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# IO Tools (Text, CSV, HDF5, ...)

The Pandas I/O api is a set of top level `reader` functions accessed like `pd.read_csv()` that generally return a `pandas` object.

> - read_csv
> - read_excel
> - read_hdf
> - read_sql
> - read_json
> - read_msgpack (experimental)
> - read_html
> - read_gbq (experimental)
> - read_stata
> - read_clipboard
> - read_pickle

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

> - to_csv
> - to_excel
> - to_hdf
> - to_sql
> - to_json
> - to_msgpack (experimental)
> - to_html
> - to_gbq (experimental)
> - to_stata
> - to_clipboard
> - to_pickle

## CSV & Text files

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

They can take a number of arguments:

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

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

    In [1]: print(open('foo.csv').read())
    date,A,B,C
    20090101,a,1,2
    20090102,b,3,4
    20090103,c,4,5

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

    In [2]: pd.read_csv('foo.csv')
    Out[2]:
           date  A  B  C
    0  20090101  a  1  2
    1  20090102  b  3  4
    2  20090103  c  4  5

    [3 rows x 4 columns]

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

    In [3]: pd.read_csv('foo.csv', index_col=0)
    Out[3]:
              A  B  C
    date
    20090101  a  1  2
    20090102  b  3  4
    20090103  c  4  5

    [3 rows x 3 columns]

    In [4]: pd.read_csv('foo.csv', index_col='date')
    Out[4]:
              A  B  C
    date
    20090101  a  1  2
    20090102  b  3  4
    20090103  c  4  5

    [3 rows x 3 columns]

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

    In [5]: pd.read_csv('foo.csv', index_col=[0, 'A'])
    Out[5]:
                B  C
    date     A
    20090101 a  1  2
    20090102 b  3  4
    20090103 c  4  5

    [3 rows x 2 columns]

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 `csv.Dialect` instance.

Suppose you had data with unenclosed quotes:

    In [6]: print(data)
    label1,label2,label3
    index1,"a,c,e
    index2,b,d,f

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`

    In [7]: dia = csv.excel()

    In [8]: dia.quoting = csv.QUOTE_NONE

    In [9]: pd.read_csv(StringIO(data), dialect=dia)
    Out[9]:
           label1 label2 label3
    index1     "a      c      e
    index2      b      d      f

    [2 rows x 3 columns]

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

    In [10]: data = 'a,b,c~1,2,3~4,5,6'

    In [11]: pd.read_csv(StringIO(data), lineterminator='~')
    Out[11]:
       a  b  c
    0  1  2  3
    1  4  5  6

    [2 rows x 3 columns]

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

    In [12]: data = 'a, b, c\n1, 2, 3\n4, 5, 6'

    In [13]: print(data)
    a, b, c
    1, 2, 3
    4, 5, 6

    In [14]: pd.read_csv(StringIO(data), skipinitialspace=True)
    Out[14]:
       a  b  c
    0  1  2  3
    1  4  5  6

    [2 rows x 3 columns]

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:

    In [15]: data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'

    In [16]: print(data)
    a,b,c
    1,2,3
    4,5,6
    7,8,9

    In [17]: df = pd.read_csv(StringIO(data), dtype=object)

    In [18]: df
    Out[18]:
       a  b  c
    0  1  2  3
    1  4  5  6
    2  7  8  9

    [3 rows x 3 columns]

    In [19]: df['a'][0]
    Out[19]: '1'

    In [20]: df = pd.read_csv(StringIO(data), dtype={'b': object, 'c': np.float64})

    In [21]: df.dtypes
    Out[21]:
    a      int64
    b     object
    c    float64
    dtype: object

### 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:

    In [22]: from StringIO import StringIO

    In [23]: data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'

    In [24]: print(data)
    a,b,c
    1,2,3
    4,5,6
    7,8,9

    In [25]: pd.read_csv(StringIO(data))
    Out[25]:
       a  b  c
    0  1  2  3
    1  4  5  6
    2  7  8  9

    [3 rows x 3 columns]

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):

    In [26]: print(data)
    a,b,c
    1,2,3
    4,5,6
    7,8,9

    In [27]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0)
    Out[27]:
       foo  bar  baz
    0    1    2    3
    1    4    5    6
    2    7    8    9

    [3 rows x 3 columns]

    In [28]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None)
    Out[28]:
      foo bar baz
    0   a   b   c
    1   1   2   3
    2   4   5   6
    3   7   8   9

    [4 rows x 3 columns]

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

    In [29]: data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9'

    In [30]: pd.read_csv(StringIO(data), header=1)
    Out[30]:
       a  b  c
    0  1  2  3
    1  4  5  6
    2  7  8  9

    [3 rows x 3 columns]

### 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:

    In [31]: data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'

    In [32]: pd.read_csv(StringIO(data))
    Out[32]:
       a  b  c    d
    0  1  2  3  foo
    1  4  5  6  bar
    2  7  8  9  baz

    [3 rows x 4 columns]

    In [33]: pd.read_csv(StringIO(data), usecols=['b', 'd'])
    Out[33]:
       b    d
    0  2  foo
    1  5  bar
    2  8  baz

    [3 rows x 2 columns]

    In [34]: pd.read_csv(StringIO(data), usecols=[0, 2, 3])
    Out[34]:
       a  c    d
    0  1  3  foo
    1  4  6  bar
    2  7  9  baz

    [3 rows x 3 columns]

### 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:

    In [35]: data = b'word,length\nTr\xc3\xa4umen,7\nGr\xc3\xbc\xc3\x9fe,5'.decode('utf8').encode('latin-1')

    In [36]: df = pd.read_csv(StringIO(data), encoding='latin-1')

    In [37]: df
    Out[37]:
          word  length
    0  Träumen       7
    1    Grüße       5

    [2 rows x 2 columns]

    In [38]: df['word'][1]
    Out[38]: u'Gr\xfc\xdfe'

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:

    In [39]: data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'

    In [40]: pd.read_csv(StringIO(data))
    Out[40]:
            a    b     c
    4   apple  bat   5.7
    8  orange  cow  10.0

    [2 rows x 3 columns]

    In [41]: data = 'index,a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'

    In [42]: pd.read_csv(StringIO(data), index_col=0)
    Out[42]:
                a    b     c
    index
    4       apple  bat   5.7
    8      orange  cow  10.0

    [2 rows x 3 columns]

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`:

    In [43]: data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'

    In [44]: print(data)
    a,b,c
    4,apple,bat,
    8,orange,cow,

    In [45]: pd.read_csv(StringIO(data))
    Out[45]:
            a    b   c
    4   apple  bat NaN
    8  orange  cow NaN

    [2 rows x 3 columns]

    In [46]: pd.read_csv(StringIO(data), index_col=False)
    Out[46]:
       a       b    c
    0  4   apple  bat
    1  8  orange  cow

    [2 rows x 3 columns]

### Specifying Date Columns

To better facilitate working with datetime data, `read_csv()` and `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`:

    # Use a column as an index, and parse it as dates.
    In [47]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)

    In [48]: df
    Out[48]:
                A  B  C
    date
    2009-01-01  a  1  2
    2009-01-02  b  3  4
    2009-01-03  c  4  5

    [3 rows x 3 columns]

    # These are python datetime objects
    In [49]: df.index
    Out[49]:
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2009-01-01, ..., 2009-01-03]
    Length: 3, Freq: None, Timezone: None

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:

    In [50]: print(open('tmp.csv').read())
    KORD,19990127, 19:00:00, 18:56:00, 0.8100
    KORD,19990127, 20:00:00, 19:56:00, 0.0100
    KORD,19990127, 21:00:00, 20:56:00, -0.5900
    KORD,19990127, 21:00:00, 21:18:00, -0.9900
    KORD,19990127, 22:00:00, 21:56:00, -0.5900
    KORD,19990127, 23:00:00, 22:56:00, -0.5900

    In [51]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]])

    In [52]: df
    Out[52]:
                      1_2                 1_3     0     4
    0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
    1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
    2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
    3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
    4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
    5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

    [6 rows x 4 columns]

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

    In [53]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]],
       ....:                  keep_date_col=True)
       ....:

    In [54]: df
    Out[54]:
                      1_2                 1_3     0         1          2  \
    0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  19990127   19:00:00
    1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  19990127   20:00:00
    2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD  19990127   21:00:00
    3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD  19990127   21:00:00
    4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD  19990127   22:00:00
    5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD  19990127   23:00:00

               3     4
    0   18:56:00  0.81
    1   19:56:00  0.01
    2   20:56:00 -0.59
    3   21:18:00 -0.99
    4   21:56:00 -0.59
    5   22:56:00 -0.59

    [6 rows x 7 columns]

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:

    In [55]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

    In [56]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec)

    In [57]: df
    Out[57]:
                  nominal              actual     0     4
    0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
    1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
    2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
    3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
    4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
    5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

    [6 rows x 4 columns]

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:

    In [58]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

    In [59]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
       ....:                  index_col=0) #index is the nominal column
       ....:

    In [60]: df
    Out[60]:
                                     actual     0     4
    nominal
    1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
    1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
    1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
    1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
    1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
    1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

    [6 rows x 3 columns]

Note

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

Note

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

### Date Parsing Functions

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

    In [61]: import pandas.io.date_converters as conv

    In [62]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
       ....:                  date_parser=conv.parse_date_time)
       ....:

    In [63]: df
    Out[63]:
                  nominal              actual     0     4
    0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
    1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
    2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
    3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
    4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
    5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

    [6 rows x 4 columns]

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.

### Inferring Datetime Format

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

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

- “20111230”
- “2011/12/30”
- “20111230 00:00:00”
- “12/30/2011 00:00:00”
- “30/Dec/2011 00:00:00”
- “30/December/2011 00:00:00”

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

    # Try to infer the format for the index column
    In [64]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
       ....:                  infer_datetime_format=True)
       ....:

    In [65]: df
    Out[65]:
                A  B  C
    date
    2009-01-01  a  1  2
    2009-01-02  b  3  4
    2009-01-03  c  4  5

    [3 rows x 3 columns]

### 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:

    In [66]: print(open('tmp.csv').read())
    date,value,cat
    1/6/2000,5,a
    2/6/2000,10,b
    3/6/2000,15,c

    In [67]: pd.read_csv('tmp.csv', parse_dates=[0])
    Out[67]:
            date  value cat
    0 2000-01-06      5   a
    1 2000-02-06     10   b
    2 2000-03-06     15   c

    [3 rows x 3 columns]

    In [68]: pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0])
    Out[68]:
            date  value cat
    0 2000-06-01      5   a
    1 2000-06-02     10   b
    2 2000-06-03     15   c

    [3 rows x 3 columns]

### Thousand Separators

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

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

    In [69]: print(open('tmp.csv').read())
    ID|level|category
    Patient1|123,000|x
    Patient2|23,000|y
    Patient3|1,234,018|z

    In [70]: df = pd.read_csv('tmp.csv', sep='|')

    In [71]: df
    Out[71]:
             ID      level category
    0  Patient1    123,000        x
    1  Patient2     23,000        y
    2  Patient3  1,234,018        z

    [3 rows x 3 columns]

    In [72]: df.level.dtype
    Out[72]: dtype('O')

The `thousands` keyword allows integers to be parsed correctly

    In [73]: print(open('tmp.csv').read())
    ID|level|category
    Patient1|123,000|x
    Patient2|23,000|y
    Patient3|1,234,018|z

    In [74]: df = pd.read_csv('tmp.csv', sep='|', thousands=',')

    In [75]: df
    Out[75]:
             ID    level category
    0  Patient1   123000        x
    1  Patient2    23000        y
    2  Patient3  1234018        z

    [3 rows x 3 columns]

    In [76]: df.level.dtype
    Out[76]: dtype('int64')

### NA Values

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

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

    read_csv(path, na_values=[5])

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

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

only an empty field will be `NaN`

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

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

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

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

### Infinity

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

### Comments

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

    In [77]: print(open('tmp.csv').read())
    ID,level,category
    Patient1,123000,x # really unpleasant
    Patient2,23000,y # wouldn't take his medicine
    Patient3,1234018,z # awesome

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

    In [78]: df = pd.read_csv('tmp.csv')

    In [79]: df
    Out[79]:
             ID    level                        category
    0  Patient1   123000           x # really unpleasant
    1  Patient2    23000  y # wouldn't take his medicine
    2  Patient3  1234018                     z # awesome

    [3 rows x 3 columns]

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

    In [80]: df = pd.read_csv('tmp.csv', comment='#')

    In [81]: df
    Out[81]:
             ID    level category
    0  Patient1   123000       x
    1  Patient2    23000       y
    2  Patient3  1234018       z

    [3 rows x 3 columns]

### Returning Series

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

    In [82]: print(open('tmp.csv').read())
    level
    Patient1,123000
    Patient2,23000
    Patient3,1234018

    In [83]: output =  pd.read_csv('tmp.csv', squeeze=True)

    In [84]: output
    Out[84]:
    Patient1     123000
    Patient2      23000
    Patient3    1234018
    Name: level, dtype: int64

    In [85]: type(output)
    Out[85]: pandas.core.series.Series

### 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:

    In [86]: data= 'a,b,c\n1,Yes,2\n3,No,4'

    In [87]: print(data)
    a,b,c
    1,Yes,2
    3,No,4

    In [88]: pd.read_csv(StringIO(data))
    Out[88]:
       a    b  c
    0  1  Yes  2
    1  3   No  4

    [2 rows x 3 columns]

    In [89]: pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])
    Out[89]:
       a      b  c
    0  1   True  2
    1  3  False  4

    [2 rows x 3 columns]

### 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:

    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:

    In [90]: data = 'a,b\n"hello, \\"Bob\\", nice to see you",5'

    In [91]: print(data)
    a,b
    "hello, \"Bob\", nice to see you",5

    In [92]: pd.read_csv(StringIO(data), escapechar='\\')
    Out[92]:
                                   a  b
    0  hello, "Bob", nice to see you  5

    [1 rows x 2 columns]

### Files with Fixed Width Columns

While `read_csv` reads delimited data, the `read_fwf()` function works with data files that have known and fixed column widths. The function parameters to `read_fwf` are largely the same as read_csv with two extra parameters:

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

Consider a typical fixed-width data file:

    In [93]: print(open('bar.csv').read())
    id8141    360.242940   149.910199   11950.7
    id1594    444.953632   166.985655   11788.4
    id1849    364.136849   183.628767   11806.2
    id1230    413.836124   184.375703   11916.8
    id1948    502.953953   173.237159   12468.3

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:

    #Column specifications are a list of half-intervals
    In [94]: colspecs = [(0, 6), (8, 20), (21, 33), (34, 43)]

    In [95]: df = pd.read_fwf('bar.csv', colspecs=colspecs, header=None, index_col=0)

    In [96]: df
    Out[96]:
                     1           2        3
    0
    id8141  360.242940  149.910199  11950.7
    id1594  444.953632  166.985655  11788.4
    id1849  364.136849  183.628767  11806.2
    id1230  413.836124  184.375703  11916.8
    id1948  502.953953  173.237159  12468.3

    [5 rows x 3 columns]

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:

    #Widths are a list of integers
    In [97]: widths = [6, 14, 13, 10]

    In [98]: df = pd.read_fwf('bar.csv', widths=widths, header=None)

    In [99]: df
    Out[99]:
            0           1           2        3
    0  id8141  360.242940  149.910199  11950.7
    1  id1594  444.953632  166.985655  11788.4
    2  id1849  364.136849  183.628767  11806.2
    3  id1230  413.836124  184.375703  11916.8
    4  id1948  502.953953  173.237159  12468.3

    [5 rows x 4 columns]

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.

New in version 0.13.0.

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

    In [100]: df = pd.read_fwf('bar.csv', header=None, index_col=0)

    In [101]: df
    Out[101]:
                     1           2        3
    0
    id8141  360.242940  149.910199  11950.7
    id1594  444.953632  166.985655  11788.4
    id1849  364.136849  183.628767  11806.2
    id1230  413.836124  184.375703  11916.8
    id1948  502.953953  173.237159  12468.3

    [5 rows x 3 columns]

### Files with an “implicit” index column

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

    In [102]: print(open('foo.csv').read())
    A,B,C
    20090101,a,1,2
    20090102,b,3,4
    20090103,c,4,5

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

    In [103]: pd.read_csv('foo.csv')
    Out[103]:
              A  B  C
    20090101  a  1  2
    20090102  b  3  4
    20090103  c  4  5

    [3 rows x 3 columns]

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

    In [104]: df = pd.read_csv('foo.csv', parse_dates=True)

    In [105]: df.index
    Out[105]:
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2009-01-01, ..., 2009-01-03]
    Length: 3, Freq: None, Timezone: None

### Reading an index with a `MultiIndex`

Suppose you have data indexed by two columns:

    In [106]: print(open('data/mindex_ex.csv').read())
    year,indiv,zit,xit
    1977,"A",1.2,.6
    1977,"B",1.5,.5
    1977,"C",1.7,.8
    1978,"A",.2,.06
    1978,"B",.7,.2
    1978,"C",.8,.3
    1978,"D",.9,.5
    1978,"E",1.4,.9
    1979,"C",.2,.15
    1979,"D",.14,.05
    1979,"E",.5,.15
    1979,"F",1.2,.5
    1979,"G",3.4,1.9
    1979,"H",5.4,2.7
    1979,"I",6.4,1.2

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

    In [107]: df = pd.read_csv("data/mindex_ex.csv", index_col=[0,1])

    In [108]: df
    Out[108]:
                 zit   xit
    year indiv
    1977 A      1.20  0.60
         B      1.50  0.50
         C      1.70  0.80
    1978 A      0.20  0.06
         B      0.70  0.20
         C      0.80  0.30
         D      0.90  0.50
         E      1.40  0.90
    1979 C      0.20  0.15
         D      0.14  0.05
         E      0.50  0.15
         F      1.20  0.50
         G      3.40  1.90
         H      5.40  2.70
         I      6.40  1.20

    [15 rows x 2 columns]

    In [109]: df.ix[1978]
    Out[109]:
           zit   xit
    indiv
    A      0.2  0.06
    B      0.7  0.20
    C      0.8  0.30
    D      0.9  0.50
    E      1.4  0.90

    [5 rows x 2 columns]

### Reading columns with a `MultiIndex`

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

    In [110]: from pandas.util.testing import makeCustomDataframe as mkdf

    In [111]: df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)

    In [112]: df.to_csv('mi.csv')

    In [113]: print(open('mi.csv').read())
    C0,,C_l0_g0,C_l0_g1,C_l0_g2
    C1,,C_l1_g0,C_l1_g1,C_l1_g2
    C2,,C_l2_g0,C_l2_g1,C_l2_g2
    C3,,C_l3_g0,C_l3_g1,C_l3_g2
    R0,R1,,,
    R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2
    R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2
    R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2
    R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2
    R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2

    In [114]: pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1])
    Out[114]:
    C0              C_l0_g0 C_l0_g1 C_l0_g2
    C1              C_l1_g0 C_l1_g1 C_l1_g2
    C2              C_l2_g0 C_l2_g1 C_l2_g2
    C3              C_l3_g0 C_l3_g1 C_l3_g2
    R0      R1
    R_l0_g0 R_l1_g0    R0C0    R0C1    R0C2
    R_l0_g1 R_l1_g1    R1C0    R1C1    R1C2
    R_l0_g2 R_l1_g2    R2C0    R2C1    R2C2
    R_l0_g3 R_l1_g3    R3C0    R3C1    R3C2
    R_l0_g4 R_l1_g4    R4C0    R4C1    R4C2

    [5 rows x 3 columns]

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

    In [115]: print(open('mi2.csv').read())
    ,a,a,a,b,c,c
    ,q,r,s,t,u,v
    one,1,2,3,4,5,6
    two,7,8,9,10,11,12

    In [116]: pd.read_csv('mi2.csv',header=[0,1],index_col=0)
    Out[116]:
         a         b   c
         q  r  s   t   u   v
    one  1  2  3   4   5   6
    two  7  8  9  10  11  12

    [2 rows x 6 columns]

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

### Automatically “sniffing” the delimiter

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

    In [117]: print(open('tmp2.sv').read())
    :0:1:2:3
    0:0.4691122999071863:-0.2828633443286633:-1.5090585031735124:-1.1356323710171934
    1:1.2121120250208506:-0.17321464905330858:0.11920871129693428:-1.0442359662799567
    2:-0.8618489633477999:-2.1045692188948086:-0.4949292740687813:1.071803807037338
    3:0.7215551622443669:-0.7067711336300845:-1.0395749851146963:0.27185988554282986
    4:-0.42497232978883753:0.567020349793672:0.27623201927771873:-1.0874006912859915
    5:-0.6736897080883706:0.1136484096888855:-1.4784265524372235:0.5249876671147047
    6:0.4047052186802365:0.5770459859204836:-1.7150020161146375:-1.0392684835147725
    7:-0.3706468582364464:-1.1578922506419993:-1.344311812731667:0.8448851414248841
    8:1.0757697837155533:-0.10904997528022223:1.6435630703622064:-1.4693879595399115
    9:0.35702056413309086:-0.6746001037299882:-1.776903716971867:-0.9689138124473498

    In [118]: pd.read_csv('tmp2.sv')
    Out[118]:
                                                :0:1:2:3
    0  0:0.4691122999071863:-0.2828633443286633:-1.50...
    1  1:1.2121120250208506:-0.17321464905330858:0.11...
    2  2:-0.8618489633477999:-2.1045692188948086:-0.4...
    3  3:0.7215551622443669:-0.7067711336300845:-1.03...
    4  4:-0.42497232978883753:0.567020349793672:0.276...
    5  5:-0.6736897080883706:0.1136484096888855:-1.47...
    6  6:0.4047052186802365:0.5770459859204836:-1.715...
    7  7:-0.3706468582364464:-1.1578922506419993:-1.3...
    8  8:1.0757697837155533:-0.10904997528022223:1.64...
    9  9:0.35702056413309086:-0.6746001037299882:-1.7...

    [10 rows x 1 columns]

### 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:

    In [119]: print(open('tmp.sv').read())
    |0|1|2|3
    0|0.4691122999071863|-0.2828633443286633|-1.5090585031735124|-1.1356323710171934
    1|1.2121120250208506|-0.17321464905330858|0.11920871129693428|-1.0442359662799567
    2|-0.8618489633477999|-2.1045692188948086|-0.4949292740687813|1.071803807037338
    3|0.7215551622443669|-0.7067711336300845|-1.0395749851146963|0.27185988554282986
    4|-0.42497232978883753|0.567020349793672|0.27623201927771873|-1.0874006912859915
    5|-0.6736897080883706|0.1136484096888855|-1.4784265524372235|0.5249876671147047
    6|0.4047052186802365|0.5770459859204836|-1.7150020161146375|-1.0392684835147725
    7|-0.3706468582364464|-1.1578922506419993|-1.344311812731667|0.8448851414248841
    8|1.0757697837155533|-0.10904997528022223|1.6435630703622064|-1.4693879595399115
    9|0.35702056413309086|-0.6746001037299882|-1.776903716971867|-0.9689138124473498

    In [120]: table = pd.read_table('tmp.sv', sep='|')

    In [121]: table
    Out[121]:
       Unnamed: 0         0         1         2         3
    0           0  0.469112 -0.282863 -1.509059 -1.135632
    1           1  1.212112 -0.173215  0.119209 -1.044236
    2           2 -0.861849 -2.104569 -0.494929  1.071804
    3           3  0.721555 -0.706771 -1.039575  0.271860
    4           4 -0.424972  0.567020  0.276232 -1.087401
    5           5 -0.673690  0.113648 -1.478427  0.524988
    6           6  0.404705  0.577046 -1.715002 -1.039268
    7           7 -0.370647 -1.157892 -1.344312  0.844885
    8           8  1.075770 -0.109050  1.643563 -1.469388
    9           9  0.357021 -0.674600 -1.776904 -0.968914

    [10 rows x 5 columns]

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

    In [122]: reader = pd.read_table('tmp.sv', sep='|', chunksize=4)

    In [123]: reader
    Out[123]: <pandas.io.parsers.TextFileReader at 0xb5de090>

    In [124]: for chunk in reader:
       .....:     print(chunk)
       .....:
       Unnamed: 0         0         1         2         3
    0           0  0.469112 -0.282863 -1.509059 -1.135632
    1           1  1.212112 -0.173215  0.119209 -1.044236
    2           2 -0.861849 -2.104569 -0.494929  1.071804
    3           3  0.721555 -0.706771 -1.039575  0.271860

    [4 rows x 5 columns]
       Unnamed: 0         0         1         2         3
    0           4 -0.424972  0.567020  0.276232 -1.087401
    1           5 -0.673690  0.113648 -1.478427  0.524988
    2           6  0.404705  0.577046 -1.715002 -1.039268
    3           7 -0.370647 -1.157892 -1.344312  0.844885

    [4 rows x 5 columns]
       Unnamed: 0         0        1         2         3
    0           8  1.075770 -0.10905  1.643563 -1.469388
    1           9  0.357021 -0.67460 -1.776904 -0.968914

    [2 rows x 5 columns]

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

    In [125]: reader = pd.read_table('tmp.sv', sep='|', iterator=True)

    In [126]: reader.get_chunk(5)
    Out[126]:
       Unnamed: 0         0         1         2         3
    0           0  0.469112 -0.282863 -1.509059 -1.135632
    1           1  1.212112 -0.173215  0.119209 -1.044236
    2           2 -0.861849 -2.104569 -0.494929  1.071804
    3           3  0.721555 -0.706771 -1.039575  0.271860
    4           4 -0.424972  0.567020  0.276232 -1.087401

    [5 rows x 5 columns]

### Writing to CSV format

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

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

### Writing a formatted string

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.

## JSON

Read and write `JSON` format files and strings.

### Writing JSON

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

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

- `orient` :

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

  The format of the JSON string

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

- `date_format` : string, type of date conversion, ‘epoch’ for timestamp, ‘iso’ for ISO8601.

- `double_precision` : The number of decimal places to use when encoding floating point values, default 10.

- `force_ascii` : force encoded string to be ASCII, default True.

- `date_unit` : The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’ or ‘ns’ for seconds, milliseconds, microseconds and nanoseconds respectively. Default ‘ms’.

- `default_handler` : The handler to call if an object cannot otherwise be converted to a suitable format for JSON. Takes a single argument, which is the object to convert, and returns a serialisable object.

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

    In [127]: dfj = DataFrame(randn(5, 2), columns=list('AB'))

    In [128]: json = dfj.to_json()

    In [129]: json
    Out[129]: '{"A":{"0":-1.2945235903,"1":0.2766617129,"2":-0.0139597524,"3":-0.0061535699,"4":0.8957173022},"B":{"0":0.4137381054,"1":-0.472034511,"2":-0.3625429925,"3":-0.923060654,"4":0.8052440254}}'

#### Orient Options

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

    In [130]: dfjo = DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
       .....:                  columns=list('ABC'), index=list('xyz'))
       .....:

    In [131]: dfjo
    Out[131]:
       A  B  C
    x  1  4  7
    y  2  5  8
    z  3  6  9

    [3 rows x 3 columns]

    In [132]: sjo = Series(dict(x=15, y=16, z=17), name='D')

    In [133]: sjo
    Out[133]:
    x    15
    y    16
    z    17
    Name: D, dtype: int64

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

    In [134]: dfjo.to_json(orient="columns")
    Out[134]: '{"A":{"x":1,"y":2,"z":3},"B":{"x":4,"y":5,"z":6},"C":{"x":7,"y":8,"z":9}}'

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

    In [135]: dfjo.to_json(orient="index")
    Out[135]: '{"x":{"A":1,"B":4,"C":7},"y":{"A":2,"B":5,"C":8},"z":{"A":3,"B":6,"C":9}}'

    In [136]: sjo.to_json(orient="index")
    Out[136]: '{"x":15,"y":16,"z":17}'

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

    In [137]: dfjo.to_json(orient="records")
    Out[137]: '[{"A":1,"B":4,"C":7},{"A":2,"B":5,"C":8},{"A":3,"B":6,"C":9}]'

    In [138]: sjo.to_json(orient="records")
    Out[138]: '[15,16,17]'

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

    In [139]: dfjo.to_json(orient="values")
    Out[139]: '[[1,4,7],[2,5,8],[3,6,9]]'

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

    In [140]: dfjo.to_json(orient="split")
    Out[140]: '{"columns":["A","B","C"],"index":["x","y","z"],"data":[[1,4,7],[2,5,8],[3,6,9]]}'

    In [141]: sjo.to_json(orient="split")
    Out[141]: '{"name":"D","index":["x","y","z"],"data":[15,16,17]}'

Note

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

#### Date Handling

Writing in iso date format

    In [142]: dfd = DataFrame(randn(5, 2), columns=list('AB'))

    In [143]: dfd['date'] = Timestamp('20130101')

    In [144]: dfd = dfd.sort_index(1, ascending=False)

    In [145]: json = dfd.to_json(date_format='iso')

    In [146]: json
    Out[146]: '{"date":{"0":"2013-01-01T00:00:00.000Z","1":"2013-01-01T00:00:00.000Z","2":"2013-01-01T00:00:00.000Z","3":"2013-01-01T00:00:00.000Z","4":"2013-01-01T00:00:00.000Z"},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Writing in iso date format, with microseconds

    In [147]: json = dfd.to_json(date_format='iso', date_unit='us')

    In [148]: json
    Out[148]: '{"date":{"0":"2013-01-01T00:00:00.000000Z","1":"2013-01-01T00:00:00.000000Z","2":"2013-01-01T00:00:00.000000Z","3":"2013-01-01T00:00:00.000000Z","4":"2013-01-01T00:00:00.000000Z"},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Epoch timestamps, in seconds

    In [149]: json = dfd.to_json(date_format='epoch', date_unit='s')

    In [150]: json
    Out[150]: '{"date":{"0":1356998400,"1":1356998400,"2":1356998400,"3":1356998400,"4":1356998400},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

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

    In [151]: dfj2 = dfj.copy()

    In [152]: dfj2['date'] = Timestamp('20130101')

    In [153]: dfj2['ints'] = list(range(5))

    In [154]: dfj2['bools'] = True

    In [155]: dfj2.index = date_range('20130101', periods=5)

    In [156]: dfj2.to_json('test.json')

    In [157]: open('test.json').read()
    Out[157]: '{"A":{"1356998400000":-1.2945235903,"1357084800000":0.2766617129,"1357171200000":-0.0139597524,"1357257600000":-0.0061535699,"1357344000000":0.8957173022},"B":{"1356998400000":0.4137381054,"1357084800000":-0.472034511,"1357171200000":-0.3625429925,"1357257600000":-0.923060654,"1357344000000":0.8052440254},"date":{"1356998400000":1356998400000,"1357084800000":1356998400000,"1357171200000":1356998400000,"1357257600000":1356998400000,"1357344000000":1356998400000},"ints":{"1356998400000":0,"1357084800000":1,"1357171200000":2,"1357257600000":3,"1357344000000":4},"bools":{"1356998400000":true,"1357084800000":true,"1357171200000":true,"1357257600000":true,"1357344000000":true}}'

#### Fallback Behavior

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

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

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

    In [141]: from datetime import timedelta

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

    In [143]: dftd.to_json()

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

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

    In [158]: dftd.to_json(default_handler=str)
    Out[158]: '{"0":{"0":"23 days, 0:00:00","1":"0:00:05","2":42}}'

    In [159]: def my_handler(obj):
       .....:    return obj.total_seconds()
       .....:

### Reading JSON

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

- `filepath_or_buffer` : a VALID JSON string or file handle / StringIO. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.json

- `typ` : type of object to recover (series or frame), default ‘frame’

- `orient` :

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

  The format of the JSON string

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

- `dtype` : if True, infer dtypes, if a dict of column to dtype, then use those, if False, then don’t infer dtypes at all, default is True, apply only to the data

- `convert_axes` : boolean, try to convert the axes to the proper dtypes, default is True

- `convert_dates` : a list of columns to parse for dates; If True, then try to parse datelike columns, default is True

- `keep_default_dates` : boolean, default True. If parsing dates, then parse the default datelike columns

- `numpy` : direct decoding to numpy arrays. default is False; Supports numeric data only, although labels may be non-numeric. Also note that the JSON ordering MUST be the same for each term if `numpy=True`

- `precise_float` : boolean, default `False`. Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (`False`) is to use fast but less precise builtin functionality

- `date_unit` : string, the timestamp unit to detect if converting dates. Default None. By default the timestamp precision will be detected, if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force timestamp precision to seconds, milliseconds, microseconds or nanoseconds respectively.

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

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

#### Data Conversion

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

Note

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

Warning

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

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

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

Reading from a JSON string:

    In [160]: pd.read_json(json)
    Out[160]:
              A         B       date
    0 -1.206412  2.565646 2013-01-01
    1  1.431256  1.340309 2013-01-01
    2 -1.170299 -0.226169 2013-01-01
    3  0.410835  0.813850 2013-01-01
    4  0.132003 -0.827317 2013-01-01

    [5 rows x 3 columns]

Reading from a file:

    In [161]: pd.read_json('test.json')
    Out[161]:
                       A         B bools       date  ints
    2013-01-01 -1.294524  0.413738  True 2013-01-01     0
    2013-01-02  0.276662 -0.472035  True 2013-01-01     1
    2013-01-03 -0.013960 -0.362543  True 2013-01-01     2
    2013-01-04 -0.006154 -0.923061  True 2013-01-01     3
    2013-01-05  0.895717  0.805244  True 2013-01-01     4

    [5 rows x 5 columns]

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

    In [162]: pd.read_json('test.json', dtype=object).dtypes
    Out[162]:
    A        object
    B        object
    bools    object
    date     object
    ints     object
    dtype: object

Specify dtypes for conversion:

    In [163]: pd.read_json('test.json', dtype={'A' : 'float32', 'bools' : 'int8'}).dtypes
    Out[163]:
    A               float32
    B               float64
    bools              int8
    date     datetime64[ns]
    ints              int64
    dtype: object

Preserve string indicies:

    In [164]: si = DataFrame(np.zeros((4, 4)),
       .....:          columns=list(range(4)),
       .....:          index=[str(i) for i in range(4)])
       .....:

    In [165]: si
    Out[165]:
       0  1  2  3
    0  0  0  0  0
    1  0  0  0  0
    2  0  0  0  0
    3  0  0  0  0

    [4 rows x 4 columns]

    In [166]: si.index
    Out[166]: Index([u'0', u'1', u'2', u'3'], dtype='object')

    In [167]: si.columns
    Out[167]: Int64Index([0, 1, 2, 3], dtype='int64')

    In [168]: json = si.to_json()

    In [169]: sij = pd.read_json(json, convert_axes=False)

    In [170]: sij
    Out[170]:
       0  1  2  3
    0  0  0  0  0
    1  0  0  0  0
    2  0  0  0  0
    3  0  0  0  0

    [4 rows x 4 columns]

    In [171]: sij.index
    Out[171]: Index([u'0', u'1', u'2', u'3'], dtype='object')

    In [172]: sij.columns
    Out[172]: Index([u'0', u'1', u'2', u'3'], dtype='object')

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

    In [173]: json = dfj2.to_json(date_unit='ns')

    # Try to parse timestamps as millseconds -> Won't Work
    In [174]: dfju = pd.read_json(json, date_unit='ms')

    In [175]: dfju
    Out[175]:
                                A         B bools                 date  ints
    1356998400000000000 -1.294524  0.413738  True  1356998400000000000     0
    1357084800000000000  0.276662 -0.472035  True  1356998400000000000     1
    1357171200000000000 -0.013960 -0.362543  True  1356998400000000000     2
    1357257600000000000 -0.006154 -0.923061  True  1356998400000000000     3
    1357344000000000000  0.895717  0.805244  True  1356998400000000000     4

    [5 rows x 5 columns]

    # Let Pandas detect the correct precision
    In [176]: dfju = pd.read_json(json)

    In [177]: dfju
    Out[177]:
                       A         B bools       date  ints
    2013-01-01 -1.294524  0.413738  True 2013-01-01     0
    2013-01-02  0.276662 -0.472035  True 2013-01-01     1
    2013-01-03 -0.013960 -0.362543  True 2013-01-01     2
    2013-01-04 -0.006154 -0.923061  True 2013-01-01     3
    2013-01-05  0.895717  0.805244  True 2013-01-01     4

    [5 rows x 5 columns]

    # Or specify that all timestamps are in nanoseconds
    In [178]: dfju = pd.read_json(json, date_unit='ns')

    In [179]: dfju
    Out[179]:
                       A         B bools       date  ints
    2013-01-01 -1.294524  0.413738  True 2013-01-01     0
    2013-01-02  0.276662 -0.472035  True 2013-01-01     1
    2013-01-03 -0.013960 -0.362543  True 2013-01-01     2
    2013-01-04 -0.006154 -0.923061  True 2013-01-01     3
    2013-01-05  0.895717  0.805244  True 2013-01-01     4

    [5 rows x 5 columns]

#### The Numpy Parameter

Note

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

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

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

    In [180]: randfloats = np.random.uniform(-100, 1000, 10000)

    In [181]: randfloats.shape = (1000, 10)

    In [182]: dffloats = DataFrame(randfloats, columns=list('ABCDEFGHIJ'))

    In [183]: jsonfloats = dffloats.to_json()

    In [184]: timeit read_json(jsonfloats)
    100 loops, best of 3: 12.1 ms per loop

    In [185]: timeit read_json(jsonfloats, numpy=True)
    100 loops, best of 3: 6 ms per loop

The speedup is less noticable for smaller datasets:

    In [186]: jsonfloats = dffloats.head(100).to_json()

    In [187]: timeit read_json(jsonfloats)
    100 loops, best of 3: 3.96 ms per loop

    In [188]: timeit read_json(jsonfloats, numpy=True)
    100 loops, best of 3: 3.22 ms per loop

Warning

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

> - data is numeric.
> - data is uniform. The dtype is sniffed from the first value decoded. A `ValueError` may be raised, or incorrect output may be produced if this condition is not satisfied.
> - labels are ordered. Labels are only read from the first container, it is assumed that each subsequent row / column has been encoded in the same order. This should be satisfied if the data was encoded using `to_json` but may not be the case if the JSON is from another source.

### Normalization

New in version 0.13.0.

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

    In [189]: from pandas.io.json import json_normalize

    In [190]: data = [{'state': 'Florida',
       .....:           'shortname': 'FL',
       .....:           'info': {
       .....:                'governor': 'Rick Scott'
       .....:           },
       .....:           'counties': [{'name': 'Dade', 'population': 12345},
       .....:                       {'name': 'Broward', 'population': 40000},
       .....:                       {'name': 'Palm Beach', 'population': 60000}]},
       .....:          {'state': 'Ohio',
       .....:           'shortname': 'OH',
       .....:           'info': {
       .....:                'governor': 'John Kasich'
       .....:           },
       .....:           'counties': [{'name': 'Summit', 'population': 1234},
       .....:                        {'name': 'Cuyahoga', 'population': 1337}]}]
       .....:

    In [191]: json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']])
    Out[191]:
             name  population info.governor    state shortname
    0        Dade       12345    Rick Scott  Florida        FL
    1     Broward       40000    Rick Scott  Florida        FL
    2  Palm Beach       60000    Rick Scott  Florida        FL
    3      Summit        1234   John Kasich     Ohio        OH
    4    Cuyahoga        1337   John Kasich     Ohio        OH

    [5 rows x 5 columns]

## HTML

### Reading HTML Content

Warning

We highly encourage you to read the HTML parsing gotchas regarding the issues surrounding the BeautifulSoup4/html5lib/lxml parsers.

New in version 0.12.0.

The top-level `read_html()` function can accept an HTML string/file/url and will parse HTML tables into list of pandas DataFrames. Let’s look at a few examples.

Note

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

Read a URL with no options

    In [192]: url = 'http://www.fdic.gov/bank/individual/failed/banklist.html'

    In [193]: dfs = read_html(url)

    In [194]: dfs
    Out[194]:
    [                                            Bank Name             City  ST  \
     0                                        Syringa Bank            Boise  ID
     1                                   The Bank of Union          El Reno  OK
     2                                DuPage National Bank     West Chicago  IL
     3          Texas Community Bank, National Association    The Woodlands  TX
     4                              Bank of Jackson County       Graceville  FL
     5   First National Bank also operating as The  Nat...         Edinburg  TX
     6                                The Community's Bank       Bridgeport  CT
     7                             Sunrise Bank of Arizona          Phoenix  AZ
     8                                Community South Bank          Parsons  TN
     9                                      Bank of Wausau           Wausau  WI
     10  First Community Bank of Southwest Florida (als...       Fort Myers  FL
     11                             Mountain National Bank      Sevierville  TN
     12                                  1st Commerce Bank  North Las Vegas  NV
     13           Banks of Wisconsin d/b/a Bank of Kenosha          Kenosha  WI
     14                               Central Arizona Bank       Scottsdale  AZ
                                                       ...              ... ...

          CERT                       Acquiring Institution Closing Date  \
     0   34296                                Sunwest Bank   2014-01-31
     1   17967                                   BancFirst   2014-01-24
     2    5732                    Republic Bank of Chicago   2014-01-17
     3   57431                   Spirit of Texas Bank, SSB   2013-12-13
     4   14794               First Federal Bank of Florida   2013-10-30
     5   14318                          PlainsCapital Bank   2013-09-13
     6   57041                                 No Acquirer   2013-09-13
     7   34707   First Fidelity Bank, National Association   2013-08-23
     8   19849                             CB&S Bank, Inc.   2013-08-23
     9   35016                       Nicolet National Bank   2013-08-09
     10  34943                                     C1 Bank   2013-08-02
     11  34789  First Tennessee Bank, National Association   2013-06-07
     12  58358                                  Plaza Bank   2013-06-06
     13  35386                       North Shore Bank, FSB   2013-05-31
     14  34527                          Western State Bank   2013-05-14
           ...                                         ...          ...

        Updated Date Loss Share Type
     0    2014-01-31             NaN
     1    2014-01-28             NaN
     2    2014-01-27             NaN
     3    2014-01-13             NaN
     4    2013-12-09            none
     5    2013-11-01         SFR/NSF
     6    2013-12-20            none
     7    2013-11-01            none
     8    2013-11-01            none
     9    2013-10-24            none
     10   2013-10-24            none
     11   2013-07-12            none
     12   2013-07-12             NSF
     13   2013-10-29            none
     14   2013-07-12            none
                 ...             ...

     [519 rows x 8 columns]]

Note

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

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

    In [195]: with open(file_path, 'r') as f:
       .....:     dfs = read_html(f.read())
       .....:

    In [196]: dfs
    Out[196]:
    [                                   Bank Name              City  ST   CERT  \
     0   Banks of Wisconsin d/b/a Bank of Kenosha           Kenosha  WI  35386
     1                       Central Arizona Bank        Scottsdale  AZ  34527
     2                               Sunrise Bank          Valdosta  GA  58185
     3                      Pisgah Community Bank         Asheville  NC  58701
     4                        Douglas County Bank      Douglasville  GA  21649
     5                               Parkway Bank            Lenoir  NC  57158
     6                     Chipola Community Bank          Marianna  FL  58034
     7             Heritage Bank of North Florida       Orange Park  FL  26680
     8                         First Federal Bank         Lexington  KY  29594
     9                           Gold Canyon Bank       Gold Canyon  AZ  58066
     10                             Frontier Bank          LaGrange  GA  16431
     11                             Covenant Bank           Chicago  IL  22476
     12                          1st Regents Bank           Andover  MN  57157
     13                   Westside Community Bank  University Place  WA  33997
     14              Community Bank of the Ozarks     Sunrise Beach  MO  27331
                                              ...               ... ...    ...

                               Acquiring Institution Closing Date Updated Date
     0                         North Shore Bank, FSB   2013-05-31   2013-05-31
     1                            Western State Bank   2013-05-14   2013-05-20
     2                                  Synovus Bank   2013-05-10   2013-05-21
     3                            Capital Bank, N.A.   2013-05-10   2013-05-14
     4                           Hamilton State Bank   2013-04-26   2013-05-16
     5              CertusBank, National Association   2013-04-26   2013-05-17
     6                 First Federal Bank of Florida   2013-04-19   2013-05-16
     7                            FirstAtlantic Bank   2013-04-19   2013-05-16
     8                           Your Community Bank   2013-04-19   2013-04-23
     9   First Scottsdale Bank, National Association   2013-04-05   2013-04-09
     10                    HeritageBank of the South   2013-03-08   2013-03-26
     11               Liberty Bank and Trust Company   2013-02-15   2013-03-04
     12                         First Minnesota Bank   2013-01-18   2013-02-28
     13                                 Sunwest Bank   2013-01-11   2013-01-24
     14                             Bank of Sullivan   2012-12-14   2013-01-24
                                                 ...          ...          ...

     [506 rows x 7 columns]]

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

    In [197]: from cStringIO import StringIO

    In [198]: with open(file_path, 'r') as f:
       .....:     sio = StringIO(f.read())
       .....:

    In [199]: dfs = read_html(sio)

    In [200]: dfs
    Out[200]:
    [                                   Bank Name              City  ST   CERT  \
     0   Banks of Wisconsin d/b/a Bank of Kenosha           Kenosha  WI  35386
     1                       Central Arizona Bank        Scottsdale  AZ  34527
     2                               Sunrise Bank          Valdosta  GA  58185
     3                      Pisgah Community Bank         Asheville  NC  58701
     4                        Douglas County Bank      Douglasville  GA  21649
     5                               Parkway Bank            Lenoir  NC  57158
     6                     Chipola Community Bank          Marianna  FL  58034
     7             Heritage Bank of North Florida       Orange Park  FL  26680
     8                         First Federal Bank         Lexington  KY  29594
     9                           Gold Canyon Bank       Gold Canyon  AZ  58066
     10                             Frontier Bank          LaGrange  GA  16431
     11                             Covenant Bank           Chicago  IL  22476
     12                          1st Regents Bank           Andover  MN  57157
     13                   Westside Community Bank  University Place  WA  33997
     14              Community Bank of the Ozarks     Sunrise Beach  MO  27331
                                              ...               ... ...    ...

                               Acquiring Institution Closing Date Updated Date
     0                         North Shore Bank, FSB   2013-05-31   2013-05-31
     1                            Western State Bank   2013-05-14   2013-05-20
     2                                  Synovus Bank   2013-05-10   2013-05-21
     3                            Capital Bank, N.A.   2013-05-10   2013-05-14
     4                           Hamilton State Bank   2013-04-26   2013-05-16
     5              CertusBank, National Association   2013-04-26   2013-05-17
     6                 First Federal Bank of Florida   2013-04-19   2013-05-16
     7                            FirstAtlantic Bank   2013-04-19   2013-05-16
     8                           Your Community Bank   2013-04-19   2013-04-23
     9   First Scottsdale Bank, National Association   2013-04-05   2013-04-09
     10                    HeritageBank of the South   2013-03-08   2013-03-26
     11               Liberty Bank and Trust Company   2013-02-15   2013-03-04
     12                         First Minnesota Bank   2013-01-18   2013-02-28
     13                                 Sunwest Bank   2013-01-11   2013-01-24
     14                             Bank of Sullivan   2012-12-14   2013-01-24
                                                 ...          ...          ...

     [506 rows x 7 columns]]

Note

The following examples are not run by the IPython evaluator due to the fact that having so many network-accessing functions slows down the documentation build. If you spot an error or an example that doesn’t run, please do not hesitate to report it over on pandas GitHub issues page.

Read a URL and match a table that contains specific text

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

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

    dfs = read_html(url, header=0)

Specify an index column

    dfs = read_html(url, index_col=0)

Specify a number of rows to skip

    dfs = read_html(url, skiprows=0)

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

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

Don’t infer numeric and date types

    dfs = read_html(url, infer_types=False)

Specify an HTML attribute

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

Use some combination of the above

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

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

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

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

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

or

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

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

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

### Writing to HTML files

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

Note

Not all of the possible options for `DataFrame.to_html` are shown here for brevity’s sake. See `to_html()` for the full set of options.

    In [201]: df = DataFrame(randn(2, 2))

    In [202]: df
    Out[202]:
              0         1
    0 -0.184744  0.496971
    1 -0.856240  1.857977

    [2 rows x 2 columns]

    In [203]: print(df.to_html())  # raw html
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>0</th>
          <th>1</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>-0.184744</td>
          <td> 0.496971</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-0.856240</td>
          <td> 1.857977</td>
        </tr>
      </tbody>
    </table>

HTML:

| 0 | 1
--- | --- | ---
0 | -0.184744 | 0.496971
1 | -0.856240 | 1.857977

The `columns` argument will limit the columns shown

    In [204]: print(df.to_html(columns=[0]))
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>-0.184744</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-0.856240</td>
        </tr>
      </tbody>
    </table>

HTML:

| 0
--- | ---
0 | -0.184744
1 | -0.856240

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

    In [205]: print(df.to_html(float_format='{0:.10f}'.format))
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>0</th>
          <th>1</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>-0.1847438576</td>
          <td>0.4969711327</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-0.8562396763</td>
          <td>1.8579766508</td>
        </tr>
      </tbody>
    </table>

HTML:

| 0 | 1
--- | --- | ---
0 | -0.1847438576 | 0.4969711327
1 | -0.8562396763 | 1.8579766508

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

    In [206]: print(df.to_html(bold_rows=False))
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>0</th>
          <th>1</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td>0</td>
          <td>-0.184744</td>
          <td> 0.496971</td>
        </tr>
        <tr>
          <td>1</td>
          <td>-0.856240</td>
          <td> 1.857977</td>
        </tr>
      </tbody>
    </table>

| 0 | 1
--- | --- | ---
0 | -0.184744 | 0.496971
1 | -0.856240 | 1.857977

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

    In [207]: print(df.to_html(classes=['awesome_table_class', 'even_more_awesome_class']))
    <table border="1" class="dataframe awesome_table_class even_more_awesome_class">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>0</th>
          <th>1</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>-0.184744</td>
          <td> 0.496971</td>
        </tr>
        <tr>
          <th>1</th>
          <td>-0.856240</td>
          <td> 1.857977</td>
        </tr>
      </tbody>
    </table>

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

    In [208]: df = DataFrame({'a': list('&<>'), 'b': randn(3)})

Escaped:

    In [209]: print(df.to_html())
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>a</th>
          <th>b</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td> &amp;</td>
          <td>-0.474063</td>
        </tr>
        <tr>
          <th>1</th>
          <td> &lt;</td>
          <td>-0.230305</td>
        </tr>
        <tr>
          <th>2</th>
          <td> &gt;</td>
          <td>-0.400654</td>
        </tr>
      </tbody>
    </table>

| a | b
--- | --- | ---
0 | & | -0.474063
1 | < | -0.230305
2 | > | -0.400654

Not escaped:

    In [210]: print(df.to_html(escape=False))
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>a</th>
          <th>b</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td> &</td>
          <td>-0.474063</td>
        </tr>
        <tr>
          <th>1</th>
          <td> <</td>
          <td>-0.230305</td>
        </tr>
        <tr>
          <th>2</th>
          <td> ></td>
          <td>-0.400654</td>
        </tr>
      </tbody>
    </table>

| a | b
--- | --- | ---
0 | & | -0.474063
1 | < | -0.230305
2 | > | -0.400654

Note

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

## Excel files

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

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

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

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

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

Note

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

New in version 0.13.

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

Using the sheet name:

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

Using the sheet index:

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

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

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

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

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

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

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

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

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

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

In order to write separate DataFrames to separate sheets in a single Excel file, one can pass an `ExcelWriter`.

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

Note

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

### Excel writer engines

New in version 0.13.

`pandas` chooses an Excel writer via two methods:

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

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

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

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

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

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

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

## Clipboard

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

      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()

    In [211]: clipdf
    Out[211]:
       A  B  C
    x  1  4  p
    y  2  5  q
    z  3  6  r

    [3 rows x 3 columns]

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

    In [212]: df=pd.DataFrame(randn(5,3))

    In [213]: df
    Out[213]:
              0         1         2
    0 -0.288267 -0.084905  0.004772
    1  1.382989  0.343635 -1.253994
    2 -0.124925  0.212244  0.496654
    3  0.525417  1.238640 -1.210543
    4 -1.175743 -0.172372 -0.734129

    [5 rows x 3 columns]

    In [214]: df.to_clipboard()

    In [215]: pd.read_clipboard()
    Out[215]:
              0         1         2
    0 -0.288267 -0.084905  0.004772
    1  1.382989  0.343635 -1.253994
    2 -0.124925  0.212244  0.496654
    3  0.525417  1.238640 -1.210543
    4 -1.175743 -0.172372 -0.734129

    [5 rows x 3 columns]

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

Note

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

## Pickling

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

    In [216]: df
    Out[216]:
              0         1         2
    0 -0.288267 -0.084905  0.004772
    1  1.382989  0.343635 -1.253994
    2 -0.124925  0.212244  0.496654
    3  0.525417  1.238640 -1.210543
    4 -1.175743 -0.172372 -0.734129

    [5 rows x 3 columns]

    In [217]: df.to_pickle('foo.pkl')

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

    In [218]: read_pickle('foo.pkl')
    Out[218]:
              0         1         2
    0 -0.288267 -0.084905  0.004772
    1  1.382989  0.343635 -1.253994
    2 -0.124925  0.212244  0.496654
    3  0.525417  1.238640 -1.210543
    4 -1.175743 -0.172372 -0.734129

    [5 rows x 3 columns]

Warning

Loading pickled data received from untrusted sources can be unsafe.

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

Warning

In 0.13, pickle preserves compatibility with pickles created prior to 0.13. These must be read with `pd.read_pickle`, rather than the default python `pickle.load`. See this question for a detailed explanation.

Note

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

## msgpack (experimental)

New in version 0.13.0.

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

Warning

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

    In [219]: df = DataFrame(np.random.rand(5,2),columns=list('AB'))

    In [220]: df.to_msgpack('foo.msg')

    In [221]: pd.read_msgpack('foo.msg')
    Out[221]:
              A         B
    0  0.154336  0.710999
    1  0.398096  0.765220
    2  0.586749  0.293052
    3  0.290293  0.710783
    4  0.988593  0.062106

    [5 rows x 2 columns]

    In [222]: s = Series(np.random.rand(5),index=date_range('20130101',periods=5))

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

    In [223]: pd.to_msgpack('foo.msg', df, 'foo', np.array([1,2,3]), s)

    In [224]: pd.read_msgpack('foo.msg')
    Out[224]:
    [          A         B
     0  0.154336  0.710999
     1  0.398096  0.765220
     2  0.586749  0.293052
     3  0.290293  0.710783
     4  0.988593  0.062106

     [5 rows x 2 columns], u'foo', array([1, 2, 3]), 2013-01-01    0.690810
     2013-01-02    0.235907
     2013-01-03    0.712756
     2013-01-04    0.119599
     2013-01-05    0.023493
     Freq: D, dtype: float64]

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

    In [225]: for o in pd.read_msgpack('foo.msg',iterator=True):
       .....:     print o
       .....:
              A         B
    0  0.154336  0.710999
    1  0.398096  0.765220
    2  0.586749  0.293052
    3  0.290293  0.710783
    4  0.988593  0.062106

    [5 rows x 2 columns]
    foo
    [1 2 3]
    2013-01-01    0.690810
    2013-01-02    0.235907
    2013-01-03    0.712756
    2013-01-04    0.119599
    2013-01-05    0.023493
    Freq: D, dtype: float64

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

    In [226]: df.to_msgpack('foo.msg',append=True)

    In [227]: pd.read_msgpack('foo.msg')
    Out[227]:
    [          A         B
     0  0.154336  0.710999
     1  0.398096  0.765220
     2  0.586749  0.293052
     3  0.290293  0.710783
     4  0.988593  0.062106

     [5 rows x 2 columns], u'foo', array([1, 2, 3]), 2013-01-01    0.690810
     2013-01-02    0.235907
     2013-01-03    0.712756
     2013-01-04    0.119599
     2013-01-05    0.023493
     Freq: D, dtype: float64,           A         B
     0  0.154336  0.710999
     1  0.398096  0.765220
     2  0.586749  0.293052
     3  0.290293  0.710783
     4  0.988593  0.062106

     [5 rows x 2 columns]]

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

    In [228]: pd.to_msgpack('foo2.msg', { 'dict' : [ { 'df' : df }, { 'string' : 'foo' }, { 'scalar' : 1. }, { 's' : s } ] })

    In [229]: pd.read_msgpack('foo2.msg')
    Out[229]:
    {u'dict': ({u'df':           A         B
       0  0.154336  0.710999
       1  0.398096  0.765220
       2  0.586749  0.293052
       3  0.290293  0.710783
       4  0.988593  0.062106

       [5 rows x 2 columns]},
      {u'string': u'foo'},
      {u'scalar': 1.0},
      {u's': 2013-01-01    0.690810
       2013-01-02    0.235907
       2013-01-03    0.712756
       2013-01-04    0.119599
       2013-01-05    0.023493
       Freq: D, dtype: float64})}

### Read/Write API

Msgpacks can also be read from and written to strings.

    In [230]: df.to_msgpack()
    Out[230]: '\x84\xa6blocks\x91\x86\xa5items\x85\xa5dtype\x11\xa3typ\xa5index\xa5klass\xa5Index\xa4data\x92\xa1A\xa1B\xa4name\xc0\xa8compress\xc0\xa5shape\x92\x02\x05\xa6values\xda\x00P\xa0\xab\xfb6H\xc1\xc3?\x98(oMgz\xd9?\x17\xaed\\\xa5\xc6\xe2?\xdc\xd0\x1bd(\x94\xd2?\xb5\xe8\xf5\x0e\x8d\xa2\xef?\x02D\xebO\x80\xc0\xe6?\x16\xbddQ\xae|\xe8?\x10?Ya[\xc1\xd2?\xa8\xfd\xcf\xa0\xbc\xbe\xe6? Z\xe1\ti\xcc\xaf?\xa5klass\xaaFloatBlock\xa5dtype\x0c\xa4axes\x92\x85\xa5dtype\x11\xa3typ\xa5index\xa5klass\xa5Index\xa4data\x92\xa1A\xa1B\xa4name\xc0\x85\xa5dtype\x07\xa3typ\xa5index\xa5klass\xaaInt64Index\xa4data\xda\x00(\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\xa4name\xc0\xa3typ\xadblock_manager\xa5klass\xa9DataFrame'

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

    In [231]: pd.read_msgpack(df.to_msgpack() + s.to_msgpack())
    Out[231]:
    [          A         B
     0  0.154336  0.710999
     1  0.398096  0.765220
     2  0.586749  0.293052
     3  0.290293  0.710783
     4  0.988593  0.062106

     [5 rows x 2 columns], 2013-01-01    0.690810
     2013-01-02    0.235907
     2013-01-03    0.712756
     2013-01-04    0.119599
     2013-01-05    0.023493
     Freq: D, dtype: float64]

## 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 cookbook for some advanced strategies

Note

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

    In [232]: store = HDFStore('store.h5')

    In [233]: print(store)
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    Empty

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

    In [234]: np.random.seed(1234)

    In [235]: index = date_range('1/1/2000', periods=8)

    In [236]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])

    In [237]: df = DataFrame(randn(8, 3), index=index,
       .....:                columns=['A', 'B', 'C'])
       .....:

    In [238]: 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
    In [239]: store['s'] = s

    In [240]: store['df'] = df

    In [241]: store['wp'] = wp

    # the type of stored data
    In [242]: store.root.wp._v_attrs.pandas_type
    Out[242]: 'wide'

    In [243]: store
    Out[243]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df            frame        (shape->[8,3])
    /s             series       (shape->[5])
    /wp            wide         (shape->[2,5,4])

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

    # store.get('df') is an equivalent method
    In [244]: store['df']
    Out[244]:
                       A         B         C
    2000-01-01  0.887163  0.859588 -0.636524
    2000-01-02  0.015696 -2.242685  1.150036
    2000-01-03  0.991946  0.953324 -2.021255
    2000-01-04 -0.334077  0.002118  0.405453
    2000-01-05  0.289092  1.321158 -1.546906
    2000-01-06 -0.202646 -0.655969  0.193421
    2000-01-07  0.553439  1.318152 -0.469305
    2000-01-08  0.675554 -1.817027 -0.183109

    [8 rows x 3 columns]

    # dotted (attribute) access provides get as well
    In [245]: store.df
    Out[245]:
                       A         B         C
    2000-01-01  0.887163  0.859588 -0.636524
    2000-01-02  0.015696 -2.242685  1.150036
    2000-01-03  0.991946  0.953324 -2.021255
    2000-01-04 -0.334077  0.002118  0.405453
    2000-01-05  0.289092  1.321158 -1.546906
    2000-01-06 -0.202646 -0.655969  0.193421
    2000-01-07  0.553439  1.318152 -0.469305
    2000-01-08  0.675554 -1.817027 -0.183109

    [8 rows x 3 columns]

Deletion of the object specified by the key

    # store.remove('wp') is an equivalent method
    In [246]: del store['wp']

    In [247]: store
    Out[247]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df            frame        (shape->[8,3])
    /s             series       (shape->[5])

Closing a Store, Context Manager

    In [248]: store.close()

    In [249]: store
    Out[249]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    File is CLOSED

    In [250]: store.is_open
    Out[250]: False

    # Working with, and automatically closing the store with the context
    # manager
    In [251]: with get_store('store.h5') as store:
       .....:     store.keys()
       .....:

### 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)

    In [252]: df_tl = DataFrame(dict(A=list(range(5)), B=list(range(5))))

    In [253]: df_tl.to_hdf('store_tl.h5','table',append=True)

    In [254]: read_hdf('store_tl.h5', 'table', where = ['index>2'])
    Out[254]:
       A  B
    3  3  3
    4  4  4

    [2 rows x 2 columns]

### Fixed Format

Note

This was prior to 0.13.0 the `Storer` format.

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

Warning

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

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

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

### Table Format

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

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

    In [255]: store = HDFStore('store.h5')

    In [256]: df1 = df[0:4]

    In [257]: df2 = df[4:]

    # append data (creates a table automatically)
    In [258]: store.append('df', df1)

    In [259]: store.append('df', df2)

    In [260]: store
    Out[260]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])

    # select the entire object
    In [261]: store.select('df')
    Out[261]:
                       A         B         C
    2000-01-01  0.887163  0.859588 -0.636524
    2000-01-02  0.015696 -2.242685  1.150036
    2000-01-03  0.991946  0.953324 -2.021255
    2000-01-04 -0.334077  0.002118  0.405453
    2000-01-05  0.289092  1.321158 -1.546906
    2000-01-06 -0.202646 -0.655969  0.193421
    2000-01-07  0.553439  1.318152 -0.469305
    2000-01-08  0.675554 -1.817027 -0.183109

    [8 rows x 3 columns]

    # the type of stored data
    In [262]: store.root.df._v_attrs.pandas_type
    Out[262]: 'frame_table'

Note

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

### 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.

    In [263]: store.put('foo/bar/bah', df)

    In [264]: store.append('food/orange', df)

    In [265]: store.append('food/apple',  df)

    In [266]: store
    Out[266]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df                     frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /food/apple             frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /food/orange            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /foo/bar/bah            frame        (shape->[8,3])

    # a list of keys are returned
    In [267]: store.keys()
    Out[267]: ['/df', '/food/apple', '/food/orange', '/foo/bar/bah']

    # remove all nodes under this level
    In [268]: store.remove('food')

    In [269]: store
    Out[269]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df                     frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /foo/bar/bah            frame        (shape->[8,3])

### 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.

    In [270]: df_mixed = DataFrame({ 'A' : randn(8),
       .....:                        'B' : randn(8),
       .....:                        'C' : np.array(randn(8),dtype='float32'),
       .....:                        'string' :'string',
       .....:                        'int' : 1,
       .....:                        'bool' : True,
       .....:                        'datetime64' : Timestamp('20010102')},
       .....:                      index=list(range(8)))
       .....:

    In [271]: df_mixed.ix[3:5,['A', 'B', 'string', 'datetime64']] = np.nan

    In [272]: store.append('df_mixed', df_mixed, min_itemsize = {'values': 50})

    In [273]: df_mixed1 = store.select('df_mixed')

    In [274]: df_mixed1
    Out[274]:
              A         B         C  bool datetime64  int  string
    0  0.704721 -1.152659 -0.430096  True 2001-01-02    1  string
    1 -0.785435  0.631979  0.767369  True 2001-01-02    1  string
    2  0.462060  0.039513  0.984920  True 2001-01-02    1  string
    3       NaN       NaN  0.270836  True        NaT    1     NaN
    4       NaN       NaN  1.391986  True        NaT    1     NaN
    5       NaN       NaN  0.079842  True        NaT    1     NaN
    6  2.007843  0.152631 -0.399965  True 2001-01-02    1  string
    7  0.226963  0.164530 -1.027851  True 2001-01-02    1  string

    [8 rows x 7 columns]

    In [275]: df_mixed1.get_dtype_counts()
    Out[275]:
    bool              1
    datetime64[ns]    1
    float32           1
    float64           2
    int64             1
    object            1
    dtype: int64

    # we have provided a minimum string column size
    In [276]: store.root.df_mixed.table
    Out[276]:
    /df_mixed/table (Table(8,)) ''
      description := {
      "index": Int64Col(shape=(), dflt=0, pos=0),
      "values_block_0": Float64Col(shape=(2,), dflt=0.0, pos=1),
      "values_block_1": Float32Col(shape=(1,), dflt=0.0, pos=2),
      "values_block_2": Int64Col(shape=(1,), dflt=0, pos=3),
      "values_block_3": Int64Col(shape=(1,), dflt=0, pos=4),
      "values_block_4": BoolCol(shape=(1,), dflt=False, pos=5),
      "values_block_5": StringCol(itemsize=50, shape=(1,), dflt='', pos=6)}
      byteorder := 'little'
      chunkshape := (689,)
      autoindex := True
      colindexes := {
        "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

### Storing Multi-Index DataFrames

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

    In [277]: 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'])
       .....:

    In [278]: df_mi = DataFrame(np.random.randn(10, 3), index=index,
       .....:                   columns=['A', 'B', 'C'])
       .....:

    In [279]: df_mi
    Out[279]:
                      A         B         C
    foo bar
    foo one   -0.584718  0.816594 -0.081947
        two   -0.344766  0.528288 -1.068989
        three -0.511881  0.291205  0.566534
    bar one    0.503592  0.285296  0.484288
        two    1.363482 -0.781105 -0.468018
    baz two    1.224574 -1.281108  0.875476
        three -1.710715 -0.450765  0.749164
    qux one   -0.203933 -0.182175  0.680656
        two   -1.818499  0.047072  0.394844
        three -0.248432 -0.617707 -0.682884

    [10 rows x 3 columns]

    In [280]: store.append('df_mi',df_mi)

    In [281]: store.select('df_mi')
    Out[281]:
                      A         B         C
    foo bar
    foo one   -0.584718  0.816594 -0.081947
        two   -0.344766  0.528288 -1.068989
        three -0.511881  0.291205  0.566534
    bar one    0.503592  0.285296  0.484288
        two    1.363482 -0.781105 -0.468018
    baz two    1.224574 -1.281108  0.875476
        three -1.710715 -0.450765  0.749164
    qux one   -0.203933 -0.182175  0.680656
        two   -1.818499  0.047072  0.394844
        three -0.248432 -0.617707 -0.682884

    [10 rows x 3 columns]

    # the levels are automatically included as data columns
    In [282]: store.select('df_mi', 'foo=bar')
    Out[282]:
                    A         B         C
    foo bar
    bar one  0.503592  0.285296  0.484288
        two  1.363482 -0.781105 -0.468018

    [2 rows x 3 columns]

### Querying a Table

Warning

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

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

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

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

Valid comparison operators are:

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

Valid boolean expressions are combined with:

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

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

Note

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

The following are valid expressions:

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

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

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

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

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

Here are some examples:

    In [283]: dfq = DataFrame(randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10))

    In [284]: store.append('dfq',dfq,format='table',data_columns=True)

Use boolean expressions, with in-line function evaluation.

    In [285]: store.select('dfq',"index>Timestamp('20130104') & columns=['A', 'B']")
    Out[285]:
                       A         B
    2013-01-05  1.210384  0.797435
    2013-01-06 -0.850346  1.176812
    2013-01-07  0.984188 -0.121728
    2013-01-08  0.796595 -0.474021
    2013-01-09 -0.804834 -2.123620
    2013-01-10  0.334198  0.536784

    [6 rows x 2 columns]

Use and inline column reference

    In [286]: store.select('dfq',where="A>0 or C>0")
    Out[286]:
                       A         B         C         D
    2013-01-01  0.436258 -1.703013  0.393711 -0.479324
    2013-01-02 -0.299016  0.694103  0.678630  0.239556
    2013-01-03  0.151227  0.816127  1.893534  0.639633
    2013-01-04 -0.962029 -2.085266  1.930247 -1.735349
    2013-01-05  1.210384  0.797435 -0.379811  0.702562
    2013-01-07  0.984188 -0.121728  2.365769  0.496143
    2013-01-08  0.796595 -0.474021 -0.056696  1.357797
    2013-01-10  0.334198  0.536784 -0.743830 -0.320204

    [8 rows x 4 columns]

Works with a Panel as well.

    In [287]: store.append('wp',wp)

    In [288]: store
    Out[288]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df                     frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /df_mi                  frame_table  (typ->appendable_multi,nrows->10,ncols->5,indexers->[index],dc->[bar,foo])
    /df_mixed               frame_table  (typ->appendable,nrows->8,ncols->7,indexers->[index])
    /dfq                    frame_table  (typ->appendable,nrows->10,ncols->4,indexers->[index],dc->[A,B,C,D])
    /wp                     wide_table   (typ->appendable,nrows->20,ncols->2,indexers->[major_axis,minor_axis])
    /foo/bar/bah            frame        (shape->[8,3])

    In [289]: store.select('wp', "major_axis>Timestamp('20000102') & minor_axis=['A', 'B']")
    Out[289]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to B

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

    In [290]: store.select('df', "columns=['A', 'B']")
    Out[290]:
                       A         B
    2000-01-01  0.887163  0.859588
    2000-01-02  0.015696 -2.242685
    2000-01-03  0.991946  0.953324
    2000-01-04 -0.334077  0.002118
    2000-01-05  0.289092  1.321158
    2000-01-06 -0.202646 -0.655969
    2000-01-07  0.553439  1.318152
    2000-01-08  0.675554 -1.817027

    [8 rows x 2 columns]

`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.

    # this is effectively what the storage of a Panel looks like
    In [291]: wp.to_frame()
    Out[291]:
                         Item1     Item2
    major      minor
    2000-01-01 A      1.058969  0.215269
               B     -0.397840  0.841009
               C      0.337438 -1.445810
               D      1.047579 -1.401973
    2000-01-02 A      1.045938 -0.100918
               B      0.863717 -0.548242
               C     -0.122092 -0.144620
               D      0.124713  0.354020
    2000-01-03 A     -0.322795 -0.035513
               B      0.841675  0.565738
               C      2.390961  1.545659
               D      0.076200 -0.974236
    2000-01-04 A     -0.566446 -0.070345
               B      0.036142  0.307969
               C     -2.074978 -0.208499
                           ...       ...

    [20 rows x 2 columns]

    # limiting the search
    In [292]: store.select('wp',"major_axis>20000102 & minor_axis=['A','B']",
       .....:              start=0, stop=10)
       .....:
    Out[292]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 1 (major_axis) x 2 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-03 00:00:00
    Minor_axis axis: A to B

Note

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

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

Using timedelta64[ns]

New in version 0.13.

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

Warning

This requires `numpy >= 1.7`

    In [293]: from datetime import timedelta

    In [294]: dftd = DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ]))

    In [295]: dftd['C'] = dftd['A']-dftd['B']

    In [296]: dftd
    Out[296]:
               A                   B                 C
    0 2013-01-01 2013-01-01 00:00:10 -0 days, 00:00:10
    1 2013-01-01 2013-01-02 00:00:10 -1 days, 00:00:10
    2 2013-01-01 2013-01-03 00:00:10 -2 days, 00:00:10
    3 2013-01-01 2013-01-04 00:00:10 -3 days, 00:00:10
    4 2013-01-01 2013-01-05 00:00:10 -4 days, 00:00:10
    5 2013-01-01 2013-01-06 00:00:10 -5 days, 00:00:10
    6 2013-01-01 2013-01-07 00:00:10 -6 days, 00:00:10
    7 2013-01-01 2013-01-08 00:00:10 -7 days, 00:00:10
    8 2013-01-01 2013-01-09 00:00:10 -8 days, 00:00:10
    9 2013-01-01 2013-01-10 00:00:10 -9 days, 00:00:10

    [10 rows x 3 columns]

    In [297]: store.append('dftd',dftd,data_columns=True)

    In [298]: store.select('dftd',"C<'-3.5D'")
    Out[298]:
               A                   B                 C
    4 2013-01-01 2013-01-05 00:00:10 -4 days, 00:00:10
    5 2013-01-01 2013-01-06 00:00:10 -5 days, 00:00:10
    6 2013-01-01 2013-01-07 00:00:10 -6 days, 00:00:10
    7 2013-01-01 2013-01-08 00:00:10 -7 days, 00:00:10
    8 2013-01-01 2013-01-09 00:00:10 -8 days, 00:00:10
    9 2013-01-01 2013-01-10 00:00:10 -9 days, 00:00:10

    [6 rows x 3 columns]

### Indexing

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

Note

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

    # we have automagically already created an index (in the first section)
    In [299]: i = store.root.df.table.cols.index.index

    In [300]: i.optlevel, i.kind
    Out[300]: (6, 'medium')

    # change an index by passing new parameters
    In [301]: store.create_table_index('df', optlevel=9, kind='full')

    In [302]: i = store.root.df.table.cols.index.index

    In [303]: i.optlevel, i.kind
    Out[303]: (9, 'full')

See here for how to create a completely-sorted-index (CSI) on an existing store.

### Query via Data Columns

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

    In [304]: df_dc = df.copy()

    In [305]: df_dc['string'] = 'foo'

    In [306]: df_dc.ix[4:6,'string'] = np.nan

    In [307]: df_dc.ix[7:9,'string'] = 'bar'

    In [308]: df_dc['string2'] = 'cool'

    In [309]: df_dc.ix[1:3,['B','C']] = 1.0

    In [310]: df_dc
    Out[310]:
                       A         B         C string string2
    2000-01-01  0.887163  0.859588 -0.636524    foo    cool
    2000-01-02  0.015696  1.000000  1.000000    foo    cool
    2000-01-03  0.991946  1.000000  1.000000    foo    cool
    2000-01-04 -0.334077  0.002118  0.405453    foo    cool
    2000-01-05  0.289092  1.321158 -1.546906    NaN    cool
    2000-01-06 -0.202646 -0.655969  0.193421    NaN    cool
    2000-01-07  0.553439  1.318152 -0.469305    foo    cool
    2000-01-08  0.675554 -1.817027 -0.183109    bar    cool

    [8 rows x 5 columns]

    # on-disk operations
    In [311]: store.append('df_dc', df_dc, data_columns = ['B', 'C', 'string', 'string2'])

    In [312]: store.select('df_dc', [ Term('B>0') ])
    Out[312]:
                       A         B         C string string2
    2000-01-01  0.887163  0.859588 -0.636524    foo    cool
    2000-01-02  0.015696  1.000000  1.000000    foo    cool
    2000-01-03  0.991946  1.000000  1.000000    foo    cool
    2000-01-04 -0.334077  0.002118  0.405453    foo    cool
    2000-01-05  0.289092  1.321158 -1.546906    NaN    cool
    2000-01-07  0.553439  1.318152 -0.469305    foo    cool

    [6 rows x 5 columns]

    # getting creative
    In [313]: store.select('df_dc', 'B > 0 & C > 0 & string == foo')
    Out[313]:
                       A         B         C string string2
    2000-01-02  0.015696  1.000000  1.000000    foo    cool
    2000-01-03  0.991946  1.000000  1.000000    foo    cool
    2000-01-04 -0.334077  0.002118  0.405453    foo    cool

    [3 rows x 5 columns]

    # this is in-memory version of this type of selection
    In [314]: df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == 'foo')]
    Out[314]:
                       A         B         C string string2
    2000-01-02  0.015696  1.000000  1.000000    foo    cool
    2000-01-03  0.991946  1.000000  1.000000    foo    cool
    2000-01-04 -0.334077  0.002118  0.405453    foo    cool

    [3 rows x 5 columns]

    # we have automagically created this index and the B/C/string/string2
    # columns are stored separately as ``PyTables`` columns
    In [315]: store.root.df_dc.table
    Out[315]:
    /df_dc/table (Table(8,)) ''
      description := {
      "index": Int64Col(shape=(), dflt=0, pos=0),
      "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
      "B": Float64Col(shape=(), dflt=0.0, pos=2),
      "C": Float64Col(shape=(), dflt=0.0, pos=3),
      "string": StringCol(itemsize=3, shape=(), dflt='', pos=4),
      "string2": StringCol(itemsize=4, shape=(), dflt='', pos=5)}
      byteorder := 'little'
      chunkshape := (1680,)
      autoindex := True
      colindexes := {
        "index": Index(6, medium, shuffle, zlib(1)).is_csi=False,
        "C": Index(6, medium, shuffle, zlib(1)).is_csi=False,
        "B": Index(6, medium, shuffle, zlib(1)).is_csi=False,
        "string2": Index(6, medium, shuffle, zlib(1)).is_csi=False,
        "string": Index(6, medium, shuffle, zlib(1)).is_csi=False}

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

### Iterator

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

    In [316]: for df in store.select('df', chunksize=3):
       .....:    print(df)
       .....:
                       A         B         C
    2000-01-01  0.887163  0.859588 -0.636524
    2000-01-02  0.015696 -2.242685  1.150036
    2000-01-03  0.991946  0.953324 -2.021255

    [3 rows x 3 columns]
                       A         B         C
    2000-01-04 -0.334077  0.002118  0.405453
    2000-01-05  0.289092  1.321158 -1.546906
    2000-01-06 -0.202646 -0.655969  0.193421

    [3 rows x 3 columns]
                       A         B         C
    2000-01-07  0.553439  1.318152 -0.469305
    2000-01-08  0.675554 -1.817027 -0.183109

    [2 rows x 3 columns]

Note

New in version 0.12.0.

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

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

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

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

    In [317]: dfeq = DataFrame({'number': np.arange(1,11)})

    In [318]: dfeq
    Out[318]:
       number
    0       1
    1       2
    2       3
    3       4
    4       5
    5       6
    6       7
    7       8
    8       9
    9      10

    [10 rows x 1 columns]

    In [319]: store.append('dfeq', dfeq, data_columns=['number'])

    In [320]: def chunks(l, n):
       .....:      return [l[i:i+n] for i in xrange(0, len(l), n)]
       .....:

    In [321]: evens = [2,4,6,8,10]

    In [322]: coordinates = store.select_as_coordinates('dfeq','number=evens')

    In [323]: for c in chunks(coordinates, 2):
       .....:      print store.select('dfeq',where=c)
       .....:
       number
    1       2
    3       4

    [2 rows x 1 columns]
       number
    5       6
    7       8

    [2 rows x 1 columns]
       number
    9      10

    [1 rows x 1 columns]

### 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.

    In [324]: store.select_column('df_dc', 'index')
    Out[324]:
    0   2000-01-01
    1   2000-01-02
    2   2000-01-03
    3   2000-01-04
    4   2000-01-05
    5   2000-01-06
    6   2000-01-07
    7   2000-01-08
    dtype: datetime64[ns]

    In [325]: store.select_column('df_dc', 'string')
    Out[325]:
    0    foo
    1    foo
    2    foo
    3    foo
    4    NaN
    5    NaN
    6    foo
    7    bar
    dtype: object

Selecting coordinates

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

    In [326]: df_coord = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))

    In [327]: store.append('df_coord',df_coord)

    In [328]: c = store.select_as_coordinates('df_coord','index>20020101')

    In [329]: c.summary()
    Out[329]: u'Int64Index: 268 entries, 732 to 999'

    In [330]: store.select('df_coord',where=c)
    Out[330]:
                       0         1
    2002-01-02 -0.667994 -0.368175
    2002-01-03  0.020119 -0.823208
    2002-01-04 -0.165481  0.720866
    2002-01-05  1.295919 -0.527767
    2002-01-06 -0.463393 -0.150792
    2002-01-07 -1.139341 -0.954387
    2002-01-08  0.051837 -0.147048
    2002-01-09 -0.383978  1.209025
    2002-01-10  0.213923 -0.113980
    2002-01-11  0.944945 -0.183393
    2002-01-12  1.714323  0.024600
    2002-01-13  0.454133  0.272278
    2002-01-14  0.305823 -0.390413
    2002-01-15  0.424165  0.208513
    2002-01-16  0.429386  1.357697
                     ...       ...

    [268 rows x 2 columns]

Selecting using a where mask

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

    In [331]: df_mask = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))

    In [332]: store.append('df_mask',df_mask)

    In [333]: c = store.select_column('df_mask','index')

    In [334]: where = c[DatetimeIndex(c).month==5].index

    In [335]: store.select('df_mask',where=where)
    Out[335]:
                       0         1
    2000-05-01 -0.098554 -0.280782
    2000-05-02  0.739851  1.627182
    2000-05-03  0.030132 -0.145601
    2000-05-04  0.227530  1.048856
    2000-05-05  1.773939  1.116887
    2000-05-06  1.081251  1.509416
    2000-05-07 -0.498694 -0.913155
    2000-05-08 -0.126945  0.079686
    2000-05-09  1.020345 -0.790110
    2000-05-10 -1.155447 -0.367505
    2000-05-11  1.263914  0.827049
    2000-05-12 -0.572469  1.211678
    2000-05-13  1.792988  1.379125
    2000-05-14  2.049917  0.992011
    2000-05-15 -0.930035 -0.249394
                     ...       ...

    [93 rows x 2 columns]

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.

    In [336]: store.get_storer('df_dc').nrows
    Out[336]: 8

### Multiple Table Queries

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

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

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

    In [337]: df_mt = DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8),
       .....:                                columns=['A', 'B', 'C', 'D', 'E', 'F'])
       .....:

    In [338]: df_mt['foo'] = 'bar'

    In [339]: df_mt.ix[1, ('A', 'B')] = np.nan

    # you can also create the tables individually
    In [340]: store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None },
       .....:                           df_mt, selector='df1_mt')
       .....:

    In [341]: store
    Out[341]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df                     frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /df1_mt                 frame_table  (typ->appendable,nrows->7,ncols->2,indexers->[index],dc->[A,B])
    /df2_mt                 frame_table  (typ->appendable,nrows->7,ncols->5,indexers->[index])
    /df_coord               frame_table  (typ->appendable,nrows->1000,ncols->2,indexers->[index])
    /df_dc                  frame_table  (typ->appendable,nrows->8,ncols->5,indexers->[index],dc->[B,C,string,string2])
    /df_mask                frame_table  (typ->appendable,nrows->1000,ncols->2,indexers->[index])
    /df_mi                  frame_table  (typ->appendable_multi,nrows->10,ncols->5,indexers->[index],dc->[bar,foo])
    /df_mixed               frame_table  (typ->appendable,nrows->8,ncols->7,indexers->[index])
    /dfeq                   frame_table  (typ->appendable,nrows->10,ncols->1,indexers->[index],dc->[number])
    /dfq                    frame_table  (typ->appendable,nrows->10,ncols->4,indexers->[index],dc->[A,B,C,D])
    /dftd                   frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])
    /wp                     wide_table   (typ->appendable,nrows->20,ncols->2,indexers->[major_axis,minor_axis])
    /foo/bar/bah            frame        (shape->[8,3])

    # individual tables were created
    In [342]: store.select('df1_mt')
    Out[342]:
                       A         B
    2000-01-01 -0.816310  1.282296
    2000-01-03  0.684353 -1.755306
    2000-01-04 -1.315814  1.455079
    2000-01-05 -0.027564  0.046757
    2000-01-06 -0.416244 -0.821168
    2000-01-07  0.665090  1.084344
    2000-01-08  0.607460  0.790907

    [7 rows x 2 columns]

    In [343]: store.select('df2_mt')
    Out[343]:
                       C         D         E         F  foo
    2000-01-01 -1.521825 -0.428670 -1.550209  0.826839  bar
    2000-01-03  1.236974 -1.328279  0.662291  1.894976  bar
    2000-01-04 -0.746478  0.851039  1.415686 -0.929096  bar
    2000-01-05 -1.452287  1.575492 -0.197377 -0.219901  bar
    2000-01-06  1.190342  2.115021  0.148762  1.073931  bar
    2000-01-07 -0.709897 -2.022441  0.714697  0.318215  bar
    2000-01-08  0.852225  0.096696 -0.379903  0.929313  bar

    [7 rows x 5 columns]

    # as a multiple
    In [344]: store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
       .....:                           selector = 'df1_mt')
       .....:
    Out[344]:
                      A         B         C         D         E         F  foo
    2000-01-07  0.66509  1.084344 -0.709897 -2.022441  0.714697  0.318215  bar
    2000-01-08  0.60746  0.790907  0.852225  0.096696 -0.379903  0.929313  bar

    [2 rows x 7 columns]

### 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.

    # returns the number of rows deleted
    In [345]: store.remove('wp', 'major_axis>20000102' )
    Out[345]: 12

    In [346]: store.select('wp')
    Out[346]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 2 (major_axis) x 4 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-02 00:00:00
    Minor_axis axis: A to D

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

### Compression

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

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

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

Compression for all objects within the file

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

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

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

ptrepack

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

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

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

### Notes & Caveats

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

Warning

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

### DataTypes

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

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

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

>     In [347]: import datetime

>     In [348]: df = DataFrame(dict(datelike=Series([datetime.datetime(2001, 1, 1),
>        .....:                                      datetime.datetime(2001, 1, 2), np.nan])))
>        .....:

>     In [349]: df
>     Out[349]:
>         datelike
>     0 2001-01-01
>     1 2001-01-02
>     2        NaT

>     [3 rows x 1 columns]

>     In [350]: df.dtypes
>     Out[350]:
>     datelike    datetime64[ns]
>     dtype: object

>     # to convert
>     In [351]: df['datelike'] = Series(df['datelike'].values, dtype='M8[ns]')

>     In [352]: df
>     Out[352]:
>         datelike
>     0 2001-01-01
>     1 2001-01-02
>     2        NaT

>     [3 rows x 1 columns]

>     In [353]: df.dtypes
>     Out[353]:
>     datelike    datetime64[ns]
>     dtype: object

### String Columns

min_itemsize

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

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

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

Note

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

    In [354]: dfs = DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5)))

    In [355]: dfs
    Out[355]:
         A    B
    0  foo  bar
    1  foo  bar
    2  foo  bar
    3  foo  bar
    4  foo  bar

    [5 rows x 2 columns]

    # A and B have a size of 30
    In [356]: store.append('dfs', dfs, min_itemsize = 30)

    In [357]: store.get_storer('dfs').table
    Out[357]:
    /dfs/table (Table(5,)) ''
      description := {
      "index": Int64Col(shape=(), dflt=0, pos=0),
      "values_block_0": StringCol(itemsize=30, shape=(2,), dflt='', pos=1)}
      byteorder := 'little'
      chunkshape := (963,)
      autoindex := True
      colindexes := {
        "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

    # A is created as a data_column with a size of 30
    # B is size is calculated
    In [358]: store.append('dfs2', dfs, min_itemsize = { 'A' : 30 })

    In [359]: store.get_storer('dfs2').table
    Out[359]:
    /dfs2/table (Table(5,)) ''
      description := {
      "index": Int64Col(shape=(), dflt=0, pos=0),
      "values_block_0": StringCol(itemsize=3, shape=(1,), dflt='', pos=1),
      "A": StringCol(itemsize=30, shape=(), dflt='', pos=2)}
      byteorder := 'little'
      chunkshape := (1598,)
      autoindex := True
      colindexes := {
        "A": Index(6, medium, shuffle, zlib(1)).is_csi=False,
        "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

nan_rep

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

    In [360]: dfss = DataFrame(dict(A = ['foo','bar','nan']))

    In [361]: dfss
    Out[361]:
         A
    0  foo
    1  bar
    2  nan

    [3 rows x 1 columns]

    In [362]: store.append('dfss', dfss)

    In [363]: store.select('dfss')
    Out[363]:
         A
    0  foo
    1  bar
    2  NaN

    [3 rows x 1 columns]

    # here you need to specify a different nan rep
    In [364]: store.append('dfss2', dfss, nan_rep='_nan_')

    In [365]: store.select('dfss2')
    Out[365]:
         A
    0  foo
    1  bar
    2  nan

    [3 rows x 1 columns]

### External Compatibility

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

>     In [366]: store_export = HDFStore('export.h5')

>     In [367]: store_export.append('df_dc', df_dc, data_columns=df_dc.columns)

>     In [368]: store_export
>     Out[368]:
>     <class 'pandas.io.pytables.HDFStore'>
>     File path: export.h5
>     /df_dc            frame_table  (typ->appendable,nrows->8,ncols->5,indexers->[index],dc->[A,B,C,string,string2])

### 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.

>     # a legacy store
>     In [369]: legacy_store = HDFStore(legacy_file_path,'r')

>     In [370]: legacy_store
>     Out[370]:
>     <class 'pandas.io.pytables.HDFStore'>
>     File path: /home/user1/src/pandas/doc/source/_static/legacy_0.10.h5
>     /a                    series       (shape->[30])
>     /b                    frame        (shape->[30,4])
>     /df1_mixed            frame_table [0.10.0] (typ->appendable,nrows->30,ncols->11,indexers->[index])
>     /p1_mixed             wide_table  [0.10.0] (typ->appendable,nrows->120,ncols->9,indexers->[major_axis,minor_axis])
>     /p4d_mixed            ndim_table  [0.10.0] (typ->appendable,nrows->360,ncols->9,indexers->[items,major_axis,minor_axis])
>     /foo/bar              wide         (shape->[3,30,4])

>     # copy (and return the new handle)
>     In [371]: new_store = legacy_store.copy('store_new.h5')

>     In [372]: new_store
>     Out[372]:
>     <class 'pandas.io.pytables.HDFStore'>
>     File path: store_new.h5
>     /a                    series       (shape->[30])
>     /b                    frame        (shape->[30,4])
>     /df1_mixed            frame_table  (typ->appendable,nrows->30,ncols->11,indexers->[index])
>     /p1_mixed             wide_table   (typ->appendable,nrows->120,ncols->9,indexers->[major_axis,minor_axis])
>     /p4d_mixed            wide_table   (typ->appendable,nrows->360,ncols->9,indexers->[items,major_axis,minor_axis])
>     /foo/bar              wide         (shape->[3,30,4])

>     In [373]: new_store.close()

### Performance

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

### Experimental

HDFStore supports `Panel4D` storage.

    In [374]: p4d = Panel4D({ 'l1' : wp })

    In [375]: p4d
    Out[375]:
    <class 'pandas.core.panelnd.Panel4D'>
    Dimensions: 1 (labels) x 2 (items) x 5 (major_axis) x 4 (minor_axis)
    Labels axis: l1 to l1
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to D

    In [376]: store.append('p4d', p4d)

    In [377]: store
    Out[377]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df                     frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /df1_mt                 frame_table  (typ->appendable,nrows->7,ncols->2,indexers->[index],dc->[A,B])
    /df2_mt                 frame_table  (typ->appendable,nrows->7,ncols->5,indexers->[index])
    /df_coord               frame_table  (typ->appendable,nrows->1000,ncols->2,indexers->[index])
    /df_dc                  frame_table  (typ->appendable,nrows->8,ncols->5,indexers->[index],dc->[B,C,string,string2])
    /df_mask                frame_table  (typ->appendable,nrows->1000,ncols->2,indexers->[index])
    /df_mi                  frame_table  (typ->appendable_multi,nrows->10,ncols->5,indexers->[index],dc->[bar,foo])
    /df_mixed               frame_table  (typ->appendable,nrows->8,ncols->7,indexers->[index])
    /dfeq                   frame_table  (typ->appendable,nrows->10,ncols->1,indexers->[index],dc->[number])
    /dfq                    frame_table  (typ->appendable,nrows->10,ncols->4,indexers->[index],dc->[A,B,C,D])
    /dfs                    frame_table  (typ->appendable,nrows->5,ncols->2,indexers->[index])
    /dfs2                   frame_table  (typ->appendable,nrows->5,ncols->2,indexers->[index],dc->[A])
    /dfss                   frame_table  (typ->appendable,nrows->3,ncols->1,indexers->[index])
    /dfss2                  frame_table  (typ->appendable,nrows->3,ncols->1,indexers->[index])
    /dftd                   frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])
    /p4d                    wide_table   (typ->appendable,nrows->40,ncols->1,indexers->[items,major_axis,minor_axis])
    /wp                     wide_table   (typ->appendable,nrows->8,ncols->2,indexers->[major_axis,minor_axis])
    /foo/bar/bah            frame        (shape->[8,3])

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.

    In [378]: store.append('p4d2', p4d, axes=['labels', 'major_axis', 'minor_axis'])

    In [379]: store
    Out[379]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df                     frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
    /df1_mt                 frame_table  (typ->appendable,nrows->7,ncols->2,indexers->[index],dc->[A,B])
    /df2_mt                 frame_table  (typ->appendable,nrows->7,ncols->5,indexers->[index])
    /df_coord               frame_table  (typ->appendable,nrows->1000,ncols->2,indexers->[index])
    /df_dc                  frame_table  (typ->appendable,nrows->8,ncols->5,indexers->[index],dc->[B,C,string,string2])
    /df_mask                frame_table  (typ->appendable,nrows->1000,ncols->2,indexers->[index])
    /df_mi                  frame_table  (typ->appendable_multi,nrows->10,ncols->5,indexers->[index],dc->[bar,foo])
    /df_mixed               frame_table  (typ->appendable,nrows->8,ncols->7,indexers->[index])
    /dfeq                   frame_table  (typ->appendable,nrows->10,ncols->1,indexers->[index],dc->[number])
    /dfq                    frame_table  (typ->appendable,nrows->10,ncols->4,indexers->[index],dc->[A,B,C,D])
    /dfs                    frame_table  (typ->appendable,nrows->5,ncols->2,indexers->[index])
    /dfs2                   frame_table  (typ->appendable,nrows->5,ncols->2,indexers->[index],dc->[A])
    /dfss                   frame_table  (typ->appendable,nrows->3,ncols->1,indexers->[index])
    /dfss2                  frame_table  (typ->appendable,nrows->3,ncols->1,indexers->[index])
    /dftd                   frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])
    /p4d                    wide_table   (typ->appendable,nrows->40,ncols->1,indexers->[items,major_axis,minor_axis])
    /p4d2                   wide_table   (typ->appendable,nrows->20,ncols->2,indexers->[labels,major_axis,minor_axis])
    /wp                     wide_table   (typ->appendable,nrows->8,ncols->2,indexers->[major_axis,minor_axis])
    /foo/bar/bah            frame        (shape->[8,3])

    In [380]: store.select('p4d2', [ Term('labels=l1'), Term('items=Item1'), Term('minor_axis=A_big_strings') ])
    Out[380]:
    <class 'pandas.core.panelnd.Panel4D'>
    Dimensions: 0 (labels) x 1 (items) x 0 (major_axis) x 0 (minor_axis)
    Labels axis: None
    Items axis: Item1 to Item1
    Major_axis axis: None
    Minor_axis axis: None

## SQL Queries

The `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 cookbook examples 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 `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:')

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

    In [381]: sql.read_sql("SELECT * FROM data;", cnx)
    Out[381]:
       id                 date Col_1  Col_2  Col_3
    0  26  2010-10-18 00:00:00     X  27.50      1
    1  42  2010-10-19 00:00:00     Y -12.50      0
    2  63  2010-10-20 00:00:00     Z   5.73      1

    [3 rows x 5 columns]

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

    In [382]: sql.read_sql("SELECT * FROM data;", cnx, index_col='id')
    Out[382]:
                       date Col_1  Col_2  Col_3
    id
    26  2010-10-18 00:00:00     X  27.50      1
    42  2010-10-19 00:00:00     Y -12.50      0
    63  2010-10-20 00:00:00     Z   5.73      1

    [3 rows x 4 columns]

    In [383]: sql.read_sql("SELECT * FROM data;", cnx, index_col='date')
    Out[383]:
                         id Col_1  Col_2  Col_3
    date
    2010-10-18 00:00:00  26     X  27.50      1
    2010-10-19 00:00:00  42     Y -12.50      0
    2010-10-20 00:00:00  63     Z   5.73      1

    [3 rows x 4 columns]

Of course, you can specify a more “complex” query.

    In [384]: sql.read_sql("SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", cnx)
    Out[384]:
       id Col_1  Col_2
    0  42     Y  -12.5

    [1 rows x 3 columns]

There are a few other available functions:

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

Note

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

## Google BigQuery (Experimental)

New in version 0.13.0.

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

For specifics on the service itself, see here

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

    from pandas.io import gbq

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

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

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

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

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

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

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

To add more rows to this, simply:

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

Note

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

There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the BigQuery SQL query language has some oddities, see here

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

Warning

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

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

## STATA Format

New in version 0.12.0.

### Writing to STATA format

The method `to_stata()` will write a DataFrame into a .dta file. The format version of this file is always 115 (Stata 12).

    In [385]: df = DataFrame(randn(10, 2), columns=list('AB'))

    In [386]: df.to_stata('stata.dta')

### Reading from STATA format

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

    In [387]: pd.read_stata('stata.dta')
    Out[387]:
       index         A         B
    0      0  0.811031 -0.356817
    1      1  1.047085  0.664705
    2      2 -0.086919  0.416905
    3      3 -0.764381 -0.287229
    4      4 -0.089351 -1.035115
    5      5  0.489131 -0.253340
    6      6 -1.948100 -0.116556
    7      7  0.800597 -0.796154
    8      8 -0.382952 -0.397373
    9      9 -0.717627  0.156995

    [10 rows x 3 columns]

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

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

The StataReader supports .dta Formats 104, 105, 108, 113-115 and 117. Alternatively, the function `read_stata()` can be used

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