Notes

Data Transformation and Indexing:
What is the difference between loc[] and iloc[] in Pandas?
Answer: loc[] is label-based, while iloc[] is position-based.
How would you add or remove a column from a DataFrame?
Add: df['new_column'] = value
Remove: df.drop('column', axis=1)
How do you reset or set an index in Pandas?
Example: df.reset_index() or df.set_index('column')
5. Pandas with Machine Learning:
How do you use Pandas to preprocess data for machine learning models?
Example: Handling missing values, encoding categorical variables (e.g., pd.get_dummies()), scaling, and normalizing data.
How can you use Pandas to split data into training and testing sets?
You can use train_test_split from scikit-learn, but you can also use Pandas’ .sample() method to randomly split data.
How can you calculate correlation between features using Pandas?
Example: df.corr()
6. Project-Specific Pandas Questions:
Given your project experience in Westpac Banking Software:

How did you use Pandas to handle data in your testing of banking software?
This could involve data validation, preparing test datasets, or generating reports.
How do you handle and manipulate large transactional data using Pandas?
Example: Efficient data filtering, aggregation, or time-series analysis.
How do you ensure data integrity while testing functionalities of mobile applications using Pandas?
Example: Checking for duplicates, ensuring column consistency, or comparing expected vs actual results.
7. Advanced Pandas:
What are some optimization techniques you used when working with large data sets in Pandas?
Answer: Use of astype() to convert data types, working with chunks (chunksize), using pd.Categorical for categorical data, and avoiding loops by vectorizing operations.
Explain the use of apply(), map(), and applymap() in Pandas.
These methods are used to apply functions to elements of a DataFrame or Series.
8. Pandas for Data Visualization (if relevant):
How do you visualize data in Pandas?
Example: Pandas has a .plot() function for quick visualizations (e.g., bar charts, histograms). For more advanced plots, you can use libraries like matplotlib and seaborn.
Possible Scenario-Based Questions:
How would you handle a scenario where you need to clean, manipulate, and analyze transaction data with Pandas?
You might need to discuss steps like removing null values, performing aggregation (e.g., total transaction per customer), and generating insights or reports.
If you had to test a feature in a banking app where you need to verify account balances over time, how would you use Pandas for this?
This may involve time series analysis, data comparison, and ensuring the correctness of values.
These questions would test your understanding of Pandas in the context of data processing, manipulation, and its application in real-world projects, including your Westpac Banking Software experience.