pandas IO tools

pandas has a number of functions for reading table data as DataFrame objects, including

Function	Description
pandas.read_csv	loads CSV data from a file, URL or file-like object; usually a comma is used as separator
pandas.read_fwf	loads fwf, which is data in column format with a fixed width
pandas.read_clipboard	reads data from the clipboard and passes it to read_csv; useful for converting tables from web pages, among other things
pandas.read_excel	reads table data from an Excel XLS or XLSX file
pandas.read_hdf	reads HDF5 files
pandas.read_html	reads all tables from the specified HTML document
pandas.read_json	reads data from a JSON file
pandas.read_feather	reads the Feather binary file format
pandas.read_orc	reads Apache ORC binary data
pandas.read_parquet	reads Apache Parquet binary file format
pandas.read_pickle	reads any object stored in Python Pickle format
pandas.read_sas	reads a SAS data set
pandas.read_spss	reads a data file created by SPSS
pandas.read_sql	reads the results of an SQL query (with SQLAlchemy) as a pandas DataFrame
pandas.read_sql_table	reads an entire SQL table (with SQLAlchemy) as a pandas DataFrame (corresponds to a query that selects everything Rin this table with read_sql)
pandas.read_stata	reads a data set from the Stata file format

See also

pandas I/O API

The pandas I/O API is a collection of reader functions that return a pandas object. In most cases, corresponding writer methods are also available.

First, I will give an overview of some of these functions that are designed to convert text and excel data into a pandas DataFrame: CSV, JSON and Excel. The optional arguments for these functions can be divided into the following categories:

Indexing

Can one or more columns index the returned DataFrame, and whether the column names should be retrieved from the file, the arguments you specify, or not at all.

Type inference and data conversion

This includes the custom value conversions and the custom list of missing value flags.

Date and time parsing

This includes the combining capability, including combining date and time information spread across multiple columns into a single column in the result.

Iteration

Support for iteration over parts of very large files.

Problems with unclean data

Skipping of rows or footers, comments or other trivia such as numeric data with thousands separated by commas.

Since data can be very messy in the real world, some of the data loading functions (especially read_csv) have accumulated a long list of optional arguments over time. The online documentation for pandas contains many examples of each function.

Some of these functions, like pandas.read_csv, perform type inference because the data types of the columns are not part of the data format. This means that you don’t necessarily have to specify which columns are numeric, integer, boolean or string. With other data formats such as HDF5, ORC and Parquet, however, the data type information is already embedded in the format.