In this chapter, we discussed how pandas and NumPy can provide you with all the tools to load and effectively mung your data.
We started with pandas and its data structures, DataFrames and series, and went through to the final NumPy two-dimensional arrays with a data structure suitable for subsequent experimentation and machine learning. In doing so, we touched upon subjects such as the manipulation of vectors and matrices, categorical data encoding, textual data processing, fixing missing data and errors, slicing and dicing, merging, and stacking.
pandas and NumPy surely offer many more functions than the essential building blocks we presented here, as well as the commands and procedures illustrated. You can now take any available raw data and apply all the cleaning and shaping transformations necessary for your data science project.
In the next chapter, we will take...
