Learn Python by Building Data Science Applications

Beginning with pandas

Of course, not all data—and data analysis—is numeric. To address that gap, and inspired by the R language's dataframe objects, another package—pandas—was created by Wes McKinney in 2008. While it heavily relies on NumPy for numeric computations, its core interface objects are dataframes (2-dimensional multitype tables) and series (1-dimensional arrays). Dataframes, in comparison to NumPy matrices, don't require all data to be of the same type. On the contrary, they allow you to mix numeric values with Boolean, strings, DateTimes, and any other arbitrary Python objects. It does require (and enforce), however, the data type to be uniform vertically—within the same columns. Compared to NumPy, it also allows dataframe columns ...