Summary
This chapter gave you a high-level walkthrough of the key features that make pandas a vital tool in the data analytics life cycle. You started by learning briefly about the library's architecture and the topics that are going to be covered in this book. You discovered the library's capabilities with the help of hands-on examples. Then, you learned about data objects such as Series and DataFrames, data types such as int64, float, and object, and different methods you can use to input data from external sources and also write data to formats such as CSV. After that, you implemented different methods to manipulate data, such as data selection and indexing. Later, you performed data transformation using aggregation and grouping methods and implemented various data visualization techniques. You also worked with time series data and discovered ways to optimize code in pandas. Finally, you learned how pandas can be used for preparing data for modeling.
In the next chapter, you will learn about the main data structures in pandas: Series and DataFrames.