This module contains various modules for data I/O. These are as follows:

• api.py: This defines various imports for the data I/O API.
• common.py: This defines the common functionality for the I/O API.
• clipboards.py: This contains cross-platform clipboard methods to enable the copy and paste functions from the keyboard. The pandas I/O API includes functions such as pandas.read_clipboard() and pandas.to_clipboard(..).

• date_converters.py: This defines date conversion functions.
• excel.py: This module parses and converts Excel data. This defines the ExcelFile and ExcelWriter classes.
• feather_format.py: This module reads and writes data in Feather format.
• gbq.py : This is the module for Google's BigQuery.
• html.py: This is the module for dealing with HTML I/O.
• json.py: This is the module for dealing with JSON I/O in pandas. This defines the Writer, SeriesWriter, FrameWriter, Parser, SeriesParser, and FrameParser classes.
• msgpack: This module reads and writes data to msgpack format.
• packer.py: This is an msgpack serializer support for reading and writing pandas data structures to disk.
• parquet.py: This module reads and writes data in Parquet format.
• parsers.py: This is the module that defines various functions and classes that are used in parsing and processing files to create pandas DataFrames. All of the three read_* functions discussed in the following list have multiple configurable options for reading. For more details, see http://bit.ly/1EKDYbP:
  • read_csv(..): This defines the pandas.read_csv() function that is used to read the contents of a CSV file into a DataFrame.
  • read_table(..): This reads a tab-separated table file into a DataFrame.
  • read_fwf(..): This reads a fixed-width format file into a DataFrame.
  • TextFileReader: This is the class that is used for reading text files.
  • ParserBase: This is the base class for parser objects.
  • CParserWrapper, PythonParser: These are the parser for C and Python respectively. They both inherit from ParserBase.
  • FixedWidthReader: This is the class for reading fixed-width data. A fixed-width data file contains fields in specific positions within the file.
  • FixedWithFieldParser: This is the class for parsing fixed-width fields that have been inherited from PythonParser.
• pickle.py: This provides methods for pickling (serializing) pandas objects. These are as follows:
  • to_pickle(..): This serializes an object to file.
  • read_pickle(..): This reads serialized objects from a file into a pandas object. It should only be used with trusted sources.
• pytables.py: This is an interface for PyTables module for reading and writing pandas data structures to files on disk.
• sql.py: This is a collection of classes and functions that enable the retrieval of data from relational databases that attempts to be database-agnostic. These classes and functions are as follows:
  • PandasSQL: This is the base class for interfacing pandas with SQL. It provides dummy read_sql and to_sql methods that must be implemented by subclasses.
  • PandasSQLAlchemy: This is the subclass of PandasSQL that enables conversions between DataFrame and SQL databases using SQLAlchemy.
  • PandasSQLTable: This maps pandas tables (DataFrame) to SQL tables.
  • pandasSQL_builder(..): This returns the correct PandasSQL subclass based on the provided parameters.
  • PandasSQLTableLegacy: This class is the legacy support version of PandasSQLTable.
  • PandasSQLLegacy: This class is the legacy support version of PandasSQLTable.
  • get_schema(..): This gets the SQL database table schema for a given frame.
  • read_sql_table(..): This reads an SQL database table into a DataFrame.
  • read_sql_query(..): This reads an SQL query into a DataFrame.
  • read_sql(..): This reads an SQL query/table into a DataFrame.
• stata.py: This contains tools for processing Stata files into pandas DataFrames.
• sas: This module contains submodules that help to read data from SAS outputs.
• S3.py: This module provides remote connectivity to S3 buckets.