Exploring NumPy, Pandas, and Matplotlib

What is the primary purpose of NumPy?

• To create graphical user interfaces.

• To manage databases and data storage.

• To provide support for web development.

• To provide support for numerical computing with arrays and matrices.

Which data structures does Pandas introduce?

• List and Table

• Series and DataFrame

• Array and Matrix

• Record and Collection

How does Matplotlib help in data analysis?

• Matplotlib is used for data storage and management.

• Matplotlib only supports 3D modeling and not data visualization.

• Matplotlib is primarily a statistical analysis tool without visualization capabilities.

• Matplotlib helps in data analysis by providing powerful visualization tools that allow users to create a variety of plots to identify trends and patterns in data.

What type of data is Pandas primarily used for?

• Structured data

• Unstructured data

• Time series data

• Image data

What are the benefits of using NumPy for numerical computations?

• NumPy is primarily used for string manipulation.

• NumPy only supports 1D arrays and not multi-dimensional arrays.

• NumPy is slower than traditional for-loops for numerical computations.

• Benefits of using NumPy include efficient array operations, speed through vectorization, and compatibility with other scientific libraries.

Can you name a common operation you can perform with a Pandas DataFrame?

• Filtering rows based on a condition

• Creating a scatter plot

• Merging two DataFrames

• Sorting columns alphabetically

What types of visualizations can Matplotlib create?

• 3D surface plots

• Gantt charts

• Line plots, bar charts, histograms, scatter plots, pie charts, box plots, heatmaps, contour plots.

• Network diagrams

Why is NumPy considered efficient for scientific computing?

• NumPy is efficient because it uses linked lists for data storage.

• NumPy is efficient for scientific computing because it uses contiguous memory for arrays, supports vectorized operations, and enables broadcasting.

• NumPy is primarily designed for text processing tasks.

• NumPy is slow due to its reliance on Python loops.