.. _io:

.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import os
   import csv
   from StringIO import StringIO
   import pandas as pd
   ExcelWriter = pd.ExcelWriter

   import numpy as np
   np.random.seed(123456)
   randn = np.random.randn
   np.set_printoptions(precision=4, suppress=True)

   import matplotlib.pyplot as plt
   plt.close('all')

   from pandas import *
   options.display.max_rows=15
   import pandas.util.testing as tm
   clipdf = DataFrame({'A':[1,2,3],'B':[4,5,6],'C':['p','q','r']},
                      index=['x','y','z'])

*******************************
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.

    * :ref:`read_csv<io.read_csv_table>`
    * :ref:`read_excel<io.excel>`
    * :ref:`read_hdf<io.hdf5>`
    * :ref:`read_sql<io.sql>`
    * :ref:`read_json<io.json_reader>`
    * :ref:`read_msgpack<io.msgpack>` (experimental)
    * :ref:`read_html<io.read_html>`
    * :ref:`read_gbq<io.bigquery>` (experimental)
    * :ref:`read_stata<io.stata_reader>`
    * :ref:`read_clipboard<io.clipboard>`
    * :ref:`read_pickle<io.pickle>`

The corresponding ``writer`` functions are object methods that are accessed like ``df.to_csv()``

    * :ref:`to_csv<io.store_in_csv>`
    * :ref:`to_excel<io.excel>`
    * :ref:`to_hdf<io.hdf5>`
    * :ref:`to_sql<io.sql>`
    * :ref:`to_json<io.json_writer>`
    * :ref:`to_msgpack<io.msgpack>` (experimental)
    * :ref:`to_html<io.html>`
    * :ref:`to_gbq<io.bigquery>` (experimental)
    * :ref:`to_stata<io.stata_writer>`
    * :ref:`to_clipboard<io.clipboard>`
    * :ref:`to_pickle<io.pickle>`

.. _io.read_csv_table:

CSV & Text files
----------------

The two workhorse functions for reading text files (a.k.a. flat files) are
:func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`.
They both use the same parsing code to intelligently convert tabular
data into a DataFrame object. See the :ref:`cookbook<cookbook.csv>`
for some advanced strategies

They can take a number of arguments:

  - ``filepath_or_buffer``: Either a string path to a file, url
    (including http, ftp, and s3 locations), or any object with a ``read``
    method (such as an open file or ``StringIO``).
  - ``sep`` or ``delimiter``: A delimiter / separator to split fields
    on. `read_csv` is capable of inferring the delimiter automatically in some
    cases by "sniffing." The separator may be specified as a regular
    expression; for instance you may use '\|\\s*' to indicate a pipe plus
    arbitrary whitespace.
  - ``delim_whitespace``: Parse whitespace-delimited (spaces or tabs) file
    (much faster than using a regular expression)
  - ``compression``: decompress ``'gzip'`` and ``'bz2'`` formats on the fly.
  - ``dialect``: string or :class:`python:csv.Dialect` instance to expose more
    ways to specify the file format
  - ``dtype``: A data type name or a dict of column name to data type. If not
    specified, data types will be inferred.
  - ``header``: row number(s) to use as the column names, and the start of the
    data.  Defaults to 0 if no ``names`` passed, otherwise ``None``. Explicitly
    pass ``header=0`` to be able to replace existing names. The header can be
    a list of integers 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 are skipped)
  - ``skiprows``: A collection of numbers for rows in the file to skip. Can
    also be an integer to skip the first ``n`` rows
  - ``index_col``: column number, column name, or list of column numbers/names,
    to use as the ``index`` (row labels) of the resulting DataFrame. By default,
    it will number the rows without using any column, unless there is one more
    data column than there are headers, in which case the first column is taken
    as the index.
  - ``names``: List of column names to use as column names. To replace header
    existing in file, explicitly pass ``header=0``.
  - ``na_values``: optional list of strings to recognize as NaN (missing
    values), either in addition to or in lieu of the default set.
  - ``true_values``: list of strings to recognize as ``True``
  - ``false_values``: list of strings to recognize as ``False``
  - ``keep_default_na``: whether to include the default set of missing values
    in addition to the ones specified in ``na_values``
  - ``parse_dates``: if True then index will be parsed as dates
    (False by default). You can specify more complicated options to parse
    a subset of columns or a combination of columns into a single date column
    (list of ints or names, list of lists, or dict)
    [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column
    [[1, 3]] -> combine columns 1 and 3 and parse as a single date column
    {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'
  - ``keep_date_col``: if True, then date component columns passed into
    ``parse_dates`` will be retained in the output (False by default).
  - ``date_parser``: function to use to parse strings into datetime
    objects. If ``parse_dates`` is True, it defaults to the very robust
    ``dateutil.parser``. Specifying this implicitly sets ``parse_dates`` as True.
    You can also use functions from community supported date converters from
    date_converters.py
  - ``dayfirst``: if True then uses the DD/MM international/European date format
    (This is False by default)
  - ``thousands``: specifies the thousands separator. If not None, this character will
    be stripped from numeric dtypes. However, if it is the first character in a field,
    that column will be imported as a string. In the PythonParser, if not None,
    then parser will try to look for it in the output and parse relevant data to numeric
    dtypes. Because it has to essentially scan through the data again, this causes a
    significant performance hit so only use if necessary.
  - ``lineterminator`` : string (length 1), default ``None``, Character to break file into lines. Only valid with C parser
  - ``quotechar`` : string, The character to used to denote the start and end of a quoted item.
    Quoted items can include the delimiter and it will be ignored.
  - ``quoting`` : int,
    Controls whether quotes should be recognized. Values are taken from `csv.QUOTE_*` values.
    Acceptable values are 0, 1, 2, and 3 for QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONE, and QUOTE_NONNUMERIC, respectively.
  - ``skipinitialspace`` : boolean, default ``False``, Skip spaces after delimiter
  - ``escapechar`` : string, to specify how to escape quoted data
  - ``comment``: denotes the start of a comment and ignores the rest of the line.
    Currently line commenting is not supported.
  - ``nrows``: Number of rows to read out of the file. Useful to only read a
    small portion of a large file
  - ``iterator``: If True, return a ``TextFileReader`` to enable reading a file
    into memory piece by piece
  - ``chunksize``: An number of rows to be used to "chunk" a file into
    pieces. Will cause an ``TextFileReader`` object to be returned. More on this
    below in the section on :ref:`iterating and chunking <io.chunking>`
  - ``skip_footer``: number of lines to skip at bottom of file (default 0)
  - ``converters``: a dictionary of functions for converting values in certain
    columns, where keys are either integers or column labels
  - ``encoding``: a string representing the encoding to use for decoding
    unicode data, e.g. ``'utf-8``` or ``'latin-1'``.
  - ``verbose``: show number of NA values inserted in non-numeric columns
  - ``squeeze``: if True then output with only one column is turned into Series
  - ``error_bad_lines``: if False then any lines causing an error will be skipped :ref:`bad lines <io.bad_lines>`
  - ``usecols``: a subset of columns to return, results in much faster parsing
    time and lower memory usage.
  - ``mangle_dupe_cols``: boolean, default True, then duplicate columns will be specified
    as 'X.0'...'X.N', rather than 'X'...'X'
  - ``tupleize_cols``: boolean, default False, if False, convert a list of tuples
    to a multi-index of columns, otherwise, leave the column index as a list of tuples

.. ipython:: python
   :suppress:

   f = open('foo.csv','w')
   f.write('date,A,B,C\n20090101,a,1,2\n20090102,b,3,4\n20090103,c,4,5')
   f.close()

Consider a typical CSV file containing, in this case, some time series data:

.. ipython:: python

   print(open('foo.csv').read())

The default for `read_csv` is to create a DataFrame with simple numbered rows:

.. ipython:: python

   pd.read_csv('foo.csv')

In the case of indexed data, you can pass the column number or column name you
wish to use as the index:

.. ipython:: python

   pd.read_csv('foo.csv', index_col=0)

.. ipython:: python

   pd.read_csv('foo.csv', index_col='date')

You can also use a list of columns to create a hierarchical index:

.. ipython:: python

   pd.read_csv('foo.csv', index_col=[0, 'A'])

.. _io.dialect:

The ``dialect`` keyword gives greater flexibility in specifying the file format.
By default it uses the Excel dialect but you can specify either the dialect name
or a :class:`python:csv.Dialect` instance.

.. ipython:: python
   :suppress:

   data = ('label1,label2,label3\n'
           'index1,"a,c,e\n'
           'index2,b,d,f')

Suppose you had data with unenclosed quotes:

.. ipython:: python

   print(data)

By default, ``read_csv`` uses the Excel dialect and treats the double quote as
the quote character, which causes it to fail when it finds a newline before it
finds the closing double quote.

We can get around this using ``dialect``

.. ipython:: python

   dia = csv.excel()
   dia.quoting = csv.QUOTE_NONE
   pd.read_csv(StringIO(data), dialect=dia)

All of the dialect options can be specified separately by keyword arguments:

.. ipython:: python

    data = 'a,b,c~1,2,3~4,5,6'
    pd.read_csv(StringIO(data), lineterminator='~')

Another common dialect option is ``skipinitialspace``, to skip any whitespace
after a delimiter:

.. ipython:: python

   data = 'a, b, c\n1, 2, 3\n4, 5, 6'
   print(data)
   pd.read_csv(StringIO(data), skipinitialspace=True)

The parsers make every attempt to "do the right thing" and not be very
fragile. Type inference is a pretty big deal. So if a column can be coerced to
integer dtype without altering the contents, it will do so. Any non-numeric
columns will come through as object dtype as with the rest of pandas objects.

.. _io.dtypes:

Specifying column data types
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Starting with v0.10, you can indicate the data type for the whole DataFrame or
individual columns:

.. ipython:: python

    data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
    print(data)

    df = pd.read_csv(StringIO(data), dtype=object)
    df
    df['a'][0]
    df = pd.read_csv(StringIO(data), dtype={'b': object, 'c': np.float64})
    df.dtypes

.. _io.headers:

Handling column names
~~~~~~~~~~~~~~~~~~~~~

A file may or may not have a header row. pandas assumes the first row should be
used as the column names:

.. ipython:: python

    from StringIO import StringIO
    data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
    print(data)
    pd.read_csv(StringIO(data))

By specifying the ``names`` argument in conjunction with ``header`` you can
indicate other names to use and whether or not to throw away the header row (if
any):

.. ipython:: python

    print(data)
    pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0)
    pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None)

If the header is in a row other than the first, pass the row number to
``header``. This will skip the preceding rows:

.. ipython:: python

    data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9'
    pd.read_csv(StringIO(data), header=1)

.. _io.usecols:

Filtering columns (``usecols``)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The ``usecols`` argument allows you to select any subset of the columns in a
file, either using the column names or position numbers:

.. ipython:: python

    data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data), usecols=['b', 'd'])
    pd.read_csv(StringIO(data), usecols=[0, 2, 3])

.. _io.unicode:

Dealing with Unicode Data
~~~~~~~~~~~~~~~~~~~~~~~~~

The ``encoding`` argument should be used for encoded unicode data, which will
result in byte strings being decoded to unicode in the result:

.. ipython:: python

   data = b'word,length\nTr\xc3\xa4umen,7\nGr\xc3\xbc\xc3\x9fe,5'.decode('utf8').encode('latin-1')
   df = pd.read_csv(StringIO(data), encoding='latin-1')
   df
   df['word'][1]

Some formats which encode all characters as multiple bytes, like UTF-16, won't
parse correctly at all without specifying the encoding.

.. _io.index_col:

Index columns and trailing delimiters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If a file has one more column of data than the number of column names, the
first column will be used as the DataFrame's row names:

.. ipython:: python

    data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
    pd.read_csv(StringIO(data))

.. ipython:: python

    data = 'index,a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
    pd.read_csv(StringIO(data), index_col=0)

Ordinarily, you can achieve this behavior using the ``index_col`` option.

There are some exception cases when a file has been prepared with delimiters at
the end of each data line, confusing the parser. To explicitly disable the
index column inference and discard the last column, pass ``index_col=False``:

.. ipython:: python

    data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'
    print(data)
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data), index_col=False)

.. _io.parse_dates:

Specifying Date Columns
~~~~~~~~~~~~~~~~~~~~~~~

To better facilitate working with datetime data,
:func:`~pandas.io.parsers.read_csv` and :func:`~pandas.io.parsers.read_table`
uses the keyword arguments ``parse_dates`` and ``date_parser`` to allow users
to specify a variety of columns and date/time formats to turn the input text
data into ``datetime`` objects.

The simplest case is to just pass in ``parse_dates=True``:

.. ipython:: python

   # Use a column as an index, and parse it as dates.
   df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)
   df

   # These are python datetime objects
   df.index

It is often the case that we may want to store date and time data separately,
or store various date fields separately. the ``parse_dates`` keyword can be
used to specify a combination of columns to parse the dates and/or times from.

You can specify a list of column lists to ``parse_dates``, the resulting date
columns will be prepended to the output (so as to not affect the existing column
order) and the new column names will be the concatenation of the component
column names:

.. ipython:: python
   :suppress:

   data =  ("KORD,19990127, 19:00:00, 18:56:00, 0.8100\n"
            "KORD,19990127, 20:00:00, 19:56:00, 0.0100\n"
            "KORD,19990127, 21:00:00, 20:56:00, -0.5900\n"
            "KORD,19990127, 21:00:00, 21:18:00, -0.9900\n"
            "KORD,19990127, 22:00:00, 21:56:00, -0.5900\n"
            "KORD,19990127, 23:00:00, 22:56:00, -0.5900")

   with open('tmp.csv', 'w') as fh:
       fh.write(data)

.. ipython:: python

    print(open('tmp.csv').read())
    df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]])
    df

By default the parser removes the component date columns, but you can choose
to retain them via the ``keep_date_col`` keyword:

.. ipython:: python

   df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]],
                    keep_date_col=True)
   df

Note that if you wish to combine multiple columns into a single date column, a
nested list must be used. In other words, ``parse_dates=[1, 2]`` indicates that
the second and third columns should each be parsed as separate date columns
while ``parse_dates=[[1, 2]]`` means the two columns should be parsed into a
single column.

You can also use a dict to specify custom name columns:

.. ipython:: python

   date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
   df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec)
   df

It is important to remember that if multiple text columns are to be parsed into
a single date column, then a new column is prepended to the data. The `index_col`
specification is based off of this new set of columns rather than the original
data columns:


.. ipython:: python

   date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
   df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
                    index_col=0) #index is the nominal column
   df

.. note::
   read_csv has a fast_path for parsing datetime strings in iso8601 format,
   e.g "2000-01-01T00:01:02+00:00" and similar variations. If you can arrange
   for your data to store datetimes in this format, load times will be
   significantly faster, ~20x has been observed.


.. note::

   When passing a dict as the `parse_dates` argument, the order of
   the columns prepended is not guaranteed, because `dict` objects do not impose
   an ordering on their keys. On Python 2.7+ you may use `collections.OrderedDict`
   instead of a regular `dict` if this matters to you. Because of this, when using a
   dict for 'parse_dates' in conjunction with the `index_col` argument, it's best to
   specify `index_col` as a column label rather then as an index on the resulting frame.


Date Parsing Functions
~~~~~~~~~~~~~~~~~~~~~~
Finally, the parser allows you can specify a custom ``date_parser`` function to
take full advantage of the flexiblity of the date parsing API:

.. ipython:: python

   import pandas.io.date_converters as conv
   df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
                    date_parser=conv.parse_date_time)
   df

You can explore the date parsing functionality in ``date_converters.py`` and
add your own. We would love to turn this module into a community supported set
of date/time parsers. To get you started, ``date_converters.py`` contains
functions to parse dual date and time columns, year/month/day columns,
and year/month/day/hour/minute/second columns. It also contains a
``generic_parser`` function so you can curry it with a function that deals with
a single date rather than the entire array.

.. ipython:: python
   :suppress:

   os.remove('tmp.csv')

.. _io.dayfirst:


Inferring Datetime Format
~~~~~~~~~~~~~~~~~~~~~~~~~
If you have ``parse_dates`` enabled for some or all of your columns, and your
datetime strings are all formatted the same way, you may get a large speed
up by setting ``infer_datetime_format=True``.  If set, pandas will attempt
to guess the format of your datetime strings, and then use a faster means
of parsing the strings.  5-10x parsing speeds have been observed.  Pandas
will fallback to the usual parsing if either the format cannot be guessed
or the format that was guessed cannot properly parse the entire column
of strings.  So in general, ``infer_datetime_format`` should not have any
negative consequences if enabled.

Here are some examples of datetime strings that can be guessed (All
representing December 30th, 2011 at 00:00:00)

- "20111230"
- "2011/12/30"
- "20111230 00:00:00"
- "12/30/2011 00:00:00"
- "30/Dec/2011 00:00:00"
- "30/December/2011 00:00:00"

``infer_datetime_format`` is sensitive to ``dayfirst``.  With
``dayfirst=True``, it will guess "01/12/2011" to be December 1st. With
``dayfirst=False`` (default) it will guess "01/12/2011" to be January 12th.

.. ipython:: python

   # Try to infer the format for the index column
   df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
                    infer_datetime_format=True)
   df

.. ipython:: python
   :suppress:

   os.remove('foo.csv')

International Date Formats
~~~~~~~~~~~~~~~~~~~~~~~~~~
While US date formats tend to be MM/DD/YYYY, many international formats use
DD/MM/YYYY instead. For convenience, a ``dayfirst`` keyword is provided:

.. ipython:: python
   :suppress:

   data = "date,value,cat\n1/6/2000,5,a\n2/6/2000,10,b\n3/6/2000,15,c"
   with open('tmp.csv', 'w') as fh:
        fh.write(data)

.. ipython:: python

   print(open('tmp.csv').read())

   pd.read_csv('tmp.csv', parse_dates=[0])
   pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0])

.. _io.thousands:

Thousand Separators
~~~~~~~~~~~~~~~~~~~
For large numbers that have been written with a thousands separator, you can
set the ``thousands`` keyword to a string of length 1 so that integers will be parsed
correctly:

.. ipython:: python
   :suppress:

   data =  ("ID|level|category\n"
            "Patient1|123,000|x\n"
            "Patient2|23,000|y\n"
            "Patient3|1,234,018|z")

   with open('tmp.csv', 'w') as fh:
       fh.write(data)

By default, numbers with a thousands separator will be parsed as strings

.. ipython:: python

    print(open('tmp.csv').read())
    df = pd.read_csv('tmp.csv', sep='|')
    df

    df.level.dtype

The ``thousands`` keyword allows integers to be parsed correctly

.. ipython:: python

    print(open('tmp.csv').read())
    df = pd.read_csv('tmp.csv', sep='|', thousands=',')
    df

    df.level.dtype

.. ipython:: python
   :suppress:

   os.remove('tmp.csv')

.. _io.na_values:

NA Values
~~~~~~~~~

To control which values are parsed as missing values (which are signified by ``NaN``), specifiy a
list of strings in ``na_values``. If you specify a number (a ``float``, like ``5.0`` or an ``integer`` like ``5``),
the corresponding equivalent values will also imply a missing value (in this case effectively
``[5.0,5]`` are recognized as ``NaN``.

To completely override the default values that are recognized as missing, specify ``keep_default_na=False``.
The default ``NaN`` recognized values are ``['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A','N/A', 'NA',
'#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan']``.

.. code-block:: python

   read_csv(path, na_values=[5])

the default values, in addition to ``5`` , ``5.0`` when interpreted as numbers are recognized as ``NaN``

.. code-block:: python

   read_csv(path, keep_default_na=False, na_values=[""])

only an empty field will be ``NaN``

.. code-block:: python

   read_csv(path, keep_default_na=False, na_values=["NA", "0"])

only ``NA`` and ``0`` as strings are ``NaN``

.. code-block:: python

   read_csv(path, na_values=["Nope"])

the default values, in addition to the string ``"Nope"`` are recognized as ``NaN``

.. _io.infinity:

Infinity
~~~~~~~~

``inf`` like values will be parsed as ``np.inf`` (positive infinity), and ``-inf`` as ``-np.inf`` (negative infinity).
These will ignore the case of the value, meaning ``Inf``, will also be parsed as ``np.inf``.


.. _io.comments:

Comments
~~~~~~~~
Sometimes comments or meta data may be included in a file:

.. ipython:: python
   :suppress:

   data =  ("ID,level,category\n"
            "Patient1,123000,x # really unpleasant\n"
            "Patient2,23000,y # wouldn't take his medicine\n"
            "Patient3,1234018,z # awesome")

   with open('tmp.csv', 'w') as fh:
       fh.write(data)

.. ipython:: python

   print(open('tmp.csv').read())

By default, the parse includes the comments in the output:

.. ipython:: python

   df = pd.read_csv('tmp.csv')
   df

We can suppress the comments using the ``comment`` keyword:

.. ipython:: python

   df = pd.read_csv('tmp.csv', comment='#')
   df

.. ipython:: python
   :suppress:

   os.remove('tmp.csv')

Returning Series
~~~~~~~~~~~~~~~~

Using the ``squeeze`` keyword, the parser will return output with a single column
as a ``Series``:

.. ipython:: python
   :suppress:

   data =  ("level\n"
            "Patient1,123000\n"
            "Patient2,23000\n"
            "Patient3,1234018")

   with open('tmp.csv', 'w') as fh:
       fh.write(data)

.. ipython:: python

   print(open('tmp.csv').read())

   output =  pd.read_csv('tmp.csv', squeeze=True)
   output

   type(output)

.. ipython:: python
   :suppress:

   os.remove('tmp.csv')

.. _io.boolean:

Boolean values
~~~~~~~~~~~~~~

The common values ``True``, ``False``, ``TRUE``, and ``FALSE`` are all
recognized as boolean. Sometime you would want to recognize some other values
as being boolean. To do this use the ``true_values`` and ``false_values``
options:

.. ipython:: python

    data= 'a,b,c\n1,Yes,2\n3,No,4'
    print(data)
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])

.. _io.bad_lines:

Handling "bad" lines
~~~~~~~~~~~~~~~~~~~~

Some files may have malformed lines with too few fields or too many. Lines with
too few fields will have NA values filled in the trailing fields. Lines with
too many will cause an error by default:

.. ipython:: python
   :suppress:

    data = 'a,b,c\n1,2,3\n4,5,6,7\n8,9,10'

.. code-block:: ipython

    In [27]: data = 'a,b,c\n1,2,3\n4,5,6,7\n8,9,10'

    In [28]: pd.read_csv(StringIO(data))
    ---------------------------------------------------------------------------
    CParserError                              Traceback (most recent call last)
    CParserError: Error tokenizing data. C error: Expected 3 fields in line 3, saw 4

You can elect to skip bad lines:

.. code-block:: ipython

    In [29]: pd.read_csv(StringIO(data), error_bad_lines=False)
    Skipping line 3: expected 3 fields, saw 4

    Out[29]:
       a  b   c
    0  1  2   3
    1  8  9  10

.. _io.quoting:

Quoting and Escape Characters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Quotes (and other escape characters) in embedded fields can be handled in any
number of ways. One way is to use backslashes; to properly parse this data, you
should pass the ``escapechar`` option:

.. ipython:: python

   data = 'a,b\n"hello, \\"Bob\\", nice to see you",5'
   print(data)
   pd.read_csv(StringIO(data), escapechar='\\')

.. _io.fwf:

Files with Fixed Width Columns
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
While ``read_csv`` reads delimited data, the :func:`~pandas.io.parsers.read_fwf`
function works with data files that have known and fixed column widths.
The function parameters to ``read_fwf`` are largely the same as `read_csv` with
two extra parameters:

  - ``colspecs``: A list of pairs (tuples) giving the extents of the
    fixed-width fields of each line as half-open intervals (i.e.,  [from, to[ ).
    String value 'infer' can be used to instruct the parser to try detecting
    the column specifications from the first 100 rows of the data. Default
    behaviour, if not specified, is to infer.
  - ``widths``: A list of field widths which can be used instead of 'colspecs'
    if the intervals are contiguous.

.. ipython:: python
   :suppress:

   f = open('bar.csv', 'w')
   data1 = ("id8141    360.242940   149.910199   11950.7\n"
            "id1594    444.953632   166.985655   11788.4\n"
            "id1849    364.136849   183.628767   11806.2\n"
            "id1230    413.836124   184.375703   11916.8\n"
            "id1948    502.953953   173.237159   12468.3")
   f.write(data1)
   f.close()

Consider a typical fixed-width data file:

.. ipython:: python

   print(open('bar.csv').read())

In order to parse this file into a DataFrame, we simply need to supply the
column specifications to the `read_fwf` function along with the file name:

.. ipython:: python

   #Column specifications are a list of half-intervals
   colspecs = [(0, 6), (8, 20), (21, 33), (34, 43)]
   df = pd.read_fwf('bar.csv', colspecs=colspecs, header=None, index_col=0)
   df

Note how the parser automatically picks column names X.<column number> when
``header=None`` argument is specified. Alternatively, you can supply just the
column widths for contiguous columns:

.. ipython:: python

   #Widths are a list of integers
   widths = [6, 14, 13, 10]
   df = pd.read_fwf('bar.csv', widths=widths, header=None)
   df

The parser will take care of extra white spaces around the columns
so it's ok to have extra separation between the columns in the file.

.. versionadded:: 0.13.0

By default, ``read_fwf`` will try to infer the file's ``colspecs`` by using the
first 100 rows of the file. It can do it only in cases when the columns are
aligned and correctly separated by the provided ``delimiter`` (default delimiter
is whitespace).

.. ipython:: python

   df = pd.read_fwf('bar.csv', header=None, index_col=0)
   df

.. ipython:: python
   :suppress:

   os.remove('bar.csv')

Files with an "implicit" index column
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. ipython:: python
   :suppress:

   f = open('foo.csv', 'w')
   f.write('A,B,C\n20090101,a,1,2\n20090102,b,3,4\n20090103,c,4,5')
   f.close()

Consider a file with one less entry in the header than the number of data
column:

.. ipython:: python

   print(open('foo.csv').read())

In this special case, ``read_csv`` assumes that the first column is to be used
as the index of the DataFrame:

.. ipython:: python

   pd.read_csv('foo.csv')

Note that the dates weren't automatically parsed. In that case you would need
to do as before:

.. ipython:: python

   df = pd.read_csv('foo.csv', parse_dates=True)
   df.index

.. ipython:: python
   :suppress:

   os.remove('foo.csv')


Reading an index with a ``MultiIndex``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. _io.csv_multiindex:

Suppose you have data indexed by two columns:

.. ipython:: python

   print(open('data/mindex_ex.csv').read())

The ``index_col`` argument to ``read_csv`` and ``read_table`` can take a list of
column numbers to turn multiple columns into a ``MultiIndex`` for the index of the
returned object:

.. ipython:: python

   df = pd.read_csv("data/mindex_ex.csv", index_col=[0,1])
   df
   df.ix[1978]

.. _io.multi_index_columns:

Reading columns with a ``MultiIndex``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

By specifying list of row locations for the ``header`` argument, you
can read in a ``MultiIndex`` for the columns. Specifying non-consecutive
rows will skip the interveaning rows. In order to have the pre-0.13 behavior
of tupleizing columns, specify ``tupleize_cols=True``.

.. ipython:: python

   from pandas.util.testing import makeCustomDataframe as mkdf
   df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
   df.to_csv('mi.csv')
   print(open('mi.csv').read())
   pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1])

Starting in 0.13.0, ``read_csv`` will be able to interpret a more common format
of multi-columns indices.

.. ipython:: python
   :suppress:

   data = ",a,a,a,b,c,c\n,q,r,s,t,u,v\none,1,2,3,4,5,6\ntwo,7,8,9,10,11,12"
   fh = open('mi2.csv','w')
   fh.write(data)
   fh.close()

.. ipython:: python

   print(open('mi2.csv').read())
   pd.read_csv('mi2.csv',header=[0,1],index_col=0)

Note: If an ``index_col`` is not specified (e.g. you don't have an index, or wrote it
with ``df.to_csv(..., index=False``), then any ``names`` on the columns index will be *lost*.

.. ipython:: python
   :suppress:

   import os
   os.remove('mi.csv')
   os.remove('mi2.csv')

.. _io.sniff:

Automatically "sniffing" the delimiter
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``read_csv`` is capable of inferring delimited (not necessarily
comma-separated) files. YMMV, as pandas uses the :class:`python:csv.Sniffer`
class of the csv module.

.. ipython:: python
   :suppress:

   df = DataFrame(np.random.randn(10, 4))
   df.to_csv('tmp.sv', sep='|')
   df.to_csv('tmp2.sv', sep=':')

.. ipython:: python

    print(open('tmp2.sv').read())
    pd.read_csv('tmp2.sv')

.. _io.chunking:

Iterating through files chunk by chunk
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Suppose you wish to iterate through a (potentially very large) file lazily
rather than reading the entire file into memory, such as the following:


.. ipython:: python

   print(open('tmp.sv').read())
   table = pd.read_table('tmp.sv', sep='|')
   table


By specifiying a ``chunksize`` to ``read_csv`` or ``read_table``, the return
value will be an iterable object of type ``TextFileReader``:

.. ipython:: python

   reader = pd.read_table('tmp.sv', sep='|', chunksize=4)
   reader

   for chunk in reader:
       print(chunk)


Specifying ``iterator=True`` will also return the ``TextFileReader`` object:

.. ipython:: python

   reader = pd.read_table('tmp.sv', sep='|', iterator=True)
   reader.get_chunk(5)

.. ipython:: python
   :suppress:

   os.remove('tmp.sv')
   os.remove('tmp2.sv')

.. _io.store_in_csv:

Writing to CSV format
~~~~~~~~~~~~~~~~~~~~~

The Series and DataFrame objects have an instance method ``to_csv`` which
allows storing the contents of the object as a comma-separated-values file. The
function takes a number of arguments. Only the first is required.

  - ``path``: A string path to the file to write
  - ``na_rep``: A string representation of a missing value (default '')
  - ``cols``: Columns to write (default None)
  - ``header``: Whether to write out the column names (default True)
  - ``index``: whether to write row (index) names (default True)
  - ``index_label``: Column label(s) for index column(s) if desired. If None
    (default), and `header` and `index` are True, then the index names are
    used. (A sequence should be given if the DataFrame uses MultiIndex).
  - ``mode`` : Python write mode, default 'w'
  - ``sep`` : Field delimiter for the output file (default ",")
  - ``encoding``: a string representing the encoding to use if the contents are
    non-ascii, for python versions prior to 3
  - ``tupleize_cols``: boolean, default False, if False, write as a list of tuples,
    otherwise write in an expanded line format suitable for ``read_csv``

Writing a formatted string
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. _io.formatting:

The DataFrame object has an instance method ``to_string`` which allows control
over the string representation of the object. All arguments are optional:

  - ``buf`` default None, for example a StringIO object
  - ``columns`` default None, which columns to write
  - ``col_space`` default None, minimum width of each column.
  - ``na_rep`` default ``NaN``, representation of NA value
  - ``formatters`` default None, a dictionary (by column) of functions each of
    which takes a single argument and returns a formatted string
  - ``float_format`` default None, a function which takes a single (float)
    argument and returns a formatted string; to be applied to floats in the
    DataFrame.
  - ``sparsify`` default True, set to False for a DataFrame with a hierarchical
    index to print every multiindex key at each row.
  - ``index_names`` default True, will print the names of the indices
  - ``index`` default True, will print the index (ie, row labels)
  - ``header`` default True, will print the column labels
  - ``justify`` default ``left``, will print column headers left- or
    right-justified

The Series object also has a ``to_string`` method, but with only the ``buf``,
``na_rep``, ``float_format`` arguments. There is also a ``length`` argument
which, if set to ``True``, will additionally output the length of the Series.

.. _io.json:

JSON
----

Read and write ``JSON`` format files and strings.

.. _io.json_writer:

Writing JSON
~~~~~~~~~~~~

A ``Series`` or ``DataFrame`` can be converted to a valid JSON string. Use ``to_json``
with optional parameters:

- ``path_or_buf`` : the pathname or buffer to write the output
  This can be ``None`` in which case a JSON string is returned
- ``orient`` :

  Series :
      - default is ``index``
      - allowed values are {``split``, ``records``, ``index``}

  DataFrame
      - default is ``columns``
      - allowed values are {``split``, ``records``, ``index``, ``columns``, ``values``}

  The format of the JSON string

  .. csv-table::
     :widths: 20, 150
     :delim: ;

     ``split``; dict like {index -> [index], columns -> [columns], data -> [values]}
     ``records``; list like [{column -> value}, ... , {column -> value}]
     ``index``; dict like {index -> {column -> value}}
     ``columns``; dict like {column -> {index -> value}}
     ``values``; just the values array

- ``date_format`` : string, type of date conversion, 'epoch' for timestamp, 'iso' for ISO8601.
- ``double_precision`` : The number of decimal places to use when encoding floating point values, default 10.
- ``force_ascii`` : force encoded string to be ASCII, default True.
- ``date_unit`` : The time unit to encode to, governs timestamp and ISO8601 precision. One of 's', 'ms', 'us' or 'ns' for seconds, milliseconds, microseconds and nanoseconds respectively. Default 'ms'.
- ``default_handler`` : The handler to call if an object cannot otherwise be converted to a suitable format for JSON. Takes a single argument, which is the object to convert, and returns a serialisable object.

Note ``NaN``'s, ``NaT``'s and ``None`` will be converted to ``null`` and ``datetime`` objects will be converted based on the ``date_format`` and ``date_unit`` parameters.

.. ipython:: python

   dfj = DataFrame(randn(5, 2), columns=list('AB'))
   json = dfj.to_json()
   json

Orient Options
++++++++++++++

There are a number of different options for the format of the resulting JSON
file / string. Consider the following DataFrame and Series:

.. ipython:: python

  dfjo = DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
                   columns=list('ABC'), index=list('xyz'))
  dfjo
  sjo = Series(dict(x=15, y=16, z=17), name='D')
  sjo

**Column oriented** (the default for ``DataFrame``) serialises the data as
nested JSON objects with column labels acting as the primary index:

.. ipython:: python

  dfjo.to_json(orient="columns")
  # Not available for Series

**Index oriented** (the default for ``Series``) similar to column oriented
but the index labels are now primary:

.. ipython:: python

  dfjo.to_json(orient="index")
  sjo.to_json(orient="index")

**Record oriented** serialises the data to a JSON array of column -> value records,
index labels are not included. This is useful for passing DataFrame data to plotting
libraries, for example the JavaScript library d3.js:

.. ipython:: python

  dfjo.to_json(orient="records")
  sjo.to_json(orient="records")

**Value oriented** is a bare-bones option which serialises to nested JSON arrays of
values only, column and index labels are not included:

.. ipython:: python

  dfjo.to_json(orient="values")
  # Not available for Series

**Split oriented** serialises to a JSON object containing separate entries for
values, index and columns. Name is also included for ``Series``:

.. ipython:: python

  dfjo.to_json(orient="split")
  sjo.to_json(orient="split")

.. note::

  Any orient option that encodes to a JSON object will not preserve the ordering of
  index and column labels during round-trip serialisation. If you wish to preserve
  label ordering use the `split` option as it uses ordered containers.

Date Handling
+++++++++++++

Writing in iso date format

.. ipython:: python

   dfd = DataFrame(randn(5, 2), columns=list('AB'))
   dfd['date'] = Timestamp('20130101')
   dfd = dfd.sort_index(1, ascending=False)
   json = dfd.to_json(date_format='iso')
   json

Writing in iso date format, with microseconds

.. ipython:: python

   json = dfd.to_json(date_format='iso', date_unit='us')
   json

Epoch timestamps, in seconds

.. ipython:: python

   json = dfd.to_json(date_format='epoch', date_unit='s')
   json

Writing to a file, with a date index and a date column

.. ipython:: python

   dfj2 = dfj.copy()
   dfj2['date'] = Timestamp('20130101')
   dfj2['ints'] = list(range(5))
   dfj2['bools'] = True
   dfj2.index = date_range('20130101', periods=5)
   dfj2.to_json('test.json')
   open('test.json').read()

Fallback Behavior
+++++++++++++++++

If the JSON serialiser cannot handle the container contents directly it will fallback in the following manner:

- if a ``toDict`` method is defined by the unrecognised object then that
  will be called and its returned ``dict`` will be JSON serialised.
- if a ``default_handler`` has been passed to ``to_json`` that will
  be called to convert the object.
- otherwise an attempt is made to convert the object to a ``dict`` by
  parsing its contents. However if the object is complex this will often fail
  with an ``OverflowError``.

Your best bet when encountering ``OverflowError`` during serialisation
is to specify a ``default_handler``. For example ``timedelta`` can cause
problems:

.. ipython:: python
   :suppress:

   from datetime import timedelta
   dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42])

.. code-block:: ipython

   In [141]: from datetime import timedelta

   In [142]: dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42])

   In [143]: dftd.to_json()

   ---------------------------------------------------------------------------
   OverflowError                             Traceback (most recent call last)
   OverflowError: Maximum recursion level reached

which can be dealt with by specifying a simple ``default_handler``:

.. ipython:: python

   dftd.to_json(default_handler=str)

   def my_handler(obj):
      return obj.total_seconds()
   dftd.to_json(default_handler=my_handler)

.. _io.json_reader:

Reading JSON
~~~~~~~~~~~~

Reading a JSON string to pandas object can take a number of parameters.
The parser will try to parse a ``DataFrame`` if ``typ`` is not supplied or
is ``None``. To explicity force ``Series`` parsing, pass ``typ=series``

- ``filepath_or_buffer`` : a **VALID** JSON string or file handle / StringIO. The string could be
  a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host
  is expected. For instance, a local file could be
  file ://localhost/path/to/table.json
- ``typ``    : type of object to recover (series or frame), default 'frame'
- ``orient`` :

  Series :
      - default is ``index``
      - allowed values are {``split``, ``records``, ``index``}

  DataFrame
      - default is ``columns``
      - allowed values are {``split``, ``records``, ``index``, ``columns``, ``values``}

  The format of the JSON string

  .. csv-table::
     :widths: 20, 150
     :delim: ;

     ``split``; dict like {index -> [index], columns -> [columns], data -> [values]}
     ``records``; list like [{column -> value}, ... , {column -> value}]
     ``index``; dict like {index -> {column -> value}}
     ``columns``; dict like {column -> {index -> value}}
     ``values``; just the values array

- ``dtype`` : if True, infer dtypes, if a dict of column to dtype, then use those, if False, then don't infer dtypes at all, default is True, apply only to the data
- ``convert_axes`` : boolean, try to convert the axes to the proper dtypes, default is True
- ``convert_dates`` : a list of columns to parse for dates; If True, then try to parse datelike columns, default is True
- ``keep_default_dates`` : boolean, default True. If parsing dates, then parse the default datelike columns
- ``numpy`` : direct decoding to numpy arrays. default is False;
  Supports numeric data only, although labels may be non-numeric. Also note that the JSON ordering **MUST** be the same for each term if ``numpy=True``
- ``precise_float`` : boolean, default ``False``. Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (``False``) is to use fast but less precise builtin functionality
- ``date_unit`` : string, the timestamp unit to detect if converting dates. Default
  None. By default the timestamp precision will be detected, if this is not desired
  then pass one of 's', 'ms', 'us' or 'ns' to force timestamp precision to
  seconds, milliseconds, microseconds or nanoseconds respectively.

The parser will raise one of ``ValueError/TypeError/AssertionError`` if the JSON is not parsable.

If a non-default ``orient`` was used when encoding to JSON be sure to pass the same
option here so that decoding produces sensible results, see `Orient Options`_ for an
overview.

Data Conversion
+++++++++++++++

The default of ``convert_axes=True``, ``dtype=True``, and ``convert_dates=True`` will try to parse the axes, and all of the data
into appropriate types, including dates. If you need to override specific dtypes, pass a dict to ``dtype``. ``convert_axes`` should only
be set to ``False`` if you need to preserve string-like numbers (e.g. '1', '2') in an axes.

.. note::

  Large integer values may be converted to dates if ``convert_dates=True`` and the data and / or column labels appear 'date-like'. The exact threshold depends on the ``date_unit`` specified.

.. warning::

   When reading JSON data, automatic coercing into dtypes has some quirks:

     * an index can be reconstructed in a different order from serialization, that is, the returned order is not guaranteed to be the same as before serialization
     * a column that was ``float`` data will be converted to ``integer`` if it can be done safely, e.g. a column of ``1.``
     * bool columns will be converted to ``integer`` on reconstruction

   Thus there are times where you may want to specify specific dtypes via the ``dtype`` keyword argument.

Reading from a JSON string:

.. ipython:: python

   pd.read_json(json)

Reading from a file:

.. ipython:: python

   pd.read_json('test.json')

Don't convert any data (but still convert axes and dates):

.. ipython:: python

   pd.read_json('test.json', dtype=object).dtypes

Specify dtypes for conversion:

.. ipython:: python

   pd.read_json('test.json', dtype={'A' : 'float32', 'bools' : 'int8'}).dtypes

Preserve string indicies:

.. ipython:: python

   si = DataFrame(np.zeros((4, 4)),
            columns=list(range(4)),
            index=[str(i) for i in range(4)])
   si
   si.index
   si.columns
   json = si.to_json()

   sij = pd.read_json(json, convert_axes=False)
   sij
   sij.index
   sij.columns

Dates written in nanoseconds need to be read back in nanoseconds:

.. ipython:: python

   json = dfj2.to_json(date_unit='ns')

   # Try to parse timestamps as millseconds -> Won't Work
   dfju = pd.read_json(json, date_unit='ms')
   dfju

   # Let Pandas detect the correct precision
   dfju = pd.read_json(json)
   dfju

   # Or specify that all timestamps are in nanoseconds
   dfju = pd.read_json(json, date_unit='ns')
   dfju

The Numpy Parameter
+++++++++++++++++++

.. note::
  This supports numeric data only. Index and columns labels may be non-numeric, e.g. strings, dates etc.

If ``numpy=True`` is passed to ``read_json`` an attempt will be made to sniff
an appropriate dtype during deserialisation and to subsequently decode directly
to numpy arrays, bypassing the need for intermediate Python objects.

This can provide speedups if you are deserialising a large amount of numeric
data:

.. ipython:: python

   randfloats = np.random.uniform(-100, 1000, 10000)
   randfloats.shape = (1000, 10)
   dffloats = DataFrame(randfloats, columns=list('ABCDEFGHIJ'))

   jsonfloats = dffloats.to_json()

.. ipython:: python

   timeit read_json(jsonfloats)

.. ipython:: python

   timeit read_json(jsonfloats, numpy=True)

The speedup is less noticable for smaller datasets:

.. ipython:: python

   jsonfloats = dffloats.head(100).to_json()

.. ipython:: python

   timeit read_json(jsonfloats)

.. ipython:: python

   timeit read_json(jsonfloats, numpy=True)

.. warning::

   Direct numpy decoding makes a number of assumptions and may fail or produce
   unexpected output if these assumptions are not satisfied:

    - data is numeric.

    - data is uniform. The dtype is sniffed from the first value decoded.
      A ``ValueError`` may be raised, or incorrect output may be produced
      if this condition is not satisfied.

    - labels are ordered. Labels are only read from the first container, it is assumed
      that each subsequent row / column has been encoded in the same order. This should be satisfied if the
      data was encoded using ``to_json`` but may not be the case if the JSON
      is from another source.

.. ipython:: python
   :suppress:

   import os
   os.remove('test.json')

.. _io.json_normalize:

Normalization
~~~~~~~~~~~~~

.. versionadded:: 0.13.0

Pandas provides a utility function to take a dict or list of dicts and *normalize* this semi-structured data
into a flat table.

.. ipython:: python

   from pandas.io.json import json_normalize
   data = [{'state': 'Florida',
             'shortname': 'FL',
             'info': {
                  'governor': 'Rick Scott'
             },
             'counties': [{'name': 'Dade', 'population': 12345},
                         {'name': 'Broward', 'population': 40000},
                         {'name': 'Palm Beach', 'population': 60000}]},
            {'state': 'Ohio',
             'shortname': 'OH',
             'info': {
                  'governor': 'John Kasich'
             },
             'counties': [{'name': 'Summit', 'population': 1234},
                          {'name': 'Cuyahoga', 'population': 1337}]}]

   json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']])

HTML
----

.. _io.read_html:

Reading HTML Content
~~~~~~~~~~~~~~~~~~~~~~

.. warning::

   We **highly encourage** you to read the :ref:`HTML parsing gotchas
   <html-gotchas>` regarding the issues surrounding the
   BeautifulSoup4/html5lib/lxml parsers.

.. versionadded:: 0.12.0

The top-level :func:`~pandas.io.html.read_html` function can accept an HTML
string/file/url and will parse HTML tables into list of pandas DataFrames.
Let's look at a few examples.

.. note::

   ``read_html`` returns a ``list`` of ``DataFrame`` objects, even if there is
   only a single table contained in the HTML content

Read a URL with no options

.. ipython:: python

   url = 'http://www.fdic.gov/bank/individual/failed/banklist.html'
   dfs = read_html(url)
   dfs

.. note::

   The data from the above URL changes every Monday so the resulting data above
   and the data below may be slightly different.

Read in the content of the file from the above URL and pass it to ``read_html``
as a string

.. ipython:: python
   :suppress:

   import os
   file_path = os.path.abspath(os.path.join('source', '_static', 'banklist.html'))

.. ipython:: python

   with open(file_path, 'r') as f:
       dfs = read_html(f.read())
   dfs

You can even pass in an instance of ``StringIO`` if you so desire

.. ipython:: python

   from cStringIO import StringIO

   with open(file_path, 'r') as f:
       sio = StringIO(f.read())

   dfs = read_html(sio)
   dfs

.. note::

   The following examples are not run by the IPython evaluator due to the fact
   that having so many network-accessing functions slows down the documentation
   build. If you spot an error or an example that doesn't run, please do not
   hesitate to report it over on `pandas GitHub issues page
   <http://www.github.com/pydata/pandas/issues>`__.


Read a URL and match a table that contains specific text

.. code-block:: python

   match = 'Metcalf Bank'
   df_list = read_html(url, match=match)

Specify a header row (by default ``<th>`` elements are used to form the column
index); if specified, the header row is taken from the data minus the parsed
header elements (``<th>`` elements).

.. code-block:: python

   dfs = read_html(url, header=0)

Specify an index column

.. code-block:: python

   dfs = read_html(url, index_col=0)

Specify a number of rows to skip

.. code-block:: python

   dfs = read_html(url, skiprows=0)

Specify a number of rows to skip using a list (``xrange`` (Python 2 only) works
as well)

.. code-block:: python

   dfs = read_html(url, skiprows=range(2))

Don't infer numeric and date types

.. code-block:: python

   dfs = read_html(url, infer_types=False)

Specify an HTML attribute

.. code-block:: python

   dfs1 = read_html(url, attrs={'id': 'table'})
   dfs2 = read_html(url, attrs={'class': 'sortable'})
   print(np.array_equal(dfs1[0], dfs2[0]))  # Should be True

Use some combination of the above

.. code-block:: python

   dfs = read_html(url, match='Metcalf Bank', index_col=0)

Read in pandas ``to_html`` output (with some loss of floating point precision)

.. code-block:: python

   df = DataFrame(randn(2, 2))
   s = df.to_html(float_format='{0:.40g}'.format)
   dfin = read_html(s, index_col=0)

The ``lxml`` backend will raise an error on a failed parse if that is the only
parser you provide (if you only have a single parser you can provide just a
string, but it is considered good practice to pass a list with one string if,
for example, the function expects a sequence of strings)

.. code-block:: python

   dfs = read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml'])

or

.. code-block:: python

   dfs = read_html(url, 'Metcalf Bank', index_col=0, flavor='lxml')

However, if you have bs4 and html5lib installed and pass ``None`` or ``['lxml',
'bs4']`` then the parse will most likely succeed. Note that *as soon as a parse
succeeds, the function will return*.

.. code-block:: python

   dfs = read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml', 'bs4'])


.. _io.html:

Writing to HTML files
~~~~~~~~~~~~~~~~~~~~~~

``DataFrame`` objects have an instance method ``to_html`` which renders the
contents of the ``DataFrame`` as an HTML table. The function arguments are as
in the method ``to_string`` described above.

.. note::

   Not all of the possible options for ``DataFrame.to_html`` are shown here for
   brevity's sake. See :func:`~pandas.core.frame.DataFrame.to_html` for the
   full set of options.

.. ipython:: python
   :suppress:

   def write_html(df, filename, *args, **kwargs):
       static = os.path.abspath(os.path.join('source', '_static'))
       with open(os.path.join(static, filename + '.html'), 'w') as f:
           df.to_html(f, *args, **kwargs)

.. ipython:: python

   df = DataFrame(randn(2, 2))
   df
   print(df.to_html())  # raw html

.. ipython:: python
   :suppress:

   write_html(df, 'basic')

HTML:

.. raw:: html
   :file: _static/basic.html

The ``columns`` argument will limit the columns shown

.. ipython:: python

   print(df.to_html(columns=[0]))

.. ipython:: python
   :suppress:

   write_html(df, 'columns', columns=[0])

HTML:

.. raw:: html
   :file: _static/columns.html

``float_format`` takes a Python callable to control the precision of floating
point values

.. ipython:: python

   print(df.to_html(float_format='{0:.10f}'.format))

.. ipython:: python
   :suppress:

   write_html(df, 'float_format', float_format='{0:.10f}'.format)

HTML:

.. raw:: html
   :file: _static/float_format.html

``bold_rows`` will make the row labels bold by default, but you can turn that
off

.. ipython:: python

   print(df.to_html(bold_rows=False))

.. ipython:: python
   :suppress:

   write_html(df, 'nobold', bold_rows=False)

.. raw:: html
   :file: _static/nobold.html

The ``classes`` argument provides the ability to give the resulting HTML
table CSS classes. Note that these classes are *appended* to the existing
``'dataframe'`` class.

.. ipython:: python

   print(df.to_html(classes=['awesome_table_class', 'even_more_awesome_class']))

Finally, the ``escape`` argument allows you to control whether the
"<", ">" and "&" characters escaped in the resulting HTML (by default it is
``True``). So to get the HTML without escaped characters pass ``escape=False``

.. ipython:: python

   df = DataFrame({'a': list('&<>'), 'b': randn(3)})


.. ipython:: python
   :suppress:

   write_html(df, 'escape')
   write_html(df, 'noescape', escape=False)

Escaped:

.. ipython:: python

   print(df.to_html())

.. raw:: html
   :file: _static/escape.html

Not escaped:

.. ipython:: python

   print(df.to_html(escape=False))

.. raw:: html
   :file: _static/noescape.html

.. note::

   Some browsers may not show a difference in the rendering of the previous two
   HTML tables.

.. _io.excel:

Excel files
-----------

The :func:`~pandas.read_excel` method can read Excel 2003 (``.xls``) and
Excel 2007 (``.xlsx``) files using the ``xlrd`` Python
module and use the same parsing code as the above to convert tabular data into
a DataFrame. See the :ref:`cookbook<cookbook.excel>` for some
advanced strategies

Besides ``read_excel`` you can also read Excel files using the ``ExcelFile``
class. The following two command are equivalent:

.. code-block:: python

    # using the ExcelFile class
    xls = pd.ExcelFile('path_to_file.xls')
    xls.parse('Sheet1', index_col=None, na_values=['NA'])

    # using the read_excel function
    read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])

The class based approach can be used to read multiple sheets or to introspect
the sheet names using the ``sheet_names`` attribute. 

.. note::

   The prior method of accessing ``ExcelFile`` has been moved from
   ``pandas.io.parsers`` to the top level namespace starting from pandas
   0.12.0.

.. versionadded:: 0.13

There are now two ways to read in sheets from an Excel file. You can provide
either the index of a sheet or its name. If the value provided is an integer
then it is assumed that the integer refers to the index of a sheet, otherwise
if a string is passed then it is assumed that the string refers to the name of
a particular sheet in the file.

Using the sheet name:

.. code-block:: python

   read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])

Using the sheet index:

.. code-block:: python

   read_excel('path_to_file.xls', 0, index_col=None, na_values=['NA'])

It is often the case that users will insert columns to do temporary computations
in Excel and you may not want to read in those columns. `read_excel` takes
a `parse_cols` keyword to allow you to specify a subset of columns to parse.

If `parse_cols` is an integer, then it is assumed to indicate the last column
to be parsed.

.. code-block:: python

   read_excel('path_to_file.xls', 'Sheet1', parse_cols=2, index_col=None, na_values=['NA'])

If `parse_cols` is a list of integers, then it is assumed to be the file column
indices to be parsed.

.. code-block:: python

   read_excel('path_to_file.xls', 'Sheet1', parse_cols=[0, 2, 3], index_col=None, na_values=['NA'])

To write a DataFrame object to a sheet of an Excel file, you can use the
``to_excel`` instance method.  The arguments are largely the same as ``to_csv``
described above, the first argument being the name of the excel file, and the
optional second argument the name of the sheet to which the DataFrame should be
written.  For example:

.. code-block:: python

   df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')

Files with a ``.xls`` extension will be written using ``xlwt`` and those with a
``.xlsx`` extension will be written using ``xlsxwriter`` (if available) or
``openpyxl``.

The Panel class also has a ``to_excel`` instance method,
which writes each DataFrame in the Panel to a separate sheet.

In order to write separate DataFrames to separate sheets in a single Excel file,
one can pass an :class:`~pandas.io.excel.ExcelWriter`.

.. code-block:: python

   with ExcelWriter('path_to_file.xlsx') as writer:
       df1.to_excel(writer, sheet_name='Sheet1')
       df2.to_excel(writer, sheet_name='Sheet2')

.. note:: Wringing a little more performance out of ``read_excel``
    Internally, Excel stores all numeric data as floats. Because this can
    produce unexpected behavior when reading in data, pandas defaults to trying
    to convert integers to floats if it doesn't lose information (``1.0 -->
    1``).  You can pass ``convert_float=False`` to disable this behavior, which
    may give a slight performance improvement.

.. _io.excel.writers:

Excel writer engines
~~~~~~~~~~~~~~~~~~~~

.. versionadded:: 0.13

``pandas`` chooses an Excel writer via two methods:

1. the ``engine`` keyword argument
2. the filename extension (via the default specified in config options)

By default, ``pandas`` uses the `XlsxWriter`_  for ``.xlsx`` and `openpyxl`_
for ``.xlsm`` files and `xlwt`_ for ``.xls`` files.  If you have multiple
engines installed, you can set the default engine through :ref:`setting the
config options <basics.working_with_options>` ``io.excel.xlsx.writer`` and
``io.excel.xls.writer``. pandas will fall back on `openpyxl`_ for ``.xlsx``
files if `Xlsxwriter`_ is not available.

.. _XlsxWriter: http://xlsxwriter.readthedocs.org
.. _openpyxl: http://packages.python.org/openpyxl/
.. _xlwt: http://www.python-excel.org

To specify which writer you want to use, you can pass an engine keyword
argument to ``to_excel`` and to ``ExcelWriter``.

.. code-block:: python

   # By setting the 'engine' in the DataFrame and Panel 'to_excel()' methods.
   df.to_excel('path_to_file.xlsx', sheet_name='Sheet1', engine='xlsxwriter')

   # By setting the 'engine' in the ExcelWriter constructor.
   writer = ExcelWriter('path_to_file.xlsx', engine='xlsxwriter')

   # Or via pandas configuration.
   from pandas import options
   options.io.excel.xlsx.writer = 'xlsxwriter'

   df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')

.. _io.clipboard:

Clipboard
---------

A handy way to grab data is to use the ``read_clipboard`` method, which takes
the contents of the clipboard buffer and passes them to the ``read_table``
method. For instance, you can copy the following
text to the clipboard (CTRL-C on many operating systems):

.. code-block:: python

     A B C
   x 1 4 p
   y 2 5 q
   z 3 6 r

And then import the data directly to a DataFrame by calling:

.. code-block:: python

   clipdf = pd.read_clipboard()

.. ipython:: python

   clipdf

The ``to_clipboard`` method can be used to write the contents of a DataFrame to
the clipboard. Following which you can paste the clipboard contents into other
applications (CTRL-V on many operating systems). Here we illustrate writing a
DataFrame into clipboard and reading it back.

.. ipython:: python

    df=pd.DataFrame(randn(5,3))
    df
    df.to_clipboard()
    pd.read_clipboard()

We can see that we got the same content back, which we had earlier written to the clipboard.

.. note::

   You may need to install xclip or xsel (with gtk or PyQt4 modules) on Linux to use these methods.

.. _io.pickle:

Pickling
--------

All pandas objects are equipped with ``to_pickle`` methods which use Python's
``cPickle`` module to save data structures to disk using the pickle format.

.. ipython:: python

   df
   df.to_pickle('foo.pkl')

The ``read_pickle`` function in the ``pandas`` namespace can be used to load
any pickled pandas object (or any other pickled object) from file:


.. ipython:: python

   read_pickle('foo.pkl')

.. ipython:: python
   :suppress:

   import os
   os.remove('foo.pkl')

.. warning::

   Loading pickled data received from untrusted sources can be unsafe.

   See: http://docs.python.org/2.7/library/pickle.html

.. warning::

   In 0.13, pickle preserves compatibility with pickles created prior to 0.13. These must
   be read with ``pd.read_pickle``, rather than the default python ``pickle.load``.
   See `this question <http://stackoverflow.com/questions/20444593/pandas-compiled-from-source-default-pickle-behavior-changed>`__
   for a detailed explanation.

.. note::

    These methods were previously ``pd.save`` and ``pd.load``, prior to 0.12.0, and are now deprecated.

.. _io.msgpack:

msgpack (experimental)
----------------------

.. versionadded:: 0.13.0

Starting in 0.13.0, pandas is supporting the ``msgpack`` format for
object serialization. This is a lightweight portable binary format, similar
to binary JSON, that is highly space efficient, and provides good performance
both on the writing (serialization), and reading (deserialization).

.. warning::

   This is a very new feature of pandas. We intend to provide certain
   optimizations in the io of the ``msgpack`` data. Since this is marked
   as an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release.

.. ipython:: python

   df = DataFrame(np.random.rand(5,2),columns=list('AB'))
   df.to_msgpack('foo.msg')
   pd.read_msgpack('foo.msg')
   s = Series(np.random.rand(5),index=date_range('20130101',periods=5))

You can pass a list of objects and you will receive them back on deserialization.

.. ipython:: python

   pd.to_msgpack('foo.msg', df, 'foo', np.array([1,2,3]), s)
   pd.read_msgpack('foo.msg')

You can pass ``iterator=True`` to iterate over the unpacked results

.. ipython:: python

   for o in pd.read_msgpack('foo.msg',iterator=True):
       print o

You can pass ``append=True`` to the writer to append to an existing pack

.. ipython:: python

   df.to_msgpack('foo.msg',append=True)
   pd.read_msgpack('foo.msg')

Unlike other io methods, ``to_msgpack`` is available on both a per-object basis,
``df.to_msgpack()`` and using the top-level ``pd.to_msgpack(...)`` where you
can pack arbitrary collections of python lists, dicts, scalars, while intermixing
pandas objects.

.. ipython:: python

   pd.to_msgpack('foo2.msg', { 'dict' : [ { 'df' : df }, { 'string' : 'foo' }, { 'scalar' : 1. }, { 's' : s } ] })
   pd.read_msgpack('foo2.msg')

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove('foo.msg')
   os.remove('foo2.msg')

Read/Write API
~~~~~~~~~~~~~~

Msgpacks can also be read from and written to strings.

.. ipython:: python

   df.to_msgpack()

Furthermore you can concatenate the strings to produce a list of the original objects.

.. ipython:: python

  pd.read_msgpack(df.to_msgpack() + s.to_msgpack())

.. _io.hdf5:

HDF5 (PyTables)
---------------

``HDFStore`` is a dict-like object which reads and writes pandas using
the high performance HDF5 format using the excellent `PyTables
<http://www.pytables.org/>`__ library. See the :ref:`cookbook <cookbook.hdf>`
for some advanced strategies

.. note::

   ``PyTables`` 3.0.0 was recently released to enable support for Python 3.
   Pandas should be fully compatible (and previously written stores should be
   backwards compatible) with all ``PyTables`` >= 2.3. For ``python >= 3.2``,
   ``pandas >= 0.12.0`` is required for compatibility.

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove('store.h5')

.. ipython:: python

   store = HDFStore('store.h5')
   print(store)

Objects can be written to the file just like adding key-value pairs to a
dict:

.. ipython:: python

   np.random.seed(1234)
   index = date_range('1/1/2000', periods=8)
   s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
   df = DataFrame(randn(8, 3), index=index,
                  columns=['A', 'B', 'C'])
   wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
              major_axis=date_range('1/1/2000', periods=5),
              minor_axis=['A', 'B', 'C', 'D'])

   # store.put('s', s) is an equivalent method
   store['s'] = s

   store['df'] = df

   store['wp'] = wp

   # the type of stored data
   store.root.wp._v_attrs.pandas_type

   store

In a current or later Python session, you can retrieve stored objects:

.. ipython:: python

   # store.get('df') is an equivalent method
   store['df']

   # dotted (attribute) access provides get as well
   store.df

Deletion of the object specified by the key

.. ipython:: python

   # store.remove('wp') is an equivalent method
   del store['wp']

   store

Closing a Store, Context Manager

.. ipython:: python

   store.close()
   store
   store.is_open

   # Working with, and automatically closing the store with the context
   # manager
   with get_store('store.h5') as store:
       store.keys()

.. ipython:: python
   :suppress:

   store.close()
   import os
   os.remove('store.h5')

Read/Write API
~~~~~~~~~~~~~~

``HDFStore`` supports an top-level API using  ``read_hdf`` for reading and ``to_hdf`` for writing,
similar to how ``read_csv`` and ``to_csv`` work. (new in 0.11.0)

.. ipython:: python

   df_tl = DataFrame(dict(A=list(range(5)), B=list(range(5))))
   df_tl.to_hdf('store_tl.h5','table',append=True)
   read_hdf('store_tl.h5', 'table', where = ['index>2'])

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove('store_tl.h5')

.. _io.hdf5-fixed:

Fixed Format
~~~~~~~~~~~~

.. note::

   This was prior to 0.13.0 the ``Storer`` format.

The examples above show storing using ``put``, which write the HDF5 to ``PyTables`` in a fixed array format, called
the ``fixed`` format. These types of stores are are **not** appendable once written (though you can simply
remove them and rewrite). Nor are they **queryable**; they must be
retrieved in their entirety. These offer very fast writing and slightly faster reading than ``table`` stores.
This format is specified by default when using ``put`` or ``to_hdf`` or by ``format='fixed'`` or ``format='f'``

.. warning::

   A ``fixed`` format will raise a ``TypeError`` if you try to retrieve using a ``where`` .

   .. code-block:: python

       DataFrame(randn(10,2)).to_hdf('test_fixed.h5','df')

       pd.read_hdf('test_fixed.h5','df',where='index>5')
       TypeError: cannot pass a where specification when reading a fixed format.
                  this store must be selected in its entirety


.. _io.hdf5-table:

Table Format
~~~~~~~~~~~~

``HDFStore`` supports another ``PyTables`` format on disk, the ``table``
format. Conceptually a ``table`` is shaped very much like a DataFrame,
with rows and columns. A ``table`` may be appended to in the same or
other sessions.  In addition, delete & query type operations are
supported. This format is specified by ``format='table'`` or ``format='t'``
to ``append`` or ``put`` or ``to_hdf``

This format can be set as an option as well ``pd.set_option('io.hdf.default_format','table')`` to
enable ``put/append/to_hdf`` to by default store in the ``table`` format.

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove('store.h5')

.. ipython:: python

   store = HDFStore('store.h5')
   df1 = df[0:4]
   df2 = df[4:]

   # append data (creates a table automatically)
   store.append('df', df1)
   store.append('df', df2)
   store

   # select the entire object
   store.select('df')

   # the type of stored data
   store.root.df._v_attrs.pandas_type

.. note::

   You can also create a ``table`` by passing ``format='table'`` or ``format='t'`` to a ``put`` operation.

.. _io.hdf5-keys:

Hierarchical Keys
~~~~~~~~~~~~~~~~~

Keys to a store can be specified as a string. These can be in a
hierarchical path-name like format (e.g. ``foo/bar/bah``), which will
generate a hierarchy of sub-stores (or ``Groups`` in PyTables
parlance). Keys can be specified with out the leading '/' and are ALWAYS
absolute (e.g. 'foo' refers to '/foo'). Removal operations can remove
everying in the sub-store and BELOW, so be *careful*.

.. ipython:: python

   store.put('foo/bar/bah', df)
   store.append('food/orange', df)
   store.append('food/apple',  df)
   store

   # a list of keys are returned
   store.keys()

   # remove all nodes under this level
   store.remove('food')
   store

.. _io.hdf5-types:

Storing Mixed Types in a Table
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Storing mixed-dtype data is supported. Strings are stored as a
fixed-width using the maximum size of the appended column. Subsequent
appends will truncate strings at this length.

Passing ``min_itemsize={`values`: size}`` as a parameter to append
will set a larger minimum for the string columns. Storing ``floats,
strings, ints, bools, datetime64`` are currently supported. For string
columns, passing ``nan_rep = 'nan'`` to append will change the default
nan representation on disk (which converts to/from `np.nan`), this
defaults to `nan`.

.. ipython:: python

    df_mixed = DataFrame({ 'A' : randn(8),
                           'B' : randn(8),
                           'C' : np.array(randn(8),dtype='float32'),
                           'string' :'string',
                           'int' : 1,
                           'bool' : True,
                           'datetime64' : Timestamp('20010102')},
                         index=list(range(8)))
    df_mixed.ix[3:5,['A', 'B', 'string', 'datetime64']] = np.nan

    store.append('df_mixed', df_mixed, min_itemsize = {'values': 50})
    df_mixed1 = store.select('df_mixed')
    df_mixed1
    df_mixed1.get_dtype_counts()

    # we have provided a minimum string column size
    store.root.df_mixed.table

Storing Multi-Index DataFrames
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Storing multi-index dataframes as tables is very similar to
storing/selecting from homogeneous index DataFrames.

.. ipython:: python

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
                                   ['one', 'two', 'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['foo', 'bar'])
        df_mi = DataFrame(np.random.randn(10, 3), index=index,
                          columns=['A', 'B', 'C'])
        df_mi

        store.append('df_mi',df_mi)
        store.select('df_mi')

        # the levels are automatically included as data columns
        store.select('df_mi', 'foo=bar')


.. _io.hdf5-query:

Querying a Table
~~~~~~~~~~~~~~~~

.. warning::

   This query capabilities have changed substantially starting in ``0.13.0``.
   Queries from prior version are accepted (with a ``DeprecationWarning``) printed
   if its not string-like.

``select`` and ``delete`` operations have an optional criterion that can
be specified to select/delete only a subset of the data. This allows one
to have a very large on-disk table and retrieve only a portion of the
data.

A query is specified using the ``Term`` class under the hood, as a boolean expression.

   - ``index`` and ``columns`` are supported indexers of a DataFrame
   - ``major_axis``, ``minor_axis``, and ``items`` are supported indexers of
     the Panel
   - if ``data_columns`` are specified, these can be used as additional indexers

Valid comparison operators are:

   - ``=, ==, !=, >, >=, <, <=``

Valid boolean expressions are combined with:

   - ``|`` : or
   - ``&`` : and
   - ``(`` and ``)`` : for grouping

These rules are similar to how boolean expressions are used in pandas for indexing.

.. note::

   - ``=`` will be automatically expanded to the comparison operator ``==``
   - ``~`` is the not operator, but can only be used in very limited
     circumstances
   - If a list/tuple of expressions is passed they will be combined via ``&``

The following are valid expressions:

   - ``'index>=date'``
   - ``"columns=['A', 'D']"``
   - ``"columns in ['A', 'D']"``
   - ``'columns=A'``
   - ``'columns==A'``
   - ``"~(columns=['A','B'])"``
   - ``'index>df.index[3] & string="bar"'``
   - ``'(index>df.index[3] & index<=df.index[6]) | string="bar"'``
   - ``"ts>=Timestamp('2012-02-01')"``
   - ``"major_axis>=20130101"``

The ``indexers`` are on the left-hand side of the sub-expression:

   - ``columns``, ``major_axis``, ``ts``

The right-hand side of the sub-expression (after a comparsion operator) can be:

   - functions that will be evaluated, e.g. ``Timestamp('2012-02-01')``
   - strings, e.g. ``"bar"``
   - date-like, e.g. ``20130101``, or ``"20130101"``
   - lists, e.g. ``"['A','B']"``
   - variables that are defined in the local names space, e.g. ``date``

Here are some examples:

.. ipython:: python

    dfq = DataFrame(randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10))
    store.append('dfq',dfq,format='table',data_columns=True)

Use boolean expressions, with in-line function evaluation.

.. ipython:: python

    store.select('dfq',"index>Timestamp('20130104') & columns=['A', 'B']")

Use and inline column reference

.. ipython:: python

   store.select('dfq',where="A>0 or C>0")

Works with a Panel as well.

.. ipython:: python

   store.append('wp',wp)
   store
   store.select('wp', "major_axis>Timestamp('20000102') & minor_axis=['A', 'B']")

The ``columns`` keyword can be supplied to select a list of columns to be
returned, this is equivalent to passing a
``'columns=list_of_columns_to_filter'``:

.. ipython:: python

   store.select('df', "columns=['A', 'B']")

``start`` and ``stop`` parameters can be specified to limit the total search
space. These are in terms of the total number of rows in a table.

.. ipython:: python

   # this is effectively what the storage of a Panel looks like
   wp.to_frame()

   # limiting the search
   store.select('wp',"major_axis>20000102 & minor_axis=['A','B']",
                start=0, stop=10)

.. note::

   ``select`` will raise a ``ValueError`` if the query expression has an unknown
   variable reference. Usually this means that you are trying to select on a column
   that is **not** a data_column.

   ``select`` will raise a ``SyntaxError`` if the query expression is not valid.


.. _io.hdf5-timedelta:

**Using timedelta64[ns]**

.. versionadded:: 0.13

Beginning in 0.13.0, you can store and query using the ``timedelta64[ns]`` type. Terms can be
specified in the format: ``<float>(<unit>)``, where float may be signed (and fractional), and unit can be
``D,s,ms,us,ns`` for the timedelta. Here's an example:

.. warning::

   This requires ``numpy >= 1.7``

.. ipython:: python

   from datetime import timedelta
   dftd = DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ]))
   dftd['C'] = dftd['A']-dftd['B']
   dftd
   store.append('dftd',dftd,data_columns=True)
   store.select('dftd',"C<'-3.5D'")

Indexing
~~~~~~~~

You can create/modify an index for a table with ``create_table_index``
after data is already in the table (after and ``append/put``
operation). Creating a table index is **highly** encouraged. This will
speed your queries a great deal when you use a ``select`` with the
indexed dimension as the ``where``.

.. note::

   Indexes are automagically created (starting ``0.10.1``) on the indexables
   and any data columns you specify. This behavior can be turned off by passing
   ``index=False`` to ``append``.

.. ipython:: python

   # we have automagically already created an index (in the first section)
   i = store.root.df.table.cols.index.index
   i.optlevel, i.kind

   # change an index by passing new parameters
   store.create_table_index('df', optlevel=9, kind='full')
   i = store.root.df.table.cols.index.index
   i.optlevel, i.kind

See `here <http://stackoverflow.com/questions/17893370/ptrepack-sortby-needs-full-index>`__ for how to create a completely-sorted-index (CSI) on an existing store.

Query via Data Columns
~~~~~~~~~~~~~~~~~~~~~~

You can designate (and index) certain columns that you want to be able
to perform queries (other than the `indexable` columns, which you can
always query). For instance say you want to perform this common
operation, on-disk, and return just the frame that matches this
query. You can specify ``data_columns = True`` to force all columns to
be data_columns

.. ipython:: python

   df_dc = df.copy()
   df_dc['string'] = 'foo'
   df_dc.ix[4:6,'string'] = np.nan
   df_dc.ix[7:9,'string'] = 'bar'
   df_dc['string2'] = 'cool'
   df_dc.ix[1:3,['B','C']] = 1.0
   df_dc

   # on-disk operations
   store.append('df_dc', df_dc, data_columns = ['B', 'C', 'string', 'string2'])
   store.select('df_dc', [ Term('B>0') ])

   # getting creative
   store.select('df_dc', 'B > 0 & C > 0 & string == foo')

   # this is in-memory version of this type of selection
   df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == 'foo')]

   # we have automagically created this index and the B/C/string/string2
   # columns are stored separately as ``PyTables`` columns
   store.root.df_dc.table

There is some performance degredation by making lots of columns into
`data columns`, so it is up to the user to designate these. In addition,
you cannot change data columns (nor indexables) after the first
append/put operation (Of course you can simply read in the data and
create a new table!)

Iterator
~~~~~~~~

Starting in ``0.11.0``, you can pass, ``iterator=True`` or ``chunksize=number_in_a_chunk``
to ``select`` and ``select_as_multiple`` to return an iterator on the results.
The default is 50,000 rows returned in a chunk.

.. ipython:: python

   for df in store.select('df', chunksize=3):
      print(df)

.. note::

   .. versionadded:: 0.12.0

   You can also use the iterator with ``read_hdf`` which will open, then
   automatically close the store when finished iterating.

   .. code-block:: python

      for df in read_hdf('store.h5','df', chunsize=3):
          print(df)

Note, that the chunksize keyword applies to the **source** rows. So if you
are doing a query, then the chunksize will subdivide the total rows in the table
and the query applied, returning an iterator on potentially unequal sized chunks.

Here is a recipe for generating a query and using it to create equal sized return
chunks.

.. ipython:: python

   dfeq = DataFrame({'number': np.arange(1,11)})
   dfeq

   store.append('dfeq', dfeq, data_columns=['number'])

   def chunks(l, n):
        return [l[i:i+n] for i in xrange(0, len(l), n)]

   evens = [2,4,6,8,10]
   coordinates = store.select_as_coordinates('dfeq','number=evens')
   for c in chunks(coordinates, 2):
        print store.select('dfeq',where=c)

Advanced Queries
~~~~~~~~~~~~~~~~

**Select a Single Column**

To retrieve a single indexable or data column, use the
method ``select_column``. This will, for example, enable you to get the index
very quickly. These return a ``Series`` of the result, indexed by the row number.
These do not currently accept the ``where`` selector.

.. ipython:: python

   store.select_column('df_dc', 'index')
   store.select_column('df_dc', 'string')

.. _io.hdf5-selecting_coordinates:

**Selecting coordinates**

Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an
``Int64Index`` of the resulting locations. These coordinates can also be passed to subsequent
``where`` operations.

.. ipython:: python

   df_coord = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
   store.append('df_coord',df_coord)
   c = store.select_as_coordinates('df_coord','index>20020101')
   c.summary()
   store.select('df_coord',where=c)

.. _io.hdf5-where_mask:

**Selecting using a where mask**

Sometime your query can involve creating a list of rows to select. Usually this ``mask`` would
be a resulting ``index`` from an indexing operation. This example selects the months of
a datetimeindex which are 5.

.. ipython:: python

   df_mask = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
   store.append('df_mask',df_mask)
   c = store.select_column('df_mask','index')
   where = c[DatetimeIndex(c).month==5].index
   store.select('df_mask',where=where)

**Storer Object**

If you want to inspect the stored object, retrieve via
``get_storer``. You could use this programmatically to say get the number
of rows in an object.

.. ipython:: python

   store.get_storer('df_dc').nrows


Multiple Table Queries
~~~~~~~~~~~~~~~~~~~~~~

New in 0.10.1 are the methods ``append_to_multiple`` and
``select_as_multiple``, that can perform appending/selecting from
multiple tables at once. The idea is to have one table (call it the
selector table) that you index most/all of the columns, and perform your
queries. The other table(s) are data tables with an index matching the
selector table's index. You can then perform a very fast query
on the selector table, yet get lots of data back. This method is similar to
having a very wide table, but enables more efficient queries.

The ``append_to_multiple`` method splits a given single DataFrame
into multiple tables according to ``d``, a dictionary that maps the
table names to a list of 'columns' you want in that table. If `None`
is used in place of a list, that table will have the remaining
unspecified columns of the given DataFrame. The argument ``selector``
defines which table is the selector table (which you can make queries from).
The argument ``dropna`` will drop rows from the input DataFrame to ensure
tables are synchronized.  This means that if a row for one of the tables
being written to is entirely ``np.NaN``, that row will be dropped from all tables.

If ``dropna`` is False, **THE USER IS RESPONSIBLE FOR SYNCHRONIZING THE TABLES**.
Remember that entirely ``np.Nan`` rows are not written to the HDFStore, so if
you choose to call ``dropna=False``, some tables may have more rows than others,
and therefore ``select_as_multiple`` may not work or it may return unexpected
results.

.. ipython:: python

   df_mt = DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8),
                                  columns=['A', 'B', 'C', 'D', 'E', 'F'])
   df_mt['foo'] = 'bar'
   df_mt.ix[1, ('A', 'B')] = np.nan

   # you can also create the tables individually
   store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None },
                             df_mt, selector='df1_mt')
   store

   # individual tables were created
   store.select('df1_mt')
   store.select('df2_mt')

   # as a multiple
   store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
                             selector = 'df1_mt')


Delete from a Table
~~~~~~~~~~~~~~~~~~~

You can delete from a table selectively by specifying a ``where``. In
deleting rows, it is important to understand the ``PyTables`` deletes
rows by erasing the rows, then **moving** the following data. Thus
deleting can potentially be a very expensive operation depending on the
orientation of your data. This is especially true in higher dimensional
objects (``Panel`` and ``Panel4D``). To get optimal performance, it's
worthwhile to have the dimension you are deleting be the first of the
``indexables``.

Data is ordered (on the disk) in terms of the ``indexables``. Here's a
simple use case. You store panel-type data, with dates in the
``major_axis`` and ids in the ``minor_axis``. The data is then
interleaved like this:

   - date_1
        - id_1
        - id_2
        -  .
        - id_n
   - date_2
        - id_1
        -  .
        - id_n

It should be clear that a delete operation on the ``major_axis`` will be
fairly quick, as one chunk is removed, then the following data moved. On
the other hand a delete operation on the ``minor_axis`` will be very
expensive. In this case it would almost certainly be faster to rewrite
the table using a ``where`` that selects all but the missing data.

.. ipython:: python

   # returns the number of rows deleted
   store.remove('wp', 'major_axis>20000102' )
   store.select('wp')

Please note that HDF5 **DOES NOT RECLAIM SPACE** in the h5 files
automatically. Thus, repeatedly deleting (or removing nodes) and adding
again **WILL TEND TO INCREASE THE FILE SIZE**. To *clean* the file, use
``ptrepack`` (see below).

Compression
~~~~~~~~~~~

``PyTables`` allows the stored data to be compressed. Tthis applies to
all kinds of stores, not just tables.

   - Pass ``complevel=int`` for a compression level (1-9, with 0 being no
     compression, and the default)
   - Pass ``complib=lib`` where lib is any of ``zlib, bzip2, lzo, blosc`` for
     whichever compression library you prefer.

``HDFStore`` will use the file based compression scheme if no overriding
``complib`` or ``complevel`` options are provided. ``blosc`` offers very
fast compression, and is my most used. Note that ``lzo`` and ``bzip2``
may not be installed (by Python) by default.

Compression for all objects within the file

   - ``store_compressed = HDFStore('store_compressed.h5', complevel=9, complib='blosc')``

Or on-the-fly compression (this only applies to tables). You can turn
off file compression for a specific table by passing ``complevel=0``

   - ``store.append('df', df, complib='zlib', complevel=5)``

**ptrepack**

``PyTables`` offers better write performance when tables are compressed after
they are written, as opposed to turning on compression at the very
beginning. You can use the supplied ``PyTables`` utility
``ptrepack``. In addition, ``ptrepack`` can change compression levels
after the fact.

   - ``ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5``

Furthermore ``ptrepack in.h5 out.h5`` will *repack* the file to allow
you to reuse previously deleted space. Aalternatively, one can simply
remove the file and write again, or use the ``copy`` method.

Notes & Caveats
~~~~~~~~~~~~~~~

   - Once a ``table`` is created its items (Panel) / columns (DataFrame)
     are fixed; only exactly the same columns can be appended
   - If a row has ``np.nan`` for **EVERY COLUMN** (having a ``nan``
     in a string, or a ``NaT`` in a datetime-like column counts as having
     a value), then those rows **WILL BE DROPPED IMPLICITLY**. This limitation
     *may* be addressed in the future.
   - ``HDFStore`` is **not-threadsafe for writing**. The underlying
     ``PyTables`` only supports concurrent reads (via threading or
     processes). If you need reading and writing *at the same time*, you
     need to serialize these operations in a single thread in a single
     process. You will corrupt your data otherwise. See the issue
     (:`2397`) for more information.
   - If you use locks to manage write access between multiple processes, you
     may want to use :py:func:`~os.fsync` before releasing write locks. For
     convenience you can use ``store.flush(fsync=True)`` to do this for you.
   - ``PyTables`` only supports fixed-width string columns in
     ``tables``. The sizes of a string based indexing column
     (e.g. *columns* or *minor_axis*) are determined as the maximum size
     of the elements in that axis or by passing the parameter

.. warning::

   ``PyTables`` will show a ``NaturalNameWarning`` if a  column name
   cannot be used as an attribute selector. Generally identifiers that
   have spaces, start with numbers, or ``_``, or have ``-`` embedded are not considered
   *natural*. These types of identifiers cannot be used in a ``where`` clause
   and are generally a bad idea.

DataTypes
~~~~~~~~~

``HDFStore`` will map an object dtype to the ``PyTables`` underlying
dtype. This means the following types are known to work:

    - floating : ``float64, float32, float16`` *(using* ``np.nan`` *to
      represent invalid values)*
    - integer : ``int64, int32, int8, uint64, uint32, uint8``
    - bool
    - datetime64[ns] *(using* ``NaT`` *to represent invalid values)*
    - object : ``strings`` *(using* ``np.nan`` *to represent invalid
      values)*

Currently, ``unicode`` and ``datetime`` columns (represented with a
dtype of ``object``), **WILL FAIL**. In addition, even though a column
may look like a ``datetime64[ns]``, if it contains ``np.nan``, this
**WILL FAIL**. You can try to convert datetimelike columns to proper
``datetime64[ns]`` columns, that possibily contain ``NaT`` to represent
invalid values. (Some of these issues have been addressed and these
conversion may not be necessary in future versions of pandas)

    .. ipython:: python

       import datetime
       df = DataFrame(dict(datelike=Series([datetime.datetime(2001, 1, 1),
                                            datetime.datetime(2001, 1, 2), np.nan])))
       df
       df.dtypes

       # to convert
       df['datelike'] = Series(df['datelike'].values, dtype='M8[ns]')
       df
       df.dtypes

String Columns
~~~~~~~~~~~~~~

**min_itemsize**

The underlying implementation of ``HDFStore`` uses a fixed column width (itemsize) for string columns.
A string column itemsize is calculated as the maximum of the
length of data (for that column) that is passed to the ``HDFStore``, **in the first append**. Subsequent appends,
may introduce a string for a column **larger** than the column can hold, an Exception will be raised (otherwise you
could have a silent truncation of these columns, leading to loss of information). In the future we may relax this and
allow a user-specified truncation to occur.

Pass ``min_itemsize`` on the first table creation to a-priori specifiy the minimum length of a particular string column.
``min_itemsize`` can be an integer, or a dict mapping a column name to an integer. You can pass ``values`` as a key to
allow all *indexables* or *data_columns* to have this min_itemsize.

Starting in 0.11.0, passing a ``min_itemsize`` dict will cause all passed columns to be created as *data_columns* automatically.

.. note::

   If you are not passing any *data_columns*, then the min_itemsize will be the maximum of the length of any string passed

.. ipython:: python

   dfs = DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5)))
   dfs

   # A and B have a size of 30
   store.append('dfs', dfs, min_itemsize = 30)
   store.get_storer('dfs').table

   # A is created as a data_column with a size of 30
   # B is size is calculated
   store.append('dfs2', dfs, min_itemsize = { 'A' : 30 })
   store.get_storer('dfs2').table

**nan_rep**

String columns will serialize a ``np.nan`` (a missing value) with the ``nan_rep`` string representation. This defaults to the string value ``nan``.
You could inadvertently turn an actual ``nan`` value into a missing value.

.. ipython:: python

   dfss = DataFrame(dict(A = ['foo','bar','nan']))
   dfss

   store.append('dfss', dfss)
   store.select('dfss')

   # here you need to specify a different nan rep
   store.append('dfss2', dfss, nan_rep='_nan_')
   store.select('dfss2')

External Compatibility
~~~~~~~~~~~~~~~~~~~~~~

``HDFStore`` write ``table`` format objects in specific formats suitable for
producing loss-less roundtrips to pandas objects. For external
compatibility, ``HDFStore`` can read native ``PyTables`` format
tables. It is possible to write an ``HDFStore`` object that can easily
be imported into ``R`` using the ``rhdf5`` library. Create a table
format store like this:

     .. ipython:: python

        store_export = HDFStore('export.h5')
	    store_export.append('df_dc', df_dc, data_columns=df_dc.columns)
	    store_export

     .. ipython:: python
        :suppress:

        store_export.close()
        import os
        os.remove('export.h5')

Backwards Compatibility
~~~~~~~~~~~~~~~~~~~~~~~

0.10.1 of ``HDFStore`` can read tables created in a prior version of pandas,
however query terms using the
prior (undocumented) methodology are unsupported. ``HDFStore`` will
issue a warning if you try to use a legacy-format file. You must
read in the entire file and write it out using the new format, using the
method ``copy`` to take advantage of the updates. The group attribute
``pandas_version`` contains the version information. ``copy`` takes a
number of options, please see the docstring.


     .. ipython:: python
        :suppress:

        import os
        legacy_file_path = os.path.abspath('source/_static/legacy_0.10.h5')

     .. ipython:: python

        # a legacy store
        legacy_store = HDFStore(legacy_file_path,'r')
        legacy_store

        # copy (and return the new handle)
	    new_store = legacy_store.copy('store_new.h5')
	    new_store
        new_store.close()

     .. ipython:: python
        :suppress:

        legacy_store.close()
        import os
        os.remove('store_new.h5')


Performance
~~~~~~~~~~~

   - ``Tables`` come with a writing performance penalty as compared to
     regular stores. The benefit is the ability to append/delete and
     query (potentially very large amounts of data).  Write times are
     generally longer as compared with regular stores. Query times can
     be quite fast, especially on an indexed axis.
   - You can pass ``chunksize=<int>`` to ``append``, specifying the
     write chunksize (default is 50000). This will signficantly lower
     your memory usage on writing.
   - You can pass ``expectedrows=<int>`` to the first ``append``,
     to set the TOTAL number of expected rows that ``PyTables`` will
     expected. This will optimize read/write performance.
   - Duplicate rows can be written to tables, but are filtered out in
     selection (with the last items being selected; thus a table is
     unique on major, minor pairs)
   - A ``PerformanceWarning`` will be raised if you are attempting to
     store types that will be pickled by PyTables (rather than stored as
     endemic types). See
     `Here <http://stackoverflow.com/questions/14355151/how-to-make-pandas-hdfstore-put-operation-faster/14370190#14370190>`__
     for more information and some solutions.

Experimental
~~~~~~~~~~~~

HDFStore supports ``Panel4D`` storage.

.. ipython:: python

   p4d = Panel4D({ 'l1' : wp })
   p4d
   store.append('p4d', p4d)
   store

These, by default, index the three axes ``items, major_axis,
minor_axis``. On an ``AppendableTable`` it is possible to setup with the
first append a different indexing scheme, depending on how you want to
store your data. Pass the ``axes`` keyword with a list of dimensions
(currently must by exactly 1 less than the total dimensions of the
object). This cannot be changed after table creation.

.. ipython:: python

   store.append('p4d2', p4d, axes=['labels', 'major_axis', 'minor_axis'])
   store
   store.select('p4d2', [ Term('labels=l1'), Term('items=Item1'), Term('minor_axis=A_big_strings') ])

.. ipython:: python
   :suppress:

   store.close()
   import os
   os.remove('store.h5')


.. _io.sql:

SQL Queries
-----------

The :mod:`pandas.io.sql` module provides a collection of query wrappers to both
facilitate data retrieval and to reduce dependency on DB-specific API. These
wrappers only support the Python database adapters which respect the `Python
DB-API <http://www.python.org/dev/peps/pep-0249/>`__. See some
:ref:`cookbook examples <cookbook.sql>` for some advanced strategies

For example, suppose you want to query some data with different types from a
table such as:


+-----+------------+-------+-------+-------+
| id  |    Date    | Col_1 | Col_2 | Col_3 |
+=====+============+=======+=======+=======+
| 26  | 2012-10-18 |   X   |  25.7 | True  |
+-----+------------+-------+-------+-------+
| 42  | 2012-10-19 |   Y   | -12.4 | False |
+-----+------------+-------+-------+-------+
| 63  | 2012-10-20 |   Z   |  5.73 | True  |
+-----+------------+-------+-------+-------+


Functions from :mod:`pandas.io.sql` can extract some data into a DataFrame. In
the following example, we use the `SQlite <http://www.sqlite.org/>`__ SQL database
engine. You can use a temporary SQLite database where data are stored in
"memory". Just do:

.. code-block:: python

   import sqlite3
   from pandas.io import sql
   # Create your connection.
   cnx = sqlite3.connect(':memory:')

.. ipython:: python
   :suppress:

   import sqlite3
   from pandas.io import sql
   cnx = sqlite3.connect(':memory:')

.. ipython:: python
   :suppress:

   cu = cnx.cursor()
   # Create a table named 'data'.
   cu.execute("""CREATE TABLE data(id integer,
                                   date date,
                                   Col_1 string,
                                   Col_2 float,
                                   Col_3 bool);""")
   cu.executemany('INSERT INTO data VALUES (?,?,?,?,?)',
                  [(26, datetime.datetime(2010,10,18), 'X', 27.5, True),
                   (42, datetime.datetime(2010,10,19), 'Y', -12.5, False),
                   (63, datetime.datetime(2010,10,20), 'Z', 5.73, True)])


Let ``data`` be the name of your SQL table. With a query and your database
connection, just use the :func:`~pandas.io.sql.read_sql` function to get the
query results into a DataFrame:

.. ipython:: python

   sql.read_sql("SELECT * FROM data;", cnx)

You can also specify the name of the column as the DataFrame index:

.. ipython:: python

   sql.read_sql("SELECT * FROM data;", cnx, index_col='id')
   sql.read_sql("SELECT * FROM data;", cnx, index_col='date')

Of course, you can specify a more "complex" query.

.. ipython:: python

   sql.read_sql("SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", cnx)

.. ipython:: python
   :suppress:

   cu.close()
   cnx.close()


There are a few other available functions:

  - ``tquery`` returns a list of tuples corresponding to each row.
  - ``uquery`` does the same thing as tquery, but instead of returning results
    it returns the number of related rows.
  - ``write_frame`` writes records stored in a DataFrame into the SQL table.
  - ``has_table`` checks if a given SQLite table exists.

.. note::

   For now, writing your DataFrame into a database works only with
   **SQLite**. Moreover, the **index** will currently be **dropped**.

.. _io.bigquery:

Google BigQuery (Experimental)
------------------------------

.. versionadded:: 0.13.0

The :mod:`pandas.io.gbq` module provides a wrapper for Google's BigQuery
analytics web service to simplify retrieving results from BigQuery tables
using SQL-like queries. Result sets are parsed into a pandas
DataFrame with a shape derived from the source table. Additionally,
DataFrames can be uploaded into BigQuery datasets as tables
if the source datatypes are compatible with BigQuery ones.

For specifics on the service itself, see `here <https://developers.google.com/bigquery/>`__

As an example, suppose you want to load all data from an existing table
: `test_dataset.test_table`
into BigQuery and pull it into a DataFrame.

.. code-block:: python

   from pandas.io import gbq

   # Insert your BigQuery Project ID Here
   # Can be found in the web console, or
   # using the command line tool `bq ls`
   projectid = "xxxxxxxx"

   data_frame = gbq.read_gbq('SELECT * FROM test_dataset.test_table', project_id = projectid)

The user will then be authenticated by the `bq` command line client -
this usually involves the default browser opening to a login page,
though the process can be done entirely from command line if necessary.
Datasets and additional parameters can be either configured with `bq`,
passed in as options to `read_gbq`, or set using Google's gflags (this
is not officially supported by this module, though care was taken
to ensure that they should be followed regardless of how you call the
method).

Additionally, you can define which column to use as an index as well as a preferred column order as follows:

.. code-block:: python

   data_frame = gbq.read_gbq('SELECT * FROM test_dataset.test_table',
                             index_col='index_column_name',
                             col_order='[col1, col2, col3,...]', project_id = projectid)

Finally, if you would like to create a BigQuery table, `my_dataset.my_table`, from the rows of DataFrame, `df`:

.. code-block:: python

   df = pandas.DataFrame({'string_col_name' : ['hello'],
         'integer_col_name' : [1],
         'boolean_col_name' : [True]})
   schema = ['STRING', 'INTEGER', 'BOOLEAN']
   data_frame = gbq.to_gbq(df, 'my_dataset.my_table',
                           if_exists='fail', schema = schema, project_id = projectid)

To add more rows to this, simply:

.. code-block:: python

   df2 = pandas.DataFrame({'string_col_name' : ['hello2'],
         'integer_col_name' : [2],
         'boolean_col_name' : [False]})
   data_frame = gbq.to_gbq(df2, 'my_dataset.my_table', if_exists='append', project_id = projectid)

.. note::

   A default project id can be set using the command line:
   `bq init`.

   There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the BigQuery SQL query language has some oddities,
   see `here <https://developers.google.com/bigquery/query-reference>`__

   You can access the management console to determine project id's by:
   <https://code.google.com/apis/console/b/0/?noredirect>

.. warning::

   To use this module, you will need a BigQuery account. See
   <https://cloud.google.com/products/big-query> for details.

   As of 1/28/14, a known bug is present that could possibly cause data duplication in the resultant dataframe. A fix is imminent,
   but any client changes will not make it into 0.13.1. See:
   http://stackoverflow.com/questions/20984592/bigquery-results-not-including-page-token/21009144?noredirect=1#comment32090677_21009144

.. _io.stata:

STATA Format
------------

.. versionadded:: 0.12.0

.. _io.stata_writer:

Writing to STATA format
~~~~~~~~~~~~~~~~~~~~~~~

The method :func:`~pandas.core.frame.DataFrame.to_stata` will write a DataFrame
into a .dta file. The format version of this file is always 115 (Stata 12).

.. ipython:: python

   df = DataFrame(randn(10, 2), columns=list('AB'))
   df.to_stata('stata.dta')

.. _io.stata_reader:

Reading from STATA format
~~~~~~~~~~~~~~~~~~~~~~~~~

The top-level function ``read_stata`` will read a dta format file
and return a DataFrame:
The class :class:`~pandas.io.stata.StataReader` will read the header of the
given dta file at initialization. Its method
:func:`~pandas.io.stata.StataReader.data` will read the observations,
converting them to a DataFrame which is returned:

.. ipython:: python

   pd.read_stata('stata.dta')

Currently the ``index`` is retrieved as a column on read back.

The parameter ``convert_categoricals`` indicates wheter value labels should be
read and used to create a ``Categorical`` variable from them. Value labels can
also be retrieved by the function ``variable_labels``, which requires data to be
called before (see ``pandas.io.stata.StataReader``).

The StataReader supports .dta Formats 104, 105, 108, 113-115 and 117.
Alternatively, the function :func:`~pandas.io.stata.read_stata` can be used

.. ipython:: python
   :suppress:

   import os
   os.remove('stata.dta')
