Basic Data Science Questions in Python

When it comes to data science in Python, there are many libraries available for performing various tasks such as data analysis, visualization, and manipulation. In this article, we will explore some of the most commonly asked questions related to data science in Python.

Question 1: Choosing a Visualization Library

When asked about choosing a visualization library in Python, the interviewer wants to understand how proficient you are at creating visualizations using different libraries. While both Seaborn and Matplotlib are popular choices, they have their own strengths and weaknesses. Seaborn is built on top of Matplotlib and offers more customization options, but it can make the work slower. On the other hand, Matplotlib provides fine-tuning options, making it a better choice for complex visualizations. Therefore, the answer depends on the task at hand and your familiarity with each library.

Question 2: Lists vs Tuples in Python

Lists and tuples are two fundamental data structures in Python that store one or more objects or values. The main difference between them is their mutability. Lists are mutable, which means they can be modified after being created, whereas tuples are immutable, making them faster but less flexible. Lists also have additional functionalities such as inserting, popping, and sorting elements.

Question 3: Negative Indexing in Python

Negative indexing is a powerful feature in Python that allows you to access elements in a list or array from the end of the string, moving backwards towards the first value. For example, `n-1` will show the last item in a list, while `n-2` will show the second-to-last item.

Question 4: Data Analysis Libraries in Python

Python has an extensive collection of data analysis libraries that include functions, tools, and methods for managing and analyzing data. Some of the most commonly used libraries are Pandas, NumPy, Matplotlib, and Scikit-learn. These libraries enable data scientists to perform tasks such as data cleaning, processing, visualization, and machine learning.

In conclusion, Python is a popular programming language for data science due to its extensive collection of data analysis libraries. By understanding the strengths and weaknesses of these libraries and being proficient in using them, you can efficiently analyze and visualize complex data sets.

Basic data science questions