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Q1. What is the difference between list and tuples in Python? Q2. What are the key features of Python? Q3. What type of language is python? Q4. How is Python an interpreted language? Q5. What is pep 8? Q6. How is memory managed in Python? Q7. What is name space in Python? Q8. What is PYTHON PATH? Q9. What are python modules? Q10. What are local variables and global variables in Python? We have compiled a list of top Python interview questions which are classified into 7 sections, namely: Basic Interview Questions OOPS Interview Questions Basic Python Programs Python Libraries Interview Questions Web Scraping Interview Questions Data Analysis Interview Questions Multiple Choice Questions (MCQ) Before moving ahead, you may go through the recording of Python Interview Questions where our instructor has shared his experience and expertise that will help you to crack any Python Interview: Python Interview Questions And Answers 2023 | Python Training | Edureka If you have other doubts regard...

Python Language Introduction: Python is a high-level, interpreted programming language that is widely used for web development, data analysis, artificial intelligence, and scientific computing. It was first released in 1991 by Guido van Rossum. Python has a simple and easy-to-learn syntax, making it a popular choice for beginners and experienced programmers alike. It also has a large and active community, which has developed many libraries and frameworks for a wide range of tasks. Some popular libraries include NumPy for scientific computing, pandas for data analysis, and Django for web development. Python's popularity and versatility make it a great choice for many different projects.

General topics that are likely to be popular in the field of data science in the coming years : 1. Machine learning: This is a broad field that involves using algorithms to learn patterns in data and make predictions or decisions without being explicitly programmed. 2. Deep learning: This is a subfield of machine learning that involves using neural networks to learn patterns in data. 3. Natural language processing: This involves using machine learning and other techniques to analyze and understand human language. 4. Data visualization: This involves using tools and techniques to create visual representations of data, such as charts, graphs, and maps. 5. Data engineering: This involves designing and building systems for storing, processing, and analyzing large volumes of data. 6. Big data: This involves working with and analyzing very large datasets, often using distributed computing systems. 7. Predictive modeling: This involves using statistical and machine learning techniques ...