Q What Are Some Drawbacks Of The Python Language
For starters, if you know a language well, you know its drawbacks, so responses such as “there’s nothing I don’t like about it” or “it has no drawbacks” are very telling indeed.
The two most common valid answers to this question are:
- The Global Interpreter Lock . CPython is not fully thread safe. In order to support multi-threaded Python programs, CPython provides a global lock that must be held by the current thread before it can safely access Python objects. As a result, no matter how many threads or processors are present, only one thread is ever being executed at any given time. In comparison, it is worth noting that the PyPy implementation discussed earlier in this article provides a stackless mode that supports micro-threads for massive concurrency.
- Execution speed. Python can be slower than compiled languages since it is interpreted.
Q100 What Is The Difference Between Numpy And Scipy
Ans:
| It refers to Numerical python. | It refers to Scientific python. |
| It has fewer new scientific computing features. | Most new scientific computing features belong in SciPy. |
| It contains less linear algebra functions. | It has more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. |
| NumPy has a faster processing speed. | SciPy on the other hand has slower computational speed. |
How To Nail Your Next Tech Interview
It’s always helpful to know what kind of advanced Python interview questions to expect during your coding interview. This article provides some Python advanced interview questions and answers to help you prepare for your interview, as well as a list of sample Python advanced interview questions to help you gauge your preparation level.
If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation ebook to get interview-ready! Also, read , , and for specific insights and guidance on Coding interview preparation.
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In this article, weâll discuss advanced Python interview questions and answers for fresher and experienced developers.
Weâll look at:
- Advanced Python Interview Questions and Answers
- Sample Advanced Python Interview Questions for Practice
- FAQs on Advanced Python Interview Questions
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Is Python Useful For Data Analysis Could You Explain How
Answers that candidates provide should explain that Python can be used at every stage of bigger data analysis projects, particularly because of its libraries.
Your candidates might also describe three of the specific ways in which they would use Python for data analysis: data visualization, data mining, and data processing.
What Does A Python Interview Look Like
Hiring managers or senior-level software engineers interviewing you will likely ask about your education, practical experience, familiarity with coding and technical expertise. Be prepared for traditional interview questions, technical questions and perhaps, problem-solving questions using Python.
Below are some examples of the different types of questions your interviewer might ask.
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Whats The Difference Between Stack And Array
|
Stack |
Array |
|
Stack follows a Last In First Out pattern. What this means is that data access necessarily follows a particular sequence where the last data to be stored is the first one that will be extracted. |
On the other hand, Arrays do not follow a specific order, but instead can be accessed or called by referring to the indexed element within the array. |
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Q86 Mention The Differences Between Django Pyramid And Flask
Ans:
- Flask is a microframework primarily build for a small application with simpler requirements. In flask, you have to use external libraries. Flask is ready to use.
- Pyramid is built for larger applications. It provides flexibility and lets the developer use the right tools for their project. The developer can choose the database, URL structure, templating style and more. Pyramid is heavy configurable.
- Django can also be used for larger applications just like Pyramid. It includes an ORM.
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Write A Program To Generate A List Of Fibonacci Numbers
Recall that the Fibonacci sequence is generated by starting with two integers: 1 and 2. The next number in the sequence is found by adding the previous two numbers together. This question can be answered in a number of different ways. One possible solution uses Python generators and is shown below.
This would be a good opportunity to impress the interviewer with your knowledge of Python generator functions and how they can be used to return lazy iterators in your code. You can speak to a generatorâs advantages such as time and memory efficiency as well as making Python code concise and clear.
Explain What Polymorphism Means
Polymorphism is an approach for enabling objects to take different forms. Candidates might also explain that the main benefit of polymorphism is that engineers can carry out the same action using different approaches.
It means that objects can be processed in different ways, which rely on the class or data type.
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Differentiate Between Linear And Non
|
Linear data structure |
Non-linear data structure |
|
It is a structure in which data elements are adjacent to each other |
It is a structure in which each data element can connect to over two adjacent data elements |
|
Examples of linear data structure include linked lists, arrays, queues, and stacks |
Examples of nonlinear data structure include graphs and trees |
What Are Decorators In Python
in Python are essentially functions that add functionality to an existing function in Python without changing the structure of the function itself. They are represented the @decorator_name in Python and are called in a bottom-up fashion. For example:
# decorator function to convert to lowercasedeflowercase_decorator:defwrapper: func = function string_lowercase = func.lowerreturn string_lowercasereturn wrapper# decorator function to split wordsdefsplitter_decorator:defwrapper: func = function string_split = func.splitreturn string_splitreturn wrapper@splitter_decorator # this is executed next@lowercase_decorator # this is executed firstdefhello:return'Hello World'hello # output =>
The beauty of the decorators lies in the fact that besides adding functionality to the output of the method, they can even accept arguments for functions and can further modify those arguments before passing it to the function itself. The inner nested function, i.e. ‘wrapper’ function, plays a significant role here. It is implemented to enforce encapsulation and thus, keep itself hidden from the global scope.
# decorator function to capitalize namesdefnames_decorator:defwrapper: arg1 = arg1.capitalize arg2 = arg2.capitalize string_hello = functionreturn string_helloreturn wrapper@names_decoratordefsay_hello:return'Hello ' + name1 + '! Hello ' + name2 + '!'say_hello # output => 'Hello Sara! Hello Ansh!'
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What Is Scope Resolution In Python
Sometimes objects within the same scope have the same name but function differently. In such cases, scope resolution comes into play in Python automatically. A few examples of such behavior are:
- Python modules namely ‘math’ and ‘cmath’ have a lot of functions that are common to both of them – log10, acos, exp etc. To resolve this ambiguity, it is necessary to prefix them with their respective module, like math.exp and cmath.exp.
- Consider the code below, an object temp has been initialized to 10 globally and then to 20 on function call. However, the function call didn’t change the value of the temp globally. Here, we can observe that Python draws a clear line between global and local variables, treating their namespaces as separate identities.
temp = 10# global-scope variabledeffunc: temp = 20# local-scope variableprintprint # output => 10func # output => 20print # output => 10
This behavior can be overridden using the global keyword inside the function, as shown in the following example:
temp = 10# global-scope variabledeffunc:global temp temp = 20# local-scope variableprintprint # output => 10func # output => 20print # output => 20
What Is Namespace In Python
In Python, a namespace is a system that assigns a unique name to each and every object. A variable or a method might be considered an object. Python has its own namespace, which is kept in the form of a Python dictionary. Lets look at a directory-file system structure in a computer as an example. It should go without saying that a file with the same name might be found in numerous folders. However, by supplying the absolute path of the file, one may be routed to it if desired.
A namespace is essentially a technique for ensuring that all of the names in a programme are distinct and may be used interchangeably. You may already be aware that everything in Python is an object, including strings, lists, functions, and so on. Another notable thing is that Python uses dictionaries to implement namespaces. A name-to-object mapping exists, with the names serving as keys and the objects serving as values. The same name can be used by many namespaces, each mapping it to a distinct object. Here are a few namespace examples:
Local Namespace: This namespace stores the local names of functions. This namespace is created when a function is invoked and only lives till the function returns.
Global Namespace: Names from various imported modules that you are utilizing in a project are stored in this namespace. Its formed when the module is added to the project and lasts till the script is completed.
Built-in Namespace: This namespace contains the names of built-in functions and exceptions.
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What Is The Difference Between Append And Extend Methods
Both append and extend methods are methods used to add elements at the end of a list.
- append: Adds the given element at the end of the list that called this append method
- extend: Adds the elements of another list at the end of the list that called this extend method
For in-depth knowledge, check out our Python Tutorial and boost your Python skills!
Give A Brief About Django Template
Django Templates generate dynamic web pages. Using templates, you can show the static data and the data from various databases connected to the app through a context dictionary. You can create any number of templates based on project requirements. Even it’s OK to have none of them.
Django template engine handles the templating in the Django web framework. Some template syntaxes declare variables, filters, control logic, and comments.
Django ships built-in backends for its template system called the Django template language .
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Q Executing Dml Commands Through Python Programs
DML Commands are used to modify the data of the database objectsWhenever we execute DML Commands the records are going to be modified temporarily.
Whenever we run “rollback” command the modified records will come back to its original state.
To modify the records of the database objects permanently we use “commit” command
After executing the commit command even though we execute “rollback” command, the modified records will not come back to its original state.
Create the emp1 table in the database by using following commandCreate table emp1 as select * from emp
Whenever we run the DML commands through the python program, then the no.of records which are modified because of that command will be stored into the rowcount attribute of cursor object.After executing the DML Command through the python program we have to call commit method of cursor object.
Q How Do You Perform Pattern Matching In Python Explain
Regular Expressions/REs/ regexes enable us to specify expressions that can match specific “parts” of a given string. For instance, we can define a regular expression to match a single character or a digit, a telephone number, or an email address, etc. The Python’s “re” module provides regular expression patterns and was introduce from later versions of Python 2.5. “re” module is providing methods for search text strings, or replacing text strings along with methods for splitting text strings based on the pattern defined.
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Q68 What Is Monkey Patching In Python
Ans: In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.
Consider the below example:
# m.pyclass MyClass:def f:print "f"
We can then run the monkey-patch testing like this:
import mdef monkey_f:print "monkey_f"m.MyClass.f = monkey_fobj = m.MyClassobj.f
The output will be as below:
monkey_f
As we can see, we did make some changes in the behavior of f in MyClass using the function we defined, monkey_f, outside of the module m.
What Are Some Of The Most Commonly Used Built
Python modules are the files having python code which can be functions, variables or classes. These go by .py extension. The most commonly available built-in modules are:
"abdc1321".isalnum #Output: True"xyz@123$".isalnum #Output: False
Another way is to use match method from the re module as shown:
import reprint)) # Output: Trueprint)) # Output: False
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Q Where Is Mathpy Source File
If you can’t find a source file for a module, it may be a built-in or dynamically loaded module implemented in C, C++ or other compiled language. In this case you may not have the source file or it may be something like mathmodule.c, somewhere in a C source directory . There are three kinds of modules in Python:
- Modules written in Python
- Modules written in C and dynamically loaded
- Modules written in C and linked with the interpreter to get a list of these, type Import sys print sys.builtin_module_names
What Are The Python Coding Interview Questions
Python coding interview questions are asked to test your Python coding expertise and analytical skills. For example, you can expect the questions related to keywords, architecture, ORM, frameworks, how to solve a particular scenario, how to write a code or what is the output of the program, etc. Learn 30+ Python coding interview questions and answers here in this blog.
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Define Default Values In Dictionaries With Get And Setdefault
One of the most common programming tasks involves adding, modifying, or retrieving an item that may or may not be in a dictionary. Python dictionaries have elegant functionality to make these tasks clean and easy, but developers often check explicitly for values when it isnt necessary.
Imagine you have a dictionary named cowboy, and you want to get that cowboys name. One approach is to explicitly check for the key with a conditional:
> > > cowboy=> > > if'name'incowboy:... name=cowboy... else:... name='The Man with No Name'...> > > name'The Man with No Name'
This approach first checks if the name key exists in the dictionary, and if so, it returns the corresponding value. Otherwise, it returns a default value.
While explicitly checking for keys does work, it can easily be replaced with one line if you use .get:
> > > name=cowboy.get
get performs the same operations that were performed in the first approach, but now theyre handled automatically. If the key exists, then the proper value will be returned. Otherwise, the default value will get returned.
But what if you want to update the dictionary with a default value while still accessing the name key? .get doesnt really help you here, so youre left with explicitly checking for the value again:
> > > if'name'notincowboy:... cowboy='The Man with No Name'...> > > name=cowboy
Checking for the value and setting a default is a valid approach and is easy to read, but again Python offers a more elegant method with .setdefault:
What Are Dynamic Data Structures
Dynamic data structures have the feature where they expand and contract as a program runs. It provides a very flexible method of data manipulation because adjusts based on the size of the data to be manipulated.
These 20 coding interview questions that test the conceptual understanding of the candidates give the interview a clear idea on how strong the candidates fundamentals are
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How To Remove Values To A Python Array
Elements can be removed from the python array using pop or remove methods.
pop: This function will return the removed element .
remove:It will not return the removed element.
Consider the below example :
x=arr.arrayprint)print)x.removeprint
Output:
3.61.1 # element popped at 3 rd indexarray
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Python Coding Interview Question #1: Customer Revenue In March
The last question is by Meta/Facebook:
Calculate the total revenue from each customer in March 2019. Include only customers who were active in March 2019.
Output the revenue along with the customer id and sort the results based on the revenue in descending order.
Link to the question:
Youll need to_datetime on the column order_date. Then extract March and the year 2019 from the same column. Finally, group by the cust_id and sum the column total_order_cost, which will be the revenue youre looking for. Use the sort_values to sort the output according to revenue in descending order.
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Describe The Inheritance Styles In Django
Django offers three inheritance styles:
