In this chapter, we have explored the NumPy and pandas libraries. Both libraries help deal with arrays and DataFrames. NumPy arrays have the capability to deal with n-dimensional arrays. We have learned about various array properties and operations. Our main focus is on data types, data type as an object, reshaping, stacking, splitting, slicing, and indexing.
We also focused on the pandas library for Python data analysis. We saw how pandas mimics the relational database table functionality. It offers functionality to query, aggregate, manipulate, and join data efficiently.
NumPy and pandas work well together as a tool and make it possible to perform basic data analysis. At this point, you might be tempted to think that pandas is all we need for data analysis. However, there is more to data analysis than meets the eye.
Having picked up the fundamentals, it's time to proceed to data analysis with the commonly used statistics functions in Chapter 3, Statistics. This includes...
