IMP Questions of Python for Data Science

Python for Data Science

Question Bank


Note: Whatever you explain, explain with an example. Write relevant codes.



  1. What makes python programming suitable for data science?  List some of the unique features of python.
  2. Compare all the data structures of python like List, Tuple, Set, String, Dictionary using mutability and indexing parameter. Also compare operations of these data structures.
  3. Explain slicing with all data structures i.e.  List, Tuple, Set, String and Dictionary.


  1. What is data science? Compare data science with big data and AI.
  2. What is data science pipeline? Explain with suitable diagram.
  3. What are the technologies that can replace and competent with data science.
  4. What is an IDE and what it must comprise of?


  1. Explain various data formats with suitable example?  How to read CSV, EXCEL, JSON file formats of data with suitable library?
  2. Explain easiness of using Jupiter notebook. Discuss key features for the same.
  3.  Write short notes on :
  1. Interaction with Data from NoSQL Databases
  2. Categorical variable.
  3.  Data cleaning.
  4. Bag of words and N-Grams.
  5. TF / IDF transformations.
  6. Unicode encoding.
  7. Parsing XML and HTML.
  8. Stemming and removing stop words.
  1. Explain the powers of Numpy. List some of the unique features for the same.
  2. Compare the uses of Numpy and Panda.
  1. Explain the powers of Panda. List some of the unique features for the same.
  2. Differentiate series,data frame and panel.  


  1. Explain Data visualization in python in detail. What is the need of data visualization and what are the available libraries in python.
  2.  Write short notes on :
  1. pie charts
  2. bar charts
  3. histograms
  4. boxplots
  5. scatterplots
  6. Plotting Time Series
  1. Define a plot by drawing multiple lines and plots with suitable example. Explain how to save your work to the disk.
  2.  In a given plot explain following terms and also method to set them in python :  
  1. Axis
  2. Ticks
  3. Grids
  4. Getting and formatting the axes
  5. Line Appearance
  6. Using colors
  7. Adding markers, Labels, Annotations, and Legends.


  1. Discuss scikit -learn library in python with some examples. Also explain classes in scikit- learn. How scikit-learn helps in Data Science?
  2. Explain Hashing trick in python.
  3. Explain how to achieve parallelism in python.
  4. Consider you have Irish dataset; Write a note about the dataset.
  5. What is EDA, perform EDA on Irish dataset. How statistical analysis helps in getting insight from the data? 

Important topic :-

1. Embedding plots and other images
2. Managing Data from Relational Databases
3. Slicing and Dicing
4. Parsing XML and HTML
5. Performing the Hashing Trick
6. Measuring variance and range
7. Python data structures including String, Array, List.
8. Python including data types, variables, expressions.
9. Linking data science, big data, and AI
10. Rapid Prototyping and Experimentation
11. Multiple lines and plots
12. Basemap to plot geographic data