts as a basic capability of any high-level language. At a basic level, String concatenation using the “+” operator might seem inefficient and also difficult to make expressive. This is where Python’s string formatting starting from the “%” formatting to the format() method comes into action. They exhibit great potential when it comes to crafting strings. How can we concatenate strings in Python? Python comprises of a number of ways when it comes to concatenate or combine strings together. Since Python is an object-oriented programming language, everything in Python is an object. So, the new string that is created after concatenation is also referred to as a string object in Python.  Let us see what are the different ways by which we can concatenate strings in Python. Using the + operator The simplest and most common method of concatenating a string is using the plus symbol (“+”). Let us see an example to understand it better: a = “Python”  b = “is”  c = “cool”  print(a + b + c) PythoniscoolHere, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the “+” operator and display the output using the print statement. The output is the combination of the three strings together.  You might use the “+” operator when you have few strings to concatenate. This is because strings are immutable i.e. they cannot be changed once created. So, for each concatenating statement, the interpreter creates a new object. Thus, it will be quite inefficient if you try to concatenate many strings using the “+” operator. Another disadvantage of the “+” operator is that it does not allow any separator or delimiter between the strings. If you want to concatenate “Hello” and “World” with whitespace as a separator, you need to fo something like this “Hello” + “ ” + “World” and the output will be “Hello World”. Using the * operator The asterisk (*) operator is used when you want to concatenate the same string repeatedly. For example, if you have a string “red” and you want the same string to be concatenated three times, you use the * operator. The result will be “redredred”.  An example to illustrate concatenation of string using “*” operator: a = "Python"  print(a * 3) PythonPythonPython Here, we have declared a single string variable “a” with a string value. Then, we concatenate the string with the help of the “*” operator and display the output using the print statement. The output combines the string with the same string three times repeatedly. Using the join() method The join() method is the most flexible way of concatenating strings in Python. If you have many strings and you want to combine them together, use thejoin() method. It is a string method and the most interesting thing about join() is that you can combine strings using a separator. It works on iterators like lists, tuples, string, dictionaries, etc.  An example to illustrate concatenation of string using “*” operator: a = "Welcome"  b = "to"  c = "Python"  print(“-”.join([a,b,c])) Welcome-to-Python Here, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the join() method with “-” as a separator and display the output using the print statement. The output is the combination of the three strings together with dash (“-”) operator in between the strings. Using the % operator The modulus operator (“%”) can be used for both string formatting and string concatenation. It is useful for cases in which you need to combine strings and also perform basic formatting. An example to illustrate concatenation of string using “%” operator: a = "Apple"  b = "Shake"  print(“% s % s” % (a, b)) Apple Shake Here, we have declared two string variables “a”, and “b”with two different string values. Then, we concatenate the two strings with the help of the (“%”) and display the output using the print statement.  The “% s” denotes the string data type in Python and the modulus (“%”) operator combines the string stored in the two variables “a” and “b”. The string value in the variables is passed to the string data type and the output is displayed as the combination of two strings. Using the format() function The str.format() function is a powerful function in Python which is also used for both String formatting and String Concatenation. This function combines different elements within a string through positional formatting.     An example to illustrate concatenation of string using format() function: a = "Virgin"  b = "Mojito"  print(“{} {}”.format(a, b)) Virgin Mojito Here, we have declared two string variables “a” and “b”with two different string values. Then, we concatenate the two strings with the help of the format() function and display the output using the print statement.  The curly braces (“{}”) used here are used to fix the string position. The first variable is stored in the first curly braces and the second one in the second curly braces. The job of format() function is to concatenate the strings stored in variables “a” and “b” and display the combined string. Using the f-string  Formatted string literals or f-strings, in short, are string literals in Python. They contain an f at the beginning and curly braces that contain the expressions. It calls the str() method when an object argument is used as field replacement. Let us see an example to illustrate the concatenation of string using f-string: a = "Moscow"  b = "Mule"  print(f’{a} {b}‘) Moscow Mule Here, we have declared two string variables “a” and “b”with two different string values. Then, we concatenate the two strings with the help of the f-string and display the output using the print statement.  The f-string expressions are evaluated at runtime and they are being formatted using the __format__ protocol in Python. It is considered to be a cleaner and easier way of concatenating strings in Python when compared to the format() function.Using StringIO String concatenation using StringIO is also a very flexible way for combining different strings in Python. In this method, we have to import the StringIO() function from the IO module.  An example to illustrate the concatenation of string using StringIO: from io import StringIO  a = StringIO()  a.write(“Machine ”)  a.write(“Learning”)  print(a.getvalue()) Machine Learning Here, we have declared two string variables “a” and “b”with two different string values. Then, we concatenate the two strings with the help of the StringIO() imported from the IO module and display the output using the print statement.  Here, the variable “a”acts as a file object in Python. The write() function is used here to write the string to the file and the getvalue() function returns the entire content of the file. Miscellaneous concatenations in Python We have covered all the ways by which we can concatenate different strings in Python. Let us see some few more miscellaneous examples to understand String Concatenation better. Concatenate multiple strings There are various ways by which you can concatenate multiple strings in Python. The most common among them is using the plus (“+”) operator. You can combine both string variables and string literals using the “+” operator. However, there’s another method that allows an easy way of concatenating multiple strings. It is using the in-place (+=) operator. The in-place operator concatenates the sequence with the right operand and the result gets assigned to that sequence. Let us see an example of string concatenation using the (“+=”) operator: a = "Artificial "  b = "Intelligence"  a += b  print(a) Artificial Intelligence Here, two string variables “a” and “b” are declared with two different string values. The string on the right side of the “+=” operator is combined with the string variable on the left side. Then, the output is displayed using the print statement.  You can also add a string to the end of a string variable using the “+=” operator: a = "Basket"  a += "ball"  print(a) Basketball Another way of concatenating multiple strings in Python is just by writing string literals consecutively: a = "Red""Green""Blue"  print(a) RedGreenBlueConcatenate strings and numbers There are numerous ways of concatenating strings in Python. However, not all methods can concatenate strings and numbers. If you use the “+” operator to combine strings and numbers, it will raise errors. This is because strings can hold any recorded characters but numbers like  integers or floats are recorded number value. a = "Rolls Royce "  b = 1948  print(a + b) Traceback (most recent call last):   File "<string>", line 6, in <module>  TypeError: can only concatenate str (not "int") to str The error shows that the interpreter can concatenate a string value with another string value but cannot concatenate a string value with an integer. Although, you can overcome this problem with the help of the str() function in Python. It converts any integer or floating-point number into a string.  Let us see the same example with the str() function: a = "Rolls Royce "  b = str(1948)  print(a + b) Rolls Royce 1948 The str() function converts the integer value 1948 into a string and then it is concatenated with variable “a” and the output is displayed using the print statement. You can also use the format() function when you need to convert a number with decimal places or zero padding. Concatenate a list of strings into one string You can concatenate a list of strings into one string using the join() method. It takes a character as a delimiter string. If you use an empty string as the delimiter, the list of strings will be simply concatenated without any separator.  Let us see an example to concatenate a list of strings using the join() function: a = ["Apple", "Orange", “Banana”, “Mango”]  print(“\n”.join(a)) Apple  Orange  Banana  Mango Here, the variable “a” is a list declaredwith four different string values. We have used newline (“\n”) as the delimiter in the join() method which inserts a newline for each of the strings. The output is the four strings with each string in a newline. You can use any other delimiter like comma (,) or hyphen (-) in the join() method and then perform concatenation. Also, note that thejoin() method can also concatenate other iterators like tuples, sets, dictionaries, etcConcatenate a list of numbers into one string Python does not allow the concatenation of strings with numbers or numbers with numbers. However, you can convert a numeric value into a string using the str() method and then perform concatenation. If you want to combine a list of numbers into one string, the first thing you need to do is convert each integer in a list to a string using the str() function. Then, combine all the converted strings into a single string with the join() method. Let us see an example to understand it better: a = [1, 2, 3, 4, 5]  b = [str(a) for a in a]  print(“;”.join(b)) 1;2;3;4;5 Here, the variable “a” is a list declared with five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter.Here, the variable “a” is a list declared with five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter. Some useful tips on concatenation Now let me give you some useful tips on String concatenation in Python: The string-formatting operator “%” is a potentially fast and suitable operator when you need to concatenate a few pieces of string. Also, you don’t need to call the str() function when combining numbers because this operator does it implicitly. It also enhances the readability of the code. The join() method is the fastest, cleanest, and most elegant and readable method when you need to concatenate many small pieces of string into a larger string. When you have many small pieces of strings that come either from input or computation and are not in a sequence, always use a list to contain the strings. You can use list comprehension or append method in Python to arrange your list in a sequence.  Conclusion Let us summarize what we have learned in this article so far –  Concatenation and its importance. Different ways of concatenating strings. Some miscellaneous concatenation methods. Important tips on concatenating strings. Concatenation is a crucial part of String manipulation in Python. There are numerous ways to perform concatenation. However, some are more useful than others in some cases.  Now that you have quite an experience in concatenating strings, you can look out for other string formatting methods that Python provides or you can check out the PEP article on Advanced String Formatting on Python.org for more information. 
Rated 4.0/5 based on 14 customer reviews

How to concatenate strings using Python

10K
How to concatenate strings using Python

The string data type in Python is a very important building block of programming. It is basically a sequence of one or more characters that represent Unicode characters. The characters could be letters, numbers, or symbols. The strings are immutable in nature, which means they are unchanging.  

You can implement string formatting techniques in Python like merging or splitting strings in Python. When you merge or combine two or more strings in Python, it is called string concatenation

In this article, we will understand what concatenation is and its importance. We will delve into different ways of concatenating strings including the + operator, * operator and % operator and take you through various concatenation methods including the join() method, format() function, the f-string and StringIO methods. 

What is concatenation in Python? 

String Concatenation is the operation of joining character strings end-to-end. If you have just started working on Python, you might come through a time when you will need to merge or combine the contents of two or more strings together. In technical terms, this merging or combining of strings together into a single string is called String concatenation. 

The simplest way to explain concatenation in Python is when you take two separate strings stored in the Interpreter and combine them so that they become one single string. 

For example, if you take one string as “foot” and another string as “ball” and then merge them into using concatenation technique it comes out to be a single string “football”. 

There are several ways in which you can perform string concatenation in Python. However, the simplest method is using the “+” operator. 

What is the need for String formatting in Python? 

String formatting in Python is a robust and important part of the toolkit of any Python programmer. String formatting techniques have greatly evolved since the time Python was developed. Almost every piece of production software created has its advantage in some way or the other.  

Formatted strings in Python are evaluated at run time which acts as a basic capability of any high-level language. At a basic level, String concatenation using the “+” operator might seem inefficient and also difficult to make expressive. This is where Python’s string formatting starting from the “%” formatting to the format() method comes into action. They exhibit great potential when it comes to crafting strings. 

How can we concatenate strings in Python? 

Python comprises of a number of ways when it comes to concatenate or combine strings together. Since Python is an object-oriented programming language, everything in Python is an object. So, the new string that is created after concatenation is also referred to as a string object in Python.  

Let us see what are the different ways by which we can concatenate strings in Python. 

Using the + operator 

The simplest and most common method of concatenating a string is using the plus symbol (“+”). Let us see an example to understand it better: 

a = “Python” 
b = “is” 
c = “cool” 
print(a + b + c) 

Pythoniscool

Here, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the “+” operator and display the output using the print statement. The output is the combination of the three strings together.  

You might use the “+” operator when you have few strings to concatenate. This is because strings are immutable i.e. they cannot be changed once created. So, for each concatenating statement, the interpreter creates a new object. Thus, it will be quite inefficient if you try to concatenate many strings using the “+” operator. 

Another disadvantage of the “+” operator is that it does not allow any separator or delimiter between the strings. If you want to concatenate “Hello” and “World” with whitespace as a separator, you need to fo something like this “Hello” + “ ” + “World” and the output will be “Hello World”

Using the * operator 

The asterisk (*) operator is used when you want to concatenate the same string repeatedly. For example, if you have a string “red” and you want the same string to be concatenated three times, you use the * operator. The result will be redredred.  

An example to illustrate concatenation of string using “*” operator: 

a = "Python" 
print(a * 3) 

PythonPythonPython 

Here, we have declared a single string variable “a” with a string value. Then, we concatenate the string with the help of the “*” operator and display the output using the print statement. The output combines the string with the same string three times repeatedly. 

Using the join() method 

The join() method is the most flexible way of concatenating strings in Python. If you have many strings and you want to combine them together, use thejoin(method. It is a string method and the most interesting thing about join() is that you can combine strings using a separator. It works on iterators like lists, tuples, string, dictionaries, etc.  

An example to illustrate concatenation of string using “*” operator: 

a = "Welcome" 
b = "to" 
c = "Python" 
print(“-”.join([a,b,c])) 

Welcome-to-Python 

Here, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the join(method with “-” as a separator and display the output using the print statement. The output is the combination of the three strings together with dash (“-”) operator in between the strings. 

Using the % operator 

The modulus operator (“%”) can be used for both string formatting and string concatenation. It is useful for cases in which you need to combine strings and also perform basic formatting. 

An example to illustrate concatenation of string using “%” operator: 

a = "Apple" 
b = "Shake" 
print(“% s % s” % (a, b)) 

Apple Shake 

Here, we have declared two string variables “a”, and b”with two different string values. Then, we concatenate the two strings with the help of the (“%”) and display the output using the print statement.  

The “% s” denotes the string data type in Python and the modulus (“%”) operator combines the string stored in the two variables “a” and “b”. The string value in the variables is passed to the string data type and the output is displayed as the combination of two strings. 

Using the format() function 

The str.format() function is a powerful function in Python which is also used for both String formatting and String Concatenation. This function combines different elements within a string through positional formatting.     

An example to illustrate concatenation of string using format() function: 

a = "Virgin" 
b = "Mojito" 
print(“{} {}”.format(a, b)) 

Virgin Mojito 

Here, we have declared two string variables “a” and b”with two different string values. Then, we concatenate the two strings with the help of the format(function and display the output using the print statement.  

The curly braces (“{}”) used here are used to fix the string position. The first variable is stored in the first curly braces and the second one in the second curly braces. The job of format(function is to concatenate the strings stored in variables “a” and “b” and display the combined string. 

Using the f-string  

Formatted string literals or f-strings, in short, are string literals in Python. They contain an at the beginning and curly braces that contain the expressions. It calls the str() method when an object argument is used as field replacement. 

Let us see an example to illustrate the concatenation of string using f-string

a = "Moscow" 
b = "Mule" 
print(f’{a} {b}‘) 

Moscow Mule 

Here, we have declared two string variables “a” and b”with two different string values. Then, we concatenate the two strings with the help of the f-string and display the output using the print statement.  

The f-string expressions are evaluated at runtime and they are being formatted using the __format__ protocol in Python. It is considered to be a cleaner and easier way of concatenating strings in Python when compared to the format() function.

Using StringIO 

String concatenation using StringIO is also a very flexible way for combining different strings in Python. In this method, we have to import the StringIO() function from the IO module.  

An example to illustrate the concatenation of string using StringIO

from io import StringIO 
a = StringIO() 
a.write(“Machine ”) 
a.write(“Learning”) 
print(a.getvalue()) 

Machine Learning 

Here, we have declared two string variables “a” and b”with two different string values. Then, we concatenate the two strings with the help of the StringIO() imported from the IO module and display the output using the print statement.  

Here, the variable “a”acts as a file object in Python. The write() function is used here to write the string to the file and the getvalue() function returns the entire content of the file. 

Miscellaneous concatenations in Python 

We have covered all the ways by which we can concatenate different strings in Python. Let us see some few more miscellaneous examples to understand String Concatenation better. 

Concatenate multiple strings 

There are various ways by which you can concatenate multiple strings in Python. The most common among them is using the plus (“+”) operator. You can combine both string variables and string literals using the “+” operator. 

However, there’s another method that allows an easy way of concatenating multiple strings. It is using the in-place (+=) operator. The in-place operator concatenates the sequence with the right operand and the result gets assigned to that sequence. 

Let us see an example of string concatenation using the (“+=”) operator: 

a = "Artificial " 
b = "Intelligence" 
a += b 
print(a) 

Artificial Intelligence 

Here, two string variables “a” and “b” are declared with two different string values. The string on the right side of the “+=” operator is combined with the string variable on the left side. Then, the output is displayed using the print statement.  

You can also add a string to the end of a string variable using the “+=” operator: 

a = "Basket" 
a += "ball" 
print(a) 

Basketball 

Another way of concatenating multiple strings in Python is just by writing string literals consecutively: 

a = "Red""Green""Blue" 
print(a) 

RedGreenBlue

Concatenate strings and numbers 

There are numerous ways of concatenating strings in Python. However, not all methods can concatenate strings and numbers. If you use the “+” operator to combine strings and numbers, it will raise errors. This is because strings can hold any recorded characters but numbers like  integers or floats are recorded number value. 

a = "Rolls Royce " 
b = 1948 
print(a + b) 
Traceback (most recent call last): 
  File "<string>", line 6, in <module> 
TypeError: can only concatenate str (not "int") to str 

The error shows that the interpreter can concatenate a string value with another string value but cannot concatenate a string value with an integer. Although, you can overcome this problem with the help of the str() function in Python. It converts any integer or floating-point number into a string.  

Let us see the same example with the str() function: 

a = "Rolls Royce " 
b = str(1948) 
print(a + b) 

Rolls Royce 1948 

The str() function converts the integer value 1948 into a string and then it is concatenated with variable “a” and the output is displayed using the print statement. 

You can also use the format() function when you need to convert a number with decimal places or zero padding. 

Concatenate a list of strings into one string 

You can concatenate a list of strings into one string using the join(method. It takes a character as a delimiter string. If you use an empty string as the delimiter, the list of strings will be simply concatenated without any separator.  

Let us see an example to concatenate a list of strings using the join() function: 

a = ["Apple", "Orange", “Banana”, “Mango”] 
print(“\n”.join(a)) 
Apple 
Orange 
Banana 
Mango 

Here, the variable “a” is a list declaredwith four different string values. We have used newline (“\n”) as the delimiter in the join() method which inserts a newline for each of the strings. 

The output is the four strings with each string in a newline. 

You can use any other delimiter like comma (,) or hyphen (-) in the join() method and then perform concatenation. Also, note that thejoin(method can also concatenate other iterators like tuples, sets, dictionaries, etc

Concatenate a list of numbers into one string 

Python does not allow the concatenation of strings with numbers or numbers with numbers. However, you can convert a numeric value into a string using the str() method and then perform concatenation. 

If you want to combine a list of numbers into one string, the first thing you need to do is convert each integer in a list to a string using the str() function. Then, combine all the converted strings into a single string with the join(method. 

Let us see an example to understand it better: 

a = [1, 2, 3, 4, 5] 
b = [str(a) for a in a] 
print(“;”.join(b)) 
1;2;3;4;5 

Here, the variable “a” is a list declared with five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter.

Here, the variable “a” is a list declared with five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter. 

Some useful tips on concatenation 

Now let me give you some useful tips on String concatenation in Python: 

  • The string-formatting operator “%” is a potentially fast and suitable operator when you need to concatenate a few pieces of string. Also, you don’t need to call the str() function when combining numbers because this operator does it implicitly. It also enhances the readability of the code. 
  • The join() method is the fastest, cleanest, and most elegant and readable method when you need to concatenate many small pieces of string into a larger string. 
  • When you have many small pieces of strings that come either from input or computation and are not in a sequence, always use a list to contain the strings. You can use list comprehension or append method in Python to arrange your list in a sequence.  

Conclusion 

Let us summarize what we have learned in this article so far –  

  • Concatenation and its importance. 
  • Different ways of concatenating strings. 
  • Some miscellaneous concatenation methods. 
  • Important tips on concatenating strings. 

Concatenation is a crucial part of String manipulation in Python. There are numerous ways to perform concatenation. However, some are more useful than others in some cases.  

Now that you have quite an experience in concatenating strings, you can look out for other string formatting methods that Python provides or you can check out the PEP article on Advanced String Formatting on Python.org for more information. 

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
8242
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
7929
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
5988
How to use sys.argv in Python

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

20% Discount