.. _io: .. 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, ...) ******************************* .. _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` 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 ` - ``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 ` .. 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 = '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. .. _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 .. 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') .. _io.dayfirst: 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 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') .. _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 [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. 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`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. _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``: .. ipython:: python df = pd.read_csv("data/mindex_ex.csv", index_col=[0,1]) df df.ix[1978] .. _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 ``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 ~~~~~~~~~~~~~~~~~~~~~ .. _io.store_in_csv: 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. _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. Writing to HTML format ~~~~~~~~~~~~~~~~~~~~~~ .. _io.html: 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 --------- .. _io.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): .. 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(delim_whitespace=True) .. ipython:: python clipdf .. _io.excel: 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` for some advanced strategies To use it, create the ``ExcelFile`` object: .. code-block:: python 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: .. code-block:: python 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. .. code-block:: python 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. .. code-block:: python 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: .. 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 ``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: .. code-block:: python writer = ExcelWriter('path_to_file.xlsx') df1.to_excel(writer, sheet_name='sheet1') df2.to_excel(writer, sheet_name='sheet2') writer.save() .. _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 `__ library. See the :ref:`cookbook` 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 .. 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. .. _io.hdf5-table: 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 .. _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=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')) .. _io.hdf5-query: 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 to filter a list of the return columns, 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 that 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 ~~~~~~~~~~~~~~~~ **Unique** To retrieve the *unique* values of an indexable or data column, use the method ``unique``. This will, for example, enable you to get the index very quickly. Note ``nan`` are excluded from the result set. .. ipython:: python store.unique('df_dc', 'index') store.unique('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_multple`` 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 that are indexed the same as the selector table. 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') .. _io.hdf5-delete: 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 deletion speed, it pays 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`` offer better write performance when compressed after writing them, 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 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 ``min_itemsize`` on the first table creation (``min_itemsize`` can be an integer or a dict of column name to an integer). If subsequent appends introduce elements in the indexing axis that are larger than the supported indexer, an Exception will be raised (otherwise you could have a silent truncation of these indexers, leading to loss of information). Just to be clear, this fixed-width restriction applies to **indexables** (the indexing columns) and **string values** in a mixed_type table. .. ipython:: python store.append('wp_big_strings', wp, min_itemsize = { 'minor_axis' : 30 }) wp = wp.rename_axis(lambda x: x + '_big_strings', axis=2) store.append('wp_big_strings', wp) store.select('wp_big_strings') # we have provided a minimum minor_axis indexable size store.root.wp_big_strings.table 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 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`` is backwards compatible for reading 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 prior-version 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=an integer`` to ``append``, to change the writing chunksize (default is 50000). This will signficantly lower your memory usage on writing. - You can pass ``expectedrows=an integer`` to the first ``append``, to set the TOTAL number of expectedrows 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 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 dimension (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. There wrappers only support the Python database adapters which respect the `Python DB-API `_. See some :ref:`cookbook examples ` for some advanced strategies 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 `SQlite `_ 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_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 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 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**.