Python Interviews

Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch. Here are the links to the Python series Complete Python Topics for Data Analyst: https://t.me/sqlspecialist/548 Part-1: https://t.me/sqlspecialist/562 Part-2: https://t.me/sqlspecialist/564 Part-3: https://t.me/sqlspecialist/565 Part-4: https://t.me/sqlspecialist/566 Part-5: https://t.me/sqlspecialist/568 Part-6: https://t.me/sqlspecialist/570 Part-7: https://t.me/sqlspecialist/571 Part-8: https://t.me/sqlspecialist/572 Part-9: https://t.me/sqlspecialist/578 Part-10: https://t.me/sqlspecialist/577 Part-11: https://t.me/sqlspecialist/578 Part-12: https://t.me/sqlspecialist/581 Part-13: https://t.me/sqlspecialist/583 Part-14: https://t.me/sqlspecialist/584 Part-15: https://t.me/sqlspecialist/585 I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content. But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand. You can refer these amazing resources for Python Interview Preparation. Complete SQL Topics for Data Analysts: https://t.me/sqlspecialist/523 Complete Power BI Topics for Data Analysts: https://t.me/sqlspecialist/588 I'll continue with learning series on Excel & Tableau. Thanks to all who support our channel and share the content with proper credits. You guys are really amazing. Hope it helps :)

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.