In this chapter, we examined several techniques of combining and reshaping data in one or more DataFrame objects. We started the chapter by examining how to combine data from multiple pandas objects. Then, we examined how to concatenate multiple DataFrame objects, both along the row and column axes. From this, we then examined how pandas can be used to perform database-like joins and merges of data based on values in multiple DataFrame objects.

We then examined how to reshape data in DataFrame using pivots, stacking, and melting. Through this, we saw how each of these processes provides several variations on how to move data around by changing the shape of the indexes, and by moving data in and out of indexes. This showed us how to organize data in formats that are efficient for lookup from other forms that may be more of convenience for the producer of the data.

In the next chapter, we will learn about grouping, and aggregate analysis of data in those groups, which allows us to derive results based upon like-values in the data.