Type-stable Operations on Type-heterogeneous Collections

TypeSortedCollections 

TypeSortedCollections provides the TypeSortedCollection type, which can be used to store type-heterogeneous data in a way that allows operations on the data to be performed in a type-stable manner. It does so by sorting a type-heterogeneous input collection by type upon construction, and storing these elements in a Tuple of concretely typed Vectors, one for each type. TypeSortedCollections provides type stable methods for map!, foreach, broadcast!, and mapreduce that take at least one TypeSortedCollection.

An example:

julia> using TypeSortedCollections

julia> f(x::Int64, y::Float64) = x * y
f (generic function with 2 methods)

julia> f(x::Float64, y::Float64) = round(Int64, -x * y)
f (generic function with 2 methods)

julia> xs = Number[1.; 2; 3];

julia> sortedxs = TypeSortedCollection(xs);

julia> ys = [1.; 2.; 3.];

julia> results = Vector{Int64}(length(xs));

julia> map!(f, results, sortedxs, ys)
3-element Array{Int64,1}:
 -1
  4
  9

julia> @allocated map!(f, results, sortedxs, ys)
0

Use cases

TypeSortedCollections are appropriate when the number of different types in a heterogeneous collection is (much) smaller than the number of elements of the collection. If the number of types is approximately the same as the number of elements, a plain Tuple may be a better choice.

Note that construction of a TypeSortedCollection is of course not type stable, so the intended usage is not to construct TypeSortedCollections in tight loops.

See also FunctionWrappers.jl for a solution to the related problem of storing and calling multiple callables in a type-stable manner, and Unrolled.jl for a macro-based solution.

Iteration order

By default, TypeSortedCollections do not preserve iteration order, in the sense that the order in which elements are processed in map!, foreach, broadcast!, and mapreduce will not be the same as if these functions were called on the original type-heterogeneous vector:

julia> xs = Number[1.; 2; 3.];

julia> sortedxs = TypeSortedCollection(xs);

julia> foreach(println, sortedxs)
1.0
3.0
2

If this is not desired, a TypeSortedCollection that does preserve iteration order can be constructed by passing in an additional constructor argument:

julia> xs = Number[1.; 2; 3.];

julia> sortedxs = TypeSortedCollection(xs, true);

julia> foreach(println, sortedxs)
1.0
2
3.0

The cost of preserving iteration order is that the number of Vectors stored in the TypeSortedCollection becomes equal to the number of contiguous subsequences of the input collection that have the same type, as opposed to simply the number of different types in the input collection. Note that calls to map! and foreach with both TypeSortedCollection and AbstractVector arguments are correctly indexed, regardless of whether iteration order is preserved:

julia> xs = Number[1.; 2; 3.];

julia> sortedxs = TypeSortedCollection(xs); # doesn't preserve iteration order

julia> results = similar(xs);

julia> map!(identity, results, sortedxs) # results of applying `identity` end up in the right location
3-element Array{Number,1}:
 1.0
 2
 3.0

Working with multiple TypeSortedCollections

Consider the following example:

julia> xs = Number[Float32(1); 2; 3.; 4.];

julia> ys = Number[1.; 2.; 3; 4];

julia> results = Vector{Float64}(length(xs));

julia> sortedxs = TypeSortedCollection(xs);

julia> sortedys = TypeSortedCollection(ys);

julia> map!(*, results, sortedxs, sortedys) # Error!

The error happens because xs and ys don't have the same number of different element types. This problem can be solved by aligning the indices of sortedys with those of sortedxs:

julia> sortedys = TypeSortedCollection(ys, TypeSortedCollections.indices(sortedxs));

julia> map!(*, results, sortedxs, sortedys)
4-element Array{Float64,1}:
  1.0
  4.0
  9.0
 16.0

Broadcasting

Broadcasting (in place) is implemented for AbstractVector return types:

julia> x = 4;

julia> ys = Number[1.; 2; 3];

julia> sortedys = TypeSortedCollection(ys);

julia> results = similar(ys, Float64);

julia> results .= x .* sortedys
3-element Array{Float64,1}:
  4.0
  8.0
 12.0

julia> @allocated results .= x .* sortedys
0

Download Details:

Author: Tkoolen
Source Code: https://github.com/tkoolen/TypeSortedCollections.jl 
License: View license

#julia #type 

What is GEEK

Buddha Community

Collection vs Collections in Java: Difference Between Collection & Collections in Java

Introduction

This article will be looking into one of the most popular questions in Java Language – What is Collection in Java? Also, what do you mean by Collections in Java? Are Collection and Collections the same or different in Java?

What is Collection?

What is Collections?

Conclusion

#full stack development #collection #collection vs collections in java #collections in java #difference between collection and collections in java

Ternary operator in Python?

1. Ternary Operator in Python

What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a condition in a single line replacing the multiline if-else code.

syntax : condition ? value_if_true : value_if_false

condition: A boolean expression evaluates true or false

value_if_true: a value to be assigned if the expression is evaluated to true.

value_if_false: A value to be assigned if the expression is evaluated to false.

How to use ternary operator in python here are some examples of Python ternary operator if-else.

Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.

#python #python ternary operator #ternary operator #ternary operator in if-else #ternary operator in python #ternary operator with dict #ternary operator with lambda

Type-stable Operations on Type-heterogeneous Collections

TypeSortedCollections 

TypeSortedCollections provides the TypeSortedCollection type, which can be used to store type-heterogeneous data in a way that allows operations on the data to be performed in a type-stable manner. It does so by sorting a type-heterogeneous input collection by type upon construction, and storing these elements in a Tuple of concretely typed Vectors, one for each type. TypeSortedCollections provides type stable methods for map!, foreach, broadcast!, and mapreduce that take at least one TypeSortedCollection.

An example:

julia> using TypeSortedCollections

julia> f(x::Int64, y::Float64) = x * y
f (generic function with 2 methods)

julia> f(x::Float64, y::Float64) = round(Int64, -x * y)
f (generic function with 2 methods)

julia> xs = Number[1.; 2; 3];

julia> sortedxs = TypeSortedCollection(xs);

julia> ys = [1.; 2.; 3.];

julia> results = Vector{Int64}(length(xs));

julia> map!(f, results, sortedxs, ys)
3-element Array{Int64,1}:
 -1
  4
  9

julia> @allocated map!(f, results, sortedxs, ys)
0

Use cases

TypeSortedCollections are appropriate when the number of different types in a heterogeneous collection is (much) smaller than the number of elements of the collection. If the number of types is approximately the same as the number of elements, a plain Tuple may be a better choice.

Note that construction of a TypeSortedCollection is of course not type stable, so the intended usage is not to construct TypeSortedCollections in tight loops.

See also FunctionWrappers.jl for a solution to the related problem of storing and calling multiple callables in a type-stable manner, and Unrolled.jl for a macro-based solution.

Iteration order

By default, TypeSortedCollections do not preserve iteration order, in the sense that the order in which elements are processed in map!, foreach, broadcast!, and mapreduce will not be the same as if these functions were called on the original type-heterogeneous vector:

julia> xs = Number[1.; 2; 3.];

julia> sortedxs = TypeSortedCollection(xs);

julia> foreach(println, sortedxs)
1.0
3.0
2

If this is not desired, a TypeSortedCollection that does preserve iteration order can be constructed by passing in an additional constructor argument:

julia> xs = Number[1.; 2; 3.];

julia> sortedxs = TypeSortedCollection(xs, true);

julia> foreach(println, sortedxs)
1.0
2
3.0

The cost of preserving iteration order is that the number of Vectors stored in the TypeSortedCollection becomes equal to the number of contiguous subsequences of the input collection that have the same type, as opposed to simply the number of different types in the input collection. Note that calls to map! and foreach with both TypeSortedCollection and AbstractVector arguments are correctly indexed, regardless of whether iteration order is preserved:

julia> xs = Number[1.; 2; 3.];

julia> sortedxs = TypeSortedCollection(xs); # doesn't preserve iteration order

julia> results = similar(xs);

julia> map!(identity, results, sortedxs) # results of applying `identity` end up in the right location
3-element Array{Number,1}:
 1.0
 2
 3.0

Working with multiple TypeSortedCollections

Consider the following example:

julia> xs = Number[Float32(1); 2; 3.; 4.];

julia> ys = Number[1.; 2.; 3; 4];

julia> results = Vector{Float64}(length(xs));

julia> sortedxs = TypeSortedCollection(xs);

julia> sortedys = TypeSortedCollection(ys);

julia> map!(*, results, sortedxs, sortedys) # Error!

The error happens because xs and ys don't have the same number of different element types. This problem can be solved by aligning the indices of sortedys with those of sortedxs:

julia> sortedys = TypeSortedCollection(ys, TypeSortedCollections.indices(sortedxs));

julia> map!(*, results, sortedxs, sortedys)
4-element Array{Float64,1}:
  1.0
  4.0
  9.0
 16.0

Broadcasting

Broadcasting (in place) is implemented for AbstractVector return types:

julia> x = 4;

julia> ys = Number[1.; 2; 3];

julia> sortedys = TypeSortedCollection(ys);

julia> results = similar(ys, Float64);

julia> results .= x .* sortedys
3-element Array{Float64,1}:
  4.0
  8.0
 12.0

julia> @allocated results .= x .* sortedys
0

Download Details:

Author: Tkoolen
Source Code: https://github.com/tkoolen/TypeSortedCollections.jl 
License: View license

#julia #type 

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Python I: Data Types and Operators, variable assignment, and print()

Learn the Python basics so that you can use it for your data science projects.

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

I. About Python 🐍

Created by Dutch programmer Guido van Rossum at Centrum Wiskunde & Informatica, Python made its debut in 1991. Over thirty years it has gained popularity earned a reputation of being the “Swiss army knife of programming languages.” Here are a few reasons why:

• A robust community has cultivated a wealth of free libraries and packages that allow Python to interface with technologies in different domains. Python is popular for data science, but you can also use it for web development, finance, designing games, and more.
• Python is also a multi-paradigm language — in other words it permits both structured/functional and object-oriented programming.
• As a high-level language the syntax is designed to be intuitive and easy to read. This feature makes it attractive to beginners as well as large companies like Instagram and Google.

In emerging fields like data science, artificial intelligence, and machine learning, a robust community, plenty of packages, paradigm flexibility, and syntactical simplicity, allow beginners and professionals to focus on insights and innovation.

#python3 #variables-in-python #data-types-in-python #operators-in-python #python #python i: data types and operators, variable assignment, and print()