After reading this chapter, the following points have been observed:

• pandas provides powerful methods so that we can read from and write to a variety of data structures and a variety of sources.
• The read_csv method in pandas can be used for reading CSV files, TXT files, and tables. This method has a multitude of arguments in order to specify delimiters, which rows to skip while reading, reading a file in smaller chunks, and so on.
• pandas can be used to read data directly from URLs or S3.
• DataFrames can be converted into JSON and vice versa. JSON can be stored in text files that can be read.
• JSONs have dictionary-like structures that can be nested an infinite number of times. This nested data can be subsetted just like a dictionary with keys.
• Pandas provide methods so that we can read data from the HD5, HTML, SAS, SQL, parquet, feather, and Google BigQuery data formats.
• Serialization helps in dumping data structures or objects to physical files, storing them in a database, or transmitting them through a message.

In the next chapter, we will learn how to access and select data from panda data structures. We will also look in detail at basic indexing and label-, integer-, and mixed indexing.