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Introduction to Python Compilers

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Python was created in 1991 by Guido Van Rossum.

What is Python Compiler?

A compiler is a program that converts high-level programming language into lower-level language, which can be understood by the assembly and interpreted into logical inputs. Python is miscategorized as an interpreted language as it has various implementation versions like CPython, Pypy, and Iron Python.

CPython is a standard version. It follows conversion to bytecode, leading to the misconception that python has interpreted. These interpreted codes are not understandable for the CPU unit and require an interpreter. Python Virtual machine converts bytecode into machine code.

Types of Python Compilers

Let us look at different types of Python Compilers. Let’s see the significance of the individual Compilers of Python in detail –

1. CPython

It is the most widely used interpreter in Python, developed in C and python; the bindings for the interpreter must be written in a foreign language other than Python. CPython uses a Global Interpreter Lock (GIL) on each process; thus, python bytecode for a single process is executed on a single thread.

This interpreter is not suitable for CPU-intensive algorithms. CPython finds its use as many libraries are C optimized, i.e., many libraries will run their processes faster in a C-based code. Also, Python is a dynamic programming language as it allocates resources on the go, not considering future consequences.

However, when the same code is defined for CPython-based compiler systems, the type definition is considered. The compilation steps are:- Decoding, Tokenizing, Parsing, AST(Abstract Syntax Tree), and Compiling.

2. Jython or Jpython

Jython is an implementation designed to integrate Python code over a Java virtual machine seamlessly; this integration provides an opportunity to amalgamate a popular scripting language, python, into a vast library of the Java virtual machine. Jython compiles files to .class extensions.

The Jython programs can inherit and run any Java class and compile the code to bytecode. Along with this, Jython can be used to implement Java-based packages, especially desirable for creating solutions using Servlets, Swing, SWT, and AWT packages. Jython was created in 1997 by Jim Hugunin. Jython uses the Global interpreter lock (GIL) like CPython.

3. IronPython

Iron Python is a python implementation designed with the target of the dot net framework. This project is maintained presently by a small community of users on Github. To be used for scripting requires installing the Python tools for visual studio, which is available as an extension for visual studio IDE. The full implementation is written in C#.

Iron Python uses the Dynamic language runtime framework, available in the dot net framework, to write the dynamic language. The iron python interprets Python code to in-memory bytecode before execution. The primary aim behind the design of IronPython is to implement the dot net framework to utilize the full potential of the vast User interface libraries available for the dot net framework.

4. ActivePython

ActivePython is a commercial version of the Python scripting platform designed and developed by the Open source organization ActiveState. It provides Python bundles along with some additional packages.

5. PyJS also is formerly known as Pyjamas

PyJs is a rich internet application mainly used to develop client-side web and desktop applications using Python scripting. The PyJs has a compiler that translates Python to JavaScript and is primarily designed over the Ajax framework.

6. Nuitka

Nuitka is an ideal example of a source-to-source compiler. The compiler allows the user to feed python codes and produce C/C++ extensions even if the computer has no python version installed.

7. Stackless

The interpreter gets its name because it does not engage C call stacks but rather frees the same during function calls. It results in something called a micro thread approach.

8. PyPy

This is a prevalent implementation as an alternative to traditional python owing to its fast and compliant nature. PyPy uses the Just in time compiler, a runtime compiler proficient for the source code’s dynamic compilation. The space occupied by PyPy codes is smaller in terms of memory requirements.

Conclusion Recommended Articles

We hope that this EDUCBA information on “Python Compilers” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

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Python Program To Convert List Of String To Comma Separated String

Python is derived from many other languages including ABC, Modula-3, C, C++, Algol-68, SmallTalk, and UnixShell, and many other scripting languages.

Now, as we know this article is all about converting a list of strings to comma-separated strings. Before diving deep into it, it’s necessary to know in detail about strings and lists in python. Let’s move ahead with the topic and understand them in detail. Starting off with string.

What is a String?

Python strings are sequences of characters, which means they are ordered collections of characters.

"This is a string." 'This is also a string.'

Strings in Python are arrays of bytes representing Unicode characters. Python strings are “immutable” which means they cannot be changed after they are created. This means that its internal data elements, the characters, can be accessed, but cannot be replaced, inserted, or removed for input and output. A string is a data structure. A data structure is a compound unit that consists of several other pieces of data. A string is a sequence of zero or more characters.

A string length is the number of characters it contains. Python’s len() function is used to return the length of the string.

Syntax len(str) Example len("WELCOME") Output 7

Each character occupies a position in the string. The positions of a string’s characters are numbered from 0, on the left, to the length of the string minus 1, on the right. Now, moving on to the topic of the list.

What is a List?

A list is a collection of items in a particular order. You can make a list that includes the letters of the alphabet, and the digits from 0–9. In Python, square brackets ([]) indicate a list, and individual elements in the list are separated by commas.

Example bicycles = ['trek', 'Cannondale', 'redline', 'specialized'] print(bicycles) Output

['trek', 'Cannondale', 'redline', 'specialized']

A list is a value that contains multiple values in an ordered sequence. The term list value refers to the list itself (which is a value that can be stored in a variable or passed to a function like any other value), not the values inside the list value. A list value looks like this: [‘cat’, ‘bat’, ‘rat’, ‘elephant’]. Values inside the list are also called items. Items are separated with commas (that is, they are comma-delimited).

Here we’ll look at how to change a string into a comma-separated string. It is important to keep in mind that the given string can be a list of strings as well.

Converting Python List Into A Comma-Separated String

When we convert a list of strings into a comma-separated string, it results in a string where each element of the list is separated by a comma.

For example, if we convert [“my”, “name”, “is”, “Nikita”] to a comma-separated string, it will result in “my, name, is, Nikita”.

Using the join() Function

An iterable component are combined by the join() function, which also returns a string. The character that will be utilized to separate the string’s elements must be specified.

Here we are supposed to create a comma-separated string, so we will use the comma as a separator.

Example

The following program creates a list and joins them together into one comma separated string by using join() function.

List = ["Apple", "Banana", "Mango", "Orange"] String = ', '.join(List) print("List of String:") print(List) print("Comma Separated String:") print(String) Output

List of String: ['Apple', 'Banana', 'Mango', 'Orange'] Comma Separated String: Apple, Banana, Mango, Orange

The above approach is only applicable to a list of strings.

To work with a list of integers or other elements, we can use list comprehension and the str() function. We can quickly run through the elements with list comprehension in a single line using the for loop, then convert each element to a string using the str() function.

Example

In the following program, a list of strings is created and stored in the variable List. A new string is then created by joining each element from the list with a comma as a separator and stored in the variable String.

List = ['235', '3754', '856', '964'] String = ', '.join([str(i) for i in List]) print("List of String:") print(List) print("Comma Separated String:") print(String) Output List of String: ['235', '3754', '856', '964'] Comma Separated String: 235, 3754, 856, 964

Using the map() function, we can also eliminate list comprehension. By applying the str() function to each element of the list, the map() function can be used to convert all elements of the list to strings.

Example

List = ['235', '3754', '856', '964'] String = ', '.join(map(str,List)) print("List of String:") print(List) print("Comma Separated String:") print(String) Output List of String: ['235', '3754', '856', '964'] Comma Separated String: 235, 3754, 856, 964 Using the StringIO Module

The StringIO object is comparable to a file object, however it works with text in memory. It can be imported directly in Python 2 by utilising the StringIO module. It was saved in the io module in Python 3.

Example

To write the list as a comma-separated row for a CSV file in the StringIO object, we can use the csv.writerow() function. To do so, we must first create a csv.writer object. Using the getvalue() function, we can then store the contents of this object in a string.

import io import csv List = ['235', '3754', '856', '964'] String_io = io.StringIO() w = csv.writer(String_io) w.writerow(List) String = String_io.getvalue() print("List of String:") print(List) print("Comma Separated String:") print(String) Output List of String: ['235', '3754', '856', '964'] Comma Separated String: 235,3754,856,964

We may also use the unpack operator with the print() function. The unpack operator * unpacks all of the elements of an iterable and saves them in the StringIO object via the file parameter in the print() function.

Example

A list is created with the values 8, 9, 4 and 1. A StringIO object String_io is then created, allowing the string to be treated as a file. The list is printed as a StringIO object with file=String_io and sep=’,’ and end=”. This separates each element in the list with a comma and does not add a new line or character to the end of the line. The string stored in string_io is retrieved by the getvalue() method and stored in a variable named “String”.

import io List = [8,9,4,1] String_io = io.StringIO() print(*List, file=String_io, sep=',', end='') String = String_io.getvalue() print("List of String:") print(List) print("Comma Separated String:") print(String) Output List of String: [8, 9, 4, 1] Comma Separated String: 8,9,4,1 Conclusion

In this article, we have discussed about different methods to convert a list of string to comma separated string.

List Of Top 8 Dynatrace Competitors

Introduction to Dynatrace Competitors

Dynatrace is a type of tool that is used to provide the APM (application performance management), monitoring of cloud infrastructure, AIOps (Artificial intelligence for operations), DEM (digital experience management) to the IT teams of the organization. Dynatrace is capable of monitoring the software performance and also provide the functionality for real user monitoring, synthetic monitoring, and also network monitoring.

List of Dynatrace Competitors

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1. AppDynamics

AppDynamics is an application that is used as a performance management tool for the monitoring of servers and make metrics so that server health and performance can be continuous monitor. The code-level details monitoring is supported by the AppDynamics. The bottlenecks are also figured out by the application along with the calculation of trend performance for the server. All the performance-related issues to the server easily find out using AppDynamics.

2. Splunk Enterprise

Splunk Enterprise is an application that helps to search, visualize, and analyze data that can be collected from business and IT infrastructure. The Splunk enterprise collects data from applications, websites, devices and sensors. The data indexing operation is done by the Splunk Enterprise. The search operation is very fast in Splunk Enterprise as the application handles a huge chunk of data and the tool is capable to retrieve the data very fast. The tool offers an interactive dashboard in which there is a search box, charts, and fields and they display the results to the user. The application is also capable of sending the alerts to the user when any search query is fired by the user.

3. Sumo Logic 4. Datadog

The Datadog is a type of monitoring tool used for cloud-based applications and also used to monitor the servers, databases, and provide other services. The Datadog application uses the SaaS platform to provides its functionalities. The event monitoring is the main feature offered by the tool used to monitor the performance of the cloud applications. The performance metric can be generated by the tool. The tool supports multiple operating systems like Windows, Mac, Linux that makes the tool popular. The tool offers customized dashboards to the user to show the graphs so that real-time monitoring of data can be possible. The IT teams of the organization can have a single view of infrastructure. When any issues are reported in the tool user is notified via emails and SMS.

5. SignalFx

The SignalFx is a SaaS-based type monitoring tool used by the customers to analyze, automate, and visualize data generated from applications, containers, microservices, and functions. The tool is able to send the alerts when any issue via SMS or email. In the tool, there is streaming architecture in which metric data is divided into two forms human-readable form and other is time series value. The tool is a very powerful tool as the tool has the capability to process millions of data in one second and send alerts for any data related issues. The tool is capable to find the performance bottlenecks of the software application.

6. LogicMonitor 7. Zabbix

The Zabbix is a type of monitoring tool that is available as an open-source tool so that monitoring can be done for the IT components which include servers, networks, cloud service, and virtual machines. The XML type components is used for the tool configuration. The Zabbix tool supports different types of operating systems like Unix, Unix, Mac OS, Solaris, and other operating systems. the tool monitors the statistics like CPU load, disk space, network utilization, and other statistics. The Zabbix agent needs to be installed in the machine and monitor the statistics so that issues can find out and can be resolved.

8. PagerDuty

The PagerDuty is a type of incident management application that helps to send a reliable notification, scheduling via on-call, issues notifications, and other features to the IT team so that infrastructure issues can be identified and can be easily fixed. The tool is able to help the organization by analyzing the incidents that can affect the business operations of the organization. The tool is also able to generate alerts and send alerts to the IT team so that appropriate actions can be taken for the issues.

Conclusion

Dynatrace software is a powerful tool used for monitoring of the network and application performance and finds the issues so that it can be solved. There are several alternatives to the Dynatrace tool that can be used by the organization to monitor the applications and find the performance bottlenecks so that it can be solved and performance can be escalated for the software application used by the organization.

Find Sum Of Even Factors Of A Number In Python Program

Let’s start our article with an explanation of how to find the sum of even factors of a remainder or we can say factors are the multiples of the number.

Example 1 − If we are given a number 60

Factors of 60 (6*10=2*3*2*5) are 2, 5 and 3

But according to the question, we must find out the sum of even factors so, in the above given example, the even factor will be only 2.

Also, if the given number is an odd number, then the factors will not be even numbers.

Example 2 − 55 is the given number

Its factors are 5 and 11 we can see that both the factors are not even hence the odd number will not have an even factor.

purpose of solving the mathematics functions and as our question is dealing with a math problem we have to import and add such modules in our codes.

So before writing our codes let’s see what concepts we can apply in our code as per the question given to us. This method will help us find the most appropriate function and improve our approach towards the question.

What should be our approach and why?

Since our question is completely based on the mathematic tools hence our first approach should be finding out such tools which will help us completing the code. So, we will import math module which will allow us to use the functions.

If one continues to have a doubt as to what if the number is odd what we will do so to solve this problem our next approach should be to think about a statement which will help us make choice or we can say, make decision for us and according to our need our work can be made easy using the IF statement and then use FOR and WHILE statements as per use. Now let’s write our program.

FINDING SUM OF EVEN FACTORS OF A NUMBER Example

def

sum_EF

(

)

(

)

return

for

range

(

int

)

(

math

sqrt

(

)

num

sum_n

num_t

while

(

)

num

(

2and num

)

sum_n

num_t

i sum_n

sum_n

num_t ut

sum_n ut

(

)

return

ut N

(

sum_EF

(

)

Output None

As we have written the codes for our question let’s understand some of the key points from the start. Very first thing which we have added to our code is that we have imported the MATH module.

Then the next important thing is finding out whether the asked number is odd or even. Since we know computer is not an intelligent system so we have to guide it that’s why first we have written code for checking the number whether it is odd or even.

Example −

if (N % 2 != 0) : return 0

Here we have used IF – statement for checking so if the number will be odd it will return the code to zero otherwise continue.

Now moving further with the code let’s assume we’ll entered a even number, now our work will be to check the factors of the input number starting from 1.

Now for checking the factors we have created a range and as you can see we have written math.sqrt . this is an in-built function in python which helps us to return the square root of the number.

Example −

for i in range(2, (int)(math.sqrt(N)) + 1)

Next is to remove prime numbers (numbers which get divisible only by themselves example 1,3,5,7etc) from the factors of the number since all odd numbers are not prime numbers, but all prime numbers are odd numbers.

Example −

ut = ut * (1 + N)

Return it is used when there will be a prime number.

Next, we have used−

if (i == 2 and num == 1) : num_sm = 0 num_tm = num_tm * i num_sm = num_sm + num_tm ut = ut * num_sm

for removing the 20 value which gives the value 1.

N=N//i − which we might get in between solving the problem.

In the end, we have given a value in the code with the purpose of finding out the sum of even factors.

A Comprehensive List Of The Different Python Data Types

Introduction

Python is one of the most preferred programming languages these days. It allows developers to focus all their efforts on implementation instead of complex programs and data types in python are used for that purpose only to make our work easy.

Overview of the Different Data Types

The classification or categorization of data items is known as Data types. A kind of value is signified by data type which determines what operations need to be performed on that particular data.

It is used for integral values. In python 2 versions, there was different Int and long for small and big integral values, but from python 3 it is int only.

We can represent int in 4 forms.

Decimal form ( it is by default and digits are from 0-9)

Binary form (base-2, 0, and 1)a=1111, so if do print (a)It will be a normal chúng tôi to convert it into binary, we make a change like a=0B1111So basically zero(0) small “ b” or zero(0) capital” B” is used in prefix.

Octal form (0-7 )So in this zero(0) and capital “0” or small “o” is used in prefix.

Hexadecimal (0-9, A-F)In this zero(0) and small or capital “x” is used.

Now the thing to remember is by default the answer we will get will be in decimal forms, so we can change that as well.

Float:

Float is a data type in which decimal values are there. In float, we can only have decimal no binary, oct, or hex. Eg: a=15.5 So when we see the type of “a” It will be float type.

Complex :

Print (type(x)) #so it will be complex

The real part can be binary, octal, or hex anything, but the image should be decimal only.

We can type real part in any form like binary octal or hexadecimal but not for imaginary

OB111 + 20j it is correct. But 14+ 0B101 it is incorrect

So we can do x+y, we can do x*y or we can do x/y or x-y

So addition or subtraction of the complex numbers is simple. We simply do addition or subtraction of the real part of both numbers and imaginary parts of both numbers.

(a+bi) * (c+di) = (ac-bd) + (ad+bc)i

So whenever we multiply two complex numbers the answer we will get is by this formulae Or we have actually simplified the formula.

Simply let’s say we take two complex numbers.

a= (2 + 4j) , b= (10 + 10 j)

So now when we multiply them It will be multiplied like this-– 2 (10 + 10j) + 4j(10+10j)

– 20 + 20j + 40j + 40j^2

And we know ^2 of imaginary part is -1. So it will become-

-20 + 60j

a= p+qj

b= r+sj

So division would be

a/b = pr + qs + qr – ps , x= under root of (r^2 + s^2)

——— ——–

X x

This complex is used much in machine learning or mathematics specific queries.

Boolean

But one thing is to remember that the first letters should be in the capital True, False

a=True Type (a)

It will show bool

So let’s take an example.

it will show false

The type would be

Print (type(c))

Bool

Now one more thing to remember is True =1 and False =0

So if we print (True + True)

The answer would be 2 (1+1)

And if we print (True-False) The answer would be 1 (1-0)

Strings

So in string data type, we can use single, double, or triple quotes. So for eg: S= ”Aashish”

S=’Aashish’

We can print type(s) it will give us string

Also s = ”a”

It will also give string unlike java etc because in java etc it must have given it as a character. but there is no data type as a character in python

Now comes the triple quotes. If we have to write the string in multiple lines, then we cannot use single or double quotes, what we can use is single triple quotes or double triple quotes

Like eg :

Aashish’’’

Aashish”””

One more thing we need to know now is the let say we have one statement. Like-

s= Aashish is a very ‘good’ boy

Now in the above statement if we want that Good should be in quotes, so while writing the string we should know that the statement should not be in single quotes. Like-

“Aashish is a very ‘good’ boy”.

And the same goes if we want something in double-quotes, then we have to make the whole statement in a single quote. Now if we want to use both single and double quotes in a statement.

“Aashish” is a very ‘good’ boy

Then we have to put the whole statement in triple quotes.

Positive and Negative Index in strings

So there is indexing in python string, so if let say we want to access the character of the word for eg: from a= Aashish, we want to access “i”

So we can do it like-

Print (a[4])

The first alphabet is represented by 0. Also, we can do like in python a[-1], which represents the last character.

So it will print h

Slice Operator in String: basically it’s a part of the string which is known as a slice. So to fetch that slice we use it.

Eg: “Aashish”

In slicing let say we want only “shis”

format is s[begin : end]

Which returns from beginning to end-1. So if let’s say put s[2:6]. It will return to the 5th. Now if we are not specifying begin. Then it will start at 0 s[:5].

If are not specifying it will continue to the end s[2:]. And one case is s[:]. It will return the total string.

One more case s[5:2]. We will get an empty string, as it cannot move from 5 to 2.

And we want the output as “Aashish”

So we can do it like.

output= s[0].upper() + s[1:] print(output)

So we can do it like

output = s[0:len(s)-1] + s[-1].upper() Print (output)

Now let’s say we need to have the first character in the capital and the last character in the capital and remaining as it is. So what we need to do is simply :

Output = s[0].upper() + s[1:len(s)-1] + s[-1].upper() Print (output )

And * operator for string data type :

Is used for concatenation, and both the arguments should be string only.

So it can be “String” + “String” + “string” + …..

It cannot be like “String” + “Int”

Now * operator

s=”SUN” *3

So it is a string repetition operator. And used to multiply the string. One thing to notice here is one argument should be a string and the other should be an integer

TypeCasting

The process of converting from one type to another type. 5 inbuilt functions are there-

Int

Float

Complex

bool()

str()

Int: To convert from other types to the int type

Float to the int function, so like if we have 10.989 is there, so after the decimal point all digits will be gone so it will be done like int(10.989), it will give 10.

Complex to int: 10+ 20j, so int (10+20j), it will give type error, as we cannot convert complex to int type.

Bool to int: int (True) will give 1

String to int :String internally contains an only integral value or in base 10 only, so no hexadecimal or binary, etc.Therefore int(“15”) will give chúng tôi (0B1111) will give an error

int(“10.5”) , again error as it is not integral value it is a float value.

Float: Convert into the float type

Or Float(0XFace) will give 64206.0

Complex to float is not possible :

float(False), then the answer would be 0.0

String to float type is also possible, only in the case when the string contains an integral value or float value but they should be specified in base 10.

Float (10) will give 10.0

Float (0XFACE) error

Float (“Aashish”) error

Complex Type

Complex (x)

If only one argument then it will become a real value

Complex (10)

The answer would be 10 + 0j

Complex (0B1111) would be 15+0j

Complex (10.5) would be 10.5 + 0j

Complex (True) would be 1+0j

Complex (False) would be 0j

Complex (“String”) it should have either int or float value in base 10

Complex (x,y)

When two arguments are there one will go to real and one to the imaginary complex(10, 20) would be 10+20j.

Complex (10.5, 20.6) would be 10.5 + 20.6j

Complex (“10”, “20”)

It will give an error if we are having the string argument, we can’t have the second value as a string as well

Complex (10, “20”)

It will also give an error, the second argument can’t be a string in complex

Bool()

bool(10)

If it is non zero then it will be True

bool(10) will give True

bool(0) will give false

Float

bool(0.1) true

bool(0+0j) False

bool(1+0j) True

String

bool(“True”) True

bool(“False”) True

bool(“Yes”) True

bool (“No”) True

bool( “ ”) False

If the string is empty then only it is false, otherwise if it not empty then it will be True

String

str(10) “10”

str(0B1111) “15”

str(10.5) “10.5”

str(10+20j) “10+20j”

str(True) “True”

str(False) “False”

Fundamental Data Types vs Immutability

Immutable → we cannot change

Once we create an object, we cannot perform any changes in that object. If we try to change, then the new object would be created. This non-changeable behavior is immutability.

Python Data Type: Lists

These are the collection related data type.

l=[07, “Aashish”, 20,30, “Nimish”] Print type(l)

It will give a list. The list is always in square brackets. If we print (l), the order will be in the same in which we have given. So the order of perseverance is there. Duplicates objects are allowed.

Heterogenous objects are allowed, like int, string, etc. Indexing and slicing are applicable. Now,

print (l[0])

So will give the first element. And similarly -1 for the last. Now if let say we have an empty list.

l=[ ]

So we can add elements like-

l.append(10)

We can remove elements like-

l.remove(10)

Now let say, we have-

l=[10, 20, 30, 40]

Now the list is mutable so at l[0] we can add value. Like-

l[0]= 100

So now 100 will be added to the starting. List will become l[100, 10, 20, 30, 40].

Python Data Type: Tuple

Now we will learn the data type Tuple. It is the same as the list except it is immutable, if we create one object so we cannot add or remove any object or value. It is like a read-only version of a list. We represent tuple by (10, 20,”aashish”).

Performance is better than the list. And it also uses less memory than a list.

Python Data Type: Set

Now we will learn about the new data type i.e Set. It is used for the group of objects or entities but duplicates are not allowed and I don’t care about the order. So if we have such a type of requirement then we go for the Set.

So-

s1 = {1,2,3} s2 = {3,1,2}

So basically s1 and s2 are equal. So in the set, there is no first element no last element kind of concept. Curly brackets are used inset.

s= {10, 10, 20, “Aashish”, 30}

But output will come s= {10, 20, “Aashish”, 30}

But order can be anything. So indexing cannot be there or slice operator-

Print s[0]

Will give an error.

Heterogeneous objects are allowed.

s.add(50)

In the list, it was appended. In set, it is added.

Append vs add

Append was used in the list to add items in the last. So add is used in the set, as we don’t know where it is going to get added.

s= { }

So it is not known as an empty set it is known as an empty dictionary. As the dictionary gets privilege because of the frequent use of it more than the set.

If we want to create the empty set then we can create it like

s= set() Python Data Type: Frozen set

It is exactly like a set except it is immutable. Because inset we can make changes, but not in the frozen set. So we create the frozen set like-

s={ 10, 20, 30, 40} fr= frozenset(s) Print type(fr)

It will be frozen set s.

So now we cannot add or remove the values from the frozen set and will get the attribute error if we try to do that.

Python Data Type: Range

Now we will learn about the Range.

R = range(10)

So we will get 10 values starting from the 0. So basically it is an inbuilt function used in python. If we print r-

It will give 0,1,2,3,4,5,6,7,8,9

We can use it like

For x in r : print(x)

So we will get all the values printed. How we can make the range objects.

Form: range (n)

So will give values to 0 to n-1

Form : range(begin : end)

So will give values from the beginning to the n-1

r= range(1, 10) For x in r : Print (x)

So we will get 1,2,3 …. 9

Form:

Range (begin, end, increment/Decrement)

R = range(1,21,1) So 1 to 20

1,2,3,….. 20

R = Range(1, 21,2 )

1,3,5… 19

Also, we can do like for decrement

Range (20, 1, -2)

So 20, 18 ….

So now we have known things go in order in range, so wherever order is there, we can do the indexing or slicing. But the range is immutable, as the values are in sequence and if we try to add the values, an error will occur.

Python Data Type: Dict

Now we will learn about the dictionary. Dictionary is different from the above data types in the manner that it is used to represent the key values. Represent by Dict

d={ k1: v1, k2 : V2}

So let say we have an empty dictionary

d= { }

So we can add value like

d[100]=”Aashish” d[200]= “Ayush”

So it will look like-

{ 100 : Aashish , 200 : Ayush}

Now there would be one concern about the duplicity of the values. So duplicate keys are not allowed but values can be duplicated. S-

d[100] = “Abhishek”

So the old value i.e Aashish will be replaced by the Abhishek, so the duplicate key is not allowed, anyhow there would be no error, but the value will get replaced by the new value.

No order there, similarly like a set. Heterogeneous values can be there. It is mutable, we can make changes. Slicing, indexing, etc are not there, due to no order.

Python Data Type: Bytes and BytesArray

l= [10, 20,30,40]

Till now it is a list. Now to convert it into the bytes we need to

Do-

b=bytes(l)

print(b)

Now It is used for the binary creation of the data. Bytes can only have values from 0 to 256. So basically if we add the value 256, 257 …

We will get the value error. Now in this as well indexing, slicing is there. But it is immutable, we cannot change its content. We will get an error. But if we want mutable bytes then it is known as Bytearray

l= [ 10, 20, 30, 40 ] b=bytearray(l) b[0]= 77 print(b[0])

None data type is nothing i.e no value is associated with it. To make the value available for the garbage collection-

a=None

None is also stored as object internally-

Print (type(a)) Escape Characters, Comments, and Constants :

These are –

n : for the next line

t : for the tab space

r : carriage return, means starting from the starting

b : backspace

f : form feed

‘ : for single quote

“ : for double quote

\ : for backslash symbol

We need to put # in every line.

Constants :

The things of which we cannot change the values

So as such no constant concept is there in python, but if we want the value to be constantly put it in the upper case.

MAX_VALUE=10

For the convention purpose only.

End Notes

So we learned that Datatypes are an important concept because statistical methods and certain other things can only be used with a certain python data type. You have to analyze data differently and then categorize data otherwise it would result in a wrong analysis.

Top 7 Different Categories Of Talend Components

Introduction to Talend Components

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Categories of Talend Components

Given below are different categories in which talend components are segregated:

1. Orchestration

Components under orchestration families allow users to control and manage the job execution process.

tPreJob Component: This component of the orchestration family helps in the smooth running of the job and eliminating errors during job failure. This component assigns tasks equally between the job initialization component and the main intended task component. Jobs such as testing connectivity to external services fall under pre-job tasks.

tPostJob: This component bypasses any exception and executes the job which makes it ideal for cleanups. Tasks such as temporary file deletion and disconnecting from the data based are considered as post job tasks.

tRunJob: This component is used to embed one job into another to create Talend SubJob.

2. Processing Components

tMap: This is one of the most important components used to perform data manipulating operations on the data such as data merge and filtering the data. The primary function of the tMap component is mapping the input component to the output component.

tBufferInput: This component is paired along with tBufferOutput. Data written to tBufferOutput can be read using tBufferInput component.

tBufferOutput: The tBufferOutput helps in writing the data to the buffer, which can be later accessed by tBufferInput.

tAggregateRow: This component is used to perform aggregate functions like sum, count, average on the data row.

tFilterRow: Simple conditions can be used to filter the data using the tFilterRow component.

3. Custom Code

Talend’s custom code components provide users with functionality beyond Talend’s inbuilt components. With the help of these components, the user can create a custom component by connecting multiple components together.

tJava: This component is used to execute java codes. With the help of the tJava component, users can enter personalized code which can be integrated with the Talend job.

tGroovy : This component allows users to insert Groovy script also known as simplified java syntax in order to integrate with Talend.

tJavaFlex: This component is very similar to tJava row component and includes the ability to combine the functionality of tJava and tJavaRow.

tSetGlobalVar: This component is used to add global variables to the global map.

4. File Component

Components available within the File family are used to read the data from the source file and write to the destination file.

tFileInputDelimited: This component is used to read the file row by row based on a row separator.

tFileInput Excel: This component is used to read the data from an excel file row by row.

tMySqlInput: This component extracts the data from the MySql database based on the input query.

tFileOutputDelimited: This component is used as the output component and is useful in writing the filtered data to a delimited file.

tFileOutput Excel: The data is written to Excel file with the help of tFileOutput component.

tMySqlOutput: This component writes the transformed data to Mysql database.

5. Logs & Errors Components

The component family of Logs & Errors allows you to log your job execution information. These modules, with the exception of tDie, do not play a functional role in the task-specific processing of your job; however, they play an important part in monitoring your jobs and help ensure smooth running.

tlogRow: When a user runs the Job from within Talend Studio, the tLogRow feature allows the user to write row data to the Job log file, or console window.

tAssert/tAssetCatcher: unblocked trigger messages can be caught or send using this pair of tAssert/tAssetCatcher components.

tChronometerStart/tChronometerStop: This pair of components is used to obtain the run time of a job. The run time is recorded and displayed by tChronometerStop component.

tDie: In case tAssert/tAssertCather fails to catch unblocked trigger message, tDie components send a signal to tLogCatcher and terminate the running job.

tFlowMeter/tFlowMeterCather: Data flow metrics of the job can be recorded with the help of flowmeter/tFlowMeterCatcher pair.

tLogCatcher: This component is used to catch and record the messages sent from tDie and tWarn.

tLogRow: The result of the data can be displayed on the console with the help of this component.

tStatCatcher: Statistics generated from a job can be recorded by using the tStatCatcher component.

tWarn: This component is very similar to tDie and communicates with tLogCatcher in the case of the non-blocking message.

6. Miscellaneous Components

tContextDump: This component is used for debugging or providing a record of context variables during the job execution.

tMemorizeRows: This component enables you to store data arrays that flow through your job. You should specify the number of rows to memorize. If you need to go back to previous rows within your data flow, this feature is helpful. You also choose the individual columns to memorize as well as specifying the number of rows to be memorized.

7. Internet

This family of components lets the user perform internet-related operations during the job.

tSendEmail: This component allows the Email to be sent directly from the job with the help of the simple mail transfer protocol.

tHttpRequest: This component of the internet family is used to make GET and POST request to the provided URL.

Conclusion

In this article, we have learned about multiple categories in which Talend components are categorized into. Talend provides users with nearly a thousand components in order to complete several tasks ranging from cloud migration to big data solutions. Talend components eliminate the time consumed in manually coding and provide non-developers the required tools to carry out data analysis.