.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import os
   import csv
   from StringIO import StringIO
   import pandas as pd

   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 *
   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, ...)

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, 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 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.

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: sepcifies the thousands separator. If not None, then parser will try to look for it in the output and parse relevant data to integers. Because it has to essentially scan through the data again, this causes a significant performance hit so only use if necessary.

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 TextParser 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 TextParser 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>`

.. 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'])

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.

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

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)

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])

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 = 'word,length\nTr\xe4umen,7\nGr\xfc\xdfe,5'
   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.

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)

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

.. ipython:: python
   :suppress:

   os.remove('foo.csv')

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: 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')

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])

Thousand Separators

For large integers that have been written with a thousands separator, you can set the thousands keyword to True 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, integers 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')

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')

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'])

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'

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:

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

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='\\')

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 [from, to[

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.

.. 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 DataFrame objects with `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:

.. ipython:: python

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

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')

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 TextParser:

.. 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 TextParser 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')

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

nanRep: 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

Writing a formatted string

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.

Writing to HTML format

DataFrame object has 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.

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 described in the next section. For instance, you can copy the following text to the clipboard (CTRL-C on many operating systems):

  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:

clipdf = pd.read_clipboard(delim_whitespace=True)

.. ipython:: python

   clipdf

Excel files

The ExcelFile class can read an Excel 2003 file 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

To use it, create the ExcelFile object:

xls = ExcelFile('path_to_file.xls')

Then use the parse instance method with a sheetname, then use the same additional arguments as the parsers above:

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

To read sheets from an Excel 2007 file, you can pass a filename with a .xlsx extension, in which case the openpyxl module will be used to read the file.

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. ExcelFile.parse 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.

xls.parse('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.

xls.parse('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:

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 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 use the ExcelWriter class, as in the following example:

writer = ExcelWriter('path_to_file.xlsx')
df1.to_excel(writer, sheet_name='sheet1')
df2.to_excel(writer, sheet_name='sheet2')
writer.save()

HDF5 (PyTables)

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

.. 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

   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

   # closing a store
   store.close()

   # 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')

These stores are not appendable once written (though you can simply remove them and rewrite). Nor are they queryable; they must be retrieved in their entirety.

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=range(5), B=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')

Storing in 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.

.. 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

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

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=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', Term('foo=bar'))

Querying a Table

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.

'index' and 'columns' are supported indexers of a DataFrame

'major_axis', 'minor_axis', and 'items' are supported indexers of the Panel

Valid terms can be created from dict, list, tuple, or string. Objects can be embeded as values. Allowed operations are: <, <=, >, >=, =, !=. = will be inferred as an implicit set operation (e.g. if 2 or more values are provided). The following are all valid terms.

dict(field = 'index', op = '>', value = '20121114')

('index', '>', '20121114')

'index > 20121114'

('index', '>', datetime(2012, 11, 14))

('index', ['20121114', '20121115'])

('major_axis', '=', Timestamp('2012/11/14'))

('minor_axis', ['A', 'B'])

Queries are built up using a list of Terms (currently only anding of terms is supported). An example query for a panel might be specified as follows. ['major_axis>20000102', ('minor_axis', '=', ['A', 'B']) ]. This is roughly translated to: major_axis must be greater than the date 20000102 and the minor_axis must be A or B

.. ipython:: python

   store.append('wp',wp)
   store
   store.select('wp', [ Term('major_axis>20000102'), Term('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 Term('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',[ Term('major_axis>20000102'),
                       Term('minor_axis', '=', ['A','B']) ],
                start=0, stop=10)

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. 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

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

   # 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, 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, that the chunksize keyword applies to the returned rows. So if you are doing a query, then that set will be subdivided and returned in the iterator. Keep in mind that if you do not pass a where selection criteria then the nrows of the table are considered.

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 (coming soon)

.. ipython:: python

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

Replicating or

not and or conditions are unsupported at this time; however, or operations are easy to replicate, by repeatedly applying the criteria to the table, and then concat the results.

.. ipython:: python

   crit1 = [ Term('B>0'), Term('C>0'), Term('string=foo') ]
   crit2 = [ Term('B<0'), Term('C>0'), Term('string=foo') ]

   concat([store.select('df_dc',c) for c in [crit1, crit2]])

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 works similar to having a very wide table, but is more efficient in terms of queries.

Note, THE USER IS RESPONSIBLE FOR SYNCHRONIZING THE TABLES. This means, append to the tables in the same order; append_to_multiple splits a single object to multiple tables, given a specification (as a dictionary). This dictionary is a mapping of the table names to the 'columns' you want included in that table. Pass a None for a single table (optional) to let it have the remaining columns. The argument selector defines which table is the selector table.

.. 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'

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

   # indiviual 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.

You can not append/select/delete to a non-table (table creation is determined on the first append, or by passing table=True in a put operation)

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 <pandas-dev#2397> for more information.

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

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

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, 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=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

External Compatibility

HDFStore write storer 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 <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')

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. 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 SQL database engine. You can use a temporary SQLite database where data are stored in "memory". Just do:

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_frame` function to get the query results into a DataFrame:

.. ipython:: python

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

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

.. ipython:: python

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

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

.. ipython:: python

   sql.read_frame("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.