The = (equal to) symbol is defined as assignment operator in Python. The value of Python expression on its right is assigned to a single variable on its left. The = symbol as in programming in general (and Python in particular) should not be confused with its usage in Mathematics, where it states that the expressions on the either side of the symbol are equal.
Example of Assignment Operator in Python
Consider following Python statements −
a =10
b =5
a = a + b
print(a)
At the first instance, at least for somebody new to programming but who knows maths, the statement “a=a+b” looks strange. How could a be equal to “a+b”? However, it needs to be reemphasized that the = symbol is an assignment operator here and not used to show the equality of LHS and RHS.
Because it is an assignment, the expression on right evaluates to 15, the value is assigned to a.
In the statement “a+=b”, the two operators “+” and “=” can be combined in a “+=” operator. It is called as add and assign operator. In a single statement, it performs addition of two operands “a” and “b”, and result is assigned to operand on left, i.e., “a”.
Augmented Assignment Operators in Python
In addition to the simple assignment operator, Python provides few more assignment operators for advanced use. They are called cumulative or augmented assignment operators. In this chapter, we shall learn to use augmented assignment operators defined in Python.
Python has the augmented assignment operators for all arithmetic and comparison operators.
Python augmented assignment operators combines addition and assignment in one statement. Since Python supports mixed arithmetic, the two operands may be of different types. However, the type of left operand changes to the operand of on right, if it is wider.
Example
The += operator is an augmented operator. It is also called cumulative addition operator, as it adds “b” in “a” and assigns the result back to a variable.
The following are the augmented assignment operators in Python:
Augmented Addition Operator
Augmented Subtraction Operator
Augmented Multiplication Operator
Augmented Division Operator
Augmented Modulus Operator
Augmented Exponent Operator
Augmented Floor division Operator
Augmented Addition Operator (+=)
Following examples will help in understanding how the “+=” operator works −
Open Compiler
a=10
b=5print("Augmented addition of int and int")
a+=b # equivalent to a=a+bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented addition of int and float")
a+=b # equivalent to a=a+bprint("a=",a,"type(a):",type(a))
a=10.50
b=5+6jprint("Augmented addition of float and complex")
a+=b #equivalent to a=a+bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented addition of int and int
a= 15 type(a): <class 'int'>
Augmented addition of int and float
a= 15.5 type(a): <class 'float'>
Augmented addition of float and complex
a= (15.5+6j) type(a): <class 'complex'>
Augmented Subtraction Operator (-=)
Use -= symbol to perform subtract and assign operations in a single statement. The “a-=b” statement performs “a=a-b” assignment. Operands may be of any number type. Python performs implicit type casting on the object which is narrower in size.
Open Compiler
a=10
b=5print("Augmented subtraction of int and int")
a-=b #equivalent to a=a-bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented subtraction of int and float")
a-=b #equivalent to a=a-bprint("a=",a,"type(a):",type(a))
a=10.50
b=5+6jprint("Augmented subtraction of float and complex")
a-=b #equivalent to a=a-bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented subtraction of int and int
a= 5 type(a): <class 'int'>
Augmented subtraction of int and float
a= 4.5 type(a): <class 'float'>
Augmented subtraction of float and complex
a= (5.5-6j) type(a): <class 'complex'>
Augmented Multiplication Operator (*=)
The “*=” operator works on similar principle. “a*=b” performs multiply and assign operations, and is equivalent to “a=a*b”. In case of augmented multiplication of two complex numbers, the rule of multiplication as discussed in the previous chapter is applicable.
Open Compiler
a=10
b=5print("Augmented multiplication of int and int")
a*=b #equivalent to a=a*bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented multiplication of int and float")
a*=b #equivalent to a=a*bprint("a=",a,"type(a):",type(a))
a=6+4j
b=3+2jprint("Augmented multiplication of complex and complex")
a*=b #equivalent to a=a*bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented multiplication of int and int
a= 50 type(a): <class 'int'>
Augmented multiplication of int and float
a= 55.0 type(a): <class 'float'>
Augmented multiplication of complex and complex
a= (10+24j) type(a): <class 'complex'>
Augmented Division Operator (/=)
The combination symbol “/=” acts as divide and assignment operator, hence “a/=b” is equivalent to “a=a/b”. The division operation of int or float operands is float. Division of two complex numbers returns a complex number. Given below are examples of augmented division operator.
Open Compiler
a=10
b=5print("Augmented division of int and int")
a/=b #equivalent to a=a/bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented division of int and float")
a/=b #equivalent to a=a/bprint("a=",a,"type(a):",type(a))
a=6+4j
b=3+2jprint("Augmented division of complex and complex")
a/=b #equivalent to a=a/bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented division of int and int
a= 2.0 type(a): <class 'float'>
Augmented division of int and float
a= 1.8181818181818181 type(a): <class 'float'>
Augmented division of complex and complex
a= (2+0j) type(a): <class 'complex'>
Augmented Modulus Operator (%=)
To perform modulus and assignment operation in a single statement, use the %= operator. Like the mod operator, its augmented version also is not supported for complex number.
Open Compiler
a=10
b=5print("Augmented modulus operator with int and int")
a%=b #equivalent to a=a%bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented modulus operator with int and float")
a%=b #equivalent to a=a%bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented modulus operator with int and int
a= 0 type(a): <class 'int'>
Augmented modulus operator with int and float
a= 4.5 type(a): <class 'float'>
Augmented Exponent Operator (**=)
The “**=” operator results in computation of “a” raised to “b”, and assigning the value back to “a”. Given below are some examples −
Open Compiler
a=10
b=5print("Augmented exponent operator with int and int")
a**=b #equivalent to a=a**bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented exponent operator with int and float")
a**=b #equivalent to a=a**bprint("a=",a,"type(a):",type(a))
a=6+4j
b=3+2jprint("Augmented exponent operator with complex and complex")
a**=b #equivalent to a=a**bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented exponent operator with int and int
a= 100000 type(a): <class 'int'>
Augmented exponent operator with int and float
a= 316227.7660168379 type(a): <class 'float'>
Augmented exponent operator with complex and complex
a= (97.52306038414744-62.22529992036203j) type(a): <class 'complex'>
Augmented Floor division Operator (//=)
For performing floor division and assignment in a single statement, use the “//=” operator. “a//=b” is equivalent to “a=a//b”. This operator cannot be used with complex numbers.
Open Compiler
a=10
b=5print("Augmented floor division operator with int and int")
a//=b #equivalent to a=a//bprint("a=",a,"type(a):",type(a))
a=10
b=5.5print("Augmented floor division operator with int and float")
a//=b #equivalent to a=a//bprint("a=",a,"type(a):",type(a))
It will produce the following output −
Augmented floor division operator with int and int
a= 2 type(a): <class 'int'>
Augmented floor division operator with int and float
a= 1.0 type(a): <class 'float'>
Python uses two more operators, combining “=” symbol with these two. The “<=” symbol is for less than or equal to operator and the “>=” symbol is for greater than or equal to operator.
Different Comparison Operators in Python
Python has two more comparison operators in the form of “==” and “!=”. They are for is equal to and is not equal to operators. Hence, there are six comparison operators in Python and they are listed below in this table:
<
Less than
a<b
>
Greater than
a>b
<=
Less than or equal to
a<=b
>=
Greater than or equal to
a>=b
==
Is equal to
a==b
!=
Is not equal to
a!=b
Comparison operators are binary in nature, requiring two operands. An expression involving a comparison operator is called a Boolean expression, and always returns either True or False.
Example
Open Compiler
a=5
b=7print(a>b)print(a<b)
It will produce the following output −
False
True
Both the operands may be Python literals, variables or expressions. Since Python supports mixed arithmetic, you can have any number type operands.
Example
The following code demonstrates the use of Python’s comparison operators with integer numbers −
Both operands are integer
a= 5 b= 7 a>b is False
a= 5 b= 7 a<b is True
a= 5 b= 7 a==b is False
a= 5 b= 7 a!=b is True
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Comparison of Float Number
In the following example, an integer and a float operand are compared.
Example
Open Compiler
print("comparison of int and float")
a=10
b=10.0print("a=",a,"b=",b,"a>b is", a>b)print("a=",a,"b=",b,"a<b is",a<b)print("a=",a,"b=",b,"a==b is",a==b)print("a=",a,"b=",b,"a!=b is",a!=b)
It will produce the following output −
comparison of int and float
a= 10 b= 10.0 a>b is False
a= 10 b= 10.0 a<b is False
a= 10 b= 10.0 a==b is True
a= 10 b= 10.0 a!=b is False
Comparison of Complex umbers
Although complex object is a number data type in Python, its behavior is different from others. Python doesn’t support < and > operators, however it does support equality (==) and inequality (!=) operators.
Example
Open Compiler
print("comparison of complex numbers")
a=10+1j
b=10.-1jprint("a=",a,"b=",b,"a==b is",a==b)print("a=",a,"b=",b,"a!=b is",a!=b)
It will produce the following output −
comparison of complex numbers
a= (10+1j) b= (10-1j) a==b is False
a= (10+1j) b= (10-1j) a!=b is True
You get a TypeError with less than or greater than operators.
Example
Open Compiler
print("comparison of complex numbers")
a=10+1j
b=10.-1jprint("a=",a,"b=",b,"a<b is",a<b)print("a=",a,"b=",b,"a>b is",a>b)
It will produce the following output −
comparison of complex numbers
Traceback (most recent call last):
File "C:\Users\mlath\examples\example.py", line 5, in <module>
print ("a=",a, "b=",b,"a<b is",a<b)
^^^
TypeError: '<' not supported between instances of 'complex' and
'complex
Comparison of Booleans
Boolean objects in Python are really integers: True is 1 and False is 0. In fact, Python treats any non-zero number as True. In Python, comparison of Boolean objects is possible. “False < True” is True!
Example
Open Compiler
print("comparison of Booleans")
a=True
b=Falseprint("a=",a,"b=",b,"a<b is",a<b)print("a=",a,"b=",b,"a>b is",a>b)print("a=",a,"b=",b,"a==b is",a==b)print("a=",a,"b=",b,"a!=b is",a!=b)
It will produce the following output −
comparison of Booleans
a= True b= False a<b is False
a= True b= False a>b is True
a= True b= False a==b is False
a= True b= False a!=b is True
Comparison of Sequence Types
In Python, comparison of only similar sequence objects can be performed. A string object is comparable with another string only. A list cannot be compared with a tuple, even if both have same items.
Example
Open Compiler
print("comparison of different sequence types")
a=(1,2,3)
b=[1,2,3]print("a=",a,"b=",b,"a<b is",a<b)
It will produce the following output −
comparison of different sequence types
Traceback (most recent call last):
File "C:\Users\mlath\examples\example.py", line 5, in <module>
print ("a=",a, "b=",b,"a<b is",a<b)
^^^
TypeError: '<' not supported between instances of 'tuple' and 'list'
Sequence objects are compared by lexicographical ordering mechanism. The comparison starts from item at 0th index. If they are equal, comparison moves to next index till the items at certain index happen to be not equal, or one of the sequences is exhausted. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one.
Which of the operands is greater depends on the difference in values of items at the index where they are unequal. For example, ‘BAT’>’BAR’ is True, as T comes after R in Unicode order.
If all items of two sequences compare equal, the sequences are considered equal.
Example
Open Compiler
print("comparison of strings")
a='BAT'
b='BALL'print("a=",a,"b=",b,"a<b is",a<b)print("a=",a,"b=",b,"a>b is",a>b)print("a=",a,"b=",b,"a==b is",a==b)print("a=",a,"b=",b,"a!=b is",a!=b)
It will produce the following output −
comparison of strings
a= BAT b= BALL a<b is False
a= BAT b= BALL a>b is True
a= BAT b= BALL a==b is False
a= BAT b= BALL a!=b is True
In the following example, two tuple objects are compared −
Example
Open Compiler
print("comparison of tuples")
a=(1,2,4)
b=(1,2,3)print("a=",a,"b=",b,"a<b is",a<b)print("a=",a,"b=",b,"a>b is",a>b)print("a=",a,"b=",b,"a==b is",a==b)print("a=",a,"b=",b,"a!=b is",a!=b)
The use of “<” and “>” operators for Python’s dictionary is not defined. In case of these operands, TypeError: ‘<‘ not supported between instances of ‘dict’ and ‘dict’ is reported.
Equality comparison checks if the length of both the dict items is same. Length of dictionary is the number of key-value pairs in it.
Python dictionaries are simply compared by length. The dictionary with fewer elements is considered less than a dictionary with more elements.
Example
Open Compiler
print("comparison of dictionary objects")
a={1:1,2:2}
b={2:2,1:1,3:3}print("a=",a,"b=",b,"a==b is",a==b)print("a=",a,"b=",b,"a!=b is",a!=b)
Python arithmetic operators are used to perform mathematical operations such as addition, subtraction, multiplication, division, and more on numbers. Arithmetic operators are binary operators in the sense they operate on two operands. Python fully supports mixed arithmetic. That is, the two operands can be of two different number types. In such a situation.
Types of Arithmetic Operators
Following is the table which lists down all the arithmetic operators available in Python:
Operator
Name
Example
+
Addition
a + b = 30
–
Subtraction
a – b = -10
*
Multiplication
a * b = 200
/
Division
b / a = 2
%
Modulus
b % a = 0
**
Exponent
a**b =10**20
//
Floor Division
9//2 = 4
Let us study these operators with examples.
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Addition Operator
The addition operator represents by + symbol. It is a basic arithmetic operator. It adds the two numeric operands on the either side and returns the addition result.
Example to add two integer numbers
In the following example, the two integer variables are the operands for the “+” operator.
Open Compiler
a=10
b=20print("Addition of two integers")print("a =",a,"b =",b,"addition =",a+b)
It will produce the following output −
Addition of two integers
a = 10 b = 20 addition = 30
Example to add integer and float numbers
Addition of integer and float results in a float.
Open Compiler
a=10
b=20.5print("Addition of integer and float")print("a =",a,"b =",b,"addition =",a+b)
It will produce the following output −
Addition of integer and float
a = 10 b = 20.5 addition = 30.5
Example to add two complex numbers
The result of adding float to complex is a complex number.
Open Compiler
a=10+5j
b=20.5print("Addition of complex and float")print("a=",a,"b=",b,"addition=",a+b)
It will produce the following output −
Addition of complex and float
a= (10+5j) b= 20.5 addition= (30.5+5j)
Subtraction Operator
The subtraction operator represents by – symbol. It subtracts the second operand from the first. The resultant number is negative if the second operand is larger.
Example to subtract two integer numbers
First example shows subtraction of two integers.
Open Compiler
a=10
b=20print("Subtraction of two integers:")print("a =",a,"b =",b,"a-b =",a-b)print("a =",a,"b =",b,"b-a =",b-a)
Result −
Subtraction of two integers
a = 10 b = 20 a-b = -10
a = 10 b = 20 b-a = 10
Example to subtract integer and float numbers
Subtraction of an integer and a float follows the same principle.
Open Compiler
a=10
b=20.5print("subtraction of integer and float")print("a=",a,"b=",b,"a-b=",a-b)print("a=",a,"b=",b,"b-a=",b-a)
It will produce the following output −
subtraction of integer and float
a= 10 b= 20.5 a-b= -10.5
a= 10 b= 20.5 b-a= 10.5
Example to subtract complex numbers
In the subtraction involving a complex and a float, real component is involved in the operation.
Open Compiler
a=10+5j
b=20.5print("subtraction of complex and float")print("a=",a,"b=",b,"a-b=",a-b)print("a=",a,"b=",b,"b-a=",b-a)
It will produce the following output −
subtraction of complex and float
a= (10+5j) b= 20.5 a-b= (-10.5+5j)
a= (10+5j) b= 20.5 b-a= (10.5-5j)
Multiplication Operator
The * (asterisk) symbol is defined as a multiplication operator in Python (as in many languages). It returns the product of the two operands on its either side. If any of the operands negative, the result is also negative. If both are negative, the result is positive. Changing the order of operands doesn’t change the result
Example to multiply two integers
Open Compiler
a=10
b=20print("Multiplication of two integers")print("a =",a,"b =",b,"a*b =",a*b)
It will produce the following output −
Multiplication of two integers
a = 10 b = 20 a*b = 200
Example to multiply integer and float numbers
In multiplication, a float operand may have a standard decimal point notation, or a scientific notation.
Open Compiler
a=10
b=20.5print("Multiplication of integer and float")print("a=",a,"b=",b,"a*b=",a*b)
a=-5.55
b=6.75E-3print("Multiplication of float and float")print("a =",a,"b =",b,"a*b =",a*b)
It will produce the following output −
Multiplication of integer and float
a = 10 b = 20.5 a-b = -10.5
Multiplication of float and float
a = -5.55 b = 0.00675 a*b = -0.037462499999999996
Example to multiply complex numbers
For the multiplication operation involving one complex operand, the other operand multiplies both the real part and imaginary part.
Open Compiler
a=10+5j
b=20.5print("Multiplication of complex and float")print("a =",a,"b =",b,"a*b =",a*b)
It will produce the following output −
Multiplication of complex and float
a = (10+5j) b = 20.5 a*b = (205+102.5j)
Division Operator
The “/” symbol is usually called as forward slash. The result of division operator is numerator (left operand) divided by denominator (right operand). The resultant number is negative if any of the operands is negative. Since infinity cannot be stored in the memory, Python raises ZeroDivisionError if the denominator is 0.
The result of division operator in Python is always a float, even if both operands are integers.
Example to divide two numbers
Open Compiler
a=10
b=20print("Division of two integers")print("a=",a,"b=",b,"a/b=",a/b)print("a=",a,"b=",b,"b/a=",b/a)
It will produce the following output −
Division of two integers
a= 10 b= 20 a/b= 0.5
a= 10 b= 20 b/a= 2.0
Example to divide two float numbers
In Division, a float operand may have a standard decimal point notation, or a scientific notation.
Open Compiler
a=10
b=-20.5print("Division of integer and float")print("a=",a,"b=",b,"a/b=",a/b)
a=-2.50
b=1.25E2print("Division of float and float")print("a=",a,"b=",b,"a/b=",a/b)
It will produce the following output −
Division of integer and float
a= 10 b= -20.5 a/b= -0.4878048780487805
Division of float and float
a= -2.5 b= 125.0 a/b= -0.02
Example to divide complex numbers
When one of the operands is a complex number, division between the other operand and both parts of complex number (real and imaginary) object takes place.
Open Compiler
a=7.5+7.5j
b=2.5print("Division of complex and float")print("a =",a,"b =",b,"a/b =",a/b)print("a =",a,"b =",b,"b/a =",b/a)
It will produce the following output −
Division of complex and float
a = (7.5+7.5j) b = 2.5 a/b = (3+3j)
a = (7.5+7.5j) b = 2.5 b/a = (0.16666666666666666-0.16666666666666666j)
If the numerator is 0, the result of division is always 0 except when denominator is 0, in which case, Python raises ZeroDivisionError wirh Division by Zero error message.
a= 0 b= 2.5 a/b= 0.0
Traceback (most recent call last):
File "C:\Users\mlath\examples\example.py", line 20, in <module>
print ("a=",a,"b=",b,"b/a=",b/a)
~^~
ZeroDivisionError: float division by zero
Modulus Operator
Python defines the “%” symbol, which is known aa Percent symbol, as Modulus (or modulo) operator. It returns the remainder after the denominator divides the numerator. It can also be called Remainder operator. The result of the modulus operator is the number that remains after the integer quotient. To give an example, when 10 is divided by 3, the quotient is 3 and remainder is 1. Hence, 10%3 (normally pronounced as 10 mod 3) results in 1.
Example for modulus operation on integers
If both the operands are integer, the modulus value is an integer. If numerator is completely divisible, remainder is 0. If numerator is smaller than denominator, modulus is equal to the numerator. If denominator is 0, Python raises ZeroDivisionError.
Python doesn’t accept complex numbers to be used as operand in modulus operation. It throws TypeError: unsupported operand type(s) for %.
Exponent Operator
Python uses ** (double asterisk) as the exponent operator (sometimes called raised to operator). So, for a**b, you say a raised to b, or even bth power of a.
If in the exponentiation expression, both operands are integer, result is also an integer. In case either one is a float, the result is float. Similarly, if either one operand is complex number, exponent operator returns a complex number.
If the base is 0, the result is 0, and if the index is 0 then the result is always 1.
Floor division is also called as integer division. Python uses // (double forward slash) symbol for the purpose. Unlike the modulus or modulo which returns the remainder, the floor division gives the quotient of the division of operands involved.
If both operands are positive, floor operator returns a number with fractional part removed from it. For example, the floor division of 9.8 by 2 returns 4 (pure division is 4.9, strip the fractional part, result is 4).
But if one of the operands is negative, the result is rounded away from zero (towards negative infinity). Floor division of -9.8 by 2 returns 5 (pure division is -4.9, rounded away from 0).
Multiplication of complex numbers is similar to multiplication of two binomials in algebra. If “a+bj” and “x+yj” are two complex numbers, then their multiplication is given by this formula −
(a+bj)*(x+yj)= ax+ayj+xbj+byj2 =(ax-by)+(ay+xb)j
For example,
a=6+4j
b=3+2j
c=a*b
c=(18-8)+(12+12)j
c=10+24j
The following program confirms the result −
a=6+4j
b=3+2jprint("Multplication of complex numbers - a=",a,"b=",b,"a*b=", a*b)
To understand the how the division of two complex numbers takes place, we should use the conjugate of a complex number. Python’s complex object has a conjugate() method that returns a complex number with the sign of imaginary part reversed.
>>> a=5+6j>>> a.conjugate()(5-6j)
Division of complex numbers
To divide two complex numbers, divide and multiply the numerator as well as the denominator with the conjugate of denominator.
Python operators are special symbols used to perform specific operations on one or more operands. The variables, values, or expressions can be used as operands. For example, Python’s addition operator (+) is used to perform addition operations on two variables, values, or expressions.
The following are some of the terms related to Python operators:
Unary operators: Python operators that require one operand to perform a specific operation are known as unary operators.
Binary operators: Python operators that require two operands to perform a specific operation are known as binary operators.
Operands: Variables, values, or expressions that are used with the operator to perform a specific operation.
Types of Python Operators
Python operators are categorized in the following categories −
Let us have a look at all the operators one by one.
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Python Arithmetic Operators
Python Arithmetic operators are used to perform basic mathematical operations such as addition, subtraction, multiplication, etc.
The following table contains all arithmetic operators with their symbols, names, and examples (assume that the values of a and b are 10 and 20, respectively) −
Operator
Name
Example
+
Addition
a + b = 30
–
Subtraction
a – b = -10
*
Multiplication
a * b = 200
/
Division
b / a = 2
%
Modulus
b % a = 0
**
Exponent
a**b =10**20
//
Floor Division
9//2 = 4
Example of Python Arithmetic Operators
Open Compiler
a =21
b =10
c =0
c = a + b
print("a: {} b: {} a+b: {}".format(a,b,c))
c = a - b
print("a: {} b: {} a-b: {}".format(a,b,c))
c = a * b
print("a: {} b: {} a*b: {}".format(a,b,c))
c = a / b
print("a: {} b: {} a/b: {}".format(a,b,c))
c = a % b
print("a: {} b: {} a%b: {}".format(a,b,c))
a =2
b =3
c = a**b
print("a: {} b: {} a**b: {}".format(a,b,c))
a =10
b =5
c = a//b
print("a: {} b: {} a//b: {}".format(a,b,c))
Python Comparison operators compare the values on either side of them and decide the relation among them. They are also called Relational operators.
The following table contains all comparison operators with their symbols, names, and examples (assume that the values of a and b are 10 and 20, respectively) −
Operator
Name
Example
==
Equal
(a == b) is not true.
!=
Not equal
(a != b) is true.
>
Greater than
(a > b) is not true.
<
Less than
(a < b) is true.
>=
Greater than or equal to
(a >= b) is not true.
<=
Less than or equal to
(a <= b) is true.
Example of Python Comparison Operators
Open Compiler
a =21
b =10if( a == b ):print("Line 1 - a is equal to b")else:print("Line 1 - a is not equal to b")if( a != b ):print("Line 2 - a is not equal to b")else:print("Line 2 - a is equal to b")if( a < b ):print("Line 3 - a is less than b")else:print("Line 3 - a is not less than b")if( a > b ):print("Line 4 - a is greater than b")else:print("Line 4 - a is not greater than b")
a,b=b,a #values of a and b swapped. a becomes 10, b becomes 21if( a <= b ):print("Line 5 - a is either less than or equal to b")else:print("Line 5 - a is neither less than nor equal to b")if( b >= a ):print("Line 6 - b is either greater than or equal to b")else:print("Line 6 - b is neither greater than nor equal to b")
Output
Line 1 - a is not equal to b
Line 2 - a is not equal to b
Line 3 - a is not less than b
Line 4 - a is greater than b
Line 5 - a is either less than or equal to b
Line 6 - b is either greater than or equal to b
Python Assignment Operators
Python Assignment operators are used to assign values to variables. Following is a table which shows all Python assignment operators.
The following table contains all assignment operators with their symbols, names, and examples −
Operator
Example
Same As
=
a = 10
a = 10
+=
a += 30
a = a + 30
-=
a -= 15
a = a – 15
*=
a *= 10
a = a * 10
/=
a /= 5
a = a / 5
%=
a %= 5
a = a % 5
**=
a **= 4
a = a ** 4
//=
a //= 5
a = a // 5
&=
a &= 5
a = a & 5
|=
a |= 5
a = a | 5
^=
a ^= 5
a = a ^ 5
>>=
a >>= 5
a = a >> 5
<<=
a <<= 5
a = a << 5
Example of Python Assignment Operators
Open Compiler
a =21
b =10
c =0print("a: {} b: {} c : {}".format(a,b,c))
c = a + b
print("a: {} c = a + b: {}".format(a,c))
c += a
print("a: {} c += a: {}".format(a,c))
c *= a
print("a: {} c *= a: {}".format(a,c))
c /= a
print("a: {} c /= a : {}".format(a,c))
c =2print("a: {} b: {} c : {}".format(a,b,c))
c %= a
print("a: {} c %= a: {}".format(a,c))
c **= a
print("a: {} c **= a: {}".format(a,c))
c //= a
print("a: {} c //= a: {}".format(a,c))
Output
a: 21 b: 10 c : 0
a: 21 c = a + b: 31
a: 21 c += a: 52
a: 21 c *= a: 1092
a: 21 c /= a : 52.0
a: 21 b: 10 c : 2
a: 21 c %= a: 2
a: 21 c **= a: 2097152
a: 21 c //= a: 99864
Python Bitwise Operators
Python Bitwise operator works on bits and performs bit by bit operation. These operators are used to compare binary numbers.
The following table contains all bitwise operators with their symbols, names, and examples −
Operator
Name
Example
&
AND
a & b
|
OR
a | b
^
XOR
a ^ b
~
NOT
~a
<<
Zero fill left shift
a << 3
>>
Signed right shift
a >> 3
Example of Python Bitwise Operators
Open Compiler
a =20
b =10print('a=',a,':',bin(a),'b=',b,':',bin(b))
c =0
c = a & b;print("result of AND is ", c,':',bin(c))
c = a | b;print("result of OR is ", c,':',bin(c))
c = a ^ b;print("result of EXOR is ", c,':',bin(c))
c =~a;print("result of COMPLEMENT is ", c,':',bin(c))
c = a <<2;print("result of LEFT SHIFT is ", c,':',bin(c))
c = a >>2;print("result of RIGHT SHIFT is ", c,':',bin(c))
Output
a= 20 : 0b10100 b= 10 : 0b1010
result of AND is 0 : 0b0
result of OR is 30 : 0b11110
result of EXOR is 30 : 0b11110
result of COMPLEMENT is -21 : -0b10101
result of LEFT SHIFT is 80 : 0b1010000
result of RIGHT SHIFT is 5 : 0b101
Python Logical Operators
Python logical operators are used to combile two or more conditions and check the final result. There are following logical operators supported by Python language. Assume variable a holds 10 and variable b holds 20 then
The following table contains all logical operators with their symbols, names, and examples −
Operator
Name
Example
and
AND
a and b
or
OR
a or b
not
NOT
not(a)
Example of Python Logical Operators
Open Compiler
var =5print(var >3and var <10)print(var >3or var <4)print(not(var >3and var <10))
Output
True
True
False
Python Membership Operators
Python’s membership operators test for membership in a sequence, such as strings, lists, or tuples.
There are two membership operators as explained below −
Operator
Description
Example
in
Returns True if it finds a variable in the specified sequence, false otherwise.
a in b
not in
returns True if it does not finds a variable in the specified sequence and false otherwise.
a not in b
Example of Python Membership Operators
Open Compiler
a =10
b =20list=[1,2,3,4,5]print("a:", a,"b:", b,"list:",list)if( a inlist):print("a is present in the given list")else:print("a is not present in the given list")if( b notinlist):print("b is not present in the given list")else:print("b is present in the given list")
c=b/a
print("c:", c,"list:",list)if( c inlist):print("c is available in the given list")else:print("c is not available in the given list")
Output
a: 10 b: 20 list: [1, 2, 3, 4, 5]
a is not present in the given list
b is not present in the given list
c: 2.0 list: [1, 2, 3, 4, 5]
c is available in the given list
There are two Identity operators explained below −
Operator
Description
Example
is
Returns True if both variables are the same object and false otherwise.
a is b
is not
Returns True if both variables are not the same object and false otherwise.
a is not b
Example of Python Identity Operators
Open Compiler
a =[1,2,3,4,5]
b =[1,2,3,4,5]
c = a
print(a is c)print(a is b)print(a isnot c)print(a isnot b)
Output
True
False
False
True
Python Operators Precedence
Operators precedence decides the order of the evaluation in which an operator is evaluated. Python operators have different levels of precedence. The following table contains the list of operators having highest to lowest precedence −
The following table lists all operators from highest precedence to lowest.
Sr.No.
Operator & Description
1
**Exponentiation (raise to the power)
2
~ + –Complement, unary plus and minus (method names for the last two are +@ and -@)
3
* / % //Multiply, divide, modulo and floor division
Python literals or constants are the notation for representing a fixed value in source code. In contrast to variables, literals (123, 4.3, “Hello”) are static values or you can say constants which do not change throughout the operation of the program or application. For example, in the following assignment statement.
x =10
Here 10 is a literal as numeric value representing 10, which is directly stored in memory. However,
y = x*2
Here, even if the expression evaluates to 20, it is not literally included in source code. You can also declare an int object with built-in int() function. However, this is also an indirect way of instantiation and not with literal.
x =int(10)
Different Types of Python Literals
Python provides following literals which will be explained this tutorial:
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Python Integer Literal
Any representation involving only the digit symbols (0 to 9) creates an object of int type. The object so declared may be referred by a variable using an assignment operator.
Integer literals consist three different types of different literal values decimal, octal, and hexadecimal literals.
1. Decimal Literal
Decimal literals represent the signed or unsigned numbers. Digitals from 0 to 9 are used to create a decimal literal value.
Look at the below statement assigning decimal literal to the variable −
x =10
y =-25
z =0
2. Octal Literal
Python allows an integer to be represented as an octal number or a hexadecimal number. A numeric representation with only eight digit symbols (0 to 7) but prefixed by 0o or 0O is an octal number in Python.
Look at the below statement assigning octal literal to the variable −
x =0O34
3. Hexadecimal Literal
Similarly, a series of hexadecimal symbols (0 to 9 and a to f), prefixed by 0x or 0X represents an integer in Hexedecimal form in Python.
Look at the below statement assigning hexadecimal literal to the variable −
x =0X1C
However, it may be noted that, even if you use octal or hexadecimal literal notation, Python internally treats them as of int type.
Example
Open Compiler
# Using Octal notation
x =0O34print("0O34 in octal is", x,type(x))# Using Hexadecimal notation
x =0X1cprint("0X1c in Hexadecimal is", x,type(x))
When you run this code, it will produce the following output −
0O34 in octal is 28 <class 'int'>
0X1c in Hexadecimal is 28 <class 'int'>
Python Float Literal
A floating point number consists of an integral part and a fractional part. Conventionally, a decimal point symbol (.) separates these two parts in a literal representation of a float. For example,
Example of Float Literal
x =25.55
y =0.05
z =-12.2345
For a floating point number which is too large or too small, where number of digits before or after decimal point is more, a scientific notation is used for a compact literal representation. The symbol E or e followed by positive or negative integer, follows after the integer part.
Example of Float Scientific Notation Literal
For example, a number 1.23E05 is equivalent to 123000.00. Similarly, 1.23e-2 is equivalent to 0.0123
Open Compiler
# Using normal floating point notation
x =1.23print("1.23 in normal float literal is", x,type(x))# Using Scientific notation
x =1.23E5print("1.23E5 in scientific notation is", x,type(x))
x =1.23E-2print("1.23E-2 in scientific notation is", x,type(x))
Here, you will get the following output −
1.23 in normal float literal is 1.23 <class 'float'>
1.23E5 in scientific notation is 123000.0 <class 'float''>
1.23E-2 in scientific notation is 0.0123 <class 'float''>
Python Complex Literal
A complex number comprises of a real and imaginary component. The imaginary component is any number (integer or floating point) multiplied by square root of “-1”
(√ −1). In literal representation (−1−−−√−1) is representation by “j” or “J”. Hence, a literal representation of a complex number takes a form x+yj.
Example of Complex Type Literal
Open Compiler
#Using literal notation of complex number
x =2+3jprint("2+3j complex literal is", x,type(x))
y =2.5+4.6jprint("2.5+4.6j complex literal is", x,type(x))
This code will produce the following output −
2+3j complex literal is (2+3j) <class 'complex'>
2.5+4.6j complex literal is (2+3j) <class 'complex'>
Python String Literal
A string object is one of the sequence data types in Python. It is an immutable sequence of Unicode code points. Code point is a number corresponding to a character according to Unicode standard. Strings are objects of Python’s built-in class ‘str’.
String literals are written by enclosing a sequence of characters in single quotes (‘hello’), double quotes (“hello”) or triple quotes (”’hello”’ or “””hello”””).
Example of String Literal
Open Compiler
var1='hello'print("'hello' in single quotes is:", var1,type(var1))
var2="hello"print('"hello" in double quotes is:', var1,type(var1))
var3='''hello'''print("''''hello'''' in triple quotes is:", var1,type(var1))
var4="""hello"""print('"""hello""" in triple quotes is:', var1,type(var1))
Here, you will get the following output −
'hello' in single quotes is: hello <class 'str'>
"hello" in double quotes is: hello <class 'str'>
''''hello'''' in triple quotes is: hello <class 'str'>
"""hello""" in triple quotes is: hello <class 'str'>
Example of String Literal With Double Quoted Inside String
If it is required to embed double quotes as a part of string, the string itself should be put in single quotes. On the other hand, if single quoted text is to be embedded, string should be written in double quotes.
Open Compiler
var1='Welcome to "Python Tutorial" from TutorialsPoint'print(var1)
var2="Welcome to 'Python Tutorial' from TutorialsPoint"print(var2)
It will produce the following output −
Welcome to "Python Tutorial" from TutorialsPoint
Welcome to 'Python Tutorial' from TutorialsPoint
Python List Literal
List object in Python is a collection of objects of other data type. List is an ordered collection of items not necessarily of same type. Individual object in the collection is accessed by index starting with zero.
Literal representation of a list object is done with one or more items which are separated by comma and enclosed in square brackets [].
Example of List Type Literal
Open Compiler
L1=[1,"Ravi",75.50,True]print(L1,type(L1))
It will produce the following output −
[1, 'Ravi', 75.5, True] <class 'list'>
Python Tuple Literal
Tuple object in Python is a collection of objects of other data type. Tuple is an ordered collection of items not necessarily of same type. Individual object in the collection is accessed by index starting with zero.
Literal representation of a tuple object is done with one or more items which are separated by comma and enclosed in parentheses ().
Example of Tuple Type Literal
Open Compiler
T1=(1,"Ravi",75.50,True)print(T1,type(T1))
It will produce the following output −
[1, 'Ravi', 75.5, True] <class tuple>
Example of Tuple Type Literal Without Parenthesis
Default delimiter for Python sequence is parentheses, which means a comma separated sequence without parentheses also amounts to declaration of a tuple.
Open Compiler
T1=1,"Ravi",75.50,Trueprint(T1,type(T1))
Here too, you will get the same output −
[1, 'Ravi', 75.5, True] <class tuple>
Python Dictionary Literal
Like list or tuple, dictionary is also a collection data type. However, it is not a sequence. It is an unordered collection of items, each of which is a key-value pair. Value is bound to key by the “:” symbol. One or more key:value pairs separated by comma are put inside curly brackets to form a dictionary object.
Key should be an immutable object. Number, string or tuple can be used as key. Key cannot appear more than once in one collection. If a key appears more than once, only the last one will be retained. Values can be of any data type. One value can be assigned to more than one keys. For example,
Software applications often require to display messages output in a variety in different languages such as in English, French, Japanese, Hebrew, or Hindi. Python’s string type uses the Unicode Standard for representing characters. It makes the program possible to work with all these different possible characters.
A character is the smallest possible component of a text. ‘A’, ‘B’, ‘C’, etc., are all different characters. So are ‘È’ and ‘Í’. A unicode string is a sequence of code points, which are numbers from 0 through 0x10FFFF (1,114,111 decimal). This sequence of code points needs to be represented in memory as a set of code units, and code units are then mapped to 8-bit bytes.
Character Encoding
A sequence of code points is represented in memory as a set of code units, mapped to 8-bit bytes. The rules for translating a Unicode string into a sequence of bytes are called a character encoding.
Three types of encodings are present, UTF-8, UTF-16 and UTF-32. UTF stands for Unicode Transformation Format.
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Python’s Unicode Support
Python 3.0 onwards has built-in support for Unicode. The str type contains Unicode characters, hence any string created using single, double or the triple-quoted string syntax is stored as Unicode. The default encoding for Python source code is UTF-8.
Hence, string may contain literal representation of a Unicode character (3/4) or its Unicode value (\u00BE).
Example
Open Compiler
var ="3/4"print(var)
var ="\u00BE"print(var)
This above code will produce the following output −
3/4
¾
Example
In the following example, a string ’10’ is stored using the Unicode values of 1 and 0 which are \u0031 and u0030 respectively.
Open Compiler
var ="\u0031\u0030"print(var)
It will produce the following output −
10
Strings display the text in a human-readable format, and bytes store the characters as binary data. Encoding converts data from a character string to a series of bytes. Decoding translates the bytes back to human-readable characters and symbols. It is important not
to confuse these two methods. encode is a string method, while decode is a method of the Python byte object.
Example
In the following example, we have a string variable that consists of ASCII characters. ASCII is a subset of Unicode character set. The encode() method is used to convert it into a bytes object.
From a programming point of view, a type casting refers to converting an object of one type into another. Here, we shall learn about type casting in Python Programming.
Python Type Casting is a process in which we convert a literal of one data type to another data type. Python supports two types of casting − implicit and explicit.
In Python there are different data types, such as numbers, sequences, mappings etc. There may be a situation where, you have the available data of one type but you want to use it in another form. For example, the user has input a string but you want to use it as a number. Python’s type casting mechanism let you do that.
Python Implicit Casting
When any language compiler/interpreter automatically converts object of one type into other, it is called automatic or implicit casting. Python is a strongly typed language. It doesn’t allow automatic type conversion between unrelated data types. For example, a string cannot be converted to any number type. However, an integer can be cast into a float. Other languages such as JavaScript is a weakly typed language, where an integer is coerced into a string for concatenation.
Note that memory requirement of each data type is different. For example, an integer object in Python occupies 4 bytes of memory, while a float object needs 8 bytes because of its fractional part. Hence, Python interpreter doesn’t automatically convert a float to int, because it will result in loss of data. On the other hand, int can be easily converted into float by setting its fractional part to 0.
Implicit int to float casting takes place when any arithmetic operation on int and float operands is done.
Consider we have an ,int and one float variable
<<< a=10# int object<<< b=10.5# float object
To perform their addition, 10 − the integer object is upgraded to 10.0. It is a float, but equivalent to its earlier numeric value. Now we can perform addition of two floats.
<<< c=a+b
<<<print(c)20.5
In implicit type casting, a Python object with lesser byte size is upgraded to match the bigger byte size of other object in the operation. For example, a Boolean object is first upgraded to int and then to float, before the addition with a floating point object. In the following example, we try to add a Boolean object in a float, pleae note that True is equal to 1, and False is equal to 0.
Open Compiler
a=True;
b=10.5;
c=a+b;print(c);
This will produce the following result:
11.5
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Python Explicit Casting
Although automatic or implicit casting is limited to int to float conversion, you can use Python’s built-in functions int(), float() and str() to perform the explicit conversions such as string to integer.
Python int() Function
Python’s built-in int() function converts an integer literal to an integer object, a float to integer, and a string to integer if the string itself has a valid integer literal representation.
Using int() with an int object as argument is equivalent to declaring an int object directly.
<<< a =int(10)<<< a
10
is same as −
<<< a =10<<< a
10<<<type(a)<class 'int>
If the argument to int() function is a float object or floating point expression, it returns an int object. For example −
<<< a =int(10.5)#converts a float object to int<<< a
10<<< a =int(2*3.14)#expression results float, is converted to int<<< a
6<<<type(a)<class'int'>
The int() function also returns integer 1 if a Boolean object is given as argument.
<<< a=int(True)<<< a
1<<<type(a)<class'int'>
String to Integer
The int() function returns an integer from a string object, only if it contains a valid integer representation.
<<< a =int("100")<<< a
100<<<type(a)<class'int'><<< a =("10"+"01")<<< a =int("10"+"01")<<< a
1001<<<type(a)<class'int'>
However, if the string contains a non-integer representation, Python raises ValueError.
<<< a =int("10.5")
Traceback (most recent call last):
File "<stdin>", line 1,in<module>
ValueError: invalid literal forint()with base 10:'10.5'<<< a =int("Hello World")
Traceback (most recent call last):
File "<stdin>", line 1,in<module>
ValueError: invalid literal forint()with base 10:'Hello World'
The int() function also returns integer from binary, octal and hexa-decimal string. For this, the function needs a base parameter which must be 2, 8 or 16 respectively. The string should have a valid binary/octal/Hexa-decimal representation.
Binary String to Integer
The string should be made up of 1 and 0 only, and the base should be 2.
<<< a =int("110011",2)<<< a
51
The Decimal equivalent of binary number 110011 is 51.
Octal String to Integer
The string should only contain 0 to 7 digits, and the base should be 8.
<<< a =int("20",8)<<< a
16
The Decimal equivalent of octal 20 is 16.
Hexa-Decimal String to Integer
The string should contain only the Hexadecimal symbols i.e., 0-9 and A, B, C, D, E or F. Base should be 16.
<<< a =int("2A9",16)<<< a
681
Decimal equivalent of Hexadecimal 2A9 is 681. You can easily verify these conversions with calculator app in Windows, Ubuntu or Smartphones.
Following is an example to convert number, float and string into integer data type:
Open Compiler
a =int(1)# a will be 1
b =int(2.2)# b will be 2
c =int("3")# c will be 3print(a)print(b)print(c)
This produce the following result −
1
2
3
Python float() Function
The float() is a built-in function in Python. It returns a float object if the argument is a float literal, integer or a string with valid floating point representation.
Using float() with an float object as argument is equivalent to declaring a float object directly
<<< a =float(9.99)<<< a
9.99<<<type(a)<class'float'>
is same as −
<<< a =9.99<<< a
9.99<<<type(a)<class'float'>
If the argument to float() function is an integer, the returned value is a floating point with fractional part set to 0.
<<< a =float(100)<<< a
100.0<<<type(a)<class'float'>
The float() function returns float object from a string, if the string contains a valid floating point number, otherwise ValueError is raised.
<<< a =float("9.99")<<< a
9.99<<<type(a)<class'float'><<< a =float("1,234.50")
Traceback (most recent call last):
File "<stdin>", line 1,in<module>
ValueError: could not convert string to float:'1,234.50'
The reason of ValueError here is the presence of comma in the string.
For the purpose of string to float conversion, the sceientific notation of floating point is also considered valid.
<<< a =float("1.00E4")<<< a
10000.0<<<type(a)<class'float'><<< a =float("1.00E-4")<<< a
0.0001<<<type(a)<class'float'>
Following is an example to convert number, float and string into float data type:
Open Compiler
a =float(1)# a will be 1.0
b =float(2.2)# b will be 2.2
c =float("3.3")# c will be 3.3print(a)print(b)print(c)
This produce the following result −
1.0
2.2
3.3
Python str() Function
We saw how a Python obtains integer or float number from corresponding string representation. The str() function works the opposite. It surrounds an integer or a float object with quotes (‘) to return a str object. The str() function returns the string representation of any Python object. In this section, we shall see different examples of str() function in Python.
The str() function has three parameters. First required parameter (or argument) is the object whose string representation we want. Other two operators, encoding and errors, are optional.
We shall execute str() function in Python console to easily verify that the returned object is a string, with the enclosing quotation marks (‘).
Integer to string
You can convert any integer number into a string as follows:
<<< a =str(10)<<< a
'10'<<<type(a)<class'str'>
Float to String
str() function converts floating point objects with both the notations of floating point, standard notation with a decimal point separating integer and fractional part, and the scientific notation to string object.
<<< a=str(11.10)<<< a
'11.1'<<<type(a)<class'str'><<< a =str(2/5)<<< a
'0.4'<<<type(a)<class'str'>
In the second case, a division expression is given as argument to str() function. Note that the expression is evaluated first and then result is converted to string.
Floating points in scientific notations using E or e and with positive or negative power are converted to string with str() function.
<<< a=str(10E4)<<< a
'100000.0'<<<type(a)<class'str'><<< a=str(1.23e-4)<<< a
'0.000123'<<<type(a)<class'str'>
When Boolean constant is entered as argument, it is surrounded by (‘) so that True becomes ‘True’. List and Tuple objects can also be given argument to str() function. The resultant string is the list/tuple surrounded by (‘).
<<< a=str('True')<<< a
'True'<<< a=str([1,2,3])<<< a
'[1, 2, 3]'<<< a=str((1,2,3))<<< a
'(1, 2, 3)'<<< a=str({1:100,2:200,3:300})<<< a
'{1: 100, 2: 200, 3: 300}'
Following is an example to convert number, float and string into string data type:
Open Compiler
a =str(1)# a will be "1"
b =str(2.2)# b will be "2.2"
c =str("3.3")# c will be "3.3"print(a)print(b)print(c)
This produce the following result −
1
2.2
3.3
Conversion of Sequence Types
List, Tuple and String are Python’s sequence types. They are ordered or indexed collection of items.
A string and tuple can be converted into a list object by using the list() function. Similarly, the tuple() function converts a string or list to a tuple.
We shall take an object each of these three sequence types and study their inter-conversion.
<<< a=[1,2,3,4,5]# List Object<<< b=(1,2,3,4,5)# Tupple Object<<< c="Hello"# String Object### list() separates each character in the string and builds the list<<< obj=list(c)<<< obj
['H','e','l','l','o']### The parentheses of tuple are replaced by square brackets<<< obj=list(b)<<< obj
[1,2,3,4,5]### tuple() separates each character from string and builds a tuple of characters<<< obj=tuple(c)<<< obj
('H','e','l','l','o')### square brackets of list are replaced by parentheses.<<< obj=tuple(a)<<< obj
(1,2,3,4,5)### str() function puts the list and tuple inside the quote symbols.<<< obj=str(a)<<< obj
'[1, 2, 3, 4, 5]'<<< obj=str(b)<<< obj
'(1, 2, 3, 4, 5)'
Thus Python’s explicit type casting feature allows conversion of one data type to other with the help of its built-in functions.
Data Type Conversion Functions
There are several built-in functions to perform conversion from one data type to another. These functions return a new object representing the converted value.
Sr.No.
Function & Description
1
Python int() functionConverts x to an integer. base specifies the base if x is a string.
2
Python long() functionConverts x to a long integer. base specifies the base if x is a string.
Python data types are actually classes, and the defined variables are their instances or objects. Since Python is dynamically typed, the data type of a variable is determined at runtime based on the assigned value.
In general, the data types are used to define the type of a variable. It represents the type of data we are going to store in a variable and determines what operations can be done on it.
Each programming language has its own classification of data items. With these datatypes, we can store different types of data values.
Types of Data Types in Python
Python supports the following built-in data types −
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1. Python Numeric Data Types
Python numeric data types store numeric values. Number objects are created when you assign a value to them. For example −
var1 =1# int data type
var2 =True# bool data type
var3 =10.023# float data type
var4 =10+3j# complex data type
Python supports four different numerical types and each of them have built-in classes in Python library, called int, bool, float and complex respectively −
int (signed integers)
float (floating point real values)
complex (complex numbers)
A complex number is made up of two parts – real and imaginary. They are separated by ‘+’ or ‘-‘ signs. The imaginary part is suffixed by ‘j’ which is the imaginary number. The square root of -1 (−1−−−√−1), is defined as imaginary number. Complex number in Python is represented as x+yj, where x is the real part, and y is the imaginary part. So, 5+6j is a complex number.
>>>type(5+6j)<class'complex'>
Here are some examples of numbers −
int
float
complex
10
0.0
3.14j
0O777
15.20
45.j
-786
-21.9
9.322e-36j
080
32.3+e18
.876j
0x17
-90.
-.6545+0J
-0x260
-32.54e100
3e+26J
0x69
70.2-E12
4.53e-7j
Example of Numeric Data Types
Following is an example to show the usage of Integer, Float and Complex numbers:
Open Compiler
# integer variable.
a=100print("The type of variable having value", a," is ",type(a))# float variable.
c=20.345print("The type of variable having value", c," is ",type(c))# complex variable.
d=10+3jprint("The type of variable having value", d," is ",type(d))
2. Python String Data Type
Python string is a sequence of one or more Unicode characters, enclosed in single, double or triple quotation marks (also called inverted commas). Python strings are immutable which means when you perform an operation on strings, you always produce a new string object of the same type, rather than mutating an existing string.
As long as the same sequence of characters is enclosed, single or double or triple quotes don’t matter. Hence, following string representations are equivalent.
A string in Python is an object of str class. It can be verified with type() function.
>>>type("Welcome To TutorialsPoint")<class'str'>
A string is a non-numeric data type. Obviously, we cannot perform arithmetic operations on it. However, operations such as slicing and concatenation can be done. Python’s str class defines a number of useful methods for string processing. Subsets of strings can be taken using the slice operator ([ ] and [:] ) with indexes starting at 0 in the beginning of the string and working their way from -1 at the end.
The plus (+) sign is the string concatenation operator and the asterisk (*) is the repetition operator in Python.
Example of String Data Type
Open Compiler
str='Hello World!'print(str)# Prints complete stringprint(str[0])# Prints first character of the stringprint(str[2:5])# Prints characters starting from 3rd to 5thprint(str[2:])# Prints string starting from 3rd characterprint(str*2)# Prints string two timesprint(str+"TEST")# Prints concatenated string
Sequence is a collection data type. It is an ordered collection of items. Items in the sequence have a positional index starting with 0. It is conceptually similar to an array in C or C++. There are following three sequence data types defined in Python.
List Data Type
Tuple Data Type
Range Data Type
Python sequences are bounded and iterable – Whenever we say an iterable in Python, it means a sequence data type (for example, a list).
(a) Python List Data Type
Python Lists are the most versatile compound data types. A Python list contains items separated by commas and enclosed within square brackets ([]). To some extent, Python lists are similar to arrays in C. One difference between them is that all the items belonging to a Python list can be of different data type where as C array can store elements related to a particular data type.
>>>[2023,"Python",3.11,5+6j,1.23E-4]
A list in Python is an object of list class. We can check it with type() function.
As mentioned, an item in the list may be of any data type. It means that a list object can also be an item in another list. In that case, it becomes a nested list.
>>>[['One','Two','Three'],[1,2,3],[1.0,2.0,3.0]]
A list can have items which are simple numbers, strings, tuple, dictionary, set or object of user defined class also.
The values stored in a Python list can be accessed using the slice operator ([ ] and [:]) with indexes starting at 0 in the beginning of the list and working their way to end -1. The plus (+) sign is the list concatenation operator, and the asterisk (*) is the repetition operator.
Example of List Data Type
Open Compiler
list=['abcd',786,2.23,'john',70.2]
tinylist =[123,'john']print(list)# Prints complete listprint(list[0])# Prints first element of the listprint(list[1:3])# Prints elements starting from 2nd till 3rd print(list[2:])# Prints elements starting from 3rd elementprint(tinylist *2)# Prints list two timesprint(list+ tinylist)# Prints concatenated lists
Python tuple is another sequence data type that is similar to a list. A Python tuple consists of a number of values separated by commas. Unlike lists, however, tuples are enclosed within parentheses (…).
A tuple is also a sequence, hence each item in the tuple has an index referring to its position in the collection. The index starts from 0.
>>>(2023,"Python",3.11,5+6j,1.23E-4)
In Python, a tuple is an object of tuple class. We can check it with the type() function.
tuple=('abcd',786,2.23,'john',70.2)
tinytuple =(123,'john')print(tuple)# Prints the complete tupleprint(tuple[0])# Prints first element of the tupleprint(tuple[1:3])# Prints elements of the tuple starting from 2nd till 3rd print(tuple[2:])# Prints elements of the tuple starting from 3rd elementprint(tinytuple *2)# Prints the contents of the tuple twiceprint(tuple+ tinytuple)# Prints concatenated tuples
The main differences between lists and tuples are: Lists are enclosed in brackets ( [ ] ) and their elements and size can be changed i.e. lists are mutable, while tuples are enclosed in parentheses ( ( ) ) and cannot be updated (immutable). Tuples can be thought of as read-only lists.
The following code is invalid with tuple, because we attempted to update a tuple, which is not allowed. Similar case is possible with lists −
Open Compiler
tuple=('abcd',786,2.23,'john',70.2)list=['abcd',786,2.23,'john',70.2]tuple[2]=1000# Invalid syntax with tuplelist[2]=1000# Valid syntax with list
(c) Python Range Data Type
A Python range is an immutable sequence of numbers which is typically used to iterate through a specific number of items.
It is represented by the Range class. The constructor of this class accepts a sequence of numbers starting from 0 and increments to 1 until it reaches a specified number. Following is the syntax of the function −
range(start, stop, step)
Here is the description of the parameters used −
start: Integer number to specify starting position, (Its optional, Default: 0)
stop: Integer number to specify ending position (It’s mandatory)
step: Integer number to specify increment, (Its optional, Default: 1)
Example of Range Data Type
Following is a program which uses for loop to print number from 0 to 4 −
Open Compiler
for i inrange(5):print(i)
This produce the following result −
0
1
2
3
4
Now let’s modify above program to print the number starting from 2 instead of 0 −
Open Compiler
for i inrange(2,5):print(i)
This produce the following result −
2
3
4
Again, let’s modify the program to print the number starting from 1 but with an increment of 2 instead of 1:
Open Compiler
for i inrange(1,5,2):print(i)
This produce the following result −
1
3
4. Python Binary Data Types
A binary data type in Python is a way to represent data as a series of binary digits, which are 0’s and 1’s. It is like a special language computers understand to store and process information efficiently.
This type of data is commonly used when dealing with things like files, images, or anything that can be represented using just two possible values. So, instead of using regular numbers or letters, binary sequence data types use a combination of 0s and 1s to represent information.
Python provides three different ways to represent binary data. They are as follows −
bytes
bytearray
memoryview
Let us discuss each of these data types individually −
(a) Python Bytes Data Type
The byte data type in Python represents a sequence of bytes. Each byte is an integer value between 0 and 255. It is commonly used to store binary data, such as images, files, or network packets.
We can create bytes in Python using the built-in bytes() function or by prefixing a sequence of numbers with b.
Example of Bytes Data Type
In the following example, we are using the built-in bytes() function to explicitly specify a sequence of numbers representing ASCII values −
Open Compiler
# Using bytes() function to create bytes
b1 =bytes([65,66,67,68,69])print(b1)
The result obtained is as follows −
b'ABCDE'
In here, we are using the “b” prefix before a string to automatically create a bytes object −
Open Compiler
# Using prefix 'b' to create bytes
b2 =b'Hello'print(b2)
Following is the output of the above code −
b'Hello'
(b) Python Bytearray Data Type
The bytearray data type in Python is quite similar to the bytes data type, but with one key difference: it is mutable, meaning you can modify the values stored in it after it is created.
You can create a bytearray using various methods, including by passing an iterable of integers representing byte values, by encoding a string, or by converting an existing bytes or bytearray object. For this, we use bytearray() function.
Example of Bytearray Data Type
In the example below, we are creating a bytearray by passing an iterable of integers representing byte values −
Open Compiler
# Creating a bytearray from an iterable of integers
value =bytearray([72,101,108,108,111])print(value)
The output obtained is as shown below −
bytearray(b'Hello')
Now, we are creating a bytearray by encoding a string using a “UTF-8” encoding −
Open Compiler
# Creating a bytearray by encoding a string
val =bytearray("Hello",'utf-8')print(val)
The result produced is as follows −
bytearray(b'Hello')
(c) Python Memoryview Data Type
In Python, a memoryview is a built-in object that provides a view into the memory of the original object, generally objects that support the buffer protocol, such as byte arrays (bytearray) and bytes (bytes). It allows you to access the underlying data of the original object without copying it, providing efficient memory access for large datasets.
You can create a memoryview using various methods. These methods include using the memoryview() constructor, slicing bytes or bytearray objects, extracting from array objects, or using built-in functions like open() when reading from files.
Example of Memoryview Data Type
In the given example, we are creating a memoryview object directly by passing a supported object to the memoryview() constructor. The supported objects generally include byte arrays (bytearray), bytes (bytes), and other objects that support the buffer protocol −
Open Compiler
data =bytearray(b'Hello, world!')
view =memoryview(data)print(view)
Following is the output of the above code −
<memory at 0x00000186FFAA3580>
If you have an array object, you can create a memoryview using the buffer interface as shown below −
You can also create a memoryview by slicing a bytes or bytearray object −
Open Compiler
data =b'Hello, world!'# Creating a view of the last part of the data
view =memoryview(data[7:])print(view)
The result obtained is as follows −
<memory at 0x00000200D9AA3580>
5. Python Dictionary Data Type
Python dictionaries are kind of hash table type. A dictionary key can be almost any Python type, but are usually numbers or strings. Values, on the other hand, can be any arbitrary Python object.
Python dictionary is like associative arrays or hashes found in Perl and consist of key:value pairs. The pairs are separated by comma and put inside curly brackets {}. To establish mapping between key and value, the semicolon’:’ symbol is put between the two.
>>>{1:'one',2:'two',3:'three'}
In Python, dictionary is an object of the built-in dict class. We can check it with the type() function.
>>>type({1:'one',2:'two',3:'three'})<class'dict'>
Dictionaries are enclosed by curly braces ({ }) and values can be assigned and accessed using square braces ([]).
Example of Dictionary Data Type
Open Compiler
dict={}dict['one']="This is one"dict[2]="This is two"
tinydict ={'name':'john','code':6734,'dept':'sales'}print(dict['one'])# Prints value for 'one' keyprint(dict[2])# Prints value for 2 keyprint(tinydict)# Prints complete dictionaryprint(tinydict.keys())# Prints all the keysprint(tinydict.values())# Prints all the values
This produce the following result −
This is one
This is two
{'dept': 'sales', 'code': 6734, 'name': 'john'}
['dept', 'code', 'name']
['sales', 6734, 'john']
Python’s dictionary is not a sequence. It is a collection of items but each item (key:value pair) is not identified by positional index as in string, list or tuple. Hence, slicing operation cannot be done on a dictionary. Dictionary is a mutable object, so it is possible to perform add, modify or delete actions with corresponding functionality defined in dict class. These operations will be explained in a subsequent chapter.
6. Python Set Data Type
Set is a Python implementation of set as defined in Mathematics. A set in Python is a collection, but is not an indexed or ordered collection as string, list or tuple. An object cannot appear more than once in a set, whereas in List and Tuple, same object can appear more than once.
Comma separated items in a set are put inside curly brackets or braces {}. Items in the set collection can be of different data types.
Note that items in the set collection may not follow the same order in which they are entered. The position of items is optimized by Python to perform operations over set as defined in mathematics.
Python’s Set is an object of built-in set class, as can be checked with the type() function.
A set can store only immutable objects such as number (int, float, complex or bool), string or tuple. If you try to put a list or a dictionary in the set collection, Python raises a TypeError.
Hashing is a mechanism in computer science which enables quicker searching of objects in computer’s memory. Only immutable objects are hashable.
Even if a set doesn’t allow mutable items, the set itself is mutable. Hence, add/delete/update operations are permitted on a set object, using the methods in built-in set class. Python also has a set of operators to perform set manipulation. The methods and operators are explained in latter chapters
Python boolean type is one of built-in data types which represents one of the two values either True or False. Python bool() function allows you to evaluate the value of any expression and returns either True or False based on the expression.
A Boolean number has only two possible values, as represented by the keywords, True and False. They correspond to integer 1 and 0 respectively.
Following is a program which prints the value of boolean variables a and b −
Open Compiler
a =True# display the value of aprint(a)# display the data type of aprint(type(a))
This will produce the following result −
true
<class 'bool'>
Following is another program which evaluates the expressions and prints the return values −
Open Compiler
# Returns false as a is not equal to b
a =2
b =4print(bool(a==b))# Following also prints the sameprint(a==b)# Returns False as a is None
a =Noneprint(bool(a))# Returns false as a is an empty sequence
a =()print(bool(a))# Returns false as a is 0
a =0.0print(bool(a))# Returns false as a is 10
a =10print(bool(a))
This produce the following result −
False
False
False
False
False
True
8. Python None Type
Python’s none type is represented by the “nonetype.” It is an object of its own data type. The nonetype represents the null type of values or absence of a value.
Example of None Type
In the following example, we are assigning None to a variable x and printing its type, which will be nonetyoe −
Open Compiler
# Declaring a variable# And, assigning a Null value (None)
x =None# Printing its value and typeprint("x = ", x)print("type of x = ",type(x))
This produce the following result −
x = None
type of x = <class 'NoneType'>
Getting Data Type
To get the data types in Python, you can use the type() function. The type() is a built-in function that returns the class of the given object.
Example
In the following example, we are getting the type of the values and variables −
Open Compiler
# Getting type of valuesprint(type(123))print(type(9.99))# Getting type of variables
a =10
b =2.12
c ="Hello"
d =(10,20,30)
e =[10,20,30]print(type(a))print(type(b))print(type(c))print(type(d))print(type(e))
In Python, during declaring a variable or an object, you don’t need to set the data types. Data type is set automatically based on the assigned value.
Example
The following example, demonstrating how a variable’s data type is set based on the given value −
Open Compiler
# Declaring a variable# And, assigning an integer value
x =10# Printing its value and typeprint("x = ", x)print("type of x = ",type(x))# Now, assigning string value to# the same variable
x ="Hello World!"# Printing its value and typeprint("x = ", x)print("type of x = ",type(x))
This produce the following result −
x = 10
type of x = <class 'int'>
x = Hello World!
type of x = <class 'str'>
Primitive and Non-primitive Data Types
The above-explained data types can also be categorized as primitive and non-primitive.
1. Primitive Types
The primitive data types are the fundamental data types that are used to create complex data types (sometimes called complex data structures). There are mainly four primitive data types, which are −
Integers
Floats
Booleans, and
Strings
2. Non-primitive Types
The non-primitive data types store values or collections of values. There are mainly four types of non-primitive types, which are −
Lists
Tuples
Dictionaries, and
Sets
Python Data Type Conversion
Sometimes, you may need to perform conversions between the built-in data types. To convert data between different Python data types, you simply use the type name as a function.
Following is an example which converts different values to integer, floating point and string values respectively −
Open Compiler
print("Conversion to integer data type")
a =int(1)# a will be 1
b =int(2.2)# b will be 2
c =int("3.3")# c will be 3print(a)print(b)print(c)print("Conversion to floating point number")
a =float(1)# a will be 1.0
b =float(2.2)# b will be 2.2
c =float("3.3")# c will be 3.3print(a)print(b)print(c)print("Conversion to string")
a =str(1)# a will be "1"
b =str(2.2)# b will be "2.2"
c =str("3.3")# c will be "3.3"print(a)print(b)print(c)
This produce the following result −
Conversion to integer data type
1
2
3
Conversion to floating point number
1.0
2.2
3.3
Conversion to string
1
2.2
3.3
Data Type Conversion Functions
There are several built-in functions to perform conversion from one data type to another. These functions return a new object representing the converted value.
Sr.No.
Function & Description
1
Python int() functionConverts x to an integer. base specifies the base if x is a string.
2
Python long() functionConverts x to a long integer. base specifies the base if x is a string. This function has been deprecated.
Python variables are the reserved memory locations used to store values with in a Python Program. This means that when you create a variable you reserve some space in the memory.
Based on the data type of a variable, Python interpreter allocates memory and decides what can be stored in the reserved memory. Therefore, by assigning different data types to Python variables, you can store integers, decimals or characters in these variables.
Memory Addresses
Data items belonging to different data types are stored in computer’s memory. Computer’s memory locations are having a number or address, internally represented in binary form. Data is also stored in binary form as the computer works on the principle of binary representation. In the following diagram, a string May and a number 18 is shown as stored in memory locations.
If you know the assembly language, you will covert these data items and the memory address, and give a machine language instruction. However, it is not easy for everybody. Language translator such as Python interpreter performs this type of conversion. It stores the object in a randomly chosen memory location. Python’s built-in id() function returns the address where the object is stored.
>>>"May"
May
>>>id("May")2167264641264>>>1818>>>id(18)140714055169352
Once the data is stored in the memory, it should be accessed repeatedly for performing a certain process. Obviously, fetching the data from its ID is cumbersome. High level languages like Python make it possible to give a suitable alias or a label to refer to the memory location.
In the above example, let us label the location of May as month, and location in which 18 is stored as age. Python uses the assignment operator (=) to bind an object with the label.
>>> month="May">>> age=18
The data object (May) and its name (month) have the same id(). The id() of 18 and age are also same.
The label is an identifier. It is usually called as a variable. A Python variable is a symbolic name that is a reference or pointer to an object.
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Creating Python Variables
Python variables do not need explicit declaration to reserve memory space or you can say to create a variable. A Python variable is created automatically when you assign a value to it. The equal sign (=) is used to assign values to variables.
The operand to the left of the = operator is the name of the variable and the operand to the right of the = operator is the value stored in the variable. For example −
Example to Create Python Variables
This example creates different types (an integer, a float, and a string) of variables.
counter =100# Creates an integer variable
miles =1000.0# Creates a floating point variable
name ="Zara Ali"# Creates a string variable
Printing Python Variables
Once we create a Python variable and assign a value to it, we can print it using print() function. Following is the extension of previous example and shows how to print different variables in Python:
Example to Print Python Variables
This example prints variables.
Open Compiler
counter =100# Creates an integer variable
miles =1000.0# Creates a floating point variable
name ="Zara Ali"# Creates a string variableprint(counter)print(miles)print(name)
Here, 100, 1000.0 and “Zara Ali” are the values assigned to counter, miles, and name variables, respectively. When running the above Python program, this produces the following result −
100
1000.0
Zara Ali
Deleting Python Variables
You can delete the reference to a number object by using the del statement. The syntax of the del statement is −
del var1[,var2[,var3[....,varN]]]]
You can delete a single object or multiple objects by using the del statement. For example −
del var
del var_a, var_b
Example
Following examples shows how we can delete a variable and if we try to use a deleted variable then Python interpreter will throw an error:
100
Traceback (most recent call last):
File "main.py", line 7, in <module>
print (counter)
NameError: name 'counter' is not defined
Getting Type of a Variable
You can get the data type of a Python variable using the python built-in function type() as follows.
Example: Printing Variables Type
Open Compiler
x ="Zara"
y =10
z =10.10print(type(x))print(type(y))print(type(z))
This will produce the following result:
<class 'str'>
<class 'int'>
<class 'float'>
Casting Python Variables
You can specify the data type of a variable with the help of casting as follows:
Example
This example demonstrates case sensitivity of variables.
Open Compiler
x =str(10)# x will be '10'
y =int(10)# y will be 10
z =float(10)# z will be 10.0print("x =", x )print("y =", y )print("z =", z )
This will produce the following result:
x = 10
y = 10
z = 10.0
Case-Sensitivity of Python Variables
Python variables are case sensitive which means Age and age are two different variables:
Open Compiler
age =20
Age =30print("age =", age )print("Age =", Age )
This will produce the following result:
age = 20
Age = 30
Python Variables – Multiple Assignment
Python allows to initialize more than one variables in a single statement. In the following case, three variables have same value.
>>> a=10>>> b=10>>> c=10
Instead of separate assignments, you can do it in a single assignment statement as follows −
>>> a=b=c=10>>>print(a,b,c)101010
In the following case, we have three variables with different values.
>>> a=10>>> b=20>>> c=30
These separate assignment statements can be combined in one. You need to give comma separated variable names on left, and comma separated values on the right of = operator.
>>> a,b,c =10,20,30>>>print(a,b,c)102030
Let’s try few examples in script mode: −
Open Compiler
a = b = c =100print(a)print(b)print(c)
This produces the following result:
100
100
100
Here, an integer object is created with the value 1, and all three variables are assigned to the same memory location. You can also assign multiple objects to multiple variables. For example −
Open Compiler
a,b,c =1,2,"Zara Ali"print(a)print(b)print(c)
This produces the following result:
1
2
Zara Ali
Here, two integer objects with values 1 and 2 are assigned to variables a and b respectively, and one string object with the value “Zara Ali” is assigned to the variable c.
Python Variables – Naming Convention
Every Python variable should have a unique name like a, b, c. A variable name can be meaningful like color, age, name etc. There are certain rules which should be taken care while naming a Python variable:
A variable name must start with a letter or the underscore character
A variable name cannot start with a number or any special character like $, (, * % etc.
A variable name can only contain alpha-numeric characters and underscores (A-z, 0-9, and _ )
Python variable names are case-sensitive which means Name and NAME are two different variables in Python.
Python reserved keywords cannot be used naming the variable.
If the name of variable contains multiple words, we should use these naming patterns −
Camel case − First letter is a lowercase, but first letter of each subsequent word is in uppercase. For example: kmPerHour, pricePerLitre
Pascal case − First letter of each word is in uppercase. For example: KmPerHour, PricePerLitre
Snake case − Use single underscore (_) character to separate words. For example: km_per_hour, price_per_litre
Once you use a variable to identify a data object, it can be used repeatedly without its id() value. Here, we have a variables height and width of a rectangle. We can compute the area and perimeter with these variables.
>>> width=10>>> height=20>>> area=width*height
>>> area
200>>> perimeter=2*(width+height)>>> perimeter
60
Use of variables is especially advantageous when writing scripts or programs. Following script also uses the above variables.
Save the above script with .py extension and execute from command-line. The result would be −
Area = 200
Perimeter = 60
Python Local Variables
Python Local Variables are defined inside a function. We can not access variable outside the function.
A Python functions is a piece of reusable code and you will learn more about function in Python – Functions tutorial.
Example
Following is an example to show the usage of local variables:
Open Compiler
defsum(x,y):sum= x + y
returnsumprint(sum(5,10))
This will produce the following result −
15
Python Global Variables
Any variable created outside a function can be accessed within any function and so they have global scope.
Example
Following is an example of global variables −
Open Compiler
x =5
y =10defsum():sum= x + y
returnsumprint(sum())
This will produce the following result −
15
Constants in Python
Python doesn’t have any formally defined constants, However you can indicate a variable to be treated as a constant by using all-caps names with underscores. For example, the name PI_VALUE indicates that you don’t want the variable redefined or changed in any way.
The naming convention using all-caps is sometimes referred to as screaming snake case – where the all-caps (screaming) and the underscores (snakes).
Python vs C/C++ Variables
The concept of variable works differently in Python than in C/C++. In C/C++, a variable is a named memory location. If a=10 and also b=10, both are two different memory locations. Let us assume their memory address is 100 and 200 respectively.
If a different value is assigned to “a” – say 50, 10 in the address 100 is overwritten.
A Python variable refers to the object and not the memory location. An object is stored in memory only once. Multiple variables are really the multiple labels to the same object.
The statement a=50 creates a new int object 50 in the memory at some other location, leaving the object 10 referred by “b”.
Further, if you assign some other value to b, the object 10 remains unreferred.
Python’s garbage collector mechanism releases the memory occupied by any unreferred object.
Python’s identity operator is returns True if both the operands have same id() value.
>>> a=b=10>>> a is b
True>>>id(a),id(b)(140731955278920,140731955278920)
The Python syntax defines a set of rules that are used to create a Python Program. The Python Programming Language Syntax has many similarities to Perl, C, and Java Programming Languages. However, there are some definite differences between the languages.
First Python Program
Let us execute a Python program to print “Hello, World!” in two different modes of Python Programming. (a) Interactive Mode Programming (b) Script Mode Programming.
Python – Interactive Mode Programming
We can invoke a Python interpreter from command line by typing python at the command prompt as following −
$ python3
Python 3.10.6(main, Mar 102023,10:55:28)[GCC 11.3.0] on linux
Type "help","copyright","credits"or"license"for more information.>>>
Here >>> denotes a Python Command Prompt where you can type your commands. Let’s type the following text at the Python prompt and press the Enter −
>>>print("Hello, World!")
If you are running older version of Python, like Python 2.4.x, then you would need to use print statement without parenthesis as in print “Hello, World!”. However in Python version 3.x, this produces the following result −
Hello, World!
Python – Script Mode Programming
We can invoke the Python interpreter with a script parameter which begins the execution of the script and continues until the script is finished. When the script is finished, the interpreter is no longer active.
Let us write a simple Python program in a script which is simple text file. Python files have extension .py. Type the following source code in a test.py file −
Open Compiler
print("Hello, World!")
We assume that you have Python interpreter path set in PATH variable. Now, let’s try to run this program as follows −
$ python3 test.py
This produces the following result −
Hello, World!
Let us try another way to execute a Python script. Here is the modified test.py file −
Open Compiler
#!/usr/bin/python3print("Hello, World!")
We assume that you have Python interpreter available in /usr/bin directory. Now, try to run this program as follows −
$ chmod +x test.py # This is to make file executable
$./test.py
This produces the following result −
Hello, World!
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Python Identifiers
A Python identifier is a name used to identify a variable, function, class, module or other object. An identifier starts with a letter A to Z or a to z or an underscore (_) followed by zero or more letters, underscores and digits (0 to 9).
Python does not allow punctuation characters such as @, $, and % within identifiers.
Python is a case sensitive programming language. Thus, Manpower and manpower are two different identifiers in Python.
Here are naming conventions for Python identifiers −
Python Class names start with an uppercase letter. All other identifiers start with a lowercase letter.
Starting an identifier with a single leading underscore indicates that the identifier is private identifier.
Starting an identifier with two leading underscores indicates a strongly private identifier.
If the identifier also ends with two trailing underscores, the identifier is a language-defined special name.
Python Reserved Words
The following list shows the Python keywords. These are reserved words and you cannot use them as constant or variable or any other identifier names. All the Python keywords contain lowercase letters only.
and
as
assert
break
class
continue
def
del
elif
else
except
False
finally
for
from
global
if
import
in
is
lambda
None
nonlocal
not
or
pass
raise
return
True
try
while
with
yield
Python Lines and Indentation
Python programming provides no braces to indicate blocks of code for class and function definitions or flow control. Blocks of code are denoted by line indentation, which is rigidly enforced.
The number of spaces in the indentation is variable, but all statements within the block must be indented the same amount. For example −
file_name =raw_input("Enter filename: ")iflen(file_name)==0:print"Next time please enter something"
sys.exit()try:file=open(file_name,"r")except IOError:print"There was an error reading file"
sys.exit()
file_text =file.read()file.close()print file_text
Python Multi-Line Statements
Statements in Python typically end with a new line. Python does, however, allow the use of the line continuation character (\) to denote that the line should continue. For example −
total = item_one + \
item_two + \
item_three
Statements contained within the [], {}, or () brackets do not need to use the line continuation character. For example following statement works well in Python −
days =['Monday','Tuesday','Wednesday','Thursday','Friday']
Quotations in Python
Python accepts single (‘), double (“) and triple (”’ or “””) quotes to denote string literals, as long as the same type of quote starts and ends the string.
The triple quotes are used to span the string across multiple lines. For example, all the following are legal −
word ='word'print(word)
sentence ="This is a sentence."print(sentence)
paragraph ="""This is a paragraph. It is
made up of multiple lines and sentences."""print(paragraph)
Comments in Python
A comment is a programmer-readable explanation or annotation in the Python source code. They are added with the purpose of making the source code easier for humans to understand, and are ignored by Python interpreter
Just like most modern languages, Python supports single-line (or end-of-line) and multi-line (block) comments. Python comments are very much similar to the comments available in PHP, BASH and Perl Programming languages.
A hash sign (#) that is not inside a string literal begins a comment. All characters after the # and up to the end of the physical line are part of the comment and the Python interpreter ignores them.
Open Compiler
# First commentprint("Hello, World!")# Second comment
This produces the following result −
Hello, World!
You can type a comment on the same line after a statement or expression −
name ="Madisetti"# This is again comment
You can comment multiple lines as follows −
# This is a comment.# This is a comment, too.# This is a comment, too.# I said that already.
Following triple-quoted string is also ignored by Python interpreter and can be used as a multiline comments:
'''
This is a multiline
comment.
'''
Using Blank Lines in Python Programs
A line containing only whitespace, possibly with a comment, is known as a blank line and Python totally ignores it.
In an interactive interpreter session, you must enter an empty physical line to terminate a multiline statement.
Waiting for the User
The following line of the program displays the prompt, the statement saying “Press the enter key to exit”, and waits for the user to take action −
#!/usr/bin/pythonraw_input("\n\nPress the enter key to exit.")
Here, “\n\n” is used to create two new lines before displaying the actual line. Once the user presses the key, the program ends. This is a nice trick to keep a console window open until the user is done with an application.
Multiple Statements on a Single Line
The semicolon ( ; ) allows multiple statements on the single line given that neither statement starts a new code block. Here is a sample snip using the semicolon −
Open Compiler
import sys; x ='foo'; sys.stdout.write(x +'\n')
Multiple Statement Groups as Suites
A group of individual statements, which make a single code block are called suites in Python. Compound or complex statements, such as if, while, def, and class require a header line and a suite.
Header lines begin the statement (with the keyword) and terminate with a colon ( : ) and are followed by one or more lines which make up the suite. For example −
if expression :
suite
elif expression :
suite
else:
suite
Command Line Arguments in Python
Many programs can be run to provide you with some basic information about how they should be run. Python enables you to do this with -h −
$ python3 -h
usage: python3 [option]...[-c cmd |-m mod |file|-][arg]...
Options and arguments (and corresponding environment variables):-c cmd : program passed inas string (terminates option list)-d : debug output from parser (also PYTHONDEBUG=x)-E : ignore environment variables (such as PYTHONPATH)-h :print this help message and exit
[ etc.]
You can also program your script in such a way that it should accept various options. Command Line Arguments is an advanced topic and should be studied a bit later once you have gone through rest of the Python concepts.
