What Is Comparator interface and Simple Example

What is Comparator interface with simple example

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TS-mockito: Mocking Library for TypeScript

TS-mockito

Mocking library for TypeScript inspired by http://mockito.org/

1.x to 2.x migration guide

1.x to 2.x migration guide

Main features

• Strongly typed
• IDE autocomplete
• Mock creation (mock) (also abstract classes) #example
• Spying on real objects (spy) #example
• Changing mock behavior (when) via:
  • thenReturn - return value #example
  • thenThrow - throw an error #example
  • thenCall - call custom method #example
  • thenResolve - resolve promise #example
  • thenReject - rejects promise #example
• Checking if methods were called with given arguments (verify)
  • anything, notNull, anyString, anyOfClass etc. - for more flexible comparision
  • once, twice, times, atLeast etc. - allows call count verification #example
  • calledBefore, calledAfter - allows call order verification #example
• Resetting mock (reset, resetCalls) #example, #example
• Capturing arguments passed to method (capture) #example
• Recording multiple behaviors #example
• Readable error messages (ex. 'Expected "convertNumberToString(strictEqual(3))" to be called 2 time(s). But has been called 1 time(s).')

Installation

npm install ts-mockito --save-dev

Usage

Basics

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance from mock
let foo:Foo = instance(mockedFoo);

// Using instance in source code
foo.getBar(3);
foo.getBar(5);

// Explicit, readable verification
verify(mockedFoo.getBar(3)).called();
verify(mockedFoo.getBar(anything())).called();

Stubbing method calls

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub method before execution
when(mockedFoo.getBar(3)).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.getBar(3));

// prints null, because "getBar(999)" was not stubbed
console.log(foo.getBar(999));

Stubbing getter value

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub getter before execution
when(mockedFoo.sampleGetter).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.sampleGetter);

Stubbing property values that have no getters

Syntax is the same as with getter values.

Please note, that stubbing properties that don't have getters only works if Proxy object is available (ES6).

Call count verification

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(2);
foo.getBar(2);
foo.getBar(3);

// Call count verification
verify(mockedFoo.getBar(1)).once();               // was called with arg === 1 only once
verify(mockedFoo.getBar(2)).twice();              // was called with arg === 2 exactly two times
verify(mockedFoo.getBar(between(2, 3))).thrice(); // was called with arg between 2-3 exactly three times
verify(mockedFoo.getBar(anyNumber()).times(4);    // was called with any number arg exactly four times
verify(mockedFoo.getBar(2)).atLeast(2);           // was called with arg === 2 min two times
verify(mockedFoo.getBar(anything())).atMost(4);   // was called with any argument max four times
verify(mockedFoo.getBar(4)).never();              // was never called with arg === 4

Call order verification

// Creating mock
let mockedFoo:Foo = mock(Foo);
let mockedBar:Bar = mock(Bar);

// Getting instance
let foo:Foo = instance(mockedFoo);
let bar:Bar = instance(mockedBar);

// Some calls
foo.getBar(1);
bar.getFoo(2);

// Call order verification
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(2));    // foo.getBar(1) has been called before bar.getFoo(2)
verify(mockedBar.getFoo(2)).calledAfter(mockedFoo.getBar(1));    // bar.getFoo(2) has been called before foo.getBar(1)
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(999999));    // throws error (mockedBar.getFoo(999999) has never been called)

Throwing errors

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(10)).thenThrow(new Error('fatal error'));

let foo:Foo = instance(mockedFoo);
try {
    foo.getBar(10);
} catch (error:Error) {
    console.log(error.message); // 'fatal error'
}

Custom function

You can also stub method with your own implementation

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

when(mockedFoo.sumTwoNumbers(anyNumber(), anyNumber())).thenCall((arg1:number, arg2:number) => {
    return arg1 * arg2; 
});

// prints '50' because we've changed sum method implementation to multiply!
console.log(foo.sumTwoNumbers(5, 10));

Resolving / rejecting promises

You can also stub method to resolve / reject promise

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.fetchData("a")).thenResolve({id: "a", value: "Hello world"});
when(mockedFoo.fetchData("b")).thenReject(new Error("b does not exist"));

Resetting mock calls

You can reset just mock call counter

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(1);
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
resetCalls(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset

You can also reset calls of multiple mocks at once resetCalls(firstMock, secondMock, thirdMock)

Resetting mock

Or reset mock call counter with all stubs

// Creating mock
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn("one").

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
console.log(foo.getBar(1));               // "one" - as defined in stub
console.log(foo.getBar(1));               // "one" - as defined in stub
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
reset(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset
console.log(foo.getBar(1));               // null - previously added stub has been removed

You can also reset multiple mocks at once reset(firstMock, secondMock, thirdMock)

Capturing method arguments

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

// Call method
foo.sumTwoNumbers(1, 2);

// Check first arg captor values
const [firstArg, secondArg] = capture(mockedFoo.sumTwoNumbers).last();
console.log(firstArg);    // prints 1
console.log(secondArg);    // prints 2

You can also get other calls using first(), second(), byCallIndex(3) and more...

Recording multiple behaviors

You can set multiple returning values for same matching values

const mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(anyNumber())).thenReturn('one').thenReturn('two').thenReturn('three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinitely

Another example with specific values

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(1)).thenReturn('one').thenReturn('another one');
when(mockedFoo.getBar(2)).thenReturn('two');

let foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(2));    // two
console.log(foo.getBar(1));    // another one
console.log(foo.getBar(1));    // another one - this is last defined behavior for arg '1' so it will be repeated
console.log(foo.getBar(2));    // two
console.log(foo.getBar(2));    // two - this is last defined behavior for arg '2' so it will be repeated

Short notation:

const mockedFoo:Foo = mock(Foo);

// You can specify return values as multiple thenReturn args
when(mockedFoo.getBar(anyNumber())).thenReturn('one', 'two', 'three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinity

Possible errors:

const mockedFoo:Foo = mock(Foo);

// When multiple matchers, matches same result:
when(mockedFoo.getBar(anyNumber())).thenReturn('one');
when(mockedFoo.getBar(3)).thenReturn('one');

const foo:Foo = instance(mockedFoo);
foo.getBar(3); // MultipleMatchersMatchSameStubError will be thrown, two matchers match same method call

Mocking interfaces

You can mock interfaces too, just instead of passing type to mock function, set mock function generic type Mocking interfaces requires Proxy implementation

let mockedFoo:Foo = mock<FooInterface>(); // instead of mock(FooInterface)
const foo: SampleGeneric<FooInterface> = instance(mockedFoo);

Mocking types

You can mock abstract classes

const mockedFoo: SampleAbstractClass = mock(SampleAbstractClass);
const foo: SampleAbstractClass = instance(mockedFoo);

You can also mock generic classes, but note that generic type is just needed by mock type definition

const mockedFoo: SampleGeneric<SampleInterface> = mock(SampleGeneric);
const foo: SampleGeneric<SampleInterface> = instance(mockedFoo);

Spying on real objects

You can partially mock an existing instance:

const foo: Foo = new Foo();
const spiedFoo = spy(foo);

when(spiedFoo.getBar(3)).thenReturn('one');

console.log(foo.getBar(3)); // 'one'
console.log(foo.getBaz()); // call to a real method

You can spy on plain objects too:

const foo = { bar: () => 42 };
const spiedFoo = spy(foo);

foo.bar();

console.log(capture(spiedFoo.bar).last()); // [42] 

Thanks

• Szczepan Faber (https://www.linkedin.com/in/szczepiq)
• Sebastian Konkol (https://www.linkedin.com/in/sebastiankonkol)
• Clickmeeting (http://clickmeeting.com)
• Michał Stocki (https://github.com/michalstocki)
• Łukasz Bendykowski (https://github.com/viman)
• Andrey Ermakov (https://github.com/dreef3)
• Markus Ende (https://github.com/Markus-Ende)
• Thomas Hilzendegen (https://github.com/thomashilzendegen)
• Johan Blumenberg (https://github.com/johanblumenberg)

---

Download Details:

Author: NagRock
Source Code: https://github.com/NagRock/ts-mockito 
License: MIT license

#typescript #testing #mock 

Java Comparator Interface Example | Comparator Interface in Java

Java Comparator interface is used to sort an array or list of objects based on a custom order. Custom ordering of elements is imposed by implementing Comparator.compare() method in the objects.

Java Comparator interface does the total ordering of the objects which may not have a natural ordering. For example, for a list of student objects, the natural order may be ordered by student id.

But in real-life applications, we may want to sort the list of a student by their first name, date of birth or simply any other such criteria. In such conditions, we need to use the Comparator interface.
Java Comparator Interface
Comparator interface in Java is used to order the objects of user-defined classes. A comparator object is capable of comparing two objects of two different classes.

#java #java comparator interface #comparator.compare

Laravel AJAX CRUD Example Tutorial

Hello Guys,

Today I will show you how to create laravel AJAX CRUD example tutorial. In this tutorial we are implements ajax crud operation in laravel. Also perform insert, update, delete operation using ajax in laravel 6 and also you can use this ajax crud operation in laravel 6, laravel 7. In ajax crud operation we display records in datatable.

Read More : Laravel AJAX CRUD Example Tutorial

https://www.techsolutionstuff.com/post/laravel-ajax-crud-example-tutorial

---

Read Also : Read Also : Laravel 6 CRUD Tutorial with Example

https://techsolutionstuff.com/post/laravel-6-crud-tutorial-with-example

#laravel ajax crud example tutorial #ajax crud example in laravel #laravel crud example #laravel crud example with ajax #laravel #php

3 Examples Of Design Applications With Great UX

When it comes to design, it has long been said that simplicity is key. And although I always understood the basis for this saying, I never fully appreciated what it really meant.

As a beginner it is natural to want to show all your ability by wowing your audience. And as a teacher I see my students making this mistake often. They try to build out features as much as possible and try to showcase every aspect of their ability. As many of you know, this approach does not always result in the most aesthetically pleasing design, let alone the most user friendly experience. This is why I have stressed the importance of simplicity to all my students from the first day I started teaching.

For our latest project, I assigned teams of 4 to each create a prototype for a design application.

#design #web-design #hackernoon-top-story #ux #good-ux-design-examples #good-ui-design-examples #web-application-ui-examples #web-application-design-example

Top 100 Python Interview Questions and Answers

In this article, we brought 100 essential Python interview questions to acquaint you with the skills and knowledge required to succeed in a job interview.

Our team which includes experienced Python programmers have made a careful selection of the questions to keep a balance between theory and practical knowledge. So, you can get the full advantage.

Not only the job aspirants but also the recruiters can refer this post to know the right set of questions to evaluate a candidate. Let’s now step-in to explore the Python Q&A section.

100 Essential Python Interview Questions

Let’s begin answering the fundamental-level Python interview questions.

Q-1: What is Python, what are the benefits of using it, and what do you understand of PEP 8?

Python is one of the most successful interpreted languages. When you write a Python script, it doesn’t need to get compiled before execution. Few other interpreted languages are PHP and Javascript.

Benefits of Python Programming

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

PEP 8.

PEP 8 is the latest Python coding standard, a set of coding recommendations. It guides to deliver more readable Python code.

Q-2: What is the output of the following Python code fragment? Justify your answer.

def extendList(val, list=[]):
    list.append(val)
    return list

list1 = extendList(10)
list2 = extendList(123,[])
list3 = extendList('a')

print "list1 = %s" % list1
print "list2 = %s" % list2
print "list3 = %s" % list3

The result of the above Python code snippet is:

list1 = [10, 'a']
list2 = [123]
list3 = [10, 'a']

You may erroneously expect list1 to be equal to [10] and list3 to match with [‘a’], thinking that the list argument will initialize to its default value of [] every time there is a call to the extendList.

However, the flow is like that a new list gets created once after the function is defined. And the same get used whenever someone calls the extendList method without a list argument. It works like this because the calculation of expressions (in default arguments) occurs at the time of function definition, not during its invocation.

The list1 and list3 are hence operating on the same default list, whereas list2 is running on a separate object that it has created on its own (by passing an empty list as the value of the list parameter).

The definition of the extendList function can get changed in the following manner.

def extendList(val, list=None):
  if list is None:
    list = []
  list.append(val)
  return list

With this revised implementation, the output would be:

list1 = [10]
list2 = [123]
list3 = ['a']

Q-3: What is the statement that can be used in Python if the program requires no action but requires it syntactically?

The pass statement is a null operation. Nothing happens when it executes. You should use “pass” keyword in lowercase. If you write “Pass,” you’ll face an error like “NameError: name Pass is not defined.” Python statements are case sensitive.

letter = "hai sethuraman"
for i in letter:
    if i == "a":
        pass
        print("pass statement is execute ..............")
    else:
        print(i)

Q-4: What’s the process to get the home directory using ‘~’ in Python?

You need to import the os module, and then just a single line would do the rest.

import os
print (os.path.expanduser('~'))

Output:

/home/runner

Q-5: What are the built-in types available in Python?

Here is the list of most commonly used built-in types that Python supports:

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-6: How to find bugs or perform static analysis in a Python application?

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-7: When is the Python decorator used?

Python decorator is a relative change that you do in Python syntax to adjust the functions quickly.

Q-8: What is the principal difference between a list and the tuple?

List vs. Tuple.

The principal difference between a list and the tuple is that the former is mutable while the tuple is not.

A tuple is allowed to be hashed, for example, using it as a key for dictionaries.

Q-9: How does Python handle memory management?

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-10: What are the principal differences between the lambda and def?

Lambda vs. def.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-11: Write a reg expression that confirms an email id using the python reg expression module “re”?

Python has a regular expression module “re.”

Check out the “re” expression that can check the email id for .com and .co.in subdomain.

import re
print(re.search(r"[0-9a-zA-Z.]+@[a-zA-Z]+\.(com|co\.in)$","micheal.pages@mp.com"))

Q-12: What do you think is the output of the following code fragment? Is there any error in the code?

list = ['a', 'b', 'c', 'd', 'e']
print (list[10:])

The result of the above lines of code is []. There won’t be any error like an IndexError.

You should know that trying to fetch a member from the list using an index that exceeds the member count (for example, attempting to access list[10] as given in the question) would yield an IndexError. By the way, retrieving only a slice at the starting index that surpasses the no. of items in the list won’t result in an IndexError. It will just return an empty list.

Q-13: Is there a switch or case statement in Python? If not then what is the reason for the same?

No, Python does not have a Switch statement, but you can write a Switch function and then use it.

Q-14: What is a built-in function that Python uses to iterate over a number sequence?

Range() generates a list of numbers, which is used to iterate over for loops.

for i in range(5):
    print(i)

The range() function accompanies two sets of parameters.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-15: What are the optional statements possible inside a try-except block in Python?

There are two optional clauses you can use in the try-except block.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-16: What is a string in Python?

A string in Python is a sequence of alpha-numeric characters. They are immutable objects. It means that they don’t allow modification once they get assigned a value. Python provides several methods, such as join(), replace(), or split() to alter strings. But none of these change the original object.

Q-17: What is slicing in Python?

Slicing is a string operation for extracting a part of the string, or some part of a list. In Python, a string (say text) begins at index 0, and the nth character stores at position text[n-1]. Python can also perform reverse indexing, i.e., in the backward direction, with the help of negative numbers. In Python, the slice() is also a constructor function which generates a slice object. The result is a set of indices mentioned by range(start, stop, step). The slice() method allows three parameters. 1. start – starting number for the slicing to begin. 2. stop – the number which indicates the end of slicing. 3. step – the value to increment after each index (default = 1).

Q-18: What is %s in Python?

Python has support for formatting any value into a string. It may contain quite complex expressions.

One of the common usages is to push values into a string with the %s format specifier. The formatting operation in Python has the comparable syntax as the C function printf() has.

Q-19: Is a string immutable or mutable in Python?

Python strings are indeed immutable.

Let’s take an example. We have an “str” variable holding a string value. We can’t mutate the container, i.e., the string, but can modify what it contains that means the value of the variable.

Q-20: What is the index in Python?

An index is an integer data type which denotes a position within an ordered list or a string.

In Python, strings are also lists of characters. We can access them using the index which begins from zero and goes to the length minus one.

For example, in the string “Program,” the indexing happens like this:

Program 0 1 2 3 4 5

Q-21: What is Docstring in Python?

A docstring is a unique text that happens to be the first statement in the following Python constructs:

Module, Function, Class, or Method definition.

A docstring gets added to the doc attribute of the string object.

Now, read some of the Python interview questions on functions.

Q-22: What is a function in Python programming?

A function is an object which represents a block of code and is a reusable entity. It brings modularity to a program and a higher degree of code reusability.

Python has given us many built-in functions such as print() and provides the ability to create user-defined functions.

Q-23: How many basic types of functions are available in Python?

Python gives us two basic types of functions.

Built-in, and

User-defined.

The built-in functions happen to be part of the Python language. Some of these are print(), dir(), len(), and abs() etc.

Q-24: How do we write a function in Python?

We can create a Python function in the following manner.

Step-1: to begin the function, start writing with the keyword def and then mention the function name.

Step-2: We can now pass the arguments and enclose them using the parentheses. A colon, in the end, marks the end of the function header.

Step-3: After pressing an enter, we can add the desired Python statements for execution.

Q-25: What is a function call or a callable object in Python?

A function in Python gets treated as a callable object. It can allow some arguments and also return a value or multiple values in the form of a tuple. Apart from the function, Python has other constructs, such as classes or the class instances which fits in the same category.

Q-26: What is the return keyword used for in Python?

The purpose of a function is to receive the inputs and return some output.

The return is a Python statement which we can use in a function for sending a value back to its caller.

Q-27: What is “Call by Value” in Python?

In call-by-value, the argument whether an expression or a value gets bound to the respective variable in the function.

Python will treat that variable as local in the function-level scope. Any changes made to that variable will remain local and will not reflect outside the function.

Q-28: What is “Call by Reference” in Python?

We use both “call-by-reference” and “pass-by-reference” interchangeably. When we pass an argument by reference, then it is available as an implicit reference to the function, rather than a simple copy. In such a case, any modification to the argument will also be visible to the caller.

This scheme also has the advantage of bringing more time and space efficiency because it leaves the need for creating local copies.

On the contrary, the disadvantage could be that a variable can get changed accidentally during a function call. Hence, the programmers need to handle in the code to avoid such uncertainty.

Q-29: What is the return value of the trunc() function?

The Python trunc() function performs a mathematical operation to remove the decimal values from a particular expression and provides an integer value as its output.

Q-30: Is it mandatory for a Python function to return a value?

It is not at all necessary for a function to return any value. However, if needed, we can use None as a return value.

Q-31: What does the continue do in Python?

The continue is a jump statement in Python which moves the control to execute the next iteration in a loop leaving all the remaining instructions in the block unexecuted.

The continue statement is applicable for both the “while” and “for” loops.

Q-32: What is the purpose of id() function in Python?

The id() is one of the built-in functions in Python.

Signature: id(object)

It accepts one parameter and returns a unique identifier associated with the input object.

Q-33: What does the *args do in Python?

We use *args as a parameter in the function header. It gives us the ability to pass N (variable) number of arguments.

Please note that this type of argument syntax doesn’t allow passing a named argument to the function.

Example of using the *args:

# Python code to demonstrate 
# *args for dynamic arguments 
def fn(*argList):  
    for argx in argList:  
        print (argx) 
    
fn('I', 'am', 'Learning', 'Python')

The output:

I
am
Learning
Python

Q-34: What does the **kwargs do in Python?

We can also use the **kwargs syntax in a Python function declaration. It let us pass N (variable) number of arguments which can be named or keyworded.

Example of using the **kwargs:

# Python code to demonstrate 
# **kwargs for dynamic + named arguments 
def fn(**kwargs):  
    for emp, age in kwargs.items(): 
        print ("%s's age is %s." %(emp, age)) 
    
fn(John=25, Kalley=22, Tom=32)

The output:

John's age is 25.
Kalley's age is 22.
Tom's age is 32.

Q-35: Does Python have a Main() method?

The main() is the entry point function which happens to be called first in most programming languages.

Since Python is interpreter-based, so it sequentially executes the lines of the code one-by-one.

Python also does have a Main() method. But it gets executed whenever we run our Python script either by directly clicking it or starts it from the command line.

We can also override the Python default main() function using the Python if statement. Please see the below code.

print("Welcome")
print("__name__ contains: ", __name__)
def main():
    print("Testing the main function")
if __name__ == '__main__':
    main()

The output:

Welcome
__name__ contains:  __main__
Testing the main function

Q-36: What does the __ Name __ do in Python?

The name is a unique variable. Since Python doesn’t expose the main() function, so when its interpreter gets to run the script, it first executes the code which is at level 0 indentation.

To see whether the main() gets called, we can use the name variable in an if clause compares with the value “main.”

Q-37: What is the purpose of “end” in Python?

Python’s print() function always prints a newline in the end. The print() function accepts an optional parameter known as the ‘end.’ Its value is ‘\n’ by default. We can change the end character in a print statement with the value of our choice using this parameter.

# Example: Print a  instead of the new line in the end.
print("Let's learn" , end = ' ')  
print("Python") 

# Printing a dot in the end.
print("Learn to code from techbeamers" , end = '.')  
print("com", end = ' ')

The output is:

Let's learn Python
Learn to code from techbeamers.com

Q-38: When should you use the “break” in Python?

Python provides a break statement to exit from a loop. Whenever the break hits in the code, the control of the program immediately exits from the body of the loop.

The break statement in a nested loop causes the control to exit from the inner iterative block.

Q-39: What is the difference between pass and continue in Python?

The continue statement makes the loop to resume from the next iteration.

On the contrary, the pass statement instructs to do nothing, and the remainder of the code executes as usual.

Q-40: What does the len() function do in Python?

In Python, the len() is a primary string function. It determines the length of an input string.

>>> some_string = 'techbeamers'
>>> len(some_string)
11

Q-41: What does the chr() function do in Python?

The chr() function got re-added in Python 3.2. In version 3.0, it got removed.

It returns the string denoting a character whose Unicode code point is an integer.

For example, the chr(122) returns the string ‘z’ whereas the chr(1212) returns the string ‘Ҽ’.

Q-42: What does the ord() function do in Python?

The ord(char) in Python takes a string of size one and returns an integer denoting the Unicode code format of the character in case of a Unicode type object, or the value of the byte if the argument is of 8-bit string type.

>>> ord("z")
122

Q-43: What is Rstrip() in Python?

Python provides the rstrip() method which duplicates the string but leaves out the whitespace characters from the end.

The rstrip() escapes the characters from the right end based on the argument value, i.e., a string mentioning the group of characters to get excluded.

The signature of the rstrip() is:

str.rstrip([char sequence/pre>
#Example
test_str = 'Programming    '
# The trailing whitespaces are excluded
print(test_str.rstrip())

Q-44: What is whitespace in Python?

Whitespace represents the characters that we use for spacing and separation.

They possess an “empty” representation. In Python, it could be a tab or space.

Q-45: What is isalpha() in Python?

Python provides this built-in isalpha() function for the string handling purpose.

It returns True if all characters in the string are of alphabet type, else it returns False.

Q-46: How do you use the split() function in Python?

Python’s split() function works on strings to cut a large piece into smaller chunks, or sub-strings. We can specify a separator to start splitting, or it uses the space as one by default.

#Example
str = 'pdf csv json'
print(str.split(" "))
print(str.split())

The output:

['pdf', 'csv', 'json']
['pdf', 'csv', 'json']

Q-47: What does the join method do in Python?

Python provides the join() method which works on strings, lists, and tuples. It combines them and returns a united value.

Q-48: What does the Title() method do in Python?

Python provides the title() method to convert the first letter in each word to capital format while the rest turns to Lowercase.

#Example
str = 'lEaRn pYtHoN'
print(str.title())

The output:

Learn Python

Now, check out some general purpose Python interview questions.

Q-49: What makes the CPython different from Python?

CPython has its core developed in C. The prefix ‘C’ represents this fact. It runs an interpreter loop used for translating the Python-ish code to C language.

Q-50: Which package is the fastest form of Python?

PyPy provides maximum compatibility while utilizing CPython implementation for improving its performance.

The tests confirmed that PyPy is nearly five times faster than the CPython. It currently supports Python 2.7.

Q-51: What is GIL in Python language?

Python supports GIL (the global interpreter lock) which is a mutex used to secure access to Python objects, synchronizing multiple threads from running the Python bytecodes at the same time.

Q-52: How is Python thread safe?

Python ensures safe access to threads. It uses the GIL mutex to set synchronization. If a thread loses the GIL lock at any time, then you have to make the code thread-safe.

For example, many of the Python operations execute as atomic such as calling the sort() method on a list.

Q-53: How does Python manage the memory?

Python implements a heap manager internally which holds all of its objects and data structures.

This heap manager does the allocation/de-allocation of heap space for objects.

Q-54: What is a tuple in Python?

A tuple is a collection type data structure in Python which is immutable.

They are similar to sequences, just like the lists. However, There are some differences between a tuple and list; the former doesn’t allow modifications whereas the list does.

Also, the tuples use parentheses for enclosing, but the lists have square brackets in their syntax.

Q-55: What is a dictionary in Python programming?

A dictionary is a data structure known as an associative array in Python which stores a collection of objects.

The collection is a set of keys having a single associated value. We can call it a hash, a map, or a hashmap as it gets called in other programming languages.

Q-56: What is the set object in Python?

Sets are unordered collection objects in Python. They store unique and immutable objects. Python has its implementation derived from mathematics.

Q-57: What is the use of the dictionary in Python?

A dictionary has a group of objects (the keys) map to another group of objects (the values). A Python dictionary represents a mapping of unique Keys to Values.

They are mutable and hence will not change. The values associated with the keys can be of any Python types.

Q-58: Is Python list a linked list?

A Python list is a variable-length array which is different from C-style linked lists.

Internally, it has a contiguous array for referencing to other objects and stores a pointer to the array variable and its length in the list head structure.

Here are some Python interview questions on classes and objects.

Q-59: What is Class in Python?

Python supports object-oriented programming and provides almost all OOP features to use in programs.

A Python class is a blueprint for creating the objects. It defines member variables and gets their behavior associated with them.

We can make it by using the keyword “class.” An object gets created from the constructor. This object represents the instance of the class.

In Python, we generate classes and instances in the following way.

>>>class Human:  # Create the class
...     pass
>>>man = Human()  # Create the instance
>>>print(man)

Q-60: What are Attributes and Methods in a Python class?

A class is useless if it has not defined any functionality. We can do so by adding attributes. They work as containers for data and functions. We can add an attribute directly specifying inside the class body.

>>> class Human:
...     profession = "programmer" # specify the attribute 'profession' of the class
>>> man = Human()
>>> print(man.profession)
programmer

After we added the attributes, we can go on to define the functions. Generally, we call them methods. In the method signature, we always have to provide the first argument with a self-keyword.

>>> class Human:
    profession = "programmer"
    def set_profession(self, new_profession):
        self.profession = new_profession      
>>> man = Human()
>>> man.set_profession("Manager")
>>> print(man.profession)
Manager

Q-61: How to assign values for the Class attributes at runtime?

We can specify the values for the attributes at runtime. We need to add an init method and pass input to object constructor. See the following example demonstrating this.

>>> class Human:
    def __init__(self, profession):
        self.profession = profession
    def set_profession(self, new_profession):
        self.profession = new_profession

>>> man = Human("Manager")
>>> print(man.profession)
Manager

Q-62: What is Inheritance in Python programming?

Inheritance is an OOP mechanism which allows an object to access its parent class features. It carries forward the base class functionality to the child.

We do it intentionally to abstract away the similar code in different classes.

The common code rests with the base class, and the child class objects can access it via inheritance. Check out the below example.

class PC: # Base class
    processor = "Xeon" # Common attribute
    def set_processor(self, new_processor):
        processor = new_processor

class Desktop(PC): # Derived class
    os = "Mac OS High Sierra" # Personalized attribute
    ram = "32 GB"

class Laptop(PC): # Derived class
    os = "Windows 10 Pro 64" # Personalized attribute
    ram = "16 GB"

desk = Desktop()
print(desk.processor, desk.os, desk.ram)

lap = Laptop()
print(lap.processor, lap.os, lap.ram)

The output:

Xeon Mac OS High Sierra 32 GB
Xeon Windows 10 Pro 64 16 GB

Q-63: What is Composition in Python?

The composition is also a type of inheritance in Python. It intends to inherit from the base class but a little differently, i.e., by using an instance variable of the base class acting as a member of the derived class.

See the below diagram.

To demonstrate composition, we need to instantiate other objects in the class and then make use of those instances.

class PC: # Base class
    processor = "Xeon" # Common attribute
    def __init__(self, processor, ram):
        self.processor = processor
        self.ram = ram

    def set_processor(self, new_processor):
        processor = new_processor

    def get_PC(self):
        return "%s cpu & %s ram" % (self.processor, self.ram)

class Tablet():
    make = "Intel"
    def __init__(self, processor, ram, make):
        self.PC = PC(processor, ram) # Composition
        self.make = make

    def get_Tablet(self):
        return "Tablet with %s CPU & %s ram by %s" % (self.PC.processor, self.PC.ram, self.make)

if __name__ == "__main__":
    tab = Tablet("i7", "16 GB", "Intel")
    print(tab.get_Tablet())

The output is:

Tablet with i7 CPU & 16 GB ram by Intel

Q-64: What are Errors and Exceptions in Python programs?

Errors are coding issues in a program which may cause it to exit abnormally.

On the contrary, exceptions happen due to the occurrence of an external event which interrupts the normal flow of the program.

Q-65: How do you handle exceptions with Try/Except/Finally in Python?

Python lay down Try, Except, Finally constructs to handle errors as well as Exceptions. We enclose the unsafe code indented under the try block. And we can keep our fall-back code inside the except block. Any instructions intended for execution last should come under the finally block.

try:
    print("Executing code in the try block")
    print(exception)
except:
    print("Entering in the except block")
finally:
    print("Reached to the final block")

The output is:

Executing code in the try block
Entering in the except block
Reached to the final block

Q-66: How do you raise exceptions for a predefined condition in Python?

We can raise an exception based on some condition.

For example, if we want the user to enter only odd numbers, else will raise an exception.

# Example - Raise an exception
while True:
    try:
        value = int(input("Enter an odd number- "))
        if value%2 == 0:
            raise ValueError("Exited due to invalid input!!!")
        else:
            print("Value entered is : %s" % value)
    except ValueError as ex:
        print(ex)
        break

The output is:

Enter an odd number- 2
Exited due to invalid input!!!
Enter an odd number- 1
Value entered is : 1
Enter an odd number-

Q-67: What are Python Iterators?

Iterators in Python are array-like objects which allow moving on the next element. We use them in traversing a loop, for example, in a “for” loop.

Python library has a no. of iterators. For example, a list is also an iterator and we can start a for loop over it.

Q-68: What is the difference between an Iterator and Iterable?

The collection type like a list, tuple, dictionary, and set are all iterable objects whereas they are also iterable containers which return an iterator while traversing.

Here are some advanced-level Python interview questions.

Q-69: What are Python Generators?

A Generator is a kind of function which lets us specify a function that acts like an iterator and hence can get used in a “for” loop.

In a generator function, the yield keyword substitutes the return statement.

# Simple Python function
def fn():
    return "Simple Python function."

# Python Generator function
def generate():
    yield "Python Generator function."

print(next(generate()))

The output is:

Python Generator function.

Q-70: What are Closures in Python?

Python closures are function objects returned by another function. We use them to eliminate code redundancy.

In the example below, we’ve written a simple closure for multiplying numbers.

def multiply_number(num):
    def product(number):
        'product() here is a closure'
        return num * number
    return product

num_2 = multiply_number(2)
print(num_2(11))
print(num_2(24))

num_6 = multiply_number(6)
print(num_6(1))

The output is:

22
48
6

Q-71: What are Decorators in Python?

Python decorator gives us the ability to add new behavior to the given objects dynamically. In the example below, we’ve written a simple example to display a message pre and post the execution of a function.

def decorator_sample(func):
    def decorator_hook(*args, **kwargs):
        print("Before the function call")
        result = func(*args, **kwargs)
        print("After the function call")
        return result
    return decorator_hook

@decorator_sample
def product(x, y):
    "Function to multiply two numbers."
    return x * y

print(product(3, 3))

The output is:

Before the function call
After the function call
9

Q-72: How do you create a dictionary in Python?

Let’s take the example of building site statistics. For this, we first need to break up the key-value pairs using a colon(“:”). The keys should be of an immutable type, i.e., so we’ll use the data-types which don’t allow changes at runtime. We’ll choose from an int, string, or tuple.

However, we can take values of any kind. For distinguishing the data pairs, we can use a comma(“,”) and keep the whole stuff inside curly braces({…}).

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> type(site_stats)

>>> print(site_stats)
{'type': 'organic', 'site': 'tecbeamers.com', 'traffic': 10000}

Q-73: How do you read from a dictionary in Python?

To fetch data from a dictionary, we can directly access using the keys. We can enclose a “key” using brackets […] after mentioning the variable name corresponding to the dictionary.

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> print(site_stats["traffic"])

We can even call the get method to fetch the values from a dict. It also let us set a default value. If the key is missing, then the KeyError would occur.

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> print(site_stats.get('site'))
tecbeamers.com

Q-74: How do you traverse through a dictionary object in Python?

We can use the “for” and “in” loop for traversing the dictionary object.

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> for k, v in site_stats.items():
    print("The key is: %s" % k)
    print("The value is: %s" % v)
    print("++++++++++++++++++++++++")

The output is:

The key is: type
The value is: organic
++++++++++++++++++++++++
The key is: site
The value is: tecbeamers.com
++++++++++++++++++++++++
The key is: traffic
The value is: 10000
++++++++++++++++++++++++

Q-75: How do you add elements to a dictionary in Python?

We can add elements by modifying the dictionary with a fresh key and then set the value to it.

>>> # Setup a blank dictionary
>>> site_stats = {}
>>> site_stats['site'] = 'google.com'
>>> site_stats['traffic'] = 10000000000
>>> site_stats['type'] = 'Referral'
>>> print(site_stats)
{'type': 'Referral', 'site': 'google.com', 'traffic': 10000000000}

We can even join two dictionaries to get a bigger dictionary with the help of the update() method.

>>> site_stats['site'] = 'google.co.in'
>>> print(site_stats)
{'site': 'google.co.in'}
>>> site_stats_new = {'traffic': 1000000, "type": "social media"}
>>> site_stats.update(site_stats_new)
>>> print(site_stats)
{'type': 'social media', 'site': 'google.co.in', 'traffic': 1000000}

Q-76: How do you delete elements of a dictionary in Python?

We can delete a key in a dictionary by using the del() method.

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> del site_stats["type"]
>>> print(site_stats)
{'site': 'google.co.in', 'traffic': 1000000}

Another method, we can use is the pop() function. It accepts the key as the parameter. Also, a second parameter, we can pass a default value if the key doesn’t exist.

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> print(site_stats.pop("type", None))
organic
>>> print(site_stats)
{'site': 'tecbeamers.com', 'traffic': 10000}

Q-77: How do you check the presence of a key in a dictionary?

We can use Python’s “in” operator to test the presence of a key inside a dict object.

>>> site_stats = {'site': 'tecbeamers.com', 'traffic': 10000, "type": "organic"}
>>> 'site' in site_stats
True
>>> 'traffic' in site_stats
True
>>> "type" in site_stats
True

Earlier, Python also provided the has_key() method which got deprecated.

Q-78: What is the syntax for List comprehension in Python?

The signature for the list comprehension is as follows:

[ expression(var) for var in iterable ]

For example, the below code will return all the numbers from 10 to 20 and store them in a list.

>>> alist = [var for var in range(10, 20)]
>>> print(alist)

Q-79: What is the syntax for Dictionary comprehension in Python?

A dictionary has the same syntax as was for the list comprehension but the difference is that it uses curly braces:

{ aKey, itsValue for aKey in iterable }

For example, the below code will return all the numbers 10 to 20 as the keys and will store the respective squares of those numbers as the values.

>>> adict = {var:var**2 for var in range(10, 20)}
>>> print(adict)

Q-80: What is the syntax for Generator expression in Python?

The syntax for generator expression matches with the list comprehension, but the difference is that it uses parenthesis:

( expression(var) for var in iterable )

For example, the below code will create a generator object that generates the values from 10 to 20 upon using it.

>>> (var for var in range(10, 20))
 at 0x0000000003668728>
>>> list((var for var in range(10, 20)))

Now, see more Python interview questions for practice.

Q-81: How do you write a conditional expression in Python?

We can utilize the following single statement as a conditional expression. default_statment if Condition else another_statement

>>> no_of_days = 366
>>> is_leap_year = "Yes" if no_of_days == 366 else "No"
>>> print(is_leap_year)
Yes

Q-82: What do you know about the Python enumerate?

While using the iterators, sometimes we might have a use case to store the count of iterations. Python gets this task quite easy for us by giving a built-in method known as the enumerate().

The enumerate() function attaches a counter variable to an iterable and returns it as the “enumerated” object.

We can use this object directly in the “for” loops or transform it into a list of tuples by calling the list() method. It has the following signature:

enumerate(iterable, to_begin=0)
Arguments:
iterable: array type object which enables iteration
to_begin: the base index for the counter is to get started, its default value is 0
# Example - enumerate function 
alist = ["apple","mango", "orange"] 
astr = "banana"
  
# Let's set the enumerate objects 
list_obj = enumerate(alist) 
str_obj = enumerate(astr) 
  
print("list_obj type:", type(list_obj))
print("str_obj type:", type(str_obj))

print(list(enumerate(alist)) )  
# Move the starting index to two from zero
print(list(enumerate(astr, 2)))

The output is:

list_obj type: 
str_obj type: 
[(0, 'apple'), (1, 'mango'), (2, 'orange')]
[(2, 'b'), (3, 'a'), (4, 'n'), (5, 'a'), (6, 'n'), (7, 'a')]

Q-83: What is the use of globals() function in Python?

The globals() function in Python returns the current global symbol table as a dictionary object.

Python maintains a symbol table to keep all necessary information about a program. This info includes the names of variables, methods, and classes used by the program.

All the information in this table remains in the global scope of the program and Python allows us to retrieve it using the globals() method.

Signature: globals()

Arguments: None
# Example: globals() function 
x = 9
def fn(): 
    y = 3
    z = y + x
    # Calling the globals() method
    z = globals()['x'] = z
    return z
       
# Test Code     
ret = fn() 
print(ret)

The output is:

12

Q-84: Why do you use the zip() method in Python?

The zip method lets us map the corresponding index of multiple containers so that we can use them using as a single unit.

Signature: 
 zip(*iterators)
Arguments: 
 Python iterables or collections (e.g., list, string, etc.)
Returns: 
 A single iterator object with combined mapped values
# Example: zip() function
  
emp = [ "tom", "john", "jerry", "jake" ] 
age = [ 32, 28, 33, 44 ] 
dept = [ 'HR', 'Accounts', 'R&D', 'IT' ] 
  
# call zip() to map values 
out = zip(emp, age, dept)
  
# convert all values for printing them as set 
out = set(out) 
  
# Displaying the final values  
print ("The output of zip() is : ",end="") 
print (out)

The output is:

The output of zip() is : {('jerry', 33, 'R&D'), ('jake', 44, 'IT'), ('john', 28, 'Accounts'), ('tom', 32, 'HR')}

Q-85: What are Class or Static Variables in Python programming?

In Python, all the objects share common class or static variables.

But the instance or non-static variables are altogether different for different objects.

The programming languages like C++ and Java need to use the static keyword to make a variable as the class variable. However, Python has a unique way to declare a static variable.

All names initialized with a value in the class declaration becomes the class variables. And those which get assigned values in the class methods becomes the instance variables.

# Example 
class Test: 
    aclass = 'programming' # A class variable 
    def __init__(self, ainst): 
        self.ainst = ainst # An instance variable 
  
# Objects of CSStudent class 
test1 = Test(1) 
test2 = Test(2) 
  
print(test1.aclass)
print(test2.aclass)
print(test1.ainst)
print(test2.ainst)

# A class variable is also accessible using the class name
print(Test.aclass)

The output is:

programming
programming
1
2
programming

Let’s now answer some advanced-level Python interview questions.

Q-86: How does the ternary operator work in Python?

The ternary operator is an alternative for the conditional statements. It combines true or false values with a statement that you need to test.

The syntax would look like the one given below.

[onTrue] if [Condition] else [onFalse]

x, y = 35, 75
smaller = x if x < y else y
print(smaller)

Q-87: What does the “self” keyword do?

The self is a Python keyword which represents a variable that holds the instance of an object.

In almost, all the object-oriented languages, it is passed to the methods as a hidden parameter.

Q-88: What are the different methods to copy an object in Python?

There are two ways to copy objects in Python.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-89: What is the purpose of docstrings in Python?

In Python, the docstring is what we call as the docstrings. It sets a process of recording Python functions, modules, and classes.

Q-90: Which Python function will you use to convert a number to a string?

For converting a number into a string, you can use the built-in function str(). If you want an octal or hexadecimal representation, use the inbuilt function oct() or hex().

Q-91: How do you debug a program in Python? Is it possible to step through the Python code?

Yes, we can use the Python debugger (pdb) to debug any Python program. And if we start a program using pdb, then it let us even step through the code.

Q-92: List down some of the PDB commands for debugging Python programs?

Here are a few PDB commands to start debugging Python code.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-93: What is the command to debug a Python program?

The following command helps run a Python program in debug mode.

$ python -m pdb python-script.py

Q-94: How do you monitor the code flow of a program in Python?

In Python, we can use the sys module’s settrace() method to setup trace hooks and monitor the functions inside a program.

You need to define a trace callback method and pass it to the settrace() function. The callback should specify three arguments as shown below.

import sys

def trace_calls(frame, event, arg):
    # The 'call' event occurs before a function gets executed.
    if event != 'call':
        return
    # Next, inspect the frame data and print information.
    print 'Function name=%s, line num=%s' % (frame.f_code.co_name, frame.f_lineno)
    return

def demo2():
    print 'in demo2()'

def demo1():
    print 'in demo1()'
    demo2()

sys.settrace(trace_calls)
demo1()

Q-95: Why and when do you use generators in Python?

A generator in Python is a function which returns an iterable object. We can iterate on the generator object using the yield keyword. But we can only do that once because their values don’t persist in memory, they get the values on the fly.

Generators give us the ability to hold the execution of a function or a step as long as we want to keep it. However, here are a few examples where it is beneficial to use generators.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-96: What does the yield keyword do in Python?

The yield keyword can turn any function into a generator. It works like a standard return keyword. But it’ll always return a generator object. Also, a method can have multiple calls to the yield keyword.

See the example below.

def testgen(index):
  weekdays = ['sun','mon','tue','wed','thu','fri','sat']
  yield weekdays[index]
  yield weekdays[index+1]

day = testgen(0)
print next(day), next(day)

#output: sun mon

Q-97: How to convert a list into other data types?

Sometimes, we don’t use lists as is. Instead, we have to convert them to other types.

Turn a list into a string.

We can use the ”.join() method which combines all elements into one and returns as a string.

weekdays = ['sun','mon','tue','wed','thu','fri','sat']
listAsString = ' '.join(weekdays)
print(listAsString)

#output: sun mon tue wed thu fri sat

Turn a list into a tuple.

Call Python’s tuple() function for converting a list into a tuple.

This function takes the list as its argument.

But remember, we can’t change the list after turning it into a tuple because it becomes immutable.

weekdays = ['sun','mon','tue','wed','thu','fri','sat']
listAsTuple = tuple(weekdays)
print(listAsTuple)

#output: ('sun', 'mon', 'tue', 'wed', 'thu', 'fri', 'sat')

Turn a list into a set.

Converting a list to a set poses two side-effects.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

However, we can use the set() function to convert a list into a Set.

weekdays = ['sun','mon','tue','wed','thu','fri','sat','sun','tue']
listAsSet = set(weekdays)
print(listAsSet)

#output: set(['wed', 'sun', 'thu', 'tue', 'mon', 'fri', 'sat'])

Turn a list into a dictionary.

In a dictionary, each item represents a key-value pair. So converting a list isn’t as straightforward as it were for other data types.

However, we can achieve the conversion by breaking the list into a set of pairs and then call the zip() function to return them as tuples.

Passing the tuples into the dict() function would finally turn them into a dictionary.

weekdays = ['sun','mon','tue','wed','thu','fri']
listAsDict = dict(zip(weekdays[0::2], weekdays[1::2]))
print(listAsDict)

#output: {'sun': 'mon', 'thu': 'fri', 'tue': 'wed'}

Q-98: How do you count the occurrences of each item present in the list without explicitly mentioning them?

Unlike sets, lists can have items with the same values.

In Python, the list has a count() function which returns the occurrences of a particular item.

Count the occurrences of an individual item.

weekdays = ['sun','mon','tue','wed','thu','fri','sun','mon','mon']
print(weekdays.count('mon'))

#output: 3

Count the occurrences of each item in the list.

We’ll use the list comprehension along with the count() method. It’ll print the frequency of each of the items.

weekdays = ['sun','mon','tue','wed','thu','fri','sun','mon','mon']
print([[x,weekdays.count(x)] for x in set(weekdays)])

#output: [['wed', 1], ['sun', 2], ['thu', 1], ['tue', 1], ['mon', 3], ['fri', 1]]

Q-99: What is NumPy and how is it better than a list in Python?

NumPy is a Python package for scientific computing which can deal with large data sizes. It includes a powerful N-dimensional array object and a set of advanced functions.

Also, the NumPy arrays are superior to the built-in lists. There are a no. of reasons for this.

• Python is a dynamic-typed language. It means that you don’t need to mention the data type of variables during their declaration. It allows to set variables like var1=101 and var2 =” You are an engineer.” without any error.
• Python supports object orientated programming as you can define classes along with the composition and inheritance. It doesn’t use access specifiers like public or private).
• Functions in Python are like first-class objects. It suggests you can assign them to variables, return from other methods and pass as arguments.
• Developing using Python is quick but running it is often slower than compiled languages. Luckily, Python enables to include the “C” language extensions so you can optimize your scripts.
• Python has several usages like web-based applications, test automation, data modeling, big data analytics and much more. Alternatively, you can utilize it as a “glue” layer to work with other languages.

Q-100: What are different ways to create an empty NumPy array in Python?

There are two methods which we can apply to create empty NumPy arrays.

The first method to create an empty array.

import numpy
numpy.array([])

The second method to create an empty array.

# Make an empty NumPy array
numpy.empty(shape=(0,0))

Summary – Essential Python Interview Questions

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