JAVA8:Stream API

Stream API is introduced in JDK 1.8 to support functional-style operations on streams of elements, such as map-reduce transformations on collections. Basically it follows filter-map-reduce operational pipeline technique on streams of elements to produce results.

Why Stream?

Streams differ from collections in several ways:

• No storage. A stream is not a data structure that stores elements; instead, it conveys elements from a source such as a data structure, an array, a generator function, or an I/O channel, through a pipeline of computational operations.
• Functional in nature. An operation on a stream produces a result, but does not modify its source. For example, filtering a Stream obtained from a collection produces a new Stream without the filtered elements, rather than removing elements from the source collection.
• Laziness-seeking. Many stream operations, such as filtering, mapping, or duplicate removal, can be implemented lazily, exposing opportunities for optimization. For example, "find the first String with three consecutive vowels" need not examine all the input strings. Stream operations are divided into intermediate (Stream-producing) operations and terminal (value- or side-effect-producing) operations. Intermediate operations are always lazy.
• Possibly unbounded. While collections have a finite size, streams need not. Short-circuiting operations such as limit(n) or findFirst() can allow computations on infinite streams to complete in finite time. C
• onsumable. The elements of a stream are only visited once during the life of a stream. Like an Iterator, a new stream must be generated to revisit the same elements of the source.

Multiple ways to obtain stream:

• From a Collection via the stream() and parallelStream() methods;
• From an array via Arrays.stream(Object[]);
• From static factory methods on the stream classes, such as Stream.of(Object[]), IntStream.range(int, int) or Stream.iterate(Object, UnaryOperator);
• The lines of a file can be obtained from BufferedReader.lines();
• Streams of file paths can be obtained from methods in Files;
• Streams of random numbers can be obtained from Random.ints();
• Numerous other stream-bearing methods in the JDK, including BitSet.stream(), Pattern.splitAsStream(java.lang.CharSequence), and JarFile.stream().

What is stream Operations and Pipelines?

Stream operations are divided into intermediate and terminal operations and are combined to form stream pipelines. A stream pipeline consists of a source (such as a Collection, an array, a generator function, or an I/O channel); followed by zero or more intermediate operations such as Stream.filter or Stream.map; and a terminal operation such as Stream.forEach or Stream.reduce.

• Intermediate operations:Returns a new stream. They are always lazy; executing an intermediate operation such as filter() does not actually perform any filtering, but instead creates a new stream that, when traversed, contains the elements of the initial stream that match the given predicate. Traversal of the pipeline source does not begin until the terminal operation of the pipeline is executed.  
• Terminal operations:Terminal operations, such as Stream.forEach or IntStream.sum, may traverse the stream to produce a result or a side-effect. After the terminal operation is performed, the stream pipeline is considered consumed, and can no longer be used; if you need to traverse the same data source again, you must return to the data source to get a new stream. In almost all cases, terminal operations are eager, completing their traversal of the data source and processing of the pipeline before returning. Only the terminal operations iterator() and spliterator() are not; these are provided as an "escape hatch" to enable arbitrary client-controlled pipeline traversals in the event that the existing operations are not sufficient to the task.  

Sample pipeline Examples:

Filter-map-sum     

Python Data Types

Data Types

Since python is a dynamically typed programming language so no need to define datatype manually instead it will be identified based on value data type implicitly.
Data Type is a Type of value represented by the variable.

Various Data Types defined in Python as below:
1)int
2)float
3)complex
4)bool
5)str
6)bytes
7)bytearray
8)range
9)list
10)tuple
11)set
12)frozenset
13)dict
14)None
Note: In Python every thing is an object even an method also.

Explanation about each Data type:
1) int Data Type:
Python uses int data type to represent Integral values.
Program:
>>> a=10
>>> type(a)
Output:class 'int'
In how many ways we can represent int type:By default Python considers as decimal form only.
1)Decimal Form(Base-10):0 to 9
2)Binary Form(Base-2):0 and 1
Program
a=0b1111
a=0B1111
Output:15
Program:
a=-0b1111
output:-15
3)Octal Form(Base-8):0 to 7
Program:
>>> a=0o777
>>> a
Output:511
Program:
>>> a=0O777
>>> a
Output:511
4)Hexa Decimal(Base-16):0 to 9,a to f,A to F(Here Python is not case sensitive)
Program:
>>> a=0XFace
>>> a
Output:64206
Program:
>>> a=0xBeef
>>> a
Output:48879
Program:
>>> a=0xBeer
Output:SyntaxError: invalid syntax

2) float Data Type
Program:
>>> f=123.4435
>>>
>>> f
123.4435

Note:Exponential form is allowed under float data type.Big benefit of defining into exponential form is any bigger value can be represented into a shorter way.
Program:
>>> x=1.2e3
>>> x
1200.0

3) complex Data Type
If you want to develop scientific/complex program then always recommend to go for complex data type.
Syntax for complex data type is as a+bj
Where a represents Real Part and can be any data type
b represents Imaginary Part and can be only decimal.
j^2=-1 where j is mandatory.
Program:
>>> x=10+20j
>>> x
(10+20j)
>>> type(x)


Program:
>>> p=0b111+10j
>>> p
(7+10j)

Program: Operations with two complex equations
>>> a=10+20j
>>> b=20+10j
>>> a+b
(30+30j)

>>> a-b
(-10+10j)

To know about real and imaginary part using program
>>>a=10+20j
>>> a.real Here real is pre defined attribute/property by Python
10.0
>>> a.imag Here imag is pre defined attribute/property by Python
20.0

4) bool Data Type
Allowed values are True and False for bool data type where T and F always should be in caps.

Program:
>>> a=True
>>> a
True
>>> type(a)

>>> a=true
Traceback (most recent call last):
File "", line 1, in
a=true
NameError: name 'true' is not defined

Program:
>>> a='true'
>>> a
'true'
>>> type(a)


Program:
>>> True+True
2
>>> False+False
0
>>> True+False
1


5) str Data type
Any sequence of character is know as String.
Here we can represent str by using single quote or double quote but single quote is recommended.

Program:
>>> s=pradeep
Traceback (most recent call last):
File "", line 1, in
s=pradeep
NameError: name 'pradeep' is not defined
>>> s='pradeep'
>>> s
'pradeep'
>>> s="pradeep"
>>> s
'pradeep'
Note:For multiple line string literals we always need to use triple single/double quote.

Program:
If we use single quote to print multiple line string literals.
Test.py
s='pradeep
singh'
print(s)
Result:
D:\Python\Programs>py Test.py
File "Test.py", line 1
s='pradeep
^
SyntaxError: EOL while scanning string literal


Use triple single quote
Test.py
s='''pradeep
singh'''
print(s)
Result:
D:\Python\DurgaSir\Programs>py Test.py
pradeep
singh

slice Operator
To get a sub string(piece) of a string you need to use slice operator.
Syntax: s[begin:end]=>Returns substring from begin index to end-1 index.

Program:
>>> s="I am a software engineer"
>>> s[2:5]
'am '
>>> s[1:10]
' am a sof'
>>> s[0:10]
'I am a sof'

Default value for end index is end of the string if end index is not provided.
Program:
>>> s='pradeep'
>>> s[2:]
'adeep'
Default value for begin index is Start from the string if begin index is not provided.
Program:
>>> s='pradeep'
>>> s[:3]
'pra'
If not begin and end index is provided
Program:
>>> s='pradeep'
>>> s[:]
'pradeep'

How can we achieve jump by steps while getting sub string.
Syntax: s[begin:end:step]

Program:
>>> s='iamasoftwareengineer'
>>> s[1:10:2]
'aaota'
>>> s[1:10:3]
'ast'


Note: Python supports positive(Left to right) and negative(Right to Left) index both.
Program:
>>> s='pradeep'
>>> s[0]
'p'
>>> s[1]
'r'
>>> s[2]
'a'
>>> s[3]
'd'
>>> s[4]
'e'
>>> s[-1]
'p'
>>> s[-2]
'e'
>>> s[-3]
'e'

Repetition Operator:
Syntax: s*n->Repeats s String to n times
Program:
>>> s='pradeep'
>>> s*3
'pradeeppradeeppradeep'
>>> s*10
'pradeeppradeeppradeeppradeeppradeeppradeeppradeeppradeeppradeeppradeep'
Length of string:
Program:
>>> s='pradeep'
>>> len(s)
7


Python's Fundamental datatypes:

1)char ==>str type only
2)long ==>int type only

Type Casting or Type coersion:Python supports data type casting using multiple inbuilt functions
1)int():To convert any other type value to int type.
Program:
>>> int(123.345)
123
Note:Can not convert complex type to int type
Program:
>>> int(10+20j)
Traceback (most recent call last):
File "", line 1, in
int(10+20j)
TypeError: can't convert complex to int

Boolean to int:
>>> int(True)
1
>>> int(False)
0
str to int:String should contain only base 10 value.
Program:
>>> int('10')
10
Program:
>>> int("10.5")
Traceback (most recent call last):
File "", line 1, in
int("10.5")
ValueError: invalid literal for int() with base 10: '10.5'
Program:
>>> int(0b1111)
15
>>> int('0b1111')
Traceback (most recent call last):
File "", line 1, in
int('0b1111')
ValueError: invalid literal for int() with base 10: '0b1111'
2)float()
Program:
>>> float(10)
10.0

Program:
>>> float(10+20j)
Traceback (most recent call last):
File "", line 1, in
float(10+20j)
TypeError: can't convert complex to float
Program:
>>> float(True)
1.0
>>> float(False)
0.0

Program:
>>> float('10')
10.0
>>> float('10.5')
10.5
Program:
>>> float('ten')
Traceback (most recent call last):
File "", line 1, in
float('ten')
ValueError: could not convert string to float: 'ten'

Function:complex()

Will be used to conver other types to complex type.

There  are two forms of complex function found:

1)Form1: complex(x)=>Its implies that x+0j

2)Form 2: complex(x,y)=>x+yj

Programs:

>>> complex(10)
(10+0j)
>>> complex(10,20)
(10+20j)
>>> complex(True)
(1+0j)
>>> complex(True,True)
(1+1j)
>>> complex(False)
0j
>>> complex(False,False)
0j
>>> complex(False,True)
1j
>>> complex("10")
(10+0j)
>>> complex("10.5")
(10.5+0j)
>>> complex("ten")
Traceback (most recent call last):
File "<pyshell#9>", line 1, in <module>
complex("ten")
ValueError: complex() arg is a malformed string
>>> complex(0B1111)
(15+0j)

>>> complex("5","10")>>> complex("5","10")     Traceback (most recent call last):  File "<pyshell#12>", line 1, in <module>    complex("5","10")TypeError: complex() can't take second arg if first is a string

 

Function: bool()

To convert other types to boolean.

 

Int Argument:

If we pass non zero value then bool returns always true other wise false.

Program:

>>> bool(1)
True
>>> bool(10)
True
>>> bool(0)
False
>>> bool(12)
True

float Argument:

If we pass except 0.0 value then bool returns always true.

Program:

>>> bool(0.0)
False
>>> bool(0.1)
True

complex Argument:

If both real and imaginary part are 0 then bool returns false other wise true.

Program:

>>> bool(10+20j)
True
>>> bool(0+10j)
True
>>> bool(0+0j)
False

>>> bool(0j)
False
>>> bool(1j)

str Argument:

if argument is empty string then bool returs as false other wise returns true.

>>> bool('')
False
>>> bool("pradeep")
True

>>> bool(' ')
True

Note:bool function never throws error for any data types.

 

Python Reserved Keywords

Reserved Keywords

There are 33 reserved words are available in Python.
True,False,None
and,or,not,is
if,else,elif
while,for,break,continue,return,in,yield
try,except,finally,raise(To throw custom exceptions similar like java throw),assert(For debugging purposes)
import,from,as(To create alias),class,def(To declare method),pass(Not going to do anything)
global(To declare global variable),nonlocal(To declare local variable),lambda(To create anonymous funtions)
del(To delete a variable or method or class),with(In File handling will be used)

Note:
1)All reserved words contain only alphabets.
2)Except first 3 contain lower case alphabets.

How to check reserved keywords in Python using Python Program?

>>> import keyword
>>> keyword.kwlist
['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break',
'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for',
'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or',
'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']