Python for Data Analysis, 2nd Edition

Chapter 6. Data Loading, Storage, and File Formats

Accessing data is a necessary first step for using most of the tools in this book. I’m going to be focused on data input and output using pandas, though there are numerous tools in other libraries to help with reading and writing data in various formats.

Input and output typically falls into a few main categories: reading text files and other more efficient on-disk formats, loading data from databases, and interacting with network sources like web APIs.

6.1 Reading and Writing Data in Text Format

pandas features a number of functions for reading tabular data as a DataFrame object. Table 6-1 summarizes some of them, though read_csv is likely the one you’ll use the most.

Table 6-1. Parsing functions in pandas

Function	Description
read_csv	Load delimited data from a file, URL, or file-like object; use comma as default delimiter
read_fwf	Read data in fixed-width column format (i.e., no delimiters)
read_clipboard	Version of read_csv that reads data from the clipboard; useful for converting tables from web pages
read_excel	Read tabular data from an Excel XLS or XLSX file
read_hdf	Read HDF5 files written by pandas
read_html	Read all tables found in the given HTML document
read_json	Read data from a JSON (JavaScript Object Notation) string representation
read_msgpack	Read pandas data encoded using the MessagePack binary format
read_pickle	Read an arbitrary object stored in Python pickle format
read_sas	Read a SAS dataset stored in one of the SAS system’s custom ...