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IO Tools (Text, CSV, HDF5, …)

The pandas I/O API is a set of top level reader functions accessed like pd.read_csv() that generally return a pandas object. The corresponding writer functions are object methods that are accessed like df.to_csv()

Format Type Data Description Reader Writer
text CSV read_csv to_csv
text JSON read_json to_json
text HTML read_html to_html
text Local clipboard read_clipboard to_clipboard
binary MS Excel read_excel to_excel
binary HDF5 Format read_hdf to_hdf
binary Feather Format read_feather to_feather
binary Msgpack read_msgpack to_msgpack
binary Stata read_stata to_stata
binary SAS read_sas
binary Python Pickle Format read_pickle to_pickle
SQL SQL read_sql to_sql
SQL Google Big Query read_gbq to_gbq

Here is an informal performance comparison for some of these IO methods.

Note

For examples that use the StringIO class, make sure you import it according to your Python version, i.e. from StringIO import StringIO for Python 2 and from io import StringIO for Python 3.

CSV & Text files

The two workhorse functions for reading text files (a.k.a. flat files) are read_csv() and read_table(). They both use the same parsing code to intelligently convert tabular data into a DataFrame object. See the cookbook for some advanced strategies.

Parsing options

read_csv() and read_table() accept the following arguments:

Basic

filepath_or_buffer : various
Either a path to a file (a str, pathlib.Path, or py._path.local.LocalPath), URL (including http, ftp, and S3 locations), or any object with a read() method (such as an open file or StringIO).
sep : str, defaults to ',' for read_csv(), \t for read_table()
Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used automatically. In addition, separators longer than 1 character and different from '\s+' will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example: '\\r\\t'.
delimiter : str, default None
Alternative argument name for sep.
delim_whitespace : boolean, default False

Specifies whether or not whitespace (e.g. ' ' or '\t') will be used as the delimiter. Equivalent to setting sep='\s+'. If this option is set to True, nothing should be passed in for the delimiter parameter.

New in version 0.18.1: support for the Python parser.

Column and Index Locations and Names

header : int or list of ints, default 'infer'
Row number(s) to use as the column names, and the start of the data. Default behavior is as if header=0 if no names passed, otherwise as if header=None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of ints that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.
names : array-like, default None
List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list are not allowed unless mangle_dupe_cols=True, which is the default.
index_col : int or sequence or False, default None
Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider index_col=False to force pandas to not use the first column as the index (row names).
usecols : array-like or callable, default None

Return a subset of the columns. If array-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). For example, a valid array-like usecols parameter would be [0, 1, 2] or [‘foo’, ‘bar’, ‘baz’].

If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True:

In [1]: data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'

In [2]: pd.read_csv(StringIO(data))
Out[2]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In [3]: pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ['COL1', 'COL3'])

© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.20.2/io.html