Table of content Pandas Interview Questions for Freshers Pandas Interview Questions for Experienced Python Pandas Interview Questions Pandas and Numpy Interview Questions Whether you’re new to the field or exp
4. Explain the concept of “vectorization” in NumPy. Answer: Vectorization inNumPyrefers to the practice of performing operations on entire arrays rather than individual elements. This approach leverages low-level optimizations and parallel processing, resulting in significant performance improvements over ...
In this blog, we will cover some of the most common Pythoninterview questionsyou may encounter during a job interview. We will start by discussing the importance of Python in the tech industry and why it is such a valuable skill to have. We will then cover a range of topics, includingPyt...
Here is a comprehensive compilation of Python interview questions and answers covering a wide range of topics. From basic syntax and data types to advanced concepts like object-oriented programming, data structures, and popular libraries, this resource offers a structured approach to help you prepare ...
offering a salary of ₹4 to ₹15 lakhs per annum. TCS serves customers in more than 46 countries and a variety of industries, including manufacturing, banking, and the medical and healthcare fields. Our carefully curated questions and answers will help you successfully complete the TCS inter...
Python is well-suited for a wide range of applications, including web development (with frameworks like Django and Flask), data analysis and visualization (with tools like NumPy and Matplotlib), scientific computing, automation, artificial intelligence, and more. Is Python platform independent? Yes,...
Pandas is built on top of the NumPy library, i.e., its data structures Series and DataFrame are the upgraded versions of NumPy arrays. 2. How do you quickly access the top 5 rows and last 5 rows of a pandas DataFrame? Thehead()method in pandas is used to access the first 5 rows ...
• Python code is fast to write, but it is also slower to run than compiled languages. Fortunately, Python allows C-based extensions, so bottlenecks can and are often eliminated. A good example of this is the numpy package. Since Python doesn’t do a lot of the number-crunching, it’...
2. Explain the concept of Tensors in PyTorch. In PyTorch, Tensors serve as a fundamental building block, enabling efficient numerical computations on various devices, such as CPUs, GPUs, and TPUs. They are conceptually similar to numpy.arrays while benefiting from hardware acceleration and offerin...
TP / True Positive: case was positive and predicted positive FN / False Negative: case was positive but predicted negative FP / False Positive: case was negative but predicted positive Now, your boss asks you three questions: What percent of your predictions were correct? You answer: the "acc...