Python Interviews

Top 10 basic programming concepts 1. Variables: Variables are used to store data in a program, such as numbers, text, or objects. They have a name and a value that can be changed during the program's execution. 2. Data Types: Data types define the type of data that can be stored in a variable, such as integers, floating-point numbers, strings, boolean values, and more. Different data types have different properties and operations associated with them. 3. Control Structures: Control structures are used to control the flow of a program's execution. Common control structures include if-else statements, loops (for, while, do-while), switch statements, and more. 4. Functions: Functions are blocks of code that perform a specific task. They can take input parameters, process them, and return a result. Functions help in organizing code, promoting reusability, and improving readability. 5. Conditional Statements: Conditional statements allow the program to make decisions based on certain conditions. The most common conditional statement is the if-else statement, which executes different blocks of code based on whether a condition is true or false. 6. Loops: Loops are used to repeat a block of code multiple times until a certain condition is met. Common types of loops include for loops, while loops, and do-while loops. 7. Arrays: Arrays are data structures that store a collection of elements of the same data type. Elements in an array can be accessed using an index, which represents their position in the array. 8. Classes and Objects: Object-oriented programming concepts involve classes and objects. A class is a blueprint for creating objects, which are instances of the class. Classes define attributes (variables) and behaviors (methods) that objects can exhibit. 9. Input and Output: Input and output operations allow a program to interact with the user or external devices. Common input/output operations include reading from and writing to files, displaying output to the console, and receiving input from the user. 10. Comments: Comments are used to add explanatory notes within the code that are ignored by the compiler or interpreter. They help in documenting code, explaining complex logic, and improving code readability for other developers. Join for more: https://t.me/programming_guide ENJOY LEARNING 👍👍

❤️ The str method in Python classes is a special method used to define the behavior of the class when it is converted to a string representation. 1⃣ The Point class has a str method that is used to return a string representation of an instance. 2⃣ When we call print(str(p)), the str method of the instance p is invoked, and the desired string representation (here "Point(3, 4)") is returned. ✅The str method allows customizing the string representation of class instances, which is useful when you want a readable and meaningful representation of an object.

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How to print anything in Python 👇👇 https://youtu.be/3TVm0mzQfXw?si=VKS2xo4aaxbGaKy5

Functions in Python Functions are a fundamental part of Python (and most other programming languages). Python includes many built-in functions, but you can also create your own functions. A function is a block of code which only runs when it is called. You can pass data, known as parameters, into a function. A function typically performs a specific task that you can run in your program. For example, print() is a built-in Python function that allows you to display text to the screen. Calling print("Hello") results in Hello being displayed. One of the great things about functions is that they can be used like a "black box". You do not need to know how the function is coded in order to use it. In fact, you do not even need to see the function code before using it. All you need to know is how to call the function. You call a function by referring to its name, and providing any arguments that it might require. So calling print("Hello") calls the print() function and passes an argument of "Hello". Creating a function

Dictionary in Python Of all the built-in Python data types, the dictionary is easily the most interesting. It is the only standard mapping type, and it is the backbone of every Python object. A dictionary maps keys to values. Keys need to be hashable objects, while values can be of any arbitrary type. Dictionaries are also mutable objects. In Python, a dictionary is an unordered collection of items, with each item consisting of a key: value pair (separated by a colon). Creating Dictionary in Python You can create a dictionary by enclosing comma separated key: value pairs within curly braces {}. Like this: my_dict = {"Key1": "Value1", "Key2": "Value2"}

Lists In Python Python lists are containers used to store a list of values of any data type. In simple words, we can say that a list is a collection of elements from any data type. In Python, lists are mutable i.e., Python will not create a new list if we modify an element of the list. It works as a container that holds other objects in each order. We can perform various operations like insertion and deletion on list. A list can be composed by storing a sequence of different type of values separated by commas. Python list is enclosed between square [] brackets and elements are stored in the index basis with starting index 0.

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WHILE LOOP A while loop in python runs a bunch of code or statements again and again until the given condition is true when the condition becomes false, the loop terminates its repetition. We must use the keyword “while”, along with it we have to put a condition in parenthesis and after that, a colon is placed. The condition could be either true or false. Until the condition is true, the loop will keep on executing again and again. If we use a certain sort of condition in our while loop that, it never becomes false then the program will keep on running endlessly, until we stop it by force. So, this kind of mistake in our syntax is known as logical/human error. To terminate an infinite loop, you can press CTRL+C on your system.

Some useful PYTHON libraries for data science NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++ SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices. Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot. Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community. Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction. Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data. Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets. Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data. Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information. SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code. Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient. Additional libraries, you might need: os for Operating system and file operations networkx and igraph for graph based data manipulations regular expressions for finding patterns in text data BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

👉 What is Python Data Structures? You can think of a data structure as a way of organizing and storing data such that we can access and modify it efficiently. We have primitive data types like integers, floats, Booleans, and strings. 👉 What is Python List? A list in Python is a heterogeneous container for items. This would remind you of an array in C++, but since Python does not support arrays, we have Python Lists. 👉 Python Tuple This Python Data Structure is like a, like a list in Python, is a heterogeneous container for items. But the major difference between the two (tuple and list) is that a list is mutable, but a tuple is immutable. This means that while you can reassign or delete an entire tuple, you cannot do the same to a single item or a slice. 👉 Python Dictionaries Finally, we will take a look at Python dictionaries. Think of a real-life dictionary. What is it used for? It holds word-meaning pairs. Likewise, a Python dictionary holds key-value pairs. However, you may not use an unhashable item as a key. To declare a Python dictionary, we use curly braces. But since it has key-value pairs instead of single values, this differentiates a dictionary from a set.

𝗟𝗲𝗮𝗿𝗻 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀, 𝗻𝗼𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Have you ever wondered why some technologies are still with us, and some disappeared? Here is 𝘁𝗵𝗲 𝗟𝗶𝗻𝗱𝘆 𝗘𝗳𝗳𝗲𝗰𝘁 to explain it. This effect tells me that 𝗯𝘆 𝘁𝗵𝗲 𝘁𝗶𝗺𝗲 𝗜 𝗿𝗲𝘁𝗶𝗿𝗲, 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝘀𝘁𝗶𝗹𝗹 𝗯𝗲 𝘂𝘀𝗶𝗻𝗴 𝗖# 𝗮𝗻𝗱 𝗦𝗤𝗟. It is a concept in technology and innovation that suggests that the future life expectancy of a non-perishable item is proportional to its current age. In other words, the longer an item has been in use, the longer it is likely to continue to be used. The concept was named after Lindy's Deli in New York City, where Nassim Nicholas Taleb popularized it in his book "𝗧𝗵𝗲 𝗕𝗹𝗮𝗰𝗸 𝗦𝘄𝗮𝗻." According to Taleb, the Lindy effect applies to many things, including technologies, ideas, and cultures, and evaluates their potential longevity. In software development, we see that 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗰𝗼𝗺𝗲 𝗮𝗻𝗱 𝗴𝗼, 𝗯𝘂𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗦𝗤𝗟 𝗼𝗿 𝗖# 𝗮𝗻𝗱 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗢𝗯𝗷𝗲𝗰𝘁-𝗼𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗼𝗿 𝗦𝗢𝗟𝗜𝗗 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝘀𝘁𝗮𝘆. All the energy I put into learning those technologies 10-15 years ago continues to support my work today. Some things changed, but the fundamentals stayed and even got better. So, try to 𝗹𝗲𝗮𝗿𝗻 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝗱𝗼𝗻'𝘁 𝗰𝗵𝗮𝗻𝗴𝗲 (quote from Jeff Bezos). Focus on foundations, not frameworks. I've been doing this for two decades now.