assignment operator in r programming

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Assignment Operators in R (3 Examples) | Comparing = vs. ＜- vs. ＜＜-

On this page you’ll learn how to apply the different assignment operators in the R programming language .

The content of the article is structured as follows:

Let’s dive right into the exemplifying R syntax!

Example 1: Why You Should Use ＜- Instead of = in R

Generally speaking, there is a preference in the R programming community to use an arrow (i.e. ＜-) instead of an equal sign (i.e. =) for assignment.

In my opinion, it makes a lot of sense to stick to this convention to produce scripts that are easy to read for other R programmers.

However, you should also take care about the spacing when assigning in R. False spacing can even lead to error messages .

For instance, the following R code checks whether x is smaller than minus five due to the false blank between ＜ and -:

A properly working assignment could look as follows:

However, this code is hard to read, since the missing space makes it difficult to differentiate between the different symbols and numbers.

In my opinion, the best way to assign in R is to put a blank before and after the assignment arrow:

As mentioned before, the difference between ＜- and = is mainly due to programming style . However, the following R code using an equal sign would also work:

In the following example, I’ll show a situation where ＜- and = do not lead to the same result. So keep on reading!

Example 2: When ＜- is Really Different Compared to =

In this Example, I’ll illustrate some substantial differences between assignment arrows and equal signs.

Let’s assume that we want to compute the mean of a vector ranging from 1 to 5. Then, we could use the following R code:

However, if we want to have a look at the vector x that we have used within the mean function, we get an error message:

Let’s compare this to exactly the same R code but with assignment arrow instead of an equal sign:

The output of the mean function is the same. However, the assignment arrow also stored the values in a new data object x:

This example shows a meaningful difference between = and ＜-. While the equal sign doesn’t store the used values outside of a function, the assignment arrow saves them in a new data object that can be used outside the function.

Example 3: The Difference Between ＜- and ＜＜-

So far, we have only compared ＜- and =. However, there is another assignment method we have to discuss: The double assignment arrow ＜＜- (also called scoping assignment).

The following code illustrates the difference between ＜- and ＜＜- in R. This difference mainly gets visible when applying user-defined functions .

Let’s manually create a function that contains a single assignment arrow:

Now, let’s apply this function in R:

The data object x_fun1, to which we have assigned the value 5 within the function, does not exist:

Let’s do the same with a double assignment arrow:

Let’s apply the function:

And now let’s return the data object x_fun2:

As you can see based on the previous output of the RStudio console, the assignment via ＜＜- saved the data object in the global environment outside of the user-defined function.

Video & Further Resources

I have recently released a video on my YouTube channel , which explains the R syntax of this tutorial. You can find the video below:

The YouTube video will be added soon.

In addition to the video, I can recommend to have a look at the other articles on this website.

R Programming Examples

In summary: You learned on this page how to use assignment operators in the R programming language. If you have further questions, please let me know in the comments.

assignment-operators-in-r How to use different assignment operators in R – 3 R programming examples – R programming language tutorial – Actionable R programming syntax in RStudio

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Assignment Operators

Description.

Assign a value to a name.

There are three different assignment operators: two of them have leftwards and rightwards forms.

The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

The operators <<- and ->> are normally only used in functions, and cause a search to be made through parent environments for an existing definition of the variable being assigned. If such a variable is found (and its binding is not locked) then its value is redefined, otherwise assignment takes place in the global environment. Note that their semantics differ from that in the S language, but are useful in conjunction with the scoping rules of R . See ‘The R Language Definition’ manual for further details and examples.

In all the assignment operator expressions, x can be a name or an expression defining a part of an object to be replaced (e.g., z[[1]] ). A syntactic name does not need to be quoted, though it can be (preferably by backtick s).

The leftwards forms of assignment <- = <<- group right to left, the other from left to right.

value . Thus one can use a <- b <- c <- 6 .

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language . Wadsworth & Brooks/Cole.

Chambers, J. M. (1998) Programming with Data. A Guide to the S Language . Springer (for = ).

assign (and its inverse get ), for “subassignment” such as x[i] <- v , see [<- ; further, environment .

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R operators

Learn all about the R programming language operators

There are several operators in R, such that arithmetic operators for math calculations, logical, relational or assignment operators or even the popular pipe operator. In this tutorial we will show you the R operators divided into operator types. In addition, we will show examples of use of every operator.

Arithmetic operators

The R arithmetic operators allows us to do math operations , like sums, divisions or multiplications, among others. The following table summarizes all base R arithmetic operators.

In the next block of code you will find examples of basic calculations with arithmetic operations with integers.

You can also use the basic operations with R vectors of the same length . Note that the result of these operations will be a vector with element-wise operation results.

Furthermore, you can use those arithmetic operators with matrix objects, besides the ones designed for this type of object (matrix multiplication types). Check our tutorial about matrix operations to learn more.

Logical / boolean operators

In addition, boolean or logical operators in R are used to specify multiple conditions between objects. These comparisons return TRUE and FALSE values.

Relational / comparison operators in R

Comparison or relational operators are designed to compare objects and the output of these comparisons are of type boolean. To clarify, the following table summarizes the R relational operators.

For example, you can compare integer values with these operators as follows.

If you compare vectors the output will be other vector of the same length and each element will contain the boolean corresponding to the comparison of the corresponding elements (the first element of the first vector with the first element of the second vector and so on). Moreover, you can compare each element of a matrix against other.

Assignment operators in R

The assignment operators in R allows you to assign data to a named object in order to store the data .

Note that in almost scripting programming languages you can just use the equal (=) operator. However, in R it is recommended to use the arrow assignment ( <- ) and use the equal sign only to set arguments.

The arrow assignment can be used as left or right assignment, but the right assignment is not generally used. In addition, you can use the double arrow assignment, known as scoping assignment, but we won’t enter in more detail in this tutorial, as it is for advanced users. You can know more about this assignment operator in our post about functions in R .

In the following code block you will find some examples of these operators.

If you need to use the right assignment remember that the object you want to store needs to be at the left, or an error will arise.

There are some rules when naming variables. For instance, you can use letters, numbers, dots and underscores in the variable name, but underscores can’t be the first character of the variable name.

Reserved words

There are also reserved words you can’t use, like TRUE , FALSE , NULL , among others. You can see the full list of R reserved words typing help(Reserved) or ?Reserved .

However, if for some reason you need to name your variable with a reserved word or starting with an underscore you will need to use backticks:

Miscellaneous R operators

Miscellaneous operators in R are operators used for specific purposes , as accessing data, functions, creating sequences or specifying a formula of a model. To clarify, the next table contains all the available miscellaneous operators in R.

In addition, in the following block of code we show several examples of these operators:

Infix operator

You can call an operator as a function . This is known as infix operators. Note that this type of operators are not generally used or needed.

Pipe operator in R

The pipe operator is an operator you can find in several libraries, like dplyr . The operator can be read as ‘AND THEN’ and its purpose is to simplify the syntax when writing R code. As an example, you could subset the cars dataset and then create a summary of the subset with the following code:

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Assignment & evaluation.

The first operator you’ll run into is the assignment operator. The assignment operator is used to assign a value. For instance we can assign the value 3 to the variable x using the <- assignment operator. We can then evaluate the variable by simply typing x at the command line which will return the value of x . Note that prior to the value returned you’ll see ## [1] in the command line. This simply implies that the output returned is the first output. Note that you can type any comments in your code by preceding the comment with the hashtag ( # ) symbol. Any values, symbols, and texts following # will not be evaluated.

Interestingly, R actually allows for five assignment operators:

The original assignment operator in R was <- and has continued to be the preferred among R users. The = assignment operator was added in 2001 primarily because it is the accepted assignment operator in many other languages and beginners to R coming from other languages were so prone to use it. However, R uses = to associate function arguments with values (i.e. f(x = 3) explicitly means to call function f and set the argument x to 3. Consequently, most R programmers prefer to keep = reserved for argument association and use <- for assignment.

The operators <<- is normally only used in functions which we will not get into the details. And the rightward assignment operators perform the same as their leftward counterparts, they just assign the value in an opposite direction.

Overwhelmed yet? Don’t be. This is just meant to show you that there are options and you will likely come across them sooner or later. My suggestion is to stick with the tried and true <- operator. This is the most conventional assignment operator used and is what you will find in all the base R source code…which means it should be good enough for you.

Lastly, note that R is a case sensitive programming language. Meaning all variables, functions, and objects must be called by their exact spelling:

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Assignment Operators in R – Which One to Use and Where

Assignment Operators

R has five common assignment operators:

Many style guides and traditionalists prefer the left arrow operator, <- . Why use that when it’s an extra keystroke? <- always means assignment. The equal sign is overloaded a bit taking on the roles of an assignment operator, function argument binding, or depending on the context, case statement.

Equal or “arrow” as an Assignment Operator?

In R, both the equal and arrow symbols work to assign values. Therefore, the following statements have the same effect of assigning a value on the right to the variable on the left:

There is also a right arrow, -> which assigns the value on the left, to a variable on the right:

All three assign the value of forty-two to the variable x .

So what’s the difference? Are these assignment operators interchangeable? Mostly, yes. The difference comes into play, however, when working with functions.

The equal sign can also work as an operator for function parameters.

x <- 42 y <- 18 function(value = x-y)

History of the <- Operator

The S language also didn’t have == for equality testing, so that was left to the single equal sign. Therefore, variable assignment needed to be accomplished with a different symbol, and the arrow was chosen.

There are some differences of opinion as to which assignment operator to use when it comes to = vs <-. Some believe that = is more clear. The <- operator maintains backward compatibility with S. Google’s R Style Guide recommends using the <- assignment operator, which seems to be a pretty decent reason as well. When all is said and done, though, it is like many things in programming, it depends on what your team does.

assignOps: Assignment Operators

Assignment operators, description.

Assign a value to a name.

There are three different assignment operators: two of them have leftwards and rightwards forms.

The leftwards forms of assignment <- = <<- group right to left, the other from left to right.

value . Thus one can use a <- b <- c <- 6 .

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language . Wadsworth & Brooks/Cole.

Chambers, J. M. (1998) Programming with Data. A Guide to the S Language . Springer (for = ).

assign (and its inverse get ), for “subassignment” such as x[i] <- v , see [<- ; further, environment .

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Assignment Operators in R

R provides two operators for assignment: <- and = .

Understanding their proper use is crucial for writing clear and readable R code.

Using the <- Operator

For assignments.

The <- operator is the preferred choice for assigning values to variables in R.

It clearly distinguishes assignment from argument specification in function calls.

Readability and Tradition

This usage aligns with R’s tradition and enhances code readability.

Using the = Operator

The = operator is commonly used to explicitly specify named arguments in function calls.

It helps in distinguishing argument assignment from variable assignment.

Assignment Capability

While = can also be used for assignment, this practice is less common and not recommended for clarity.

Mixing Up Operators

Potential confusion.

Using = for general assignments can lead to confusion, especially when reading or debugging code.

Mixing operators inconsistently can obscure the distinction between assignment and function argument specification.

In the example above, x = 10 might be mistaken for a function argument rather than an assignment.

Best Practices Recap

Consistency and clarity.

Use <- for variable assignments to maintain consistency and clarity.

Reserve = for specifying named arguments in function calls.

Avoiding Common Mistakes

Be mindful of the context in which you use each operator to prevent misunderstandings.

Consistently using the operators as recommended helps make your code more readable and maintainable.

Quiz: Assignment Operator Best Practices

Which of the following examples demonstrates the recommended use of assignment operators in R?

my_var = 5; mean(x = my_var)
my_var <- 5; mean(x <- my_var)
my_var <- 5; mean(x = my_var)
my_var = 5; mean(x <- my_var)
The correct answer is 3 . my_var <- 5; mean(x = my_var) correctly uses <- for variable assignment and = for specifying a named argument in a function call.

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R has many operators to carry out different mathematical and logical operations.

Operators in R can mainly be classified into the following categories.

R Arithmetic Operators

These operators are used to carry out mathematical operations like addition and multiplication. Here is a list of arithmetic operators available in R.

An example run

R Relational Operators

Relational operators are used to compare between values. Here is a list of relational operators available in R.

Operation on Vectors

The above mentioned operators work on vectors . The variables used above were in fact single element vectors.

We can use the function c() (as in concatenate) to make vectors in R.

All operations are carried out in element-wise fashion. Here is an example.

When there is a mismatch in length (number of elements) of operand vectors, the elements in shorter one is recycled in a cyclic manner to match the length of the longer one.

R will issue a warning if the length of the longer vector is not an integral multiple of the shorter vector.

R Logical Operators

Logical operators are used to carry out Boolean operations like AND , OR etc.

Operators & and | perform element-wise operation producing result having length of the longer operand.

But && and || examines only the first element of the operands resulting into a single length logical vector.

Zero is considered FALSE and non-zero numbers are taken as TRUE . An example run.

R Assignment Operators

These operators are used to assign values to variables.

The operators <- and = can be used, almost interchangeably, to assign to variable in the same environment.

The <<- operator is used for assigning to variables in the parent environments (more like global assignments). The rightward assignments, although available are rarely used.

Sorry about that.

R Data Structures

R statistics, r operators.

Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

R divides the operators in the following groups:

Arithmetic operators
Assignment operators
Comparison operators
Logical operators
Miscellaneous operators

R Arithmetic Operators

Arithmetic operators are used with numeric values to perform common mathematical operations:

R Assignment Operators

Assignment operators are used to assign values to variables:

Note: <<- is a global assigner. You will learn more about this in the Global Variable chapter .

It is also possible to turn the direction of the assignment operator.

x <- 3 is equal to 3 -> x

R Comparison Operators

Comparison operators are used to compare two values:

R Logical Operators

Logical operators are used to combine conditional statements:

R Miscellaneous Operators

Miscellaneous operators are used to manipulate data:

Note: You will learn more about Matrix multiplication and matrices in a later chapter.

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❯ R Tutorial
❯ R Operators

R Operators – Arithmetic, Relational, Logical, Assignment

R Tutorial – We shall learn about R Operators – Arithmetic, Relational, Logical, Assignment and some of the Miscellaneous Operators that R programming language provides.

There are four main categories of Operators in R programming language. They are shown in the following picture :

We shall learn about these operators in detail with Example R programs.

R Arithmetic Operators

Arithmetic Operators are used to accomplish arithmetic operations. They can be operated on the basic data types Numericals, Integers, Complex Numbers. Vectors with these basic data types can also participate in arithmetic operations, during which the operation is performed on one to one element basis.

An example for each of the arithmetic operator on Numerical values is provided below.

r_op_arithmetic.R

An example for each of the arithmetic operator on Vectors is provided below.

r_op_arithmetic_vector.R

R Relational Operators

Relational Operators are those that find out relation between the two operands provided to them. Following are the six relational operations R programming language supports.The output is boolean (TRUE or FALSE) for all of the Relational Operators in R programming language.

An example for each of the relational operator on Numberical values is provided below.

r_op_relational.R

An example for each of the relational operator on Vectors is provided below.

r_op_relational_vector.R

R Logical Operators

Logical Operators in R programming language work only for the basic data types logical, numeric and complex and vectors of these basic data types.

An example for each of the logical operators on Numerical values is provided below.

r_op_logical.R

An example for each of the logical operators on Vectors is provided below.

r_op_logical_vector.R

R Assignment Operators

Assignment Operators are those that help in assigning a value to the variable.

An example for each of the assignment operators is provided below.

r_op_assignment.R

R Miscellaneous Operators

These operators does not fall into any of the categories mentioned above, but are significantly important during R programming for manipulating data.

An example for each of the Miscellaneous operators is provided below.

r_op_misc.R

In this R Tutorial , we have learnt about R Operators – R Arithmetic Operators, R Relational Operators, R Logical Operators, R Assignment Operators, R Miscellaneous Operators with example R commands and R script files.

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The RStudio IDE is amazing. You can enhance your R productivity even more with these simple keyboard shortcuts.

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6 Keyboard Shortcuts (that will change your life)

Let’s speed up common activities with these 6 super-useful keyboard shortcuts.

1: Commenting & Uncommenting Code [Ctrl + Shift + C]

I use this all the time to turn text into commented text. Works with multiple lines too.

Go from this…

To this…

2: Add the Pipe %>% [Ctrl + Shift + M]

My students absolutely love this. You can easily add the Pipe %>% in any spot you’d like! Perfect for data wrangling with dplyr.

3: Insert The Assignment Operator [Alt + -]

My code has tons of assignment operators. This is a simple, time-saver that will make you more productive in building functions and assigning variables values.

4: Cursor-Select Multiple Lines [Ctrl + Alt + Up/Down/Click]

This is a recent addition to my portfolio of must-know keyboard shortcuts. Using Multi-Cursor Select has now become a go-to for editing R code .

Multi-Line Select

…And edit!

5: Find in Files [Ctrl + Shift + F]

THIS IS A SUPER POWER. Seriously. Learn to use this one right now!

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6: Keyboard Shortcut Cheat Sheet [Alt + Shift + K]

More shortcuts!!! Run this to get a Keyboard Shortcut Cheat Sheet.

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Getting started with Python Programming –

Python is a lot easier to code and learn. Python programs can be written on any plain text editor like notepad, notepad++, or anything of that sort. One can also use an online IDE for writing Python codes or can even install one on their system to make it more feasible to write these codes because IDEs provide a lot of features like intuitive code editor, debugger, compiler, etc. To begin with, writing Python Codes and performing various intriguing and useful operations, one must have Python installed on their System. This can be done by following the step by step instructions provided below:

What if Python already exists? Let’s check

Windows don’t come with Python preinstalled, it needs to be installed explicitly. But unlike windows, most of the Linux OS have Python pre-installed, also macOS comes with Python pre-installed. To check if your device is pre-installed with Python or not, just go to Command Line (For Windows , search for cmd in the Run dialog( + R ), for Linux open the terminal using Ctrl+Alt+T , for macOS use control+Option+Shift+T .

Now run the following command:

For Python2

For Python3

If Python is already installed, it will generate a message with the Python version available.

Download and Installation

Before starting with the installation process, you need to download it. For that all versions of Python for Windows, Linux, and MacOS are available on python.org .

Download the Python and follow the further instructions for the installation of Python.

Beginning the installation.

How to run a Python program

Let’s consider a simple Hello World Program.

Generally, there are two ways to run a Python program.

Using IDEs: You can use various IDEs(Pycharm, Jupyter Notebook, etc.) which can be used to run Python programs.
Using Command-Line: You can also use command line options to run a Python program. Below steps demonstrate how to run a Python program on Command line in Windows/Unix Operating System:

Open Commandline and then to compile the code type python HelloWorld.py . If your code has no error then it will execute properly and output will be displayed.

Open Terminal of your Unix/Linux OS and then to compile the code type python HelloWorld.py . If your code has no error then it will execute properly and output will be displayed.

Fundamentals of Python

Python indentation.

Python uses indentation to highlight the blocks of code. Whitespace is used for indentation in Python. All statements with the same distance to the right belong to the same block of code. If a block has to be more deeply nested, it is simply indented further to the right. You can understand it better by looking at the following lines of code.

The lines print(‘Logging on to geeksforgeeks…’) and print(‘retype the URL.’) are two separate code blocks. The two blocks of code in our example if-statement are both indented four spaces. The final print(‘All set!’) is not indented, and so it does not belong to the else-block.

Note: For more information, refer Indentation in Python .

Python Comments

Comments are useful information that the developers provide to make the reader understand the source code. It explains the logic or a part of it used in the code. There are two types of comment in Python:

Single line comments: Python single line comment starts with hashtag symbol with no white spaces.

Multi-line string as comment: Python multi-line comment is a piece of text enclosed in a delimiter (“””) on each end of the comment.

Note: For more information, refer Comments in Python .

Variables in Python are not “statically typed”. We do not need to declare variables before using them or declare their type. A variable is created the moment we first assign a value to it.

Note: For more information, refer Python Variables .

Operators are the main building block of any programming language. Operators allow the programmer to perform different kinds of operations on operands. These operators can be categorized based upon their different functionality:

Arithmetic operators : Arithmetic operators are used to perform mathematical operations like addition, subtraction, multiplication and division.

Relational Operators: Relational operators compares the values. It either returns True or False according to the condition.

Logical Operators: Logical operators perform Logical AND, Logical OR and Logical NOT operations.

Bitwise operators: Bitwise operator acts on bits and performs bit by bit operation.

Assignment operators: Assignment operators are used to assign values to the variables.

Special operators: Special operators are of two types-

Identity operator that contains is and is not .
Membership operator that contains in and not in .

Note: For more information, refer Basic Operators in Python .

Basics of Input/Output

Taking input from user –.

Python provides us with two inbuilt functions to read the input from the keyboard.

raw_input(): This function works in older version (like Python 2.x). This function takes exactly what is typed from the keyboard, convert it to string and then return it to the variable in which we want to store. For example:
input(): This function first takes the input from the user and then evaluates the expression, which means Python automatically identifies whether the user entered a string or a number or list. For example:

Note: For more information, refer Python input() and raw_input() .

Printing output to console –

The simplest way to produce output is using the print() function where you can pass zero or more expressions separated by commas. This function converts the expressions you pass into a string before writing to the screen.

Data types are the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data. Since everything is an object in Python programming, data types are actually classes and variables are instance (object) of these classes.

In Python, numeric data type represent the data which has numeric value. Numeric value can be interger, floating number or even complex numbers. These values are defined as int , float and complex class in Python.

Sequence Type

In Python, a sequence is the ordered collection of similar or different data types. Sequences allow storing multiple values in an organized and efficient fashion. There are several sequence types in Python –

1) String: A string is a collection of one or more characters put in a single quote, double-quote or triple quote. In python there is no character data type, a character is a string of length one. It is represented by str class. Strings in Python can be created using single quotes or double quotes or even triple quotes.

Accessing elements of string –

Deleting/Updating from a String –

In Python, Updation or deletion of characters from a String is not allowed because Strings are immutable. Only new strings can be reassigned to the same name.

Note: For more information, refer Python String .

Refer to the below articles to know more about Strings: String Slicing in Python Python String Concatenation Python String Interpolation Python programming questions on String

2) List: Lists are just like the arrays, declared in other languages. A single list may contain DataTypes like Integers, Strings, as well as Objects. The elements in a list are indexed according to a definite sequence and the indexing of a list is done with 0 being the first index. It is represented by list class.

Adding Elements to a List: Using append() , insert() and extend()

Accessing elements from the List –

Use the index operator [ ] to access an item in a list. In Python, negative sequence indexes represent positions from the end of the array. Instead of having to compute the offset as in List[len(List)-3] , it is enough to just write List[-3] .

Removing Elements from the List: Using remove() and pop()

Note: For more information, refer Python List .

Refer to the below articles to know more about List: Iterate over a list in Python Python List Comprehension and Slicing Python programming questions on List

3) Tuple: Tuple is an ordered collection of Python objects much like a list. The important difference between a list and a tuple is that tuples are immutable. It is represented by tuple class. In Python, tuples are created by placing a sequence of values separated by ‘comma’ with or without the use of parentheses for grouping of the data sequence.

Accessing element of a tuple –

Use the index operator [ ] to access an item in a tuple.

Deleting/updating elements of tuple –

Items of a tuple cannot be deleted as tuples are immutable in Python. Only new tuples can be reassigned to the same name.

Note: For more information, refer Python Tuples .

Refer to the below articles to know more about tuples: Unpacking a Tuple in Python Operations on Tuples Python programming questions on Tuples

Booleans are data type with one of the two built-in values, True or False . It is denoted by the class bool.

In Python, Set is an unordered collection of data type that is iterable, mutable and has no duplicate elements. The order of elements in a set is undefined though it may consist of various elements. Sets can be created by using the built-in set() function with an iterable object or a sequence by placing the sequence inside curly braces {} , separated by ‘comma’.

Adding elements: Using add() and update()

Accessing a Set: One can loop through the set items using a for loop as set items cannot be accessed by referring to an index.

Removing elements from a set: Using remove() , discard() , pop() and clear()

Note: For more information, refer Python Sets .

Refer to the below articles to know more about Sets: Iterate over a set in Python frozenset() in Python Python programming questions on Sets

Dictionary in Python is an unordered collection of data values, used to store data values like a map. Dictionary holds key:value pair. Each key-value pair in a Dictionary is separated by a colon : , whereas each key is separated by a ‘comma’. A Dictionary can be created by placing a sequence of elements within curly {} braces, separated by ‘comma’.

Nested Dictionary:

Note: For more information, refer Python Nested Dictionary . Adding elements to a Dictionary: One value at a time can be added to a Dictionary by defining value along with the key e.g. Dict[Key] = ‘Value’ .

Accessing elements from a Dictionary: In order to access the items of a dictionary refer to its key name or use get() method.

Removing Elements from Dictionary: Using pop() and popitem()

Note: For more information, refer Python Dictionary .

Refer to the below articles to know more about dictionary: Operations on Dictionary Iterate over a dictionary in Python Python programming questions on dictionary

Decision Making

Decision Making in programming is similar to decision making in real life. A programming language uses control statements to control the flow of execution of the program based on certain conditions. These are used to cause the flow of execution to advance and branch based on changes to the state of a program.

Decision-making statements in Python

if statement
if..else statements
nested if statements
if-elif ladder

Example 1: To demonstrate if and if-else

Example 2: To demonstrate nested-if and if-elif

Note: For more information, refer Decision Making in Python .

Control flow (Loops)

Loops in programming come into use when we need to repeatedly execute a block of statements. For example: Suppose we want to print “Hello World” 10 times. This can be done with the help of loops. The loops in Python are:

Output: Hello Geek Hello Geek Hello Geek 4 3 2 1 11 Note: For more information, refer Python While Loops .

Output: List Iteration geeks for geeks String Iteration G e e k s For-else loop G e e k s No Break G Note: For more information, refer Python For Loops .

Output: 0 1 2 3 4 2 3 4 5 6 7 8 15 18 21 24 Note: For more information, refer Python range() function .

Refer to the below articles to know more about Loops: Understanding for-loop in Python Backward iteration in Python

Loop control statements

Loop control statements change execution from its normal sequence. Following are the loop control statements provided by Python:

Break: Break statement in Python is used to bring the control out of the loop when some external condition is triggered.
Continue: Continue statement is opposite to that of break statement, instead of terminating the loop, it forces to execute the next iteration of the loop.
Pass: Pass statement is used to write empty loops. Pass is also used for empty control statement, function and classes.

Note: For more information, refer break, continue and pass in Python .

Functions are generally the block of codes or statements in a program that gives the user the ability to reuse the same code which ultimately saves the excessive use of memory, acts as a time saver and more importantly, provides better readability of the code. So basically, a function is a collection of statements that perform some specific task and return the result to the caller. A function can also perform some specific task without returning anything. In Python, def keyword is used to create functions.

Function with arguments

Default arguments: A default argument is a parameter that assumes a default value if a value is not provided in the function call for that argument.

Output: ('x: ', 10) ('y: ', 50)

Keyword arguments: The idea is to allow caller to specify argument name with values so that caller does not need to remember order of parameters.

Output: ('Geeks', 'Practice') ('Geeks', 'Practice')

Variable length arguments: In Python a function can also have variable number of arguments. This can be used in the case when we do not know in advance the number of arguments that will be passed into a function.

Output: Hello Welcome to GeeksforGeeks first == Geeks last == Geeks mid == for

Note: For more information, refer Functions in Python .

Refer to the below articles to know more about functions: Python Inner Functions Python return statement Call function from another function

Lambda functions

In Python, the lambda/anonymous function means that a function is without a name. The lambda keyword is used to create anonymous functions. Lambda function can have any number of arguments but has only one expression.

Note: For more information, refer Python lambda (Anonymous Functions) .

Refer to the below articles to know more about Lambda: Python programming questions on Lambda

Object Oriented Programming

Object-oriented programming aims to implement real-world entities like inheritance, hiding, polymorphism, etc in programming. The main aim of OOP is to bind together the data and the functions that operate on them so that no other part of the code can access this data except that function.

Classes and Objects

Class creates a user-defined data structure, which holds its own data members and member functions, which can be accessed and used by creating an instance of that class. A class is like a blueprint for an object.

An Object is an instance of a Class. A class is like a blueprint while an instance is a copy of the class with actual values.

Note: For more information, refer Python Classes and Objects .

self represents the instance of the class. By using the “ self ” keyword we can access the attributes and methods of the class in python. It binds the attributes with the given arguments.

Note: For more information, refer self in Python class .

Constructors and Destructors

Constructors: Constructors are generally used for instantiating an object.The task of constructors is to initialize(assign values) to the data members of the class when an object of class is created. In Python the __init__() method is called the constructor and is always called when an object is created. There can be two types of constructors:

Default constructor: The constructor which is called implicilty and do not accept any argument.
Parameterized constructor: Constructor which is called explicitly with parameters is known as parameterized constructor.

Note: For more information, refer Constructors in Python .

Destructors: Destructors are called when an object gets destroyed. The __del__() method is a known as a destructor method in Python. It is called when all references to the object have been deleted i.e when an object is garbage collected.

Note: For more information, refer Destructors in Python .

Inheritance

Inheritance is the ability of any class to extract and use features of other classes. It is the process by which new classes called the derived classes are created from existing classes called Base classes.

Note: For more information, refer Python inheritance .

Encapsulation

Encapsulation describes the idea of wrapping data and the methods that work on data within one unit. This puts restrictions on accessing variables and methods directly and can prevent the accidental modification of data.

Note: For more information, refer Encapsulation in Python .

Polymorphism

Polymorphism refers to the ability of OOPs programming languages to differentiate between entities with the same name efficiently. This is done by Python with the help of the signature of these entities.

Refer to the articles to know more about OOPS: Bound, unbound, and static methods in Python Multiple inheritance in Python __new__ in Python

File Handling

File handling is the ability of Python to handle files i.e. to read and write files along with many other file handling options. Python treats files differently as text or binary and this is important. Each line of code includes a sequence of characters and they form a text file. Each line of a file is terminated with a special character, called the EOL or End of Line characters like comma {, } or newline character.

Basic File Handling operations in Python are:

1) Open a file: Opening a file refers to getting the file ready either for reading or for writing. This can be done using the open() function. This function returns a file object and takes two arguments, one that accepts the file name and another that accepts the mode(Access Mode). Python provides six Access Modes:

Note: For more information, refer Open a File in Python .

2) Close the file: close() function closes the file and frees the memory space acquired by that file.

3) Reading from a File: There are three ways to read data from a text file.

read(): Returns the read bytes in form of a string. Reads n bytes, if no n specified, reads the entire file. File_object.read([n])
readline(): Reads a line of the file and returns in form of a string.For specified n, reads at most n bytes. However, does not reads more than one line, even if n exceeds the length of the line. File_object.readline([n])
readlines(): Reads all the lines and return them as each line a string element in a list. File_object.readlines()

Let’s suppose the file looks like this:

Note: For more information, refer How to read from a file in Python .

4) Writing to a file: There are two ways to write in a file.

write(): Inserts the string str1 in a single line in the text file. File_object.write(str1)
writelines(): For a list of string elements, each string is inserted in the text file. Used to insert multiple strings at a single time. File_object.writelines(L) for L = [str1, str2, str3]

Note: For more information, refer Writing to file in Python .

Refer to the below articles to know more about File-Handling: Python seek() function Python tell() function OS Module in Python Programs on OS module

Modules and Packages

A module is a self-contained Python file that contains Python statements and definitions, like a file named GFG.py , which can be considered as a module named GFG which can be imported with the help of import statement .

Let’s create a simple module named GFG.

To use the above created module, create a new Python file in the same directory and import GFG module using the import statement.

Note: For more information, refer Python Modules .

Packages are a way of structuring many packages and modules which helps in a well-organized hierarchy of data set, making the directories and modules easy to access.

To create a package in Python, we need to follow these three simple steps:

First, we create a directory and give it a package name, preferably related to its operation.
Then we put the classes and the required functions in it.
Finally we create an __init__.py file inside the directory, to let Python know that the directory is a package.

Example: Let’s create a package for cars.

First we create a directory and name it Cars.
Finally we create the __init__.py file. This file will be placed inside the Cars directory and can be left blank.

Now, let’s use the package that we created. To do this make a sample.py file in the same directory where Cars package is located and add the following code to it:

Note: For more information, refer Create and Access a Python Package .

Regular expressions(RegEx)

Python RegEx is a powerful text matching tool that uses a pre-defined pattern to match the text. It can identify the presence or absence of text by comparing it to a specific pattern, and it can also divide a pattern into one or more sub-patterns. Below is the list of metacharacters:

The most frequently used methods are:

re.findall(): Return all non-overlapping matches of pattern in string, as a list of strings. The string is scanned left-to-right, and matches are returned in the order found.

Output: ['123456789', '987654321'] In the above example, metacharacter blackslash ‘\’ has a very important role as it signals various sequences. If the blackslash is to be used without its special meaning as metacharacter, use ’\\’ .<> \d Matches any decimal digit, this is equivalent to the set class [0-9]. \D Matches any non-digit character. \s Matches any whitespace character. \S Matches any non-whitespace character \w Matches any alphanumeric character, this is equivalent to the class [a-zA-Z0-9_]. \W Matches any non-alphanumeric character.

re.compile(): Regular expressions are compiled into pattern objects, which have methods for various operations such as searching for pattern matches or performing string substitutions.

Output: ['e', 'a', 'd', 'b', 'e', 'a']

re.match(): This function attempts to match pattern to whole string. The re.match function returns a match object on success, None on failure.

Output: Given Data: Jun 24 Month: Jun Day: 24 Not a valid date

re.search(): This method either returns None (if the pattern doesn’t match), or a re.MatchObject that contains information about the matching part of the string.

Output: Match at index 14, 21 Full match: June 24 Month: June Day: 24

Note: For more information, refer Regular Expression in Python .

Exception handling

Like other languages, Python also provides the runtime errors via exception handling method with the help of try-except .

How try-except works?

First try clause is executed i.e. the code between try and except clause.
If there is no exception, then only try clause will run, except clause is finished.
If any exception occurred, try clause will be skipped and except clause will run.
If any exception occurs, but the except clause within the code doesn’t handle it, it is passed on to the outer try statements. If the exception left unhandled, then the execution stops.
A try statement can have more than one except clause.

Code 1: No exception, so try clause will run.

Code 2: There is an exception so only except clause will run.

Else Clause: In python, you can also use else clause on try-except block which must be present after all the except clauses. The code enters the else block only if the try clause does not raise an exception.

Raising Exception: The raise statement allows the programmer to force a specific exception to occur. This must be either an exception instance or an exception class. To know more about the list of exception class click here .

Note: For more information, refer Python exception handling .

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Example 1: Why You Should Use ＜- Instead of = in R

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