petl - Extract, Transform and Load (Tables of Data)¶

petl is a general purpose Python package for extracting, transforming and loading tables of data.

Documentation: http://petl.readthedocs.org/
Source Code: https://github.com/alimanfoo/petl
Download: http://pypi.python.org/pypi/petl
Mailing List: http://groups.google.com/group/python-etl

Please feel free to ask questions via the mailing list (python-etl@googlegroups.com).

To report installation problems, bugs or any other issues please email python-etl@googlegroups.com or raise an issue on GitHub.

For an example of petl in use, see the case study on comparing tables.

For an alphabetic list of all functions in the package, see the Index.

Note

Version 1.0 is a new major release of petl. See the Changes section for more information about what has changed in version 1.0.

Introduction¶

Installation¶

This package is available from the Python Package Index. If you have pip you should be able to do:

$ pip install petl

You can also download manually, extract and run python setup.py install.

To verify the installation, the test suite can be run with nose, e.g.:

$ pip install nose
$ nosetests -v petl

petl is compatible with Python versions 2.6, 2.7 and 3.4, and has been tested under Linux and Windows operating systems.

Dependencies and extensions¶

This package is written in pure Python and has no installation requirements other than the Python core modules.

Some of the functions in this package require installation of third party packages. This is indicated in the relevant parts of the documentation.

Some domain-specific and/or experimental extensions to petl are available from the petlx package.

Design goals¶

This package is designed primarily for convenience and ease of use, especially when working interactively with data that are unfamiliar, heterogeneous and/or of mixed quality.

petl transformation pipelines make minimal use of system memory and can scale to millions of rows if speed is not a priority. However if you are working with large datasets and/or performance-critical applications then other packages may be more suitable, e.g., see pandas, pytables, bcolz and blaze. See also Related Work.

ETL pipelines¶

This package makes extensive use of lazy evaluation and iterators. This means, generally, that a pipeline will not actually be executed until data is requested.

E.g., given a file at ‘example.csv’ in the current working directory:

>>> example_data = """foo,bar,baz
... a,1,3.4
... b,2,7.4
... c,6,2.2
... d,9,8.1
... """
>>> with open('example.csv', 'w') as f:
...     f.write(example_data)
...

...the following code does not actually read the file or load any of its contents into memory:

>>> import petl as etl
>>> table1 = etl.fromcsv('example.csv')

Rather, table1 is a table container (see Conventions - table containers and table iterators below) which can be iterated over, extracting data from the underlying file on demand.

Similarly, if one or more transformation functions are applied, e.g.:

>>> table2 = etl.convert(table1, 'foo', 'upper')
>>> table3 = etl.convert(table2, 'bar', int)
>>> table4 = etl.convert(table3, 'baz', float)
>>> table5 = etl.addfield(table4, 'quux', lambda row: row.bar * row.baz)

...no actual transformation work will be done until data are requested from table5 (or any of the other tables returned by the intermediate steps).

So in effect, a 5 step pipeline has been set up, and rows will pass through the pipeline on demand, as they are pulled from the end of the pipeline via iteration.

A call to a function like petl.util.vis.look(), or any of the functions which write data to a file or database (e.g., petl.io.csv.tocsv(), petl.io.text.totext(), petl.io.sqlite3.tosqlite3(), petl.io.db.todb()), will pull data through the pipeline and cause all of the transformation steps to be executed on the requested rows, e.g.:

>>> etl.look(table5)
+-----+-----+-----+--------------------+
| foo | bar | baz | quux               |
+=====+=====+=====+====================+
| 'A' |   1 | 3.4 |                3.4 |
+-----+-----+-----+--------------------+
| 'B' |   2 | 7.4 |               14.8 |
+-----+-----+-----+--------------------+
| 'C' |   6 | 2.2 | 13.200000000000001 |
+-----+-----+-----+--------------------+
| 'D' |   9 | 8.1 |  72.89999999999999 |
+-----+-----+-----+--------------------+

...although note that petl.util.vis.look() will by default only request the first 5 rows, and so the minimum amount of processing will be done to produce 5 rows.

Functional and object-oriented programming styles¶

The petl package supports both functional and object-oriented programming styles. For example, the example in the section on ETL pipelines above could also be written as:

>>> import petl as etl
>>> table = (
...     etl
...     .fromcsv('example.csv')
...     .convert('foo', 'upper')
...     .convert('bar', int)
...     .convert('baz', float)
...     .addfield('quux', lambda row: row.bar * row.baz)
... )
>>> table.look()
+-----+-----+-----+--------------------+
| foo | bar | baz | quux               |
+=====+=====+=====+====================+
| 'A' |   1 | 3.4 |                3.4 |
+-----+-----+-----+--------------------+
| 'B' |   2 | 7.4 |               14.8 |
+-----+-----+-----+--------------------+
| 'C' |   6 | 2.2 | 13.200000000000001 |
+-----+-----+-----+--------------------+
| 'D' |   9 | 8.1 |  72.89999999999999 |
+-----+-----+-----+--------------------+

A wrap() function is also provided to use the object-oriented style with any valid table container object, e.g.:

>>> l = [['foo', 'bar'], ['a', 1], ['b', 2], ['c', 2]]
>>> table = etl.wrap(l)
>>> table.look()
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+
| 'c' |   2 |
+-----+-----+

Interactive use¶

When using petl from within an interactive Python session, the default representation for table objects uses the petl.util.vis.look() function, so a table object can be returned at the prompt to inspect it, e.g.:

>>> l = [['foo', 'bar'], ['a', 1], ['b', 2], ['c', 2]]
>>> table = etl.wrap(l)
>>> table
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+
| 'c' |   2 |
+-----+-----+

By default data values are rendered using the built-in repr() function. To see the string (str()) values instead, print() the table, e.g.:

>>> print(table)
+-----+-----+
| foo | bar |
+=====+=====+
| a   |   1 |
+-----+-----+
| b   |   2 |
+-----+-----+
| c   |   2 |
+-----+-----+

IPython notebook integration¶

Table objects also implement _repr_html_() and so will be displayed as an HTML table if returned from a cell in an IPython notebook. The functions petl.util.vis.display() and petl.util.vis.displayall() also provide more control over rendering of tables within an IPython notebook.

For examples of usage see the repr_html notebook.

`petl` executable¶

Also included in the petl distribution is a script to execute simple transformation pipelines directly from the operating system shell. E.g.:

$ petl "dummytable().tocsv()" > example.csv
$ cat example.csv | petl "fromcsv().cut('foo', 'baz').convert('baz', float).selectgt('baz', 0.5).head().data().totsv()"

The petl script is extremely simple, it expects a single positional argument, which is evaluated as Python code but with all of the functions in the petl namespace imported.

Conventions - table containers and table iterators¶

This package defines the following convention for objects acting as containers of tabular data and supporting row-oriented iteration over the data.

A table container (also referred to here as a table) is any object which satisfies the following:

implements the __iter__ method
__iter__ returns a table iterator (see below)
all table iterators returned by __iter__ are independent, i.e., consuming items from one iterator will not affect any other iterators

A table iterator is an iterator which satisfies the following:

each item returned by the iterator is a sequence (e.g., tuple or list)
the first item returned by the iterator is a header row comprising a sequence of header values
each subsequent item returned by the iterator is a data row comprising a sequence of data values
a header value is typically a string (str) but may be an object of any type as long as it implements __str__ and is pickleable
a data value is any pickleable object

So, for example, a list of lists is a valid table container:

>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]

Note that an object returned by the csv.reader() function from the standard Python csv module is a table iterator and not a table container, because it can only be iterated over once. However, it is straightforward to define functions that support the table container convention and provide access to data from CSV or other types of file or data source, see e.g. the petl.io.csv.fromcsv() function.

The main reason for requiring that table containers support independent table iterators (point 3) is that data from a table may need to be iterated over several times within the same program or interactive session. E.g., when using petl in an interactive session to build up a sequence of data transformation steps, the user might want to examine outputs from several intermediate steps, before all of the steps are defined and the transformation is executed in full.

Note that this convention does not place any restrictions on the lengths of header and data rows. A table may contain a header row and/or data rows of varying lengths.

Caching¶

This package tries to make efficient use of memory by using iterators and lazy evaluation where possible. However, some transformations cannot be done without building data structures, either in memory or on disk.

An example is the petl.transform.sorts.sort() function, which will either sort a table entirely in memory, or will sort the table in memory in chunks, writing chunks to disk and performing a final merge sort on the chunks. Which strategy is used will depend on the arguments passed into the petl.transform.sorts.sort() function when it is called.

In either case, the sorting can take some time, and if the sorted data will be used more than once, it is undesirable to start again from scratch each time. It is better to cache the sorted data, if possible, so it can be re-used.

The petl.transform.sorts.sort() function, and all functions which use it internally, provide a cache keyword argument which can be used to turn on or off the caching of sorted data.

There is also an explicit petl.util.materialise.cache() function, which can be used to cache in memory up to a configurable number of rows from any table.

Acknowledgments¶

This package is primarily developed and maintained by Alistair Miles <alimanfoo@googlemail.com> with funding from the MRC Centre for Genomics and Global Health.

The following people have also contributed to the development of this package:

Alexander Stauber
Andrew Kim (andrewakim)
Caleb Lloyd (caleblloyd)
Florent Xicluna (florentx)
Jonathan Camile (deytao)
Kenneth Borthwick
Michael Rea (rea725)
Olivier Macchioni (omacchioni)
Olivier Poitrey (rs)
Peder Jakobsen (pjakobsen)
Phillip Knaus (phillipknaus)
Richard Pearson (podpearson)
Roger Woodley (rogerkwoodley)
adamsdarlingtower
imazor
james-unified
shayh
thatneat
titusz
zigen

Extract/Load - reading/writing tables from files, databases and other sources¶

Extract (read)¶

The “from...” functions extract a table from a file-like source or database. For everything except petl.io.db.fromdb() the source argument provides information about where to extract the underlying data from. If the source argument is None or a string it is interpreted as follows:

None - read from stdin
string starting with http://, https:// or ftp:// - read from URL
string ending with .gz or .bgz - read from file via gzip decompression
string ending with .bz2 - read from file via bz2 decompression
any other string - read directly from file

Some helper classes are also available for reading from other types of file-like sources, e.g., reading data from a Zip file, a string or a subprocess, see the section on I/O helper classes below for more information.

Be aware that loading data from stdin breaks the table container convention, because data can usually only be read once. If you are sure that data will only be read once in your script or interactive session then this may not be a problem, however note that some petl functions do access the underlying data source more than once and so will not work as expected with data from stdin.

Load (write)¶

The “to...” functions load data from a table into a file-like source or database. For functions that accept a source argument, if the source argument is None or a string it is interpreted as follows:

None - write to stdout
string ending with .gz or .bgz - write to file via gzip decompression
string ending with .bz2 - write to file via bz2 decompression
any other string - write directly to file

Some helper classes are also available for writing to other types of file-like sources, e.g., writing to a Zip file or string buffer, see the section on I/O helper classes below for more information.

Delimited files¶

petl.io.csv.fromcsv(source=None, encoding=None, errors=None, **csvargs)[source]¶

Extract a table from a delimited file. E.g.:

>>> import petl as etl
>>> import csv
>>> # set up a CSV file to demonstrate with
... table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 2]]
>>> with open('example.csv', 'w') as f:
...     writer = csv.writer(f)
...     writer.writerows(table1)
...
>>> # now demonstrate the use of fromcsv()
... table2 = etl.fromcsv('example.csv')
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' | '1' |
+-----+-----+
| 'b' | '2' |
+-----+-----+
| 'c' | '2' |
+-----+-----+

The source argument is the path of the delimited file, all other keyword arguments are passed to csv.reader(). So, e.g., to override the delimiter from the default CSV dialect, provide the delimiter keyword argument.

Note that all data values are strings, and any intended numeric values will need to be converted, see also petl.transform.conversions.convert().

petl.io.csv.tocsv(table, source=None, encoding=None, errors=None, write_header=True, **csvargs)[source]¶

Write the table to a CSV file. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 2]]
>>> etl.tocsv(table1, 'example.csv')
>>> # look what it did
... print(open('example.csv').read())
foo,bar
a,1
b,2
c,2

The source argument is the path of the delimited file, and the optional write_header argument specifies whether to include the field names in the delimited file. All other keyword arguments are passed to csv.writer(). So, e.g., to override the delimiter from the default CSV dialect, provide the delimiter keyword argument.

Note that if a file already exists at the given location, it will be overwritten.

petl.io.csv.appendcsv(table, source=None, encoding=None, errors=None, write_header=False, **csvargs)[source]¶

Append data rows to an existing CSV file. As petl.io.csv.tocsv() but the file is opened in append mode and the table header is not written by default.

Note that no attempt is made to check that the fields or row lengths are consistent with the existing data, the data rows from the table are simply appended to the file.

petl.io.csv.teecsv(table, source=None, encoding=None, errors=None, write_header=True, **csvargs)[source]¶: Returns a table that writes rows to a CSV file as they are iterated over.

petl.io.csv.fromtsv(source=None, encoding=None, errors=None, **csvargs)[source]¶: Convenience function, as petl.io.csv.fromcsv() but with different default dialect (tab delimited).

petl.io.csv.totsv(table, source=None, encoding=None, errors=None, write_header=True, **csvargs)[source]¶: Convenience function, as petl.io.csv.tocsv() but with different default dialect (tab delimited).

petl.io.csv.appendtsv(table, source=None, encoding=None, errors=None, write_header=False, **csvargs)[source]¶: Convenience function, as petl.io.csv.appendcsv() but with different default dialect (tab delimited).

petl.io.csv.teetsv(table, source=None, encoding=None, errors=None, write_header=True, **csvargs)[source]¶: Convenience function, as petl.io.csv.teecsv() but with different default dialect (tab delimited).

Pickle files¶

petl.io.pickle.frompickle(source=None)[source]¶

Extract a table From data pickled in the given file. The rows in the table should have been pickled to the file one at a time. E.g.:

>>> import petl as etl
>>> import pickle
>>> # set up a file to demonstrate with
... with open('example.p', 'wb') as f:
...     pickle.dump(['foo', 'bar'], f)
...     pickle.dump(['a', 1], f)
...     pickle.dump(['b', 2], f)
...     pickle.dump(['c', 2.5], f)
...
>>> # now demonstrate the use of frompickle()
... table1 = etl.frompickle('example.p')
>>> table1
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+
| 'c' | 2.5 |
+-----+-----+

petl.io.pickle.topickle(table, source=None, protocol=-1, write_header=True)[source]¶

Write the table to a pickle file. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 2]]
>>> etl.topickle(table1, 'example.p')
>>> # look what it did
... table2 = etl.frompickle('example.p')
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+
| 'c' |   2 |
+-----+-----+

Note that if a file already exists at the given location, it will be overwritten.

The pickle file format preserves type information, i.e., reading and writing is round-trippable for tables with non-string data values.

petl.io.pickle.appendpickle(table, source=None, protocol=-1, write_header=False)[source]¶

Append data to an existing pickle file. I.e., as petl.io.pickle.topickle() but the file is opened in append mode.

Note that no attempt is made to check that the fields or row lengths are consistent with the existing data, the data rows from the table are simply appended to the file.

petl.io.pickle.teepickle(table, source=None, protocol=-1, write_header=True)[source]¶: Return a table that writes rows to a pickle file as they are iterated over.

Text files¶

petl.io.text.fromtext(source=None, encoding=None, errors=None, strip=None, header=('lines', ))[source]¶

Extract a table from lines in the given text file. E.g.:

>>> import petl as etl
>>> # setup example file
... text = 'a,1\nb,2\nc,2\n'
>>> with open('example.txt', 'w') as f:
...     f.write(text)
...
12
>>> table1 = etl.fromtext('example.txt')
>>> table1
+-------+
| lines |
+=======+
| 'a,1' |
+-------+
| 'b,2' |
+-------+
| 'c,2' |
+-------+

>>> # post-process, e.g., with capture()
... table2 = table1.capture('lines', '(.*),(.*)$', ['foo', 'bar'])
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' | '1' |
+-----+-----+
| 'b' | '2' |
+-----+-----+
| 'c' | '2' |
+-----+-----+

Note that the strip() function is called on each line, which by default will remove leading and trailing whitespace, including the end-of-line character - use the strip keyword argument to specify alternative characters to strip. Set the strip argument to False to disable this behaviour and leave line endings in place.

petl.io.text.totext(table, source=None, encoding=None, errors=None, template=None, prologue=None, epilogue=None)[source]¶

Write the table to a text file. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 2]]
>>> prologue = '''{| class="wikitable"
... |-
... ! foo
... ! bar
... '''
>>> template = '''|-
... | {foo}
... | {bar}
... '''
>>> epilogue = '|}'
>>> etl.totext(table1, 'example.txt', template=template,
... prologue=prologue, epilogue=epilogue)
>>> # see what we did
... print(open('example.txt').read())
{| class="wikitable"
|-
! foo
! bar
|-
| a
| 1
|-
| b
| 2
|-
| c
| 2
|}

The template will be used to format each row via str.format.

petl.io.text.appendtext(table, source=None, encoding=None, errors=None, template=None, prologue=None, epilogue=None)[source]¶: Append the table to a text file.

petl.io.text.teetext(table, source=None, encoding=None, errors=None, template=None, prologue=None, epilogue=None)[source]¶: Return a table that writes rows to a text file as they are iterated over.

XML files¶

petl.io.xml.fromxml(source, *args, **kwargs)[source]¶

Extract data from an XML file. E.g.:

>>> import petl as etl
>>> # setup a file to demonstrate with
... d = '''<table>
...     <tr>
...         <td>foo</td><td>bar</td>
...     </tr>
...     <tr>
...         <td>a</td><td>1</td>
...     </tr>
...     <tr>
...         <td>b</td><td>2</td>
...     </tr>
...     <tr>
...         <td>c</td><td>2</td>
...     </tr>
... </table>'''
>>> with open('example1.xml', 'w') as f:
...     f.write(d)
...
212
>>> table1 = etl.fromxml('example1.xml', 'tr', 'td')
>>> table1
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' | '1' |
+-----+-----+
| 'b' | '2' |
+-----+-----+
| 'c' | '2' |
+-----+-----+

>>> # if the data values are stored in an attribute, provide the
... # attribute name as an extra positional argument
... d = '''<table>
...     <tr>
...         <td v='foo'/><td v='bar'/>
...     </tr>
...     <tr>
...         <td v='a'/><td v='1'/>
...     </tr>
...     <tr>
...         <td v='b'/><td v='2'/>
...     </tr>
...     <tr>
...         <td v='c'/><td v='2'/>
...     </tr>
... </table>'''
>>> with open('example2.xml', 'w') as f:
...     f.write(d)
...
220
>>> table2 = etl.fromxml('example2.xml', 'tr', 'td', 'v')
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' | '1' |
+-----+-----+
| 'b' | '2' |
+-----+-----+
| 'c' | '2' |
+-----+-----+

>>> # data values can also be extracted by providing a mapping of field
... # names to element paths
... d = '''<table>
...     <row>
...         <foo>a</foo><baz><bar v='1'/><bar v='3'/></baz>
...     </row>
...     <row>
...         <foo>b</foo><baz><bar v='2'/></baz>
...     </row>
...     <row>
...         <foo>c</foo><baz><bar v='2'/></baz>
...     </row>
... </table>'''
>>> with open('example3.xml', 'w') as f:
...     f.write(d)
...
223
>>> table3 = etl.fromxml('example3.xml', 'row',
...                      {'foo': 'foo', 'bar': ('baz/bar', 'v')})
>>> table3
+------------+-----+
| bar        | foo |
+============+=====+
| ('1', '3') | 'a' |
+------------+-----+
| '2'        | 'b' |
+------------+-----+
| '2'        | 'c' |
+------------+-----+

Note that the implementation is currently not streaming, i.e., the whole document is loaded into memory.

If multiple elements match a given field, all values are reported as a tuple.

If there is more than one element name used for row values, a tuple or list of paths can be provided, e.g., fromxml('example.html', './/tr', ('th', 'td')).

For writing to an XML file, see petl.io.text.totext().

HTML files¶

petl.io.html.tohtml(table, source=None, encoding=None, errors=None, caption=None, vrepr=<type 'unicode'>, lineterminator='\n', index_header=False, tr_style=None, td_styles=None, truncate=None)[source]¶

Write the table as HTML to a file. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 2]]
>>> etl.tohtml(table1, 'example.html', caption='example table')
>>> print(open('example.html').read())
<table class='petl'>
<caption>example table</caption>
<thead>
<tr>
<th>foo</th>
<th>bar</th>
</tr>
</thead>
<tbody>
<tr>
<td>a</td>
<td style='text-align: right'>1</td>
</tr>
<tr>
<td>b</td>
<td style='text-align: right'>2</td>
</tr>
<tr>
<td>c</td>
<td style='text-align: right'>2</td>
</tr>
</tbody>
</table>

The caption keyword argument is used to provide a table caption in the output HTML.

petl.io.html.teehtml(table, source=None, encoding=None, errors=None, caption=None, vrepr=<type 'unicode'>, lineterminator='\n', index_header=False, tr_style=None, td_styles=None, truncate=None)[source]¶: Return a table that writes rows to a Unicode HTML file as they are iterated over.

JSON files¶

petl.io.json.fromjson(source, *args, **kwargs)[source]¶

Extract data from a JSON file. The file must contain a JSON array as the top level object, and each member of the array will be treated as a row of data. E.g.:

>>> import petl as etl
>>> data = '''
... [{"foo": "a", "bar": 1},
... {"foo": "b", "bar": 2},
... {"foo": "c", "bar": 2}]
... '''
>>> with open('example.json', 'w') as f:
...     f.write(data)
...
74
>>> table1 = etl.fromjson('example.json')
>>> table1
+-----+-----+
| bar | foo |
+=====+=====+
|   1 | 'a' |
+-----+-----+
|   2 | 'b' |
+-----+-----+
|   2 | 'c' |
+-----+-----+

If your JSON file does not fit this structure, you will need to parse it via json.load() and select the array to treat as the data, see also petl.io.json.fromdicts().

petl.io.json.fromdicts(dicts, header=None)[source]¶

View a sequence of Python dict as a table. E.g.:

>>> import petl as etl
>>> dicts = [{"foo": "a", "bar": 1},
...          {"foo": "b", "bar": 2},
...          {"foo": "c", "bar": 2}]
>>> table1 = etl.fromdicts(dicts)
>>> table1
+-----+-----+
| bar | foo |
+=====+=====+
|   1 | 'a' |
+-----+-----+
|   2 | 'b' |
+-----+-----+
|   2 | 'c' |
+-----+-----+

Databases¶

Note

The automatic table creation feature of petl.io.db.todb() requires SQLAlchemy to be installed, e.g.:

$ pip install sqlalchemy

petl.io.db.fromdb(dbo, query, *args, **kwargs)[source]¶

Provides access to data from any DB-API 2.0 connection via a given query. E.g., using sqlite3:

>>> import petl as etl
>>> import sqlite3
>>> connection = sqlite3.connect('example.db')
>>> table = etl.fromdb(connection, 'SELECT * FROM example')

E.g., using psycopg2 (assuming you’ve installed it first):

>>> import petl as etl
>>> import psycopg2
>>> connection = psycopg2.connect('dbname=example user=postgres')
>>> table = etl.fromdb(connection, 'SELECT * FROM example')

E.g., using pymysql (assuming you’ve installed it first):

>>> import petl as etl
>>> import pymysql
>>> connection = pymysql.connect(password='moonpie', database='thangs')
>>> table = etl.fromdb(connection, 'SELECT * FROM example')

The dbo argument may also be a function that creates a cursor. N.B., each call to the function should return a new cursor. E.g.:

>>> import petl as etl
>>> import psycopg2
>>> connection = psycopg2.connect('dbname=example user=postgres')
>>> mkcursor = lambda: connection.cursor(cursor_factory=psycopg2.extras.DictCursor)
>>> table = etl.fromdb(mkcursor, 'SELECT * FROM example')

The parameter dbo may also be an SQLAlchemy engine, session or connection object.

The parameter dbo may also be a string, in which case it is interpreted as the name of a file containing an sqlite3 database.

Note that the default behaviour of most database servers and clients is for the entire result set for each query to be sent from the server to the client. If your query returns a large result set this can result in significant memory usage at the client. Some databases support server-side cursors which provide a means for client libraries to fetch result sets incrementally, reducing memory usage at the client.

To use a server-side cursor with a PostgreSQL database, e.g.:

>>> import petl as etl
>>> import psycopg2
>>> connection = psycopg2.connect('dbname=example user=postgres')
>>> table = etl.fromdb(lambda: connection.cursor(name='arbitrary'),
...                    'SELECT * FROM example')

For more information on server-side cursors see the following links:

http://initd.org/psycopg/docs/usage.html#server-side-cursors

http://mysql-python.sourceforge.net/MySQLdb.html#using-and-extending

petl.io.db.todb(table, dbo, tablename, schema=None, commit=True, create=False, drop=False, constraints=True, metadata=None, dialect=None, sample=1000)[source]¶

Load data into an existing database table via a DB-API 2.0 connection or cursor. Note that the database table will be truncated, i.e., all existing rows will be deleted prior to inserting the new data. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'],
...          ['a', 1],
...          ['b', 2],
...          ['c', 2]]
>>> # using sqlite3
... import sqlite3
>>> connection = sqlite3.connect('example.db')
>>> # assuming table "foobar" already exists in the database
... etl.todb(table, connection, 'foobar')
>>> # using psycopg2
>>> import psycopg2
>>> connection = psycopg2.connect('dbname=example user=postgres')
>>> # assuming table "foobar" already exists in the database
... etl.todb(table, connection, 'foobar')
>>> # using pymysql
>>> import pymysql
>>> connection = pymysql.connect(password='moonpie', database='thangs')
>>> # tell MySQL to use standard quote character
... connection.cursor().execute('SET SQL_MODE=ANSI_QUOTES')
>>> # load data, assuming table "foobar" already exists in the database
... etl.todb(table, connection, 'foobar')

N.B., for MySQL the statement SET SQL_MODE=ANSI_QUOTES is required to ensure MySQL uses SQL-92 standard quote characters.

A cursor can also be provided instead of a connection, e.g.:

>>> import psycopg2
>>> connection = psycopg2.connect('dbname=example user=postgres')
>>> cursor = connection.cursor()
>>> etl.todb(table, cursor, 'foobar')

The parameter dbo may also be an SQLAlchemy engine, session or connection object.

The parameter dbo may also be a string, in which case it is interpreted as the name of a file containing an sqlite3 database.

If create=True this function will attempt to automatically create a database table before loading the data. This functionality requires SQLAlchemy to be installed.

Keyword arguments:

table : table container: Table data to load
dbo : database object: DB-API 2.0 connection, callable returning a DB-API 2.0 cursor, or SQLAlchemy connection, engine or session
tablename : string: Name of the table in the database
schema : string: Name of the database schema to find the table in
commit : bool: If True commit the changes
create : bool: If True attempt to create the table before loading, inferring types from a sample of the data (requires SQLAlchemy)
drop : bool: If True attempt to drop the table before recreating (only relevant if create=True)
constraints : bool: If True use length and nullable constraints (only relevant if create=True)
metadata : sqlalchemy.MetaData: Custom table metadata (only relevant if create=True)
dialect : string: One of {‘access’, ‘sybase’, ‘sqlite’, ‘informix’, ‘firebird’, ‘mysql’, ‘oracle’, ‘maxdb’, ‘postgresql’, ‘mssql’} (only relevant if create=True)
sample : int: Number of rows to sample when inferring types etc. Set to 0 to use the whole table (only relevant if create=True)

petl.io.db.appenddb(table, dbo, tablename, schema=None, commit=True)[source]¶: Load data into an existing database table via a DB-API 2.0 connection or cursor. As petl.io.db.todb() except that the database table will be appended, i.e., the new data will be inserted into the table, and any existing rows will remain.

Excel .xls files (xlrd/xlwt)¶

Note

The following functions require xlrd and xlwt to be installed, e.g.:

$ pip install xlrd xlwt-future

petl.io.xls.fromxls(filename, sheet=None, use_view=True)[source]¶

Extract a table from a sheet in an Excel .xls file.

N.B., the sheet name is case sensitive.

petl.io.xls.toxls(tbl, filename, sheet, encoding=None, style_compression=0, styles=None)[source]¶: Write a table to a new Excel .xls file.

Excel .xlsx files (openpyxl)¶

Note

The following functions require openpyxl to be installed, e.g.:

$ pip install openpyxl

petl.io.xlsx.fromxlsx(filename, sheet=None, range_string=None, row_offset=0, column_offset=0, **kwargs)[source]¶

Extract a table from a sheet in an Excel .xlsx file.

N.B., the sheet name is case sensitive.

The sheet argument can be omitted, in which case the first sheet in the workbook is used by default.

The range_string argument can be used to provide a range string specifying a range of cells to extract.

The row_offset and column_offset arguments can be used to specify offsets.

Any other keyword arguments are passed through to openpyxl.load_workbook().

petl.io.xlsx.toxlsx(tbl, filename, sheet=None, encoding=None)[source]¶: Write a table to a new Excel .xlsx file.

Arrays (NumPy)¶

Note

The following functions require numpy to be installed, e.g.:

$ pip install numpy

petl.io.numpy.fromarray(a)[source]¶

Extract a table from a numpy structured array, e.g.:

>>> import petl as etl
>>> import numpy as np
>>> a = np.array([('apples', 1, 2.5),
...               ('oranges', 3, 4.4),
...               ('pears', 7, 0.1)],
...              dtype='U8, i4,f4')
>>> table = etl.fromarray(a)
>>> table
+-----------+----+-----------+
| f0        | f1 | f2        |
+===========+====+===========+
| 'apples'  | 1  | 2.5       |
+-----------+----+-----------+
| 'oranges' | 3  | 4.4000001 |
+-----------+----+-----------+
| 'pears'   | 7  | 0.1       |
+-----------+----+-----------+

petl.io.numpy.toarray(table, dtype=None, count=-1, sample=1000)[source]¶

Load data from the given table into a numpy structured array. E.g.:

>>> import petl as etl
>>> table = [('foo', 'bar', 'baz'),
...          ('apples', 1, 2.5),
...          ('oranges', 3, 4.4),
...          ('pears', 7, .1)]
>>> a = etl.toarray(table)
>>> a
array([('apples', 1, 2.5), ('oranges', 3, 4.4), ('pears', 7, 0.1)],
      dtype=[('foo', '<U7'), ('bar', '<i8'), ('baz', '<f8')])
>>> # the dtype can be specified as a string
... a = etl.toarray(table, dtype='a4, i2, f4')
>>> a
array([(b'appl', 1, 2.5), (b'oran', 3, 4.400000095367432),
       (b'pear', 7, 0.10000000149011612)],
      dtype=[('foo', 'S4'), ('bar', '<i2'), ('baz', '<f4')])
>>> # the dtype can also be partially specified
... a = etl.toarray(table, dtype={'foo': 'a4'})
>>> a
array([(b'appl', 1, 2.5), (b'oran', 3, 4.4), (b'pear', 7, 0.1)],
      dtype=[('foo', 'S4'), ('bar', '<i8'), ('baz', '<f8')])

If the dtype is not completely specified, sample rows will be examined to infer an appropriate dtype.

petl.io.numpy.torecarray(*args, **kwargs)[source]¶: Convenient shorthand for toarray(*args, **kwargs).view(np.recarray).

petl.io.numpy.valuestoarray(vals, dtype=None, count=-1, sample=1000)[source]¶

Load values from a table column into a numpy array, e.g.:

>>> import petl as etl
>>> table = [('foo', 'bar', 'baz'),
...          ('apples', 1, 2.5),
...          ('oranges', 3, 4.4),
...          ('pears', 7, .1)]
>>> table = etl.wrap(table)
>>> table.values('bar').array()
array([1, 3, 7])
>>> # specify dtype
... table.values('bar').array(dtype='i4')
array([1, 3, 7], dtype=int32)

DataFrames (pandas)¶

Note

The following functions require pandas to be installed, e.g.:

$ pip install pandas

petl.io.pandas.fromdataframe(df, include_index=False)[source]¶

Extract a table from a pandas DataFrame. E.g.:

>>> import petl as etl
>>> import pandas as pd
>>> records = [('apples', 1, 2.5), ('oranges', 3, 4.4), ('pears', 7, 0.1)]
>>> df = pd.DataFrame.from_records(records, columns=('foo', 'bar', 'baz'))
>>> table = etl.fromdataframe(df)
>>> table
+-----------+-----+-----+
| foo       | bar | baz |
+===========+=====+=====+
| 'apples'  |   1 | 2.5 |
+-----------+-----+-----+
| 'oranges' |   3 | 4.4 |
+-----------+-----+-----+
| 'pears'   |   7 | 0.1 |
+-----------+-----+-----+

petl.io.pandas.todataframe(table, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)[source]¶

Load data from the given table into a pandas DataFrame. E.g.:

>>> import petl as etl
>>> table = [('foo', 'bar', 'baz'),
...          ('apples', 1, 2.5),
...          ('oranges', 3, 4.4),
...          ('pears', 7, .1)]
>>> df = etl.todataframe(table)
>>> df
       foo  bar  baz
0   apples    1  2.5
1  oranges    3  4.4
2    pears    7  0.1

HDF5 files (PyTables)¶

Note

The following functions require PyTables to be installed, e.g.:

$ # install HDF5
$ apt-get install libhdf5-7 libhdf5-dev
$ # install other prerequisites
$ pip install cython
$ pip install numpy
$ pip install numexpr
$ # install PyTables
$ pip install tables

petl.io.pytables.fromhdf5(source, where=None, name=None, condition=None, condvars=None, start=None, stop=None, step=None)[source]¶

Provides access to an HDF5 table. E.g.:

>>> import petl as etl
>>> import tables
>>> # set up a new hdf5 table to demonstrate with
... h5file = tables.openFile('example.h5', mode='w',
...                          title='Example file')
>>> h5file.createGroup('/', 'testgroup', 'Test Group')
/testgroup (Group) 'Test Group'
  children := []
>>> class FooBar(tables.IsDescription):
...     foo = tables.Int32Col(pos=0)
...     bar = tables.StringCol(6, pos=2)
...
>>> h5table = h5file.createTable('/testgroup', 'testtable', FooBar,
...                              'Test Table')
>>> # load some data into the table
... table1 = (('foo', 'bar'),
...           (1, b'asdfgh'),
...           (2, b'qwerty'),
...           (3, b'zxcvbn'))
>>> for row in table1[1:]:
...     for i, f in enumerate(table1[0]):
...         h5table.row[f] = row[i]
...     h5table.row.append()
...
>>> h5file.flush()
>>> h5file.close()
>>> #
... # now demonstrate use of fromhdf5
... table1 = etl.fromhdf5('example.h5', '/testgroup', 'testtable')
>>> table1
+-----+-----------+
| foo | bar       |
+=====+===========+
|   1 | b'asdfgh' |
+-----+-----------+
|   2 | b'qwerty' |
+-----+-----------+
|   3 | b'zxcvbn' |
+-----+-----------+

>>> # alternatively just specify path to table node
... table1 = etl.fromhdf5('example.h5', '/testgroup/testtable')
>>> # ...or use an existing tables.File object
... h5file = tables.openFile('example.h5')
>>> table1 = etl.fromhdf5(h5file, '/testgroup/testtable')
>>> # ...or use an existing tables.Table object
... h5tbl = h5file.getNode('/testgroup/testtable')
>>> table1 = etl.fromhdf5(h5tbl)
>>> # use a condition to filter data
... table2 = etl.fromhdf5(h5tbl, condition='foo < 3')
>>> table2
+-----+-----------+
| foo | bar       |
+=====+===========+
|   1 | b'asdfgh' |
+-----+-----------+
|   2 | b'qwerty' |
+-----+-----------+

>>> h5file.close()

petl.io.pytables.fromhdf5sorted(source, where=None, name=None, sortby=None, checkCSI=False, start=None, stop=None, step=None)[source]¶

Provides access to an HDF5 table, sorted by an indexed column, e.g.:

>>> import petl as etl
>>> import tables
>>> # set up a new hdf5 table to demonstrate with
... h5file = tables.openFile('example.h5', mode='w', title='Test file')
>>> h5file.createGroup('/', 'testgroup', 'Test Group')
/testgroup (Group) 'Test Group'
  children := []
>>> class FooBar(tables.IsDescription):
...     foo = tables.Int32Col(pos=0)
...     bar = tables.StringCol(6, pos=2)
...
>>> h5table = h5file.createTable('/testgroup', 'testtable', FooBar, 'Test Table')
>>> # load some data into the table
... table1 = (('foo', 'bar'),
...           (3, b'asdfgh'),
...           (2, b'qwerty'),
...           (1, b'zxcvbn'))
>>> for row in table1[1:]:
...     for i, f in enumerate(table1[0]):
...         h5table.row[f] = row[i]
...     h5table.row.append()
...
>>> h5table.cols.foo.createCSIndex()  # CS index is required
0
>>> h5file.flush()
>>> h5file.close()
>>> #
... # access the data, sorted by the indexed column
... table2 = etl.fromhdf5sorted('example.h5', '/testgroup', 'testtable',
...                             sortby='foo')
>>> table2
+-----+-----------+
| foo | bar       |
+=====+===========+
|   1 | b'zxcvbn' |
+-----+-----------+
|   2 | b'qwerty' |
+-----+-----------+
|   3 | b'asdfgh' |
+-----+-----------+

petl.io.pytables.tohdf5(table, source, where=None, name=None, create=False, drop=False, description=None, title='', filters=None, expectedrows=10000, chunkshape=None, byteorder=None, createparents=False, sample=1000)[source]¶

Write to an HDF5 table. If create is False, assumes the table already exists, and attempts to truncate it before loading. If create is True, a new table will be created, and if drop is True, any existing table will be dropped first. If description is None, the description will be guessed. E.g.:

>>> import petl as etl
>>> table1 = (('foo', 'bar'),
...           (1, b'asdfgh'),
...           (2, b'qwerty'),
...           (3, b'zxcvbn'))
>>> etl.tohdf5(table1, 'example.h5', '/testgroup', 'testtable',
...            drop=True, create=True, createparents=True)
>>> etl.fromhdf5('example.h5', '/testgroup', 'testtable')
+-----+-----------+
| foo | bar       |
+=====+===========+
|   1 | b'asdfgh' |
+-----+-----------+
|   2 | b'qwerty' |
+-----+-----------+
|   3 | b'zxcvbn' |
+-----+-----------+

petl.io.pytables.appendhdf5(table, source, where=None, name=None)[source]¶: As petl.io.hdf5.tohdf5() but don’t truncate the target table before loading.

Text indexes (Whoosh)¶

Note

The following functions require Whoosh to be installed, e.g.:

$ pip install whoosh

petl.io.whoosh.fromtextindex(index_or_dirname, indexname=None, docnum_field=None)[source]¶

Extract all documents from a Whoosh index. E.g.:

>>> import petl as etl
>>> import os
>>> # set up an index and load some documents via the Whoosh API
... from whoosh.index import create_in
>>> from whoosh.fields import *
>>> schema = Schema(title=TEXT(stored=True), path=ID(stored=True),
...                 content=TEXT)
>>> dirname = 'example.whoosh'
>>> if not os.path.exists(dirname):
...     os.mkdir(dirname)
...
>>> index = create_in(dirname, schema)
>>> writer = index.writer()
>>> writer.add_document(title=u"First document", path=u"/a",
...                     content=u"This is the first document we've added!")
>>> writer.add_document(title=u"Second document", path=u"/b",
...                     content=u"The second one is even more interesting!")
>>> writer.commit()
>>> # extract documents as a table
... table = etl.fromtextindex(dirname)
>>> table
+------+-------------------+
| path | title             |
+======+===================+
| '/a' | 'First document'  |
+------+-------------------+
| '/b' | 'Second document' |
+------+-------------------+

Keyword arguments:

index_or_dirname: Either an instance of whoosh.index.Index or a string containing the directory path where the index is stored.
indexname: String containing the name of the index, if multiple indexes are stored in the same directory.
docnum_field: If not None, an extra field will be added to the output table containing the internal document number stored in the index. The name of the field will be the value of this argument.

petl.io.whoosh.searchtextindex(index_or_dirname, query, limit=10, indexname=None, docnum_field=None, score_field=None, fieldboosts=None, search_kwargs=None)[source]¶

Search a Whoosh index using a query. E.g.:

>>> import petl as etl
>>> import os
>>> # set up an index and load some documents via the Whoosh API
... from whoosh.index import create_in
>>> from whoosh.fields import *
>>> schema = Schema(title=TEXT(stored=True), path=ID(stored=True),
...                            content=TEXT)
>>> dirname = 'example.whoosh'
>>> if not os.path.exists(dirname):
...     os.mkdir(dirname)
...
>>> index = create_in('example.whoosh', schema)
>>> writer = index.writer()
>>> writer.add_document(title=u"Oranges", path=u"/a",
...                     content=u"This is the first document we've added!")
>>> writer.add_document(title=u"Apples", path=u"/b",
...                     content=u"The second document is even more "
...                             u"interesting!")
>>> writer.commit()
>>> # demonstrate the use of searchtextindex()
... table1 = etl.searchtextindex('example.whoosh', 'oranges')
>>> table1
+------+-----------+
| path | title     |
+======+===========+
| '/a' | 'Oranges' |
+------+-----------+

>>> table2 = etl.searchtextindex('example.whoosh', 'doc*')
>>> table2
+------+-----------+
| path | title     |
+======+===========+
| '/a' | 'Oranges' |
+------+-----------+
| '/b' | 'Apples'  |
+------+-----------+

Keyword arguments:

index_or_dirname: Either an instance of whoosh.index.Index or a string containing the directory path where the index is to be stored.
query: Either a string or an instance of whoosh.query.Query. If a string, it will be parsed as a multi-field query, i.e., any terms not bound to a specific field will match any field.
limit: Return at most limit results.
indexname: String containing the name of the index, if multiple indexes are stored in the same directory.
docnum_field: If not None, an extra field will be added to the output table containing the internal document number stored in the index. The name of the field will be the value of this argument.
score_field: If not None, an extra field will be added to the output table containing the score of the result. The name of the field will be the value of this argument.
fieldboosts: An optional dictionary mapping field names to boosts.
search_kwargs: Any extra keyword arguments to be passed through to the Whoosh search() method.

petl.io.whoosh.searchtextindexpage(index_or_dirname, query, pagenum, pagelen=10, indexname=None, docnum_field=None, score_field=None, fieldboosts=None, search_kwargs=None)[source]¶

Search an index using a query, returning a result page.

Keyword arguments:

index_or_dirname: Either an instance of whoosh.index.Index or a string containing the directory path where the index is to be stored.
query: Either a string or an instance of whoosh.query.Query. If a string, it will be parsed as a multi-field query, i.e., any terms not bound to a specific field will match any field.
pagenum: Number of the page to return (e.g., 1 = first page).
pagelen: Number of results per page.
indexname: String containing the name of the index, if multiple indexes are stored in the same directory.
docnum_field: If not None, an extra field will be added to the output table containing the internal document number stored in the index. The name of the field will be the value of this argument.
score_field: If not None, an extra field will be added to the output table containing the score of the result. The name of the field will be the value of this argument.
fieldboosts: An optional dictionary mapping field names to boosts.
search_kwargs: Any extra keyword arguments to be passed through to the Whoosh search() method.

petl.io.whoosh.totextindex(table, index_or_dirname, schema=None, indexname=None, merge=False, optimize=False)[source]¶

Load all rows from table into a Whoosh index. N.B., this will clear any existing data in the index before loading. E.g.:

>>> import petl as etl
>>> import datetime
>>> import os
>>> # here is the table we want to load into an index
... table = (('f0', 'f1', 'f2', 'f3', 'f4'),
...          ('AAA', 12, 4.3, True, datetime.datetime.now()),
...          ('BBB', 6, 3.4, False, datetime.datetime(1900, 1, 31)),
...          ('CCC', 42, 7.8, True, datetime.datetime(2100, 12, 25)))
>>> # define a schema for the index
... from whoosh.fields import *
>>> schema = Schema(f0=TEXT(stored=True),
...                 f1=NUMERIC(int, stored=True),
...                 f2=NUMERIC(float, stored=True),
...                 f3=BOOLEAN(stored=True),
...                 f4=DATETIME(stored=True))
>>> # load index
... dirname = 'example.whoosh'
>>> if not os.path.exists(dirname):
...     os.mkdir(dirname)
...
>>> etl.totextindex(table, dirname, schema=schema)

Keyword arguments:

table: A table container with the data to be loaded.
index_or_dirname: Either an instance of whoosh.index.Index or a string containing the directory path where the index is to be stored.
schema: Index schema to use if creating the index.
indexname: String containing the name of the index, if multiple indexes are stored in the same directory.
merge: Merge small segments during commit?
optimize: Merge all segments together?

petl.io.whoosh.appendtextindex(table, index_or_dirname, indexname=None, merge=True, optimize=False)[source]¶

Load all rows from table into a Whoosh index, adding them to any existing data in the index.

Keyword arguments:

table: A table container with the data to be loaded.
index_or_dirname: Either an instance of whoosh.index.Index or a string containing the directory path where the index is to be stored.
indexname: String containing the name of the index, if multiple indexes are stored in the same directory.
merge: Merge small segments during commit?
optimize: Merge all segments together?

I/O helper classes¶

The following classes are helpers for extract (from...()) and load (to...()) functions that use a file-like data source.

An instance of any of the following classes can be used as the source argument to data extraction functions like petl.io.csv.fromcsv() etc., with the exception of petl.io.sources.StdoutSource which is write-only.

An instance of any of the following classes can also be used as the source argument to data loading functions like petl.io.csv.tocsv() etc., with the exception of petl.io.sources.StdinSource, petl.io.sources.URLSource and petl.io.sources.PopenSource which are read-only.

The behaviour of each source can usually be configured by passing arguments to the constructor, see the source code of the petl.io.sources module for full details.

class petl.io.sources.FileSource(filename, **kwargs)[source]¶

class petl.io.sources.GzipSource(filename, **kwargs)[source]¶

class petl.io.sources.BZ2Source(filename, **kwargs)[source]¶

class petl.io.sources.ZipSource(filename, membername, pwd=None, **kwargs)[source]¶

class petl.io.sources.StdinSource[source]¶

class petl.io.sources.StdoutSource[source]¶

class petl.io.sources.URLSource(*args, **kwargs)[source]¶

class petl.io.sources.MemorySource(s=None)[source]¶

class petl.io.sources.PopenSource(*args, **kwargs)[source]¶

Transform - transforming tables¶

Basic transformations¶

petl.transform.basics.head(table, n=5)[source]¶

Select the first n data rows. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 5],
...           ['d', 7],
...           ['f', 42],
...           ['f', 3],
...           ['h', 90]]
>>> table2 = etl.head(table1, 4)
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+
| 'c' |   5 |
+-----+-----+
| 'd' |   7 |
+-----+-----+

petl.transform.basics.tail(table, n=5)[source]¶

Select the last n data rows. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 5],
...           ['d', 7],
...           ['f', 42],
...           ['f', 3],
...           ['h', 90],
...           ['k', 12],
...           ['l', 77],
...           ['q', 2]]
>>> table2 = etl.tail(table1, 4)
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'h' |  90 |
+-----+-----+
| 'k' |  12 |
+-----+-----+
| 'l' |  77 |
+-----+-----+
| 'q' |   2 |
+-----+-----+

petl.transform.basics.rowslice(table, *sliceargs)[source]¶

Choose a subsequence of data rows. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['c', 5],
...           ['d', 7],
...           ['f', 42]]
>>> table2 = etl.rowslice(table1, 2)
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+

>>> table3 = etl.rowslice(table1, 1, 4)
>>> table3
+-----+-----+
| foo | bar |
+=====+=====+
| 'b' |   2 |
+-----+-----+
| 'c' |   5 |
+-----+-----+
| 'd' |   7 |
+-----+-----+

>>> table4 = etl.rowslice(table1, 0, 5, 2)
>>> table4
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'c' |   5 |
+-----+-----+
| 'f' |  42 |
+-----+-----+

Positional arguments are used to slice the data rows. The sliceargs are passed through to itertools.islice().

petl.transform.basics.cut(table, *args, **kwargs)[source]¶

Choose and/or re-order fields. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, 2.7],
...           ['B', 2, 3.4],
...           ['B', 3, 7.8],
...           ['D', 42, 9.0],
...           ['E', 12]]
>>> table2 = etl.cut(table1, 'foo', 'baz')
>>> table2
+-----+------+
| foo | baz  |
+=====+======+
| 'A' |  2.7 |
+-----+------+
| 'B' |  3.4 |
+-----+------+
| 'B' |  7.8 |
+-----+------+
| 'D' |  9.0 |
+-----+------+
| 'E' | None |
+-----+------+

>>> # fields can also be specified by index, starting from zero
... table3 = etl.cut(table1, 0, 2)
>>> table3
+-----+------+
| foo | baz  |
+=====+======+
| 'A' |  2.7 |
+-----+------+
| 'B' |  3.4 |
+-----+------+
| 'B' |  7.8 |
+-----+------+
| 'D' |  9.0 |
+-----+------+
| 'E' | None |
+-----+------+

>>> # field names and indices can be mixed
... table4 = etl.cut(table1, 'bar', 0)
>>> table4
+-----+-----+
| bar | foo |
+=====+=====+
|   1 | 'A' |
+-----+-----+
|   2 | 'B' |
+-----+-----+
|   3 | 'B' |
+-----+-----+
|  42 | 'D' |
+-----+-----+
|  12 | 'E' |
+-----+-----+

>>> # select a range of fields
... table5 = etl.cut(table1, *range(0, 2))
>>> table5
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   1 |
+-----+-----+
| 'B' |   2 |
+-----+-----+
| 'B' |   3 |
+-----+-----+
| 'D' |  42 |
+-----+-----+
| 'E' |  12 |
+-----+-----+

Note that any short rows will be padded with None values (or whatever is provided via the missing keyword argument).

petl.transform.basics.cutout(table, *args, **kwargs)[source]¶

Remove fields. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, 2.7],
...           ['B', 2, 3.4],
...           ['B', 3, 7.8],
...           ['D', 42, 9.0],
...           ['E', 12]]
>>> table2 = etl.cutout(table1, 'bar')
>>> table2
+-----+------+
| foo | baz  |
+=====+======+
| 'A' |  2.7 |
+-----+------+
| 'B' |  3.4 |
+-----+------+
| 'B' |  7.8 |
+-----+------+
| 'D' |  9.0 |
+-----+------+
| 'E' | None |
+-----+------+

Header manipulations¶

petl.transform.headers.rename(table, *args, **kwargs)[source]¶

Replace one or more values in the table’s header row. E.g.:

>>> import petl as etl
>>> table1 = [['sex', 'age'],
...           ['m', 12],
...           ['f', 34],
...           ['-', 56]]
>>> # rename a single field
... table2 = etl.rename(table1, 'sex', 'gender')
>>> table2
+--------+-----+
| gender | age |
+========+=====+
| 'm'    |  12 |
+--------+-----+
| 'f'    |  34 |
+--------+-----+
| '-'    |  56 |
+--------+-----+

>>> # rename multiple fields by passing dictionary as second argument
... table3 = etl.rename(table1, {'sex': 'gender', 'age': 'age_years'})
>>> table3
+--------+-----------+
| gender | age_years |
+========+===========+
| 'm'    |        12 |
+--------+-----------+
| 'f'    |        34 |
+--------+-----------+
| '-'    |        56 |
+--------+-----------+

The field to rename can be specified as an index (i.e., integer representing field position).

petl.transform.headers.setheader(table, header)[source]¶

Replace header row in the given table. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2]]
>>> table2 = etl.setheader(table1, ['foofoo', 'barbar'])
>>> table2
+--------+--------+
| foofoo | barbar |
+========+========+
| 'a'    |      1 |
+--------+--------+
| 'b'    |      2 |
+--------+--------+

petl.transform.headers.extendheader(table, fields)[source]¶

Extend header row in the given table. E.g.:

>>> import petl as etl
>>> table1 = [['foo'],
...           ['a', 1, True],
...           ['b', 2, False]]
>>> table2 = etl.extendheader(table1, ['bar', 'baz'])
>>> table2
+-----+-----+-------+
| foo | bar | baz   |
+=====+=====+=======+
| 'a' |   1 | True  |
+-----+-----+-------+
| 'b' |   2 | False |
+-----+-----+-------+

petl.transform.headers.pushheader(table, header, *args)[source]¶

Push rows down and prepend a header row. E.g.:

>>> import petl as etl
>>> table1 = [['a', 1],
...           ['b', 2]]
>>> table2 = etl.pushheader(table1, ['foo', 'bar'])
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+

The header row can either be a list or positional arguments.

petl.transform.headers.prefixheader(table, prefix)[source]¶: Prefix all fields in the table header.

petl.transform.headers.suffixheader(table, suffix)[source]¶: Suffix all fields in the table header.

petl.transform.headers.skip(table, n)[source]¶

Skip n rows, including the header row. E.g.:

>>> import petl as etl
>>> table1 = [['#aaa', 'bbb', 'ccc'],
...           ['#mmm'],
...           ['foo', 'bar'],
...           ['a', 1],
...           ['b', 2]]
>>> table2 = etl.skip(table1, 2)
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+

Converting values¶

petl.transform.conversions.convert(table, *args, **kwargs)[source]¶

Transform values under one or more fields via arbitrary functions, method invocations or dictionary translations. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', '2.4', 12],
...           ['B', '5.7', 34],
...           ['C', '1.2', 56]]
>>> # using a built-in function:
... table2 = etl.convert(table1, 'bar', float)
>>> table2
+-----+-----+-----+
| foo | bar | baz |
+=====+=====+=====+
| 'A' | 2.4 |  12 |
+-----+-----+-----+
| 'B' | 5.7 |  34 |
+-----+-----+-----+
| 'C' | 1.2 |  56 |
+-----+-----+-----+

>>> # using a lambda function::
... table3 = etl.convert(table1, 'baz', lambda v: v*2)
>>> table3
+-----+-------+-----+
| foo | bar   | baz |
+=====+=======+=====+
| 'A' | '2.4' |  24 |
+-----+-------+-----+
| 'B' | '5.7' |  68 |
+-----+-------+-----+
| 'C' | '1.2' | 112 |
+-----+-------+-----+

>>> # a method of the data value can also be invoked by passing
... # the method name
... table4 = etl.convert(table1, 'foo', 'lower')
>>> table4
+-----+-------+-----+
| foo | bar   | baz |
+=====+=======+=====+
| 'a' | '2.4' |  12 |
+-----+-------+-----+
| 'b' | '5.7' |  34 |
+-----+-------+-----+
| 'c' | '1.2' |  56 |
+-----+-------+-----+

>>> # arguments to the method invocation can also be given
... table5 = etl.convert(table1, 'foo', 'replace', 'A', 'AA')
>>> table5
+------+-------+-----+
| foo  | bar   | baz |
+======+=======+=====+
| 'AA' | '2.4' |  12 |
+------+-------+-----+
| 'B'  | '5.7' |  34 |
+------+-------+-----+
| 'C'  | '1.2' |  56 |
+------+-------+-----+

>>> # values can also be translated via a dictionary
... table7 = etl.convert(table1, 'foo', {'A': 'Z', 'B': 'Y'})
>>> table7
+-----+-------+-----+
| foo | bar   | baz |
+=====+=======+=====+
| 'Z' | '2.4' |  12 |
+-----+-------+-----+
| 'Y' | '5.7' |  34 |
+-----+-------+-----+
| 'C' | '1.2' |  56 |
+-----+-------+-----+

>>> # the same conversion can be applied to multiple fields
... table8 = etl.convert(table1, ('foo', 'bar', 'baz'), str)
>>> table8
+-----+-------+------+
| foo | bar   | baz  |
+=====+=======+======+
| 'A' | '2.4' | '12' |
+-----+-------+------+
| 'B' | '5.7' | '34' |
+-----+-------+------+
| 'C' | '1.2' | '56' |
+-----+-------+------+

>>> # multiple conversions can be specified at the same time
... table9 = etl.convert(table1, {'foo': 'lower',
...                               'bar': float,
...                               'baz': lambda v: v * 2})
>>> table9
+-----+-----+-----+
| foo | bar | baz |
+=====+=====+=====+
| 'a' | 2.4 |  24 |
+-----+-----+-----+
| 'b' | 5.7 |  68 |
+-----+-----+-----+
| 'c' | 1.2 | 112 |
+-----+-----+-----+

>>> # ...or alternatively via a list
... table10 = etl.convert(table1, ['lower', float, lambda v: v*2])
>>> table10
+-----+-----+-----+
| foo | bar | baz |
+=====+=====+=====+
| 'a' | 2.4 |  24 |
+-----+-----+-----+
| 'b' | 5.7 |  68 |
+-----+-----+-----+
| 'c' | 1.2 | 112 |
+-----+-----+-----+

>>> # conversion can be conditional
... table11 = etl.convert(table1, 'baz', lambda v: v * 2,
...                       where=lambda r: r.foo == 'B')
>>> table11
+-----+-------+-----+
| foo | bar   | baz |
+=====+=======+=====+
| 'A' | '2.4' |  12 |
+-----+-------+-----+
| 'B' | '5.7' |  68 |
+-----+-------+-----+
| 'C' | '1.2' |  56 |
+-----+-------+-----+

>>> # conversion can access other values from the same row
... table12 = etl.convert(table1, 'baz',
...                       lambda v, row: v * float(row.bar),
...                       pass_row=True)
>>> table12
+-----+-------+--------------------+
| foo | bar   | baz                |
+=====+=======+====================+
| 'A' | '2.4' | 28.799999999999997 |
+-----+-------+--------------------+
| 'B' | '5.7' |              193.8 |
+-----+-------+--------------------+
| 'C' | '1.2' |               67.2 |
+-----+-------+--------------------+

Note that either field names or indexes can be given.

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

The pass_row keyword argument can be given, which if True will mean that both the value and the containing row will be passed as arguments to the conversion function (so, i.e., the conversion function should accept two arguments).

petl.transform.conversions.convertall(table, *args, **kwargs)[source]¶

Convenience function to convert all fields in the table using a common function or mapping. See also convert().

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.convertnumbers(table, strict=False, **kwargs)[source]¶

Convenience function to convert all field values to numbers where possible. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz', 'quux'],
...           ['1', '3.0', '9+3j', 'aaa'],
...           ['2', '1.3', '7+2j', None]]
>>> table2 = etl.convertnumbers(table1)
>>> table2
+-----+-----+--------+-------+
| foo | bar | baz    | quux  |
+=====+=====+========+=======+
|   1 | 3.0 | (9+3j) | 'aaa' |
+-----+-----+--------+-------+
|   2 | 1.3 | (7+2j) | None  |
+-----+-----+--------+-------+

petl.transform.conversions.replace(table, field, a, b, **kwargs)[source]¶

Convenience function to replace all occurrences of a with b under the given field. See also convert().

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.replaceall(table, a, b, **kwargs)[source]¶

Convenience function to replace all instances of a with b under all fields. See also convertall().

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.format(table, field, fmt, **kwargs)[source]¶

Convenience function to format all values in the given field using the fmt format string.

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.formatall(table, fmt, **kwargs)[source]¶

Convenience function to format all values in all fields using the fmt format string.

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.interpolate(table, field, fmt, **kwargs)[source]¶

Convenience function to interpolate all values in the given field using the fmt string.

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.interpolateall(table, fmt, **kwargs)[source]¶

Convenience function to interpolate all values in all fields using the fmt string.

The where keyword argument can be given with a callable or expression which is evaluated on each row and which should return True if the conversion should be applied on that row, else False.

petl.transform.conversions.update(table, field, value, **kwargs)[source]¶: Convenience function to convert a field to a fixed value. Accepts the where keyword argument. See also convert().

Selecting rows¶

petl.transform.selects.select(table, *args, **kwargs)[source]¶

Select rows meeting a condition. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['a', 4, 9.3],
...           ['a', 2, 88.2],
...           ['b', 1, 23.3],
...           ['c', 8, 42.0],
...           ['d', 7, 100.9],
...           ['c', 2]]
>>> # the second positional argument can be a function accepting
... # a row
... table2 = etl.select(table1,
...                     lambda rec: rec.foo == 'a' and rec.baz > 88.1)
>>> table2
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'a' |   2 | 88.2 |
+-----+-----+------+

>>> # the second positional argument can also be an expression
... # string, which will be converted to a function using petl.expr()
... table3 = etl.select(table1, "{foo} == 'a' and {baz} > 88.1")
>>> table3
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'a' |   2 | 88.2 |
+-----+-----+------+

>>> # the condition can also be applied to a single field
... table4 = etl.select(table1, 'foo', lambda v: v == 'a')
>>> table4
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'a' |   4 |  9.3 |
+-----+-----+------+
| 'a' |   2 | 88.2 |
+-----+-----+------+

The complement of the selection can be returned (i.e., the query can be inverted) by providing complement=True as a keyword argument.

petl.transform.selects.selectop(table, field, value, op, complement=False)[source]¶: Select rows where the function op applied to the given field and the given value returns True.

petl.transform.selects.selecteq(table, field, value, complement=False)[source]¶: Select rows where the given field equals the given value.

petl.transform.selects.selectne(table, field, value, complement=False)[source]¶: Select rows where the given field does not equal the given value.

petl.transform.selects.selectlt(table, field, value, complement=False)[source]¶: Select rows where the given field is less than the given value.

petl.transform.selects.selectle(table, field, value, complement=False)[source]¶: Select rows where the given field is less than or equal to the given value.

petl.transform.selects.selectgt(table, field, value, complement=False)[source]¶: Select rows where the given field is greater than the given value.

petl.transform.selects.selectge(table, field, value, complement=False)[source]¶: Select rows where the given field is greater than or equal to the given value.

petl.transform.selects.selectrangeopen(table, field, minv, maxv, complement=False)[source]¶: Select rows where the given field is greater than or equal to minv and less than or equal to maxv.

petl.transform.selects.selectrangeopenleft(table, field, minv, maxv, complement=False)[source]¶: Select rows where the given field is greater than or equal to minv and less than maxv.

petl.transform.selects.selectrangeopenright(table, field, minv, maxv, complement=False)[source]¶: Select rows where the given field is greater than minv and less than or equal to maxv.

petl.transform.selects.selectrangeclosed(table, field, minv, maxv, complement=False)[source]¶: Select rows where the given field is greater than minv and less than maxv.

petl.transform.selects.selectcontains(table, field, value, complement=False)[source]¶: Select rows where the given field contains the given value.

petl.transform.selects.selectin(table, field, value, complement=False)[source]¶: Select rows where the given field is a member of the given value.

petl.transform.selects.selectnotin(table, field, value, complement=False)[source]¶: Select rows where the given field is not a member of the given value.

petl.transform.selects.selectis(table, field, value, complement=False)[source]¶: Select rows where the given field is the given value.

petl.transform.selects.selectisnot(table, field, value, complement=False)[source]¶: Select rows where the given field is not the given value.

petl.transform.selects.selectisinstance(table, field, value, complement=False)[source]¶: Select rows where the given field is an instance of the given type.

petl.transform.selects.selecttrue(table, field, complement=False)[source]¶: Select rows where the given field evaluates True.

petl.transform.selects.selectfalse(table, field, complement=False)[source]¶: Select rows where the given field evaluates False.

petl.transform.selects.selectnone(table, field, complement=False)[source]¶: Select rows where the given field is None.

petl.transform.selects.selectnotnone(table, field, complement=False)[source]¶: Select rows where the given field is not None.

petl.transform.selects.selectusingcontext(table, query)[source]¶

Select rows based on data in the current row and/or previous and next row. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['A', 1],
...           ['B', 4],
...           ['C', 5],
...           ['D', 9]]
>>> def query(prv, cur, nxt):
...     return ((prv is not None and (cur.bar - prv.bar) < 2)
...             or (nxt is not None and (nxt.bar - cur.bar) < 2))
...
>>> table2 = etl.selectusingcontext(table1, query)
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'B' |   4 |
+-----+-----+
| 'C' |   5 |
+-----+-----+

The query function should accept three rows and return a boolean value.

petl.transform.selects.rowlenselect(table, n, complement=False)[source]¶: Select rows of length n.

petl.transform.selects.facet(table, key)[source]¶

Return a dictionary mapping field values to tables. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['a', 4, 9.3],
...           ['a', 2, 88.2],
...           ['b', 1, 23.3],
...           ['c', 8, 42.0],
...           ['d', 7, 100.9],
...           ['c', 2]]
>>> foo = etl.facet(table1, 'foo')
>>> sorted(foo.keys())
['a', 'b', 'c', 'd']
>>> foo['a']
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'a' |   4 |  9.3 |
+-----+-----+------+
| 'a' |   2 | 88.2 |
+-----+-----+------+

>>> foo['c']
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'c' |   8 | 42.0 |
+-----+-----+------+
| 'c' |   2 |      |
+-----+-----+------+

Regular expressions¶

petl.transform.regex.search(table, *args, **kwargs)[source]¶

Perform a regular expression search, returning rows that match a given pattern, either anywhere in the row or within a specific field. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['orange', 12, 'oranges are nice fruit'],
...           ['mango', 42, 'I like them'],
...           ['banana', 74, 'lovely too'],
...           ['cucumber', 41, 'better than mango']]
>>> # search any field
... table2 = etl.search(table1, '.g.')
>>> table2
+------------+-----+--------------------------+
| foo        | bar | baz                      |
+============+=====+==========================+
| 'orange'   |  12 | 'oranges are nice fruit' |
+------------+-----+--------------------------+
| 'mango'    |  42 | 'I like them'            |
+------------+-----+--------------------------+
| 'cucumber' |  41 | 'better than mango'      |
+------------+-----+--------------------------+

>>> # search a specific field
... table3 = etl.search(table1, 'foo', '.g.')
>>> table3
+----------+-----+--------------------------+
| foo      | bar | baz                      |
+==========+=====+==========================+
| 'orange' |  12 | 'oranges are nice fruit' |
+----------+-----+--------------------------+
| 'mango'  |  42 | 'I like them'            |
+----------+-----+--------------------------+

The complement can be found via petl.transform.regex.searchcomplement().

petl.transform.regex.searchcomplement(table, *args, **kwargs)[source]¶

Perform a regular expression search, returning rows that do not match a given pattern, either anywhere in the row or within a specific field. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['orange', 12, 'oranges are nice fruit'],
...           ['mango', 42, 'I like them'],
...           ['banana', 74, 'lovely too'],
...           ['cucumber', 41, 'better than mango']]
>>> # search any field
... table2 = etl.searchcomplement(table1, '.g.')
>>> table2
+----------+-----+--------------+
| foo      | bar | baz          |
+==========+=====+==============+
| 'banana' |  74 | 'lovely too' |
+----------+-----+--------------+

>>> # search a specific field
... table3 = etl.searchcomplement(table1, 'foo', '.g.')
>>> table3
+------------+-----+---------------------+
| foo        | bar | baz                 |
+============+=====+=====================+
| 'banana'   |  74 | 'lovely too'        |
+------------+-----+---------------------+
| 'cucumber' |  41 | 'better than mango' |
+------------+-----+---------------------+

This returns the complement of petl.transform.regex.search().

petl.transform.regex.sub(table, field, pattern, repl, count=0, flags=0)[source]¶: Convenience function to convert values under the given field using a regular expression substitution. See also re.sub().

petl.transform.regex.split(table, field, pattern, newfields=None, include_original=False, maxsplit=0, flags=0)[source]¶

Add one or more new fields with values generated by splitting an existing value around occurrences of a regular expression. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'variable', 'value'],
...           ['1', 'parad1', '12'],
...           ['2', 'parad2', '15'],
...           ['3', 'tempd1', '18'],
...           ['4', 'tempd2', '19']]
>>> table2 = etl.split(table1, 'variable', 'd', ['variable', 'day'])
>>> table2
+-----+-------+----------+-----+
| id  | value | variable | day |
+=====+=======+==========+=====+
| '1' | '12'  | 'para'   | '1' |
+-----+-------+----------+-----+
| '2' | '15'  | 'para'   | '2' |
+-----+-------+----------+-----+
| '3' | '18'  | 'temp'   | '1' |
+-----+-------+----------+-----+
| '4' | '19'  | 'temp'   | '2' |
+-----+-------+----------+-----+

By default the field on which the split is performed is omitted. It can be included using the include_original argument.

petl.transform.regex.capture(table, field, pattern, newfields=None, include_original=False, flags=0, fill=None)[source]¶

Add one or more new fields with values captured from an existing field searched via a regular expression. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'variable', 'value'],
...           ['1', 'A1', '12'],
...           ['2', 'A2', '15'],
...           ['3', 'B1', '18'],
...           ['4', 'C12', '19']]
>>> table2 = etl.capture(table1, 'variable', '(\w)(\d+)',
...                      ['treat', 'time'])
>>> table2
+-----+-------+-------+------+
| id  | value | treat | time |
+=====+=======+=======+======+
| '1' | '12'  | 'A'   | '1'  |
+-----+-------+-------+------+
| '2' | '15'  | 'A'   | '2'  |
+-----+-------+-------+------+
| '3' | '18'  | 'B'   | '1'  |
+-----+-------+-------+------+
| '4' | '19'  | 'C'   | '12' |
+-----+-------+-------+------+

>>> # using the include_original argument
... table3 = etl.capture(table1, 'variable', '(\w)(\d+)',
...                      ['treat', 'time'],
...                      include_original=True)
>>> table3
+-----+----------+-------+-------+------+
| id  | variable | value | treat | time |
+=====+==========+=======+=======+======+
| '1' | 'A1'     | '12'  | 'A'   | '1'  |
+-----+----------+-------+-------+------+
| '2' | 'A2'     | '15'  | 'A'   | '2'  |
+-----+----------+-------+-------+------+
| '3' | 'B1'     | '18'  | 'B'   | '1'  |
+-----+----------+-------+-------+------+
| '4' | 'C12'    | '19'  | 'C'   | '12' |
+-----+----------+-------+-------+------+

By default the field on which the capture is performed is omitted. It can be included using the include_original argument.

The fill parameter can be used to provide a list or tuple of values to use if the regular expression does not match. The fill parameter should contain as many values as there are capturing groups in the regular expression. If fill is None (default) then a petl.transform.TransformError will be raised on the first non-matching value.

Unpacking compound values¶

petl.transform.unpacks.unpack(table, field, newfields=None, include_original=False, missing=None)[source]¶

Unpack data values that are lists or tuples. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           [1, ['a', 'b']],
...           [2, ['c', 'd']],
...           [3, ['e', 'f']]]
>>> table2 = etl.unpack(table1, 'bar', ['baz', 'quux'])
>>> table2
+-----+-----+------+
| foo | baz | quux |
+=====+=====+======+
|   1 | 'a' | 'b'  |
+-----+-----+------+
|   2 | 'c' | 'd'  |
+-----+-----+------+
|   3 | 'e' | 'f'  |
+-----+-----+------+

This function will attempt to unpack exactly the number of values as given by the number of new fields specified. If there are more values than new fields, remaining values will not be unpacked. If there are less values than new fields, missing values will be added.

petl.transform.unpacks.unpackdict(table, field, keys=None, includeoriginal=False, samplesize=1000, missing=None)[source]¶

Unpack dictionary values into separate fields. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           [1, {'baz': 'a', 'quux': 'b'}],
...           [2, {'baz': 'c', 'quux': 'd'}],
...           [3, {'baz': 'e', 'quux': 'f'}]]
>>> table2 = etl.unpackdict(table1, 'bar')
>>> table2
+-----+-----+------+
| foo | baz | quux |
+=====+=====+======+
|   1 | 'a' | 'b'  |
+-----+-----+------+
|   2 | 'c' | 'd'  |
+-----+-----+------+
|   3 | 'e' | 'f'  |
+-----+-----+------+

Transforming rows¶

petl.transform.maps.fieldmap(table, mappings=None, failonerror=False, errorvalue=None)[source]¶

Transform a table, mapping fields arbitrarily between input and output. E.g.:

>>> import petl as etl
>>> from collections import OrderedDict
>>> table1 = [['id', 'sex', 'age', 'height', 'weight'],
...           [1, 'male', 16, 1.45, 62.0],
...           [2, 'female', 19, 1.34, 55.4],
...           [3, 'female', 17, 1.78, 74.4],
...           [4, 'male', 21, 1.33, 45.2],
...           [5, '-', 25, 1.65, 51.9]]
>>> mappings = OrderedDict()
>>> # rename a field
... mappings['subject_id'] = 'id'
>>> # translate a field
... mappings['gender'] = 'sex', {'male': 'M', 'female': 'F'}
>>> # apply a calculation to a field
... mappings['age_months'] = 'age', lambda v: v * 12
>>> # apply a calculation to a combination of fields
... mappings['bmi'] = lambda rec: rec['weight'] / rec['height']**2
>>> # transform and inspect the output
... table2 = etl.fieldmap(table1, mappings)
>>> table2
+------------+--------+------------+--------------------+
| subject_id | gender | age_months | bmi                |
+============+========+============+====================+
|          1 | 'M'    |        192 |  29.48870392390012 |
+------------+--------+------------+--------------------+
|          2 | 'F'    |        228 |   30.8531967030519 |
+------------+--------+------------+--------------------+
|          3 | 'F'    |        204 | 23.481883600555488 |
+------------+--------+------------+--------------------+
|          4 | 'M'    |        252 |  25.55260331279326 |
+------------+--------+------------+--------------------+
|          5 | '-'    |        300 |   19.0633608815427 |
+------------+--------+------------+--------------------+

Note also that the mapping value can be an expression string, which will be converted to a lambda function via petl.util.base.expr().

petl.transform.maps.rowmap(table, rowmapper, header, failonerror=False)[source]¶

Transform rows via an arbitrary function. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'sex', 'age', 'height', 'weight'],
...           [1, 'male', 16, 1.45, 62.0],
...           [2, 'female', 19, 1.34, 55.4],
...           [3, 'female', 17, 1.78, 74.4],
...           [4, 'male', 21, 1.33, 45.2],
...           [5, '-', 25, 1.65, 51.9]]
>>> def rowmapper(row):
...     transmf = {'male': 'M', 'female': 'F'}
...     return [row[0],
...             transmf[row['sex']] if row['sex'] in transmf else None,
...             row.age * 12,
...             row.height / row.weight ** 2]
...
>>> table2 = etl.rowmap(table1, rowmapper,
...                     header=['subject_id', 'gender', 'age_months',
...                             'bmi'])
>>> table2
+------------+--------+------------+-----------------------+
| subject_id | gender | age_months | bmi                   |
+============+========+============+=======================+
|          1 | 'M'    |        192 | 0.0003772112382934443 |
+------------+--------+------------+-----------------------+
|          2 | 'F'    |        228 | 0.0004366015456998006 |
+------------+--------+------------+-----------------------+
|          3 | 'F'    |        204 | 0.0003215689675106949 |
+------------+--------+------------+-----------------------+
|          4 | 'M'    |        252 | 0.0006509906805544679 |
+------------+--------+------------+-----------------------+
|          5 | None   |        300 | 0.0006125608384287258 |
+------------+--------+------------+-----------------------+

The rowmapper function should accept a single row and return a single row (list or tuple).

petl.transform.maps.rowmapmany(table, rowgenerator, header, failonerror=False)[source]¶

Map each input row to any number of output rows via an arbitrary function. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'sex', 'age', 'height', 'weight'],
...           [1, 'male', 16, 1.45, 62.0],
...           [2, 'female', 19, 1.34, 55.4],
...           [3, '-', 17, 1.78, 74.4],
...           [4, 'male', 21, 1.33]]
>>> def rowgenerator(row):
...     transmf = {'male': 'M', 'female': 'F'}
...     yield [row[0], 'gender',
...            transmf[row['sex']] if row['sex'] in transmf else None]
...     yield [row[0], 'age_months', row.age * 12]
...     yield [row[0], 'bmi', row.height / row.weight ** 2]
...
>>> table2 = etl.rowmapmany(table1, rowgenerator,
...                         header=['subject_id', 'variable', 'value'])
>>> table2.lookall()
+------------+--------------+-----------------------+
| subject_id | variable     | value                 |
+============+==============+=======================+
|          1 | 'gender'     | 'M'                   |
+------------+--------------+-----------------------+
|          1 | 'age_months' |                   192 |
+------------+--------------+-----------------------+
|          1 | 'bmi'        | 0.0003772112382934443 |
+------------+--------------+-----------------------+
|          2 | 'gender'     | 'F'                   |
+------------+--------------+-----------------------+
|          2 | 'age_months' |                   228 |
+------------+--------------+-----------------------+
|          2 | 'bmi'        | 0.0004366015456998006 |
+------------+--------------+-----------------------+
|          3 | 'gender'     | None                  |
+------------+--------------+-----------------------+
|          3 | 'age_months' |                   204 |
+------------+--------------+-----------------------+
|          3 | 'bmi'        | 0.0003215689675106949 |
+------------+--------------+-----------------------+
|          4 | 'gender'     | 'M'                   |
+------------+--------------+-----------------------+
|          4 | 'age_months' |                   252 |
+------------+--------------+-----------------------+

The rowgenerator function should accept a single row and yield zero or more rows (lists or tuples).

See also the petl.transform.reshape.melt() function.

petl.transform.maps.rowgroupmap(table, key, mapper, header=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶: Group rows under the given key then apply mapper to yield zero or more output rows for each input group of rows.

Sorting¶

petl.transform.sorts.sort(table, key=None, reverse=False, buffersize=None, tempdir=None, cache=True)[source]¶

Sort the table. Field names or indices (from zero) can be used to specify the key. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['C', 2],
...           ['A', 9],
...           ['A', 6],
...           ['F', 1],
...           ['D', 10]]
>>> table2 = etl.sort(table1, 'foo')
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   9 |
+-----+-----+
| 'A' |   6 |
+-----+-----+
| 'C' |   2 |
+-----+-----+
| 'D' |  10 |
+-----+-----+
| 'F' |   1 |
+-----+-----+

>>> # sorting by compound key is supported
... table3 = etl.sort(table1, key=['foo', 'bar'])
>>> table3
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   6 |
+-----+-----+
| 'A' |   9 |
+-----+-----+
| 'C' |   2 |
+-----+-----+
| 'D' |  10 |
+-----+-----+
| 'F' |   1 |
+-----+-----+

>>> # if no key is specified, the default is a lexical sort
... table4 = etl.sort(table1)
>>> table4
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   6 |
+-----+-----+
| 'A' |   9 |
+-----+-----+
| 'C' |   2 |
+-----+-----+
| 'D' |  10 |
+-----+-----+
| 'F' |   1 |
+-----+-----+

The buffersize argument should be an int or None.

If the number of rows in the table is less than buffersize, the table will be sorted in memory. Otherwise, the table is sorted in chunks of no more than buffersize rows, each chunk is written to a temporary file, and then a merge sort is performed on the temporary files.

If buffersize is None, the value of petl.config.sort_buffersize will be used. By default this is set to 100000 rows, but can be changed, e.g.:

>>> import petl.config
>>> petl.config.sort_buffersize = 500000

If petl.config.sort_buffersize is set to None, this forces all sorting to be done entirely in memory.

By default the results of the sort will be cached, and so a second pass over the sorted table will yield rows from the cache and will not repeat the sort operation. To turn off caching, set the cache argument to False.

petl.transform.sorts.mergesort(*tables, **kwargs)[source]¶

Combine multiple input tables into one sorted output table. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['A', 9],
...           ['C', 2],
...           ['D', 10],
...           ['A', 6],
...           ['F', 1]]
>>> table2 = [['foo', 'bar'],
...           ['B', 3],
...           ['D', 10],
...           ['A', 10],
...           ['F', 4]]
>>> table3 = etl.mergesort(table1, table2, key='foo')
>>> table3.lookall()
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   9 |
+-----+-----+
| 'A' |   6 |
+-----+-----+
| 'A' |  10 |
+-----+-----+
| 'B' |   3 |
+-----+-----+
| 'C' |   2 |
+-----+-----+
| 'D' |  10 |
+-----+-----+
| 'D' |  10 |
+-----+-----+
| 'F' |   1 |
+-----+-----+
| 'F' |   4 |
+-----+-----+

If the input tables are already sorted by the given key, give presorted=True as a keyword argument.

This function is equivalent to concatenating the input tables using cat() then sorting, however this function will typically be more efficient, especially if the input tables are presorted.

Keyword arguments:

key : string or tuple of strings, optional: Field name or tuple of fields to sort by (defaults to None lexical sort)
reverse : bool, optional: True if sort in reverse (descending) order (defaults to False)
presorted : bool, optional: True if inputs are already sorted by the given key (defaults to False)
missing : object: Value to fill with when input tables have different fields (defaults to None)
header : sequence of strings, optional: Specify a fixed header for the output table
buffersize : int, optional: Limit the number of rows in memory per input table when inputs are not presorted

petl.transform.sorts.issorted(table, key=None, reverse=False, strict=False)[source]¶

Return True if the table is ordered (i.e., sorted) by the given key. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['a', 1, True],
...           ['b', 3, True],
...           ['b', 2]]
>>> etl.issorted(table1, key='foo')
True
>>> etl.issorted(table1, key='bar')
False
>>> etl.issorted(table1, key='foo', strict=True)
False
>>> etl.issorted(table1, key='foo', reverse=True)
False

Joins¶

petl.transform.joins.join(left, right, key=None, lkey=None, rkey=None, presorted=False, buffersize=None, tempdir=None, cache=True, lprefix=None, rprefix=None)[source]¶

Perform an equi-join on the given tables. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [1, 'blue'],
...           [2, 'red'],
...           [3, 'purple']]
>>> table2 = [['id', 'shape'],
...           [1, 'circle'],
...           [3, 'square'],
...           [4, 'ellipse']]
>>> table3 = etl.join(table1, table2, key='id')
>>> table3
+----+----------+----------+
| id | colour   | shape    |
+====+==========+==========+
|  1 | 'blue'   | 'circle' |
+----+----------+----------+
|  3 | 'purple' | 'square' |
+----+----------+----------+

>>> # if no key is given, a natural join is tried
... table4 = etl.join(table1, table2)
>>> table4
+----+----------+----------+
| id | colour   | shape    |
+====+==========+==========+
|  1 | 'blue'   | 'circle' |
+----+----------+----------+
|  3 | 'purple' | 'square' |
+----+----------+----------+

>>> # note behaviour if the key is not unique in either or both tables
... table5 = [['id', 'colour'],
...           [1, 'blue'],
...           [1, 'red'],
...           [2, 'purple']]
>>> table6 = [['id', 'shape'],
...           [1, 'circle'],
...           [1, 'square'],
...           [2, 'ellipse']]
>>> table7 = etl.join(table5, table6, key='id')
>>> table7
+----+----------+-----------+
| id | colour   | shape     |
+====+==========+===========+
|  1 | 'blue'   | 'circle'  |
+----+----------+-----------+
|  1 | 'blue'   | 'square'  |
+----+----------+-----------+
|  1 | 'red'    | 'circle'  |
+----+----------+-----------+
|  1 | 'red'    | 'square'  |
+----+----------+-----------+
|  2 | 'purple' | 'ellipse' |
+----+----------+-----------+

>>> # compound keys are supported
... table8 = [['id', 'time', 'height'],
...           [1, 1, 12.3],
...           [1, 2, 34.5],
...           [2, 1, 56.7]]
>>> table9 = [['id', 'time', 'weight'],
...           [1, 2, 4.5],
...           [2, 1, 6.7],
...           [2, 2, 8.9]]
>>> table10 = etl.join(table8, table9, key=['id', 'time'])
>>> table10
+----+------+--------+--------+
| id | time | height | weight |
+====+======+========+========+
|  1 |    2 |   34.5 |    4.5 |
+----+------+--------+--------+
|  2 |    1 |   56.7 |    6.7 |
+----+------+--------+--------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.joins.leftjoin(left, right, key=None, lkey=None, rkey=None, missing=None, presorted=False, buffersize=None, tempdir=None, cache=True, lprefix=None, rprefix=None)[source]¶

Perform a left outer join on the given tables. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [1, 'blue'],
...           [2, 'red'],
...           [3, 'purple']]
>>> table2 = [['id', 'shape'],
...           [1, 'circle'],
...           [3, 'square'],
...           [4, 'ellipse']]
>>> table3 = etl.leftjoin(table1, table2, key='id')
>>> table3
+----+----------+----------+
| id | colour   | shape    |
+====+==========+==========+
|  1 | 'blue'   | 'circle' |
+----+----------+----------+
|  2 | 'red'    | None     |
+----+----------+----------+
|  3 | 'purple' | 'square' |
+----+----------+----------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.joins.lookupjoin(left, right, key=None, lkey=None, rkey=None, missing=None, presorted=False, buffersize=None, tempdir=None, cache=True, lprefix=None, rprefix=None)[source]¶

Perform a left join, but where the key is not unique in the right-hand table, arbitrarily choose the first row and ignore others. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'color', 'cost'],
...           [1, 'blue', 12],
...           [2, 'red', 8],
...           [3, 'purple', 4]]
>>> table2 = [['id', 'shape', 'size'],
...           [1, 'circle', 'big'],
...           [1, 'circle', 'small'],
...           [2, 'square', 'tiny'],
...           [2, 'square', 'big'],
...           [3, 'ellipse', 'small'],
...           [3, 'ellipse', 'tiny']]
>>> table3 = etl.lookupjoin(table1, table2, key='id')
>>> table3
+----+----------+------+-----------+---------+
| id | color    | cost | shape     | size    |
+====+==========+======+===========+=========+
|  1 | 'blue'   |   12 | 'circle'  | 'big'   |
+----+----------+------+-----------+---------+
|  2 | 'red'    |    8 | 'square'  | 'tiny'  |
+----+----------+------+-----------+---------+
|  3 | 'purple' |    4 | 'ellipse' | 'small' |
+----+----------+------+-----------+---------+

petl.transform.joins.rightjoin(left, right, key=None, lkey=None, rkey=None, missing=None, presorted=False, buffersize=None, tempdir=None, cache=True, lprefix=None, rprefix=None)[source]¶

Perform a right outer join on the given tables. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [1, 'blue'],
...           [2, 'red'],
...           [3, 'purple']]
>>> table2 = [['id', 'shape'],
...           [1, 'circle'],
...           [3, 'square'],
...           [4, 'ellipse']]
>>> table3 = etl.rightjoin(table1, table2, key='id')
>>> table3
+----+----------+-----------+
| id | colour   | shape     |
+====+==========+===========+
|  1 | 'blue'   | 'circle'  |
+----+----------+-----------+
|  3 | 'purple' | 'square'  |
+----+----------+-----------+
|  4 | None     | 'ellipse' |
+----+----------+-----------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.joins.outerjoin(left, right, key=None, lkey=None, rkey=None, missing=None, presorted=False, buffersize=None, tempdir=None, cache=True, lprefix=None, rprefix=None)[source]¶

Perform a full outer join on the given tables. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [1, 'blue'],
...           [2, 'red'],
...           [3, 'purple']]
>>> table2 = [['id', 'shape'],
...           [1, 'circle'],
...           [3, 'square'],
...           [4, 'ellipse']]
>>> table3 = etl.outerjoin(table1, table2, key='id')
>>> table3
+----+----------+-----------+
| id | colour   | shape     |
+====+==========+===========+
|  1 | 'blue'   | 'circle'  |
+----+----------+-----------+
|  2 | 'red'    | None      |
+----+----------+-----------+
|  3 | 'purple' | 'square'  |
+----+----------+-----------+
|  4 | None     | 'ellipse' |
+----+----------+-----------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.joins.crossjoin(*tables, **kwargs)[source]¶

Form the cartesian product of the given tables. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [1, 'blue'],
...           [2, 'red']]
>>> table2 = [['id', 'shape'],
...           [1, 'circle'],
...           [3, 'square']]
>>> table3 = etl.crossjoin(table1, table2)
>>> table3
+----+--------+----+----------+
| id | colour | id | shape    |
+====+========+====+==========+
|  1 | 'blue' |  1 | 'circle' |
+----+--------+----+----------+
|  1 | 'blue' |  3 | 'square' |
+----+--------+----+----------+
|  2 | 'red'  |  1 | 'circle' |
+----+--------+----+----------+
|  2 | 'red'  |  3 | 'square' |
+----+--------+----+----------+

If prefix is True then field names in the output table header will be prefixed by the index of the input table.

petl.transform.joins.antijoin(left, right, key=None, lkey=None, rkey=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Return rows from the left table where the key value does not occur in the right table. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [0, 'black'],
...           [1, 'blue'],
...           [2, 'red'],
...           [4, 'yellow'],
...           [5, 'white']]
>>> table2 = [['id', 'shape'],
...           [1, 'circle'],
...           [3, 'square']]
>>> table3 = etl.antijoin(table1, table2, key='id')
>>> table3
+----+----------+
| id | colour   |
+====+==========+
|  0 | 'black'  |
+----+----------+
|  2 | 'red'    |
+----+----------+
|  4 | 'yellow' |
+----+----------+
|  5 | 'white'  |
+----+----------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.joins.unjoin(table, value, key=None, autoincrement=(1, 1), presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Split a table into two tables by reversing an inner join. E.g.:

>>> import petl as etl
>>> # join key is present in the table
... table1 = (('foo', 'bar', 'baz'),
...           ('A', 1, 'apple'),
...           ('B', 1, 'apple'),
...           ('C', 2, 'orange'))
>>> table2, table3 = etl.unjoin(table1, 'baz', key='bar')
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   1 |
+-----+-----+
| 'B' |   1 |
+-----+-----+
| 'C' |   2 |
+-----+-----+

>>> table3
+-----+----------+
| bar | baz      |
+=====+==========+
|   1 | 'apple'  |
+-----+----------+
|   2 | 'orange' |
+-----+----------+

>>> # an integer join key can also be reconstructed
... table4 = (('foo', 'bar'),
...           ('A', 'apple'),
...           ('B', 'apple'),
...           ('C', 'orange'))
>>> table5, table6 = etl.unjoin(table4, 'bar')
>>> table5
+-----+--------+
| foo | bar_id |
+=====+========+
| 'A' |      1 |
+-----+--------+
| 'B' |      1 |
+-----+--------+
| 'C' |      2 |
+-----+--------+

>>> table6
+----+----------+
| id | bar      |
+====+==========+
|  1 | 'apple'  |
+----+----------+
|  2 | 'orange' |
+----+----------+

The autoincrement parameter controls how an integer join key is reconstructed, and should be a tuple of (start, step).

petl.transform.hashjoins.hashjoin(left, right, key=None, lkey=None, rkey=None, cache=True, lprefix=None, rprefix=None)[source]¶

Alternative implementation of petl.transform.joins.join(), where the join is executed by constructing an in-memory lookup for the right hand table, then iterating over rows from the left hand table.

May be faster and/or more resource efficient where the right table is small and the left table is large.

By default data from right hand table is cached to improve performance (only available when key is given).

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.hashjoins.hashleftjoin(left, right, key=None, lkey=None, rkey=None, missing=None, cache=True, lprefix=None, rprefix=None)[source]¶

Alternative implementation of petl.transform.joins.leftjoin(), where the join is executed by constructing an in-memory lookup for the right hand table, then iterating over rows from the left hand table.

May be faster and/or more resource efficient where the right table is small and the left table is large.

By default data from right hand table is cached to improve performance (only available when key is given).

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.hashjoins.hashlookupjoin(left, right, key=None, lkey=None, rkey=None, missing=None, lprefix=None, rprefix=None)[source]¶

Alternative implementation of petl.transform.joins.lookupjoin(), where the join is executed by constructing an in-memory lookup for the right hand table, then iterating over rows from the left hand table.

May be faster and/or more resource efficient where the right table is small and the left table is large.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.hashjoins.hashrightjoin(left, right, key=None, lkey=None, rkey=None, missing=None, cache=True, lprefix=None, rprefix=None)[source]¶

Alternative implementation of petl.transform.joins.rightjoin(), where the join is executed by constructing an in-memory lookup for the left hand table, then iterating over rows from the right hand table.

May be faster and/or more resource efficient where the left table is small and the right table is large.

By default data from right hand table is cached to improve performance (only available when key is given).

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

petl.transform.hashjoins.hashantijoin(left, right, key=None, lkey=None, rkey=None)[source]¶

Alternative implementation of petl.transform.joins.antijoin(), where the join is executed by constructing an in-memory set for all keys found in the right hand table, then iterating over rows from the left hand table.

May be faster and/or more resource efficient where the right table is small and the left table is large.

Left and right tables with different key fields can be handled via the lkey and rkey arguments.

Set operations¶

petl.transform.setops.complement(a, b, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Return rows in a that are not in b. E.g.:

>>> import petl as etl
>>> a = [['foo', 'bar', 'baz'],
...      ['A', 1, True],
...      ['C', 7, False],
...      ['B', 2, False],
...      ['C', 9, True]]
>>> b = [['x', 'y', 'z'],
...      ['B', 2, False],
...      ['A', 9, False],
...      ['B', 3, True],
...      ['C', 9, True]]
>>> aminusb = etl.complement(a, b)
>>> aminusb
+-----+-----+-------+
| foo | bar | baz   |
+=====+=====+=======+
| 'A' |   1 | True  |
+-----+-----+-------+
| 'C' |   7 | False |
+-----+-----+-------+

>>> bminusa = etl.complement(b, a)
>>> bminusa
+-----+---+-------+
| x   | y | z     |
+=====+===+=======+
| 'A' | 9 | False |
+-----+---+-------+
| 'B' | 3 | True  |
+-----+---+-------+

Note that the field names of each table are ignored - rows are simply compared following a lexical sort. See also the petl.transform.setops.recordcomplement() function.

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.setops.diff(a, b, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Find the difference between rows in two tables. Returns a pair of tables. E.g.:

>>> import petl as etl
>>> a = [['foo', 'bar', 'baz'],
...      ['A', 1, True],
...      ['C', 7, False],
...      ['B', 2, False],
...      ['C', 9, True]]
>>> b = [['x', 'y', 'z'],
...      ['B', 2, False],
...      ['A', 9, False],
...      ['B', 3, True],
...      ['C', 9, True]]
>>> added, subtracted = etl.diff(a, b)
>>> # rows in b not in a
... added
+-----+---+-------+
| x   | y | z     |
+=====+===+=======+
| 'A' | 9 | False |
+-----+---+-------+
| 'B' | 3 | True  |
+-----+---+-------+

>>> # rows in a not in b
... subtracted
+-----+-----+-------+
| foo | bar | baz   |
+=====+=====+=======+
| 'A' |   1 | True  |
+-----+-----+-------+
| 'C' |   7 | False |
+-----+-----+-------+

Convenient shorthand for (complement(b, a), complement(a, b)). See also petl.transform.setops.complement().

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.setops.recordcomplement(a, b, buffersize=None, tempdir=None, cache=True)[source]¶

Find records in a that are not in b. E.g.:

>>> import petl as etl
>>> a = [['foo', 'bar', 'baz'],
...      ['A', 1, True],
...      ['C', 7, False],
...      ['B', 2, False],
...      ['C', 9, True]]
>>> b = [['bar', 'foo', 'baz'],
...      [2, 'B', False],
...      [9, 'A', False],
...      [3, 'B', True],
...      [9, 'C', True]]
>>> aminusb = etl.recordcomplement(a, b)
>>> aminusb
+-----+-----+-------+
| foo | bar | baz   |
+=====+=====+=======+
| 'A' |   1 | True  |
+-----+-----+-------+
| 'C' |   7 | False |
+-----+-----+-------+

>>> bminusa = etl.recordcomplement(b, a)
>>> bminusa
+-----+-----+-------+
| bar | foo | baz   |
+=====+=====+=======+
|   3 | 'B' | True  |
+-----+-----+-------+
|   9 | 'A' | False |
+-----+-----+-------+

Note that both tables must have the same set of fields, but that the order of the fields does not matter. See also the petl.transform.setops.complement() function.

See also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.setops.recorddiff(a, b, buffersize=None, tempdir=None, cache=True)[source]¶

Find the difference between records in two tables. E.g.:

>>> import petl as etl
>>> a = [['foo', 'bar', 'baz'],
...      ['A', 1, True],
...      ['C', 7, False],
...      ['B', 2, False],
...      ['C', 9, True]]
>>> b = [['bar', 'foo', 'baz'],
...      [2, 'B', False],
...      [9, 'A', False],
...      [3, 'B', True],
...      [9, 'C', True]]
>>> added, subtracted = etl.recorddiff(a, b)
>>> added
+-----+-----+-------+
| bar | foo | baz   |
+=====+=====+=======+
|   3 | 'B' | True  |
+-----+-----+-------+
|   9 | 'A' | False |
+-----+-----+-------+

>>> subtracted
+-----+-----+-------+
| foo | bar | baz   |
+=====+=====+=======+
| 'A' |   1 | True  |
+-----+-----+-------+
| 'C' |   7 | False |
+-----+-----+-------+

Convenient shorthand for (recordcomplement(b, a), recordcomplement(a, b)). See also petl.transform.setops.recordcomplement().

See also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.setops.intersection(a, b, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Return rows in a that are also in b. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, True],
...           ['C', 7, False],
...           ['B', 2, False],
...           ['C', 9, True]]
>>> table2 = [['x', 'y', 'z'],
...           ['B', 2, False],
...           ['A', 9, False],
...           ['B', 3, True],
...           ['C', 9, True]]
>>> table3 = etl.intersection(table1, table2)
>>> table3
+-----+-----+-------+
| foo | bar | baz   |
+=====+=====+=======+
| 'B' |   2 | False |
+-----+-----+-------+
| 'C' |   9 | True  |
+-----+-----+-------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.setops.hashcomplement(a, b)[source]¶

Alternative implementation of petl.transform.setops.complement(), where the complement is executed by constructing an in-memory set for all rows found in the right hand table, then iterating over rows from the left hand table.

May be faster and/or more resource efficient where the right table is small and the left table is large.

petl.transform.setops.hashintersection(a, b)[source]¶

Alternative implementation of petl.transform.setops.intersection(), where the intersection is executed by constructing an in-memory set for all rows found in the right hand table, then iterating over rows from the left hand table.

May be faster and/or more resource efficient where the right table is small and the left table is large.

Deduplicating rows¶

petl.transform.dedup.duplicates(table, key=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Select rows with duplicate values under a given key (or duplicate rows where no key is given). E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, 2.0],
...           ['B', 2, 3.4],
...           ['D', 6, 9.3],
...           ['B', 3, 7.8],
...           ['B', 2, 12.3],
...           ['E', None, 1.3],
...           ['D', 4, 14.5]]
>>> table2 = etl.duplicates(table1, 'foo')
>>> table2
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'B' |   2 |  3.4 |
+-----+-----+------+
| 'B' |   3 |  7.8 |
+-----+-----+------+
| 'B' |   2 | 12.3 |
+-----+-----+------+
| 'D' |   6 |  9.3 |
+-----+-----+------+
| 'D' |   4 | 14.5 |
+-----+-----+------+

>>> # compound keys are supported
... table3 = etl.duplicates(table1, key=['foo', 'bar'])
>>> table3
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'B' |   2 |  3.4 |
+-----+-----+------+
| 'B' |   2 | 12.3 |
+-----+-----+------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.dedup.unique(table, key=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Select rows with unique values under a given key (or unique rows if no key is given). E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, 2],
...           ['B', '2', '3.4'],
...           ['D', 'xyz', 9.0],
...           ['B', u'3', u'7.8'],
...           ['B', '2', 42],
...           ['E', None, None],
...           ['D', 4, 12.3],
...           ['F', 7, 2.3]]
>>> table2 = etl.unique(table1, 'foo')
>>> table2
+-----+------+------+
| foo | bar  | baz  |
+=====+======+======+
| 'A' |    1 |    2 |
+-----+------+------+
| 'E' | None | None |
+-----+------+------+
| 'F' |    7 |  2.3 |
+-----+------+------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.dedup.conflicts(table, key, missing=None, include=None, exclude=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Select rows with the same key value but differing in some other field. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, 2.7],
...           ['B', 2, None],
...           ['D', 3, 9.4],
...           ['B', None, 7.8],
...           ['E', None],
...           ['D', 3, 12.3],
...           ['A', 2, None]]
>>> table2 = etl.conflicts(table1, 'foo')
>>> table2
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
| 'A' |   1 |  2.7 |
+-----+-----+------+
| 'A' |   2 | None |
+-----+-----+------+
| 'D' |   3 |  9.4 |
+-----+-----+------+
| 'D' |   3 | 12.3 |
+-----+-----+------+

Missing values are not considered conflicts. By default, None is treated as the missing value, this can be changed via the missing keyword argument.

One or more fields can be ignored when determining conflicts by providing the exclude keyword argument. Alternatively, fields to use when determining conflicts can be specified explicitly with the include keyword argument.

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.dedup.distinct(table, key=None, count=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Return only distinct rows in the table.

If the count argument is not None, it will be used as the name for an additional field, and the values of the field will be the number of duplicate rows.

If the key keyword argument is passed, the comparison is done on the given key instead of the full row.

petl.transform.dedup.isunique(table, field)[source]¶

Return True if there are no duplicate values for the given field(s), otherwise False. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b'],
...           ['b', 2],
...           ['c', 3, True]]
>>> etl.isunique(table1, 'foo')
False
>>> etl.isunique(table1, 'bar')
True

The field argument can be a single field name or index (starting from zero) or a tuple of field names and/or indexes.

Reducing rows¶

petl.transform.reductions.aggregate(table, key, aggregation=None, value=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Group rows under the given key then apply aggregation functions. E.g.:

>>> import petl as etl
>>>
>>> table1 = [['foo', 'bar', 'baz'],
...           ['a', 3, True],
...           ['a', 7, False],
...           ['b', 2, True],
...           ['b', 2, False],
...           ['b', 9, False],
...           ['c', 4, True]]
>>> # aggregate whole rows
... table2 = etl.aggregate(table1, 'foo', len)
>>> table2
+-----+-------+
| foo | value |
+=====+=======+
| 'a' |     2 |
+-----+-------+
| 'b' |     3 |
+-----+-------+
| 'c' |     1 |
+-----+-------+

>>> # aggregate single field
... table3 = etl.aggregate(table1, 'foo', sum, 'bar')
>>> table3
+-----+-------+
| foo | value |
+=====+=======+
| 'a' |    10 |
+-----+-------+
| 'b' |    13 |
+-----+-------+
| 'c' |     4 |
+-----+-------+

>>> # alternative signature using keyword args
... table4 = etl.aggregate(table1, key=('foo', 'bar'),
...                        aggregation=list, value=('bar', 'baz'))
>>> table4
+-----+-----+-------------------------+
| foo | bar | value                   |
+=====+=====+=========================+
| 'a' |   3 | [(3, True)]             |
+-----+-----+-------------------------+
| 'a' |   7 | [(7, False)]            |
+-----+-----+-------------------------+
| 'b' |   2 | [(2, True), (2, False)] |
+-----+-----+-------------------------+
| 'b' |   9 | [(9, False)]            |
+-----+-----+-------------------------+
| 'c' |   4 | [(4, True)]             |
+-----+-----+-------------------------+

>>> # aggregate multiple fields
... from collections import OrderedDict
>>> import petl as etl
>>>
>>> aggregation = OrderedDict()
>>> aggregation['count'] = len
>>> aggregation['minbar'] = 'bar', min
>>> aggregation['maxbar'] = 'bar', max
>>> aggregation['sumbar'] = 'bar', sum
>>> # default aggregation function is list
... aggregation['listbar'] = 'bar'
>>> aggregation['listbarbaz'] = ('bar', 'baz'), list
>>> aggregation['bars'] = 'bar', etl.strjoin(', ')
>>> table5 = etl.aggregate(table1, 'foo', aggregation)
>>> table5
+-----+-------+--------+--------+--------+-----------+-------------------------------------+-----------+
| foo | count | minbar | maxbar | sumbar | listbar   | listbarbaz                          | bars      |
+=====+=======+========+========+========+===========+=====================================+===========+
| 'a' |     2 |      3 |      7 |     10 | [3, 7]    | [(3, True), (7, False)]             | '3, 7'    |
+-----+-------+--------+--------+--------+-----------+-------------------------------------+-----------+
| 'b' |     3 |      2 |      9 |     13 | [2, 2, 9] | [(2, True), (2, False), (9, False)] | '2, 2, 9' |
+-----+-------+--------+--------+--------+-----------+-------------------------------------+-----------+
| 'c' |     1 |      4 |      4 |      4 | [4]       | [(4, True)]                         | '4'       |
+-----+-------+--------+--------+--------+-----------+-------------------------------------+-----------+

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.reductions.rowreduce(table, key, reducer, header=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Group rows under the given key then apply reducer to produce a single output row for each input group of rows. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 3],
...           ['a', 7],
...           ['b', 2],
...           ['b', 1],
...           ['b', 9],
...           ['c', 4]]
>>> def sumbar(key, rows):
...     return [key, sum(row[1] for row in rows)]
...
>>> table2 = etl.rowreduce(table1, key='foo', reducer=sumbar,
...                        header=['foo', 'barsum'])
>>> table2
+-----+--------+
| foo | barsum |
+=====+========+
| 'a' |     10 |
+-----+--------+
| 'b' |     12 |
+-----+--------+
| 'c' |      4 |
+-----+--------+

N.B., this is not strictly a “reduce” in the sense of the standard Python reduce() function, i.e., the reducer function is not applied recursively to values within a group, rather it is applied once to each row group as a whole.

petl.transform.reductions.mergeduplicates(table, key, missing=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Merge duplicate rows under the given key. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, 2.7],
...           ['B', 2, None],
...           ['D', 3, 9.4],
...           ['B', None, 7.8],
...           ['E', None, 42.],
...           ['D', 3, 12.3],
...           ['A', 2, None]]
>>> table2 = etl.mergeduplicates(table1, 'foo')
>>> table2
+-----+------------------+-----------------------+
| foo | bar              | baz                   |
+=====+==================+=======================+
| 'A' | Conflict({1, 2}) |                   2.7 |
+-----+------------------+-----------------------+
| 'B' |                2 |                   7.8 |
+-----+------------------+-----------------------+
| 'D' |                3 | Conflict({9.4, 12.3}) |
+-----+------------------+-----------------------+
| 'E' | None             |                  42.0 |
+-----+------------------+-----------------------+

Missing values are overridden by non-missing values. Conflicting values are reported as an instance of the Conflict class (sub-class of frozenset).

If presorted is True, it is assumed that the data are already sorted by the given key, and the buffersize, tempdir and cache arguments are ignored. Otherwise, the data are sorted, see also the discussion of the buffersize, tempdir and cache arguments under the petl.transform.sorts.sort() function.

petl.transform.reductions.merge(*tables, **kwargs)[source]¶

Convenience function to combine multiple tables (via petl.transform.sorts.mergesort()) then combine duplicate rows by merging under the given key (via petl.transform.reductions.mergeduplicates()). E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           [1, 'A', True],
...           [2, 'B', None],
...           [4, 'C', True]]
>>> table2 = [['bar', 'baz', 'quux'],
...           ['A', True, 42.0],
...           ['B', False, 79.3],
...           ['C', False, 12.4]]
>>> table3 = etl.merge(table1, table2, key='bar')
>>> table3
+-----+-----+-------------------------+------+
| bar | foo | baz                     | quux |
+=====+=====+=========================+======+
| 'A' |   1 | True                    | 42.0 |
+-----+-----+-------------------------+------+
| 'B' |   2 | False                   | 79.3 |
+-----+-----+-------------------------+------+
| 'C' |   4 | Conflict({False, True}) | 12.4 |
+-----+-----+-------------------------+------+

Keyword arguments are the same as for petl.transform.sorts.mergesort(), except key is required.

petl.transform.reductions.fold(table, key, f, value=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Reduce rows recursively via the Python standard reduce() function. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'count'],
...           [1, 3],
...           [1, 5],
...           [2, 4],
...           [2, 8]]
>>> import operator
>>> table2 = etl.fold(table1, 'id', operator.add, 'count',
...                   presorted=True)
>>> table2
+-----+-------+
| key | value |
+=====+=======+
|   1 |     8 |
+-----+-------+
|   2 |    12 |
+-----+-------+

petl.transform.reductions.groupcountdistinctvalues(table, key, value)[source]¶: Group by the key field then count the number of distinct values in the value field.

petl.transform.reductions.groupselectfirst(table, key, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶: Group by the key field then return the first row within each group.

petl.transform.reductions.groupselectmin(table, key, value, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶: Group by the key field then return the row with the maximum of the value field within each group. N.B., will only return one row for each group, even if multiple rows have the same (maximum) value.

petl.transform.reductions.groupselectmax(table, key, value, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶: Group by the key field then return the row with the minimum of the value field within each group. N.B., will only return one row for each group, even if multiple rows have the same (maximum) value.

Reshaping tables¶

petl.transform.reshape.melt(table, key=None, variables=None, variablefield='variable', valuefield='value')[source]¶

Reshape a table, melting fields into data. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'gender', 'age'],
...           [1, 'F', 12],
...           [2, 'M', 17],
...           [3, 'M', 16]]
>>> table2 = etl.melt(table1, 'id')
>>> table2.lookall()
+----+----------+-------+
| id | variable | value |
+====+==========+=======+
|  1 | 'gender' | 'F'   |
+----+----------+-------+
|  1 | 'age'    |    12 |
+----+----------+-------+
|  2 | 'gender' | 'M'   |
+----+----------+-------+
|  2 | 'age'    |    17 |
+----+----------+-------+
|  3 | 'gender' | 'M'   |
+----+----------+-------+
|  3 | 'age'    |    16 |
+----+----------+-------+

>>> # compound keys are supported
... table3 = [['id', 'time', 'height', 'weight'],
...           [1, 11, 66.4, 12.2],
...           [2, 16, 53.2, 17.3],
...           [3, 12, 34.5, 9.4]]
>>> table4 = etl.melt(table3, key=['id', 'time'])
>>> table4.lookall()
+----+------+----------+-------+
| id | time | variable | value |
+====+======+==========+=======+
|  1 |   11 | 'height' |  66.4 |
+----+------+----------+-------+
|  1 |   11 | 'weight' |  12.2 |
+----+------+----------+-------+
|  2 |   16 | 'height' |  53.2 |
+----+------+----------+-------+
|  2 |   16 | 'weight' |  17.3 |
+----+------+----------+-------+
|  3 |   12 | 'height' |  34.5 |
+----+------+----------+-------+
|  3 |   12 | 'weight' |   9.4 |
+----+------+----------+-------+

>>> # a subset of variable fields can be selected
... table5 = etl.melt(table3, key=['id', 'time'],
...                   variables=['height'])
>>> table5.lookall()
+----+------+----------+-------+
| id | time | variable | value |
+====+======+==========+=======+
|  1 |   11 | 'height' |  66.4 |
+----+------+----------+-------+
|  2 |   16 | 'height' |  53.2 |
+----+------+----------+-------+
|  3 |   12 | 'height' |  34.5 |
+----+------+----------+-------+

petl.transform.reshape.recast(table, key=None, variablefield='variable', valuefield='value', samplesize=1000, reducers=None, missing=None)[source]¶

Recast molten data. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'variable', 'value'],
...           [3, 'age', 16],
...           [1, 'gender', 'F'],
...           [2, 'gender', 'M'],
...           [2, 'age', 17],
...           [1, 'age', 12],
...           [3, 'gender', 'M']]
>>> table2 = etl.recast(table1)
>>> table2
+----+-----+--------+
| id | age | gender |
+====+=====+========+
|  1 |  12 | 'F'    |
+----+-----+--------+
|  2 |  17 | 'M'    |
+----+-----+--------+
|  3 |  16 | 'M'    |
+----+-----+--------+

>>> # specifying variable and value fields
... table3 = [['id', 'vars', 'vals'],
...           [3, 'age', 16],
...           [1, 'gender', 'F'],
...           [2, 'gender', 'M'],
...           [2, 'age', 17],
...           [1, 'age', 12],
...           [3, 'gender', 'M']]
>>> table4 = etl.recast(table3, variablefield='vars', valuefield='vals')
>>> table4
+----+-----+--------+
| id | age | gender |
+====+=====+========+
|  1 |  12 | 'F'    |
+----+-----+--------+
|  2 |  17 | 'M'    |
+----+-----+--------+
|  3 |  16 | 'M'    |
+----+-----+--------+

>>> # if there are multiple values for each key/variable pair, and no
... # reducers function is provided, then all values will be listed
... table6 = [['id', 'time', 'variable', 'value'],
...           [1, 11, 'weight', 66.4],
...           [1, 14, 'weight', 55.2],
...           [2, 12, 'weight', 53.2],
...           [2, 16, 'weight', 43.3],
...           [3, 12, 'weight', 34.5],
...           [3, 17, 'weight', 49.4]]
>>> table7 = etl.recast(table6, key='id')
>>> table7
+----+--------------+
| id | weight       |
+====+==============+
|  1 | [66.4, 55.2] |
+----+--------------+
|  2 | [53.2, 43.3] |
+----+--------------+
|  3 | [34.5, 49.4] |
+----+--------------+

>>> # multiple values can be reduced via an aggregation function
... def mean(values):
...     return float(sum(values)) / len(values)
...
>>> table8 = etl.recast(table6, key='id', reducers={'weight': mean})
>>> table8
+----+--------------------+
| id | weight             |
+====+====================+
|  1 | 60.800000000000004 |
+----+--------------------+
|  2 |              48.25 |
+----+--------------------+
|  3 |              41.95 |
+----+--------------------+

>>> # missing values are padded with whatever is provided via the
... # missing keyword argument (None by default)
... table9 = [['id', 'variable', 'value'],
...           [1, 'gender', 'F'],
...           [2, 'age', 17],
...           [1, 'age', 12],
...           [3, 'gender', 'M']]
>>> table10 = etl.recast(table9, key='id')
>>> table10
+----+------+--------+
| id | age  | gender |
+====+======+========+
|  1 |   12 | 'F'    |
+----+------+--------+
|  2 |   17 | None   |
+----+------+--------+
|  3 | None | 'M'    |
+----+------+--------+

Note that the table is scanned once to discover variables, then a second time to reshape the data and recast variables as fields. How many rows are scanned in the first pass is determined by the samplesize argument.

petl.transform.reshape.transpose(table)[source]¶

Transpose rows into columns. E.g.:

>>> import petl as etl
>>> table1 = [['id', 'colour'],
...           [1, 'blue'],
...           [2, 'red'],
...           [3, 'purple'],
...           [5, 'yellow'],
...           [7, 'orange']]
>>> table2 = etl.transpose(table1)
>>> table2
+----------+--------+-------+----------+----------+----------+
| id       | 1      | 2     | 3        | 5        | 7        |
+==========+========+=======+==========+==========+==========+
| 'colour' | 'blue' | 'red' | 'purple' | 'yellow' | 'orange' |
+----------+--------+-------+----------+----------+----------+

petl.transform.reshape.pivot(table, f1, f2, f3, aggfun, missing=None, presorted=False, buffersize=None, tempdir=None, cache=True)[source]¶

Construct a pivot table. E.g.:

>>> import petl as etl
>>> table1 = [['region', 'gender', 'style', 'units'],
...           ['east', 'boy', 'tee', 12],
...           ['east', 'boy', 'golf', 14],
...           ['east', 'boy', 'fancy', 7],
...           ['east', 'girl', 'tee', 3],
...           ['east', 'girl', 'golf', 8],
...           ['east', 'girl', 'fancy', 18],
...           ['west', 'boy', 'tee', 12],
...           ['west', 'boy', 'golf', 15],
...           ['west', 'boy', 'fancy', 8],
...           ['west', 'girl', 'tee', 6],
...           ['west', 'girl', 'golf', 16],
...           ['west', 'girl', 'fancy', 1]]
>>> table2 = etl.pivot(table1, 'region', 'gender', 'units', sum)
>>> table2
+--------+-----+------+
| region | boy | girl |
+========+=====+======+
| 'east' |  33 |   29 |
+--------+-----+------+
| 'west' |  35 |   23 |
+--------+-----+------+

>>> table3 = etl.pivot(table1, 'region', 'style', 'units', sum)
>>> table3
+--------+-------+------+-----+
| region | fancy | golf | tee |
+========+=======+======+=====+
| 'east' |    25 |   22 |  15 |
+--------+-------+------+-----+
| 'west' |     9 |   31 |  18 |
+--------+-------+------+-----+

>>> table4 = etl.pivot(table1, 'gender', 'style', 'units', sum)
>>> table4
+--------+-------+------+-----+
| gender | fancy | golf | tee |
+========+=======+======+=====+
| 'boy'  |    15 |   29 |  24 |
+--------+-------+------+-----+
| 'girl' |    19 |   24 |   9 |
+--------+-------+------+-----+

petl.transform.reshape.flatten(table)[source]¶

Convert a table to a sequence of values in row-major order. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['A', 1, True],
...           ['C', 7, False],
...           ['B', 2, False],
...           ['C', 9, True]]
>>> list(etl.flatten(table1))
['A', 1, True, 'C', 7, False, 'B', 2, False, 'C', 9, True]

petl.transform.reshape.unflatten(*args, **kwargs)[source]¶

Convert a sequence of values in row-major order into a table. E.g.:

>>> import petl as etl
>>> a = ['A', 1, True, 'C', 7, False, 'B', 2, False, 'C', 9]
>>> table1 = etl.unflatten(a, 3)
>>> table1
+-----+----+-------+
| f0  | f1 | f2    |
+=====+====+=======+
| 'A' |  1 | True  |
+-----+----+-------+
| 'C' |  7 | False |
+-----+----+-------+
| 'B' |  2 | False |
+-----+----+-------+
| 'C' |  9 | None  |
+-----+----+-------+

>>> # a table and field name can also be provided as arguments
... table2 = [['lines'],
...           ['A'],
...           [1],
...           [True],
...           ['C'],
...           [7],
...           [False],
...           ['B'],
...           [2],
...           [False],
...           ['C'],
...           [9]]
>>> table3 = etl.unflatten(table2, 'lines', 3)
>>> table3
+-----+----+-------+
| f0  | f1 | f2    |
+=====+====+=======+
| 'A' |  1 | True  |
+-----+----+-------+
| 'C' |  7 | False |
+-----+----+-------+
| 'B' |  2 | False |
+-----+----+-------+
| 'C' |  9 | None  |
+-----+----+-------+

Filling missing values¶

petl.transform.fills.filldown(table, *fields, **kwargs)[source]¶

Replace missing values with non-missing values from the row above. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           [1, 'a', None],
...           [1, None, .23],
...           [1, 'b', None],
...           [2, None, None],
...           [2, None, .56],
...           [2, 'c', None],
...           [None, 'c', .72]]
>>> table2 = etl.filldown(table1)
>>> table2.lookall()
+-----+-----+------+
| foo | bar | baz  |
+=====+=====+======+
|   1 | 'a' | None |
+-----+-----+------+
|   1 | 'a' | 0.23 |
+-----+-----+------+
|   1 | 'b' | 0.23 |
+-----+-----+------+
|   2 | 'b' | 0.23 |
+-----+-----+------+
|   2 | 'b' | 0.56 |
+-----+-----+------+
|   2 | 'c' | 0.56 |
+-----+-----+------+
|   2 | 'c' | 0.72 |
+-----+-----+------+

>>> table3 = etl.filldown(table1, 'bar')
>>> table3.lookall()
+------+-----+------+
| foo  | bar | baz  |
+======+=====+======+
|    1 | 'a' | None |
+------+-----+------+
|    1 | 'a' | 0.23 |
+------+-----+------+
|    1 | 'b' | None |
+------+-----+------+
|    2 | 'b' | None |
+------+-----+------+
|    2 | 'b' | 0.56 |
+------+-----+------+
|    2 | 'c' | None |
+------+-----+------+
| None | 'c' | 0.72 |
+------+-----+------+

>>> table4 = etl.filldown(table1, 'bar', 'baz')
>>> table4.lookall()
+------+-----+------+
| foo  | bar | baz  |
+======+=====+======+
|    1 | 'a' | None |
+------+-----+------+
|    1 | 'a' | 0.23 |
+------+-----+------+
|    1 | 'b' | 0.23 |
+------+-----+------+
|    2 | 'b' | 0.23 |
+------+-----+------+
|    2 | 'b' | 0.56 |
+------+-----+------+
|    2 | 'c' | 0.56 |
+------+-----+------+
| None | 'c' | 0.72 |
+------+-----+------+

Use the missing keyword argument to control which value is treated as missing (None by default).

petl.transform.fills.fillright(table, missing=None)[source]¶

Replace missing values with preceding non-missing values. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           [1, 'a', None],
...           [1, None, .23],
...           [1, 'b', None],
...           [2, None, None],
...           [2, None, .56],
...           [2, 'c', None],
...           [None, 'c', .72]]
>>> table2 = etl.fillright(table1)
>>> table2.lookall()
+------+-----+------+
| foo  | bar | baz  |
+======+=====+======+
|    1 | 'a' | 'a'  |
+------+-----+------+
|    1 |   1 | 0.23 |
+------+-----+------+
|    1 | 'b' | 'b'  |
+------+-----+------+
|    2 |   2 |    2 |
+------+-----+------+
|    2 |   2 | 0.56 |
+------+-----+------+
|    2 | 'c' | 'c'  |
+------+-----+------+
| None | 'c' | 0.72 |
+------+-----+------+

Use the missing keyword argument to control which value is treated as missing (None by default).

petl.transform.fills.fillleft(table, missing=None)[source]¶

Replace missing values with following non-missing values. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           [1, 'a', None],
...           [1, None, .23],
...           [1, 'b', None],
...           [2, None, None],
...           [2, None, .56],
...           [2, 'c', None],
...           [None, 'c', .72]]
>>> table2 = etl.fillleft(table1)
>>> table2.lookall()
+-----+------+------+
| foo | bar  | baz  |
+=====+======+======+
|   1 | 'a'  | None |
+-----+------+------+
|   1 | 0.23 | 0.23 |
+-----+------+------+
|   1 | 'b'  | None |
+-----+------+------+
|   2 | None | None |
+-----+------+------+
|   2 | 0.56 | 0.56 |
+-----+------+------+
|   2 | 'c'  | None |
+-----+------+------+
| 'c' | 'c'  | 0.72 |
+-----+------+------+

Use the missing keyword argument to control which value is treated as missing (None by default).

Validation¶

petl.transform.validation.validate(table, constraints=None, header=None)[source]¶

Validate a table against a set of constraints and/or an expected header, e.g.:

>>> import petl as etl
>>> # define some validation constraints
... header = ('foo', 'bar', 'baz')
>>> constraints = [
...     dict(name='foo_int', field='foo', test=int),
...     dict(name='bar_date', field='bar', test=etl.dateparser('%Y-%m-%d')),
...     dict(name='baz_enum', field='baz', assertion=lambda v: v in ['Y', 'N']),
...     dict(name='not_none', assertion=lambda row: None not in row)
... ]
>>> # now validate a table
... table = (('foo', 'bar', 'bazzz'),
...          (1, '2000-01-01', 'Y'),
...          ('x', '2010-10-10', 'N'),
...          (2, '2000/01/01', 'Y'),
...          (3, '2015-12-12', 'x'),
...          (4, None, 'N'),
...          ('y', '1999-99-99', 'z'),
...          (6, '2000-01-01'),
...          (7, '2001-02-02', 'N', True))
>>> problems = etl.validate(table, constraints=constraints, header=header)
>>> problems.lookall()
+--------------+-----+-------+--------------+------------------+
| name         | row | field | value        | error            |
+==============+=====+=======+==============+==================+
| '__header__' |   0 | None  | None         | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+
| 'foo_int'    |   2 | 'foo' | 'x'          | 'ValueError'     |
+--------------+-----+-------+--------------+------------------+
| 'bar_date'   |   3 | 'bar' | '2000/01/01' | 'ValueError'     |
+--------------+-----+-------+--------------+------------------+
| 'baz_enum'   |   4 | 'baz' | 'x'          | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+
| 'bar_date'   |   5 | 'bar' | None         | 'AttributeError' |
+--------------+-----+-------+--------------+------------------+
| 'not_none'   |   5 | None  | None         | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+
| 'foo_int'    |   6 | 'foo' | 'y'          | 'ValueError'     |
+--------------+-----+-------+--------------+------------------+
| 'bar_date'   |   6 | 'bar' | '1999-99-99' | 'ValueError'     |
+--------------+-----+-------+--------------+------------------+
| 'baz_enum'   |   6 | 'baz' | 'z'          | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+
| '__len__'    |   7 | None  |            2 | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+
| 'baz_enum'   |   7 | 'baz' | None         | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+
| '__len__'    |   8 | None  |            4 | 'AssertionError' |
+--------------+-----+-------+--------------+------------------+

Returns a table of validation problems.

Intervals (intervaltree)¶

Note

The following functions require the package intervaltree to be installed, e.g.:

$ pip install intervaltree

petl.transform.intervals.intervaljoin(left, right, lstart='start', lstop='stop', rstart='start', rstop='stop', lkey=None, rkey=None, include_stop=False, lprefix=None, rprefix=None)[source]¶

Join two tables by overlapping intervals. E.g.:

>>> import petl as etl
>>> left = [['begin', 'end', 'quux'],
...         [1, 2, 'a'],
...         [2, 4, 'b'],
...         [2, 5, 'c'],
...         [9, 14, 'd'],
...         [1, 1, 'e'],
...         [10, 10, 'f']]
>>> right = [['start', 'stop', 'value'],
...          [1, 4, 'foo'],
...          [3, 7, 'bar'],
...          [4, 9, 'baz']]
>>> table1 = etl.intervaljoin(left, right,
...                           lstart='begin', lstop='end',
...                           rstart='start', rstop='stop')
>>> table1.lookall()
+-------+-----+------+-------+------+-------+
| begin | end | quux | start | stop | value |
+=======+=====+======+=======+======+=======+
|     1 |   2 | 'a'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     3 |    7 | 'bar' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     3 |    7 | 'bar' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     4 |    9 | 'baz' |
+-------+-----+------+-------+------+-------+

>>> # include stop coordinate in intervals
... table2 = etl.intervaljoin(left, right,
...                           lstart='begin', lstop='end',
...                           rstart='start', rstop='stop',
...                           include_stop=True)
>>> table2.lookall()
+-------+-----+------+-------+------+-------+
| begin | end | quux | start | stop | value |
+=======+=====+======+=======+======+=======+
|     1 |   2 | 'a'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     3 |    7 | 'bar' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     4 |    9 | 'baz' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     3 |    7 | 'bar' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     4 |    9 | 'baz' |
+-------+-----+------+-------+------+-------+
|     9 |  14 | 'd'  |     4 |    9 | 'baz' |
+-------+-----+------+-------+------+-------+
|     1 |   1 | 'e'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+

Note start coordinates are included and stop coordinates are excluded from the interval. Use the include_stop keyword argument to include the upper bound of the interval when finding overlaps.

An additional key comparison can be made, e.g.:

>>> import petl as etl
>>> left = (('fruit', 'begin', 'end'),
...         ('apple', 1, 2),
...         ('apple', 2, 4),
...         ('apple', 2, 5),
...         ('orange', 2, 5),
...         ('orange', 9, 14),
...         ('orange', 19, 140),
...         ('apple', 1, 1))
>>> right = (('type', 'start', 'stop', 'value'),
...          ('apple', 1, 4, 'foo'),
...          ('apple', 3, 7, 'bar'),
...          ('orange', 4, 9, 'baz'))
>>> table3 = etl.intervaljoin(left, right,
...                           lstart='begin', lstop='end', lkey='fruit',
...                           rstart='start', rstop='stop', rkey='type')
>>> table3.lookall()
+----------+-------+-----+----------+-------+------+-------+
| fruit    | begin | end | type     | start | stop | value |
+==========+=======+=====+==========+=======+======+=======+
| 'apple'  |     1 |   2 | 'apple'  |     1 |    4 | 'foo' |
+----------+-------+-----+----------+-------+------+-------+
| 'apple'  |     2 |   4 | 'apple'  |     1 |    4 | 'foo' |
+----------+-------+-----+----------+-------+------+-------+
| 'apple'  |     2 |   4 | 'apple'  |     3 |    7 | 'bar' |
+----------+-------+-----+----------+-------+------+-------+
| 'apple'  |     2 |   5 | 'apple'  |     1 |    4 | 'foo' |
+----------+-------+-----+----------+-------+------+-------+
| 'apple'  |     2 |   5 | 'apple'  |     3 |    7 | 'bar' |
+----------+-------+-----+----------+-------+------+-------+
| 'orange' |     2 |   5 | 'orange' |     4 |    9 | 'baz' |
+----------+-------+-----+----------+-------+------+-------+

petl.transform.intervals.intervalleftjoin(left, right, lstart='start', lstop='stop', rstart='start', rstop='stop', lkey=None, rkey=None, include_stop=False, missing=None, lprefix=None, rprefix=None)[source]¶

Like petl.transform.intervals.intervaljoin() but rows from the left table without a match in the right table are also included. E.g.:

>>> import petl as etl
>>> left = [['begin', 'end', 'quux'],
...         [1, 2, 'a'],
...         [2, 4, 'b'],
...         [2, 5, 'c'],
...         [9, 14, 'd'],
...         [1, 1, 'e'],
...         [10, 10, 'f']]
>>> right = [['start', 'stop', 'value'],
...          [1, 4, 'foo'],
...          [3, 7, 'bar'],
...          [4, 9, 'baz']]
>>> table1 = etl.intervalleftjoin(left, right,
...                               lstart='begin', lstop='end',
...                               rstart='start', rstop='stop')
>>> table1.lookall()
+-------+-----+------+-------+------+-------+
| begin | end | quux | start | stop | value |
+=======+=====+======+=======+======+=======+
|     1 |   2 | 'a'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   4 | 'b'  |     3 |    7 | 'bar' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     1 |    4 | 'foo' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     3 |    7 | 'bar' |
+-------+-----+------+-------+------+-------+
|     2 |   5 | 'c'  |     4 |    9 | 'baz' |
+-------+-----+------+-------+------+-------+
|     9 |  14 | 'd'  | None  | None | None  |
+-------+-----+------+-------+------+-------+
|     1 |   1 | 'e'  | None  | None | None  |
+-------+-----+------+-------+------+-------+
|    10 |  10 | 'f'  | None  | None | None  |
+-------+-----+------+-------+------+-------+

Note start coordinates are included and stop coordinates are excluded from the interval. Use the include_stop keyword argument to include the upper bound of the interval when finding overlaps.

petl.transform.intervals.intervaljoinvalues(left, right, value, lstart='start', lstop='stop', rstart='start', rstop='stop', lkey=None, rkey=None, include_stop=False)[source]¶

Convenience function to join the left table with values from a specific field in the right hand table.

Note start coordinates are included and stop coordinates are excluded from the interval. Use the include_stop keyword argument to include the upper bound of the interval when finding overlaps.

petl.transform.intervals.intervalantijoin(left, right, lstart='start', lstop='stop', rstart='start', rstop='stop', lkey=None, rkey=None, include_stop=False, missing=None)[source]¶

Return rows from the left table with no overlapping rows from the right table.

Note start coordinates are included and stop coordinates are excluded from the interval. Use the include_stop keyword argument to include the upper bound of the interval when finding overlaps.

petl.transform.intervals.intervallookup(table, start='start', stop='stop', value=None, include_stop=False)[source]¶

Construct an interval lookup for the given table. E.g.:

>>> import petl as etl
>>> table = [['start', 'stop', 'value'],
...          [1, 4, 'foo'],
...          [3, 7, 'bar'],
...          [4, 9, 'baz']]
>>> lkp = etl.intervallookup(table, 'start', 'stop')
>>> lkp.search(0, 1)
[]
>>> lkp.search(1, 2)
[(1, 4, 'foo')]
>>> lkp.search(2, 4)
[(1, 4, 'foo'), (3, 7, 'bar')]
>>> lkp.search(2, 5)
[(1, 4, 'foo'), (3, 7, 'bar'), (4, 9, 'baz')]
>>> lkp.search(9, 14)
[]
>>> lkp.search(19, 140)
[]
>>> lkp.search(0)
[]
>>> lkp.search(1)
[(1, 4, 'foo')]
>>> lkp.search(2)
[(1, 4, 'foo')]
>>> lkp.search(4)
[(3, 7, 'bar'), (4, 9, 'baz')]
>>> lkp.search(5)
[(3, 7, 'bar'), (4, 9, 'baz')]

Note start coordinates are included and stop coordinates are excluded from the interval. Use the include_stop keyword argument to include the upper bound of the interval when finding overlaps.

Some examples using the include_stop and value keyword arguments:

>>> import petl as etl
>>> table = [['start', 'stop', 'value'],
...          [1, 4, 'foo'],
...          [3, 7, 'bar'],
...          [4, 9, 'baz']]
>>> lkp = etl.intervallookup(table, 'start', 'stop', include_stop=True,
...                          value='value')
>>> lkp.search(0, 1)
['foo']
>>> lkp.search(1, 2)
['foo']
>>> lkp.search(2, 4)
['foo', 'bar', 'baz']
>>> lkp.search(2, 5)
['foo', 'bar', 'baz']
>>> lkp.search(9, 14)
['baz']
>>> lkp.search(19, 140)
[]
>>> lkp.search(0)
[]
>>> lkp.search(1)
['foo']
>>> lkp.search(2)
['foo']
>>> lkp.search(4)
['foo', 'bar', 'baz']
>>> lkp.search(5)
['bar', 'baz']

petl.transform.intervals.intervallookupone(table, start='start', stop='stop', value=None, include_stop=False, strict=True)[source]¶

Construct an interval lookup for the given table, returning at most one result for each query. E.g.:

>>> import petl as etl
>>> table = [['start', 'stop', 'value'],
...          [1, 4, 'foo'],
...          [3, 7, 'bar'],
...          [4, 9, 'baz']]
>>> lkp = etl.intervallookupone(table, 'start', 'stop', strict=False)
>>> lkp.search(0, 1)
>>> lkp.search(1, 2)
(1, 4, 'foo')
>>> lkp.search(2, 4)
(1, 4, 'foo')
>>> lkp.search(2, 5)
(1, 4, 'foo')
>>> lkp.search(9, 14)
>>> lkp.search(19, 140)
>>> lkp.search(0)
>>> lkp.search(1)
(1, 4, 'foo')
>>> lkp.search(2)
(1, 4, 'foo')
>>> lkp.search(4)
(3, 7, 'bar')
>>> lkp.search(5)
(3, 7, 'bar')

If strict=True, queries returning more than one result will raise a DuplicateKeyError. If strict=False and there is more than one result, the first result is returned.

Note start coordinates are included and stop coordinates are excluded from the interval. Use the include_stop keyword argument to include the upper bound of the interval when finding overlaps.

petl.transform.intervals.intervalrecordlookup(table, start='start', stop='stop', include_stop=False)[source]¶: As petl.transform.intervals.intervallookup() but return records instead of tuples.

petl.transform.intervals.intervalrecordlookupone(table, start='start', stop='stop', include_stop=False, strict=True)[source]¶: As petl.transform.intervals.intervallookupone() but return records instead of tuples.

petl.transform.intervals.facetintervallookup(table, key, start='start', stop='stop', value=None, include_stop=False)[source]¶

Construct a faceted interval lookup for the given table. E.g.:

>>> import petl as etl
>>> table = (('type', 'start', 'stop', 'value'),
...          ('apple', 1, 4, 'foo'),
...          ('apple', 3, 7, 'bar'),
...          ('orange', 4, 9, 'baz'))
>>> lkp = etl.facetintervallookup(table, key='type', start='start', stop='stop')
>>> lkp['apple'].search(1, 2)
[('apple', 1, 4, 'foo')]
>>> lkp['apple'].search(2, 4)
[('apple', 1, 4, 'foo'), ('apple', 3, 7, 'bar')]
>>> lkp['apple'].search(2, 5)
[('apple', 1, 4, 'foo'), ('apple', 3, 7, 'bar')]
>>> lkp['orange'].search(2, 5)
[('orange', 4, 9, 'baz')]
>>> lkp['orange'].search(9, 14)
[]
>>> lkp['orange'].search(19, 140)
[]
>>> lkp['apple'].search(1)
[('apple', 1, 4, 'foo')]
>>> lkp['apple'].search(2)
[('apple', 1, 4, 'foo')]
>>> lkp['apple'].search(4)
[('apple', 3, 7, 'bar')]
>>> lkp['apple'].search(5)
[('apple', 3, 7, 'bar')]
>>> lkp['orange'].search(5)
[('orange', 4, 9, 'baz')]

petl.transform.intervals.facetintervallookupone(table, key, start='start', stop='stop', value=None, include_stop=False, strict=True)[source]¶

Construct a faceted interval lookup for the given table, returning at most one result for each query.

If strict=True, queries returning more than one result will raise a DuplicateKeyError. If strict=False and there is more than one result, the first result is returned.

petl.transform.intervals.facetintervalrecordlookup(table, key, start='start', stop='stop', include_stop=False)[source]¶: As petl.transform.intervals.facetintervallookup() but return records.

petl.transform.intervals.facetintervalrecordlookupone(table, key, start, stop, include_stop=False, strict=True)[source]¶: As petl.transform.intervals.facetintervallookupone() but return records.

petl.transform.intervals.intervalsubtract(left, right, lstart='start', lstop='stop', rstart='start', rstop='stop', lkey=None, rkey=None, include_stop=False)[source]¶: Subtract intervals in the right hand table from intervals in the left hand table.

petl.transform.intervals.collapsedintervals(table, start='start', stop='stop', key=None)[source]¶

Utility function to collapse intervals in a table.

If no facet key is given, returns an iterator over (start, stop) tuples.

If facet key is given, returns an iterator over (key, start, stop) tuples.

Utility functions¶

Basic utilities¶

petl.util.base.header(table)[source]¶

Return the header row for the given table. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> etl.header(table)
('foo', 'bar')

Note that the header row will always be returned as a tuple, regardless of what the underlying data are.

petl.util.base.fieldnames(table)[source]¶

Return the string values of the header row. If the header row contains only strings, then this function is equivalent to header(), i.e.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> etl.fieldnames(table)
('foo', 'bar')
>>> etl.header(table)
('foo', 'bar')

petl.util.base.data(table, *sliceargs)[source]¶

Return a container supporting iteration over data rows in a given table (i.e., without the header). E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> d = etl.data(table)
>>> list(d)
[['a', 1], ['b', 2]]

Positional arguments can be used to slice the data rows. The sliceargs are passed to itertools.islice().

petl.util.base.values(table, *field, **kwargs)[source]¶

Return a container supporting iteration over values in a given field or fields. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', True],
...           ['b'],
...           ['b', True],
...           ['c', False]]
>>> foo = etl.values(table1, 'foo')
>>> foo
foo: 'a', 'b', 'b', 'c'
>>> list(foo)
['a', 'b', 'b', 'c']
>>> bar = etl.values(table1, 'bar')
>>> bar
bar: True, None, True, False
>>> list(bar)
[True, None, True, False]
>>> # values from multiple fields
... table2 = [['foo', 'bar', 'baz'],
...           [1, 'a', True],
...           [2, 'bb', True],
...           [3, 'd', False]]
>>> foobaz = etl.values(table2, 'foo', 'baz')
>>> foobaz
('foo', 'baz'): (1, True), (2, True), (3, False)
>>> list(foobaz)
[(1, True), (2, True), (3, False)]

The field argument can be a single field name or index (starting from zero) or a tuple of field names and/or indexes. Multiple fields can also be provided as positional arguments.

If rows are uneven, the value of the keyword argument missing is returned.

petl.util.base.dicts(table, *sliceargs, **kwargs)[source]¶

Return a container supporting iteration over rows as dicts. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> d = etl.dicts(table)
>>> d
{'foo': 'a', 'bar': 1}
{'foo': 'b', 'bar': 2}
>>> list(d)
[{'foo': 'a', 'bar': 1}, {'foo': 'b', 'bar': 2}]

Short rows are padded with the value of the missing keyword argument.

petl.util.base.namedtuples(table, *sliceargs, **kwargs)[source]¶

View the table as a container of named tuples. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> d = etl.namedtuples(table)
>>> d
row(foo='a', bar=1)
row(foo='b', bar=2)
>>> list(d)
[row(foo='a', bar=1), row(foo='b', bar=2)]

Short rows are padded with the value of the missing keyword argument.

The name keyword argument can be given to override the name of the named tuple class (defaults to ‘row’).

petl.util.base.records(table, *sliceargs, **kwargs)[source]¶

Return a container supporting iteration over rows as records, where a record is a hybrid object supporting all possible ways of accessing values. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> d = etl.records(table)
>>> d
('a', 1)
('b', 2)
>>> list(d)
[('a', 1), ('b', 2)]
>>> [r[0] for r in d]
['a', 'b']
>>> [r['foo'] for r in d]
['a', 'b']
>>> [r.foo for r in d]
['a', 'b']

Short rows are padded with the value of the missing keyword argument.

petl.util.base.expr(s)[source]¶

Construct a function operating on a table record.

The expression string is converted into a lambda function by prepending the string with 'lambda rec: ', then replacing anything enclosed in curly braces (e.g., "{foo}") with a lookup on the record (e.g., "rec['foo']"), then finally calling eval().

So, e.g., the expression string "{foo} * {bar}" is converted to the function lambda rec: rec['foo'] * rec['bar']

petl.util.base.rowgroupby(table, key, value=None)[source]¶

Convenient adapter for itertools.groupby(). E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['a', 1, True],
...           ['b', 3, True],
...           ['b', 2]]
>>> # group entire rows
... for key, group in etl.rowgroupby(table1, 'foo'):
...     print(key, list(group))
...
a [('a', 1, True)]
b [('b', 3, True), ('b', 2)]
>>> # group specific values
... for key, group in etl.rowgroupby(table1, 'foo', 'bar'):
...     print(key, list(group))
...
a [1]
b [3, 2]

N.B., assumes the input table is already sorted by the given key.

petl.util.base.empty()[source]¶

Return an empty table. Can be useful when building up a table from a set of columns, e.g.:

>>> import petl as etl
>>> table = (
...     etl
...     .empty()
...     .addcolumn('foo', ['A', 'B'])
...     .addcolumn('bar', [1, 2])
... )
>>> table
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' |   1 |
+-----+-----+
| 'B' |   2 |
+-----+-----+

Visualising tables¶

petl.util.vis.look(table, limit=0, vrepr=None, index_header=None, style=None, truncate=None, width=None)[source]¶

Format a portion of the table as text for inspection in an interactive session. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2]]
>>> etl.look(table1)
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+
| 'b' |   2 |
+-----+-----+

>>> # alternative formatting styles
... etl.look(table1, style='simple')
===  ===
foo  bar
===  ===
'a'    1
'b'    2
===  ===

>>> etl.look(table1, style='minimal')
foo  bar
'a'    1
'b'    2

>>> # any irregularities in the length of header and/or data
... # rows will appear as blank cells
... table2 = [['foo', 'bar'],
...           ['a'],
...           ['b', 2, True]]
>>> etl.look(table2)
+-----+-----+------+
| foo | bar |      |
+=====+=====+======+
| 'a' |     |      |
+-----+-----+------+
| 'b' |   2 | True |
+-----+-----+------+

Three alternative presentation styles are available: ‘grid’, ‘simple’ and ‘minimal’, where ‘grid’ is the default. A different style can be specified using the style keyword argument. The default style can also be changed by setting petl.config.look_style.

petl.util.vis.lookall(table, **kwargs)[source]¶

Format the entire table as text for inspection in an interactive session.

N.B., this will load the entire table into memory.

petl.util.vis.see(table, limit=0, vrepr=None, index_header=None)[source]¶

Format a portion of a table as text in a column-oriented layout for inspection in an interactive session. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> etl.see(table)
foo: 'a', 'b'
bar: 1, 2

Useful for tables with a larger number of fields.

petl.util.vis.display(table, limit=0, vrepr=None, index_header=None, caption=None, tr_style=None, td_styles=None, encoding=None, truncate=None)[source]¶: Display a table inline within an IPython notebook.

petl.util.vis.displayall(table, **kwargs)[source]¶: Display all rows from a table inline within an IPython notebook (use with caution, big tables will kill your browser).

Lookup data structures¶

petl.util.lookups.lookup(table, key, value=None, dictionary=None)[source]¶

Load a dictionary with data from the given table. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['b', 3]]
>>> lkp = etl.lookup(table1, 'foo', 'bar')
>>> lkp['a']
[1]
>>> lkp['b']
[2, 3]
>>> # if no value argument is given, defaults to the whole
... # row (as a tuple)
... lkp = etl.lookup(table1, 'foo')
>>> lkp['a']
[('a', 1)]
>>> lkp['b']
[('b', 2), ('b', 3)]
>>> # compound keys are supported
... table2 = [['foo', 'bar', 'baz'],
...           ['a', 1, True],
...           ['b', 2, False],
...           ['b', 3, True],
...           ['b', 3, False]]
>>> lkp = etl.lookup(table2, ('foo', 'bar'), 'baz')
>>> lkp[('a', 1)]
[True]
>>> lkp[('b', 2)]
[False]
>>> lkp[('b', 3)]
[True, False]
>>> # data can be loaded into an existing dictionary-like
... # object, including persistent dictionaries created via the
... # shelve module
... import shelve
>>> lkp = shelve.open('example.dat', flag='n')
>>> lkp = etl.lookup(table1, 'foo', 'bar', lkp)
>>> lkp.close()
>>> lkp = shelve.open('example.dat', flag='r')
>>> lkp['a']
[1]
>>> lkp['b']
[2, 3]

petl.util.lookups.lookupone(table, key, value=None, dictionary=None, strict=False)[source]¶

Load a dictionary with data from the given table, assuming there is at most one value for each key. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['b', 3]]
>>> # if the specified key is not unique and strict=False (default),
... # the first value wins
... lkp = etl.lookupone(table1, 'foo', 'bar')
>>> lkp['a']
1
>>> lkp['b']
2
>>> # if the specified key is not unique and strict=True, will raise
... # DuplicateKeyError
... try:
...     lkp = etl.lookupone(table1, 'foo', strict=True)
... except etl.errors.DuplicateKeyError as e:
...     print(e)
...
duplicate key: 'b'
>>> # compound keys are supported
... table2 = [['foo', 'bar', 'baz'],
...           ['a', 1, True],
...           ['b', 2, False],
...           ['b', 3, True],
...           ['b', 3, False]]
>>> lkp = etl.lookupone(table2, ('foo', 'bar'), 'baz')
>>> lkp[('a', 1)]
True
>>> lkp[('b', 2)]
False
>>> lkp[('b', 3)]
True
>>> # data can be loaded into an existing dictionary-like
... # object, including persistent dictionaries created via the
... # shelve module
... import shelve
>>> lkp = shelve.open('example.dat', flag='n')
>>> lkp = etl.lookupone(table1, 'foo', 'bar', lkp)
>>> lkp.close()
>>> lkp = shelve.open('example.dat', flag='r')
>>> lkp['a']
1
>>> lkp['b']
2

petl.util.lookups.dictlookup(table, key, dictionary=None)[source]¶

Load a dictionary with data from the given table, mapping to dicts. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['b', 3]]
>>> lkp = etl.dictlookup(table1, 'foo')
>>> lkp['a']
[{'foo': 'a', 'bar': 1}]
>>> lkp['b']
[{'foo': 'b', 'bar': 2}, {'foo': 'b', 'bar': 3}]
>>> # compound keys are supported
... table2 = [['foo', 'bar', 'baz'],
...           ['a', 1, True],
...           ['b', 2, False],
...           ['b', 3, True],
...           ['b', 3, False]]
>>> lkp = etl.dictlookup(table2, ('foo', 'bar'))
>>> lkp[('a', 1)]
[{'foo': 'a', 'baz': True, 'bar': 1}]
>>> lkp[('b', 2)]
[{'foo': 'b', 'baz': False, 'bar': 2}]
>>> lkp[('b', 3)]
[{'foo': 'b', 'baz': True, 'bar': 3}, {'foo': 'b', 'baz': False, 'bar': 3}]
>>> # data can be loaded into an existing dictionary-like
... # object, including persistent dictionaries created via the
... # shelve module
... import shelve
>>> lkp = shelve.open('example.dat', flag='n')
>>> lkp = etl.dictlookup(table1, 'foo', lkp)
>>> lkp.close()
>>> lkp = shelve.open('example.dat', flag='r')
>>> lkp['a']
[{'foo': 'a', 'bar': 1}]
>>> lkp['b']
[{'foo': 'b', 'bar': 2}, {'foo': 'b', 'bar': 3}]

petl.util.lookups.dictlookupone(table, key, dictionary=None, strict=False)[source]¶

Load a dictionary with data from the given table, mapping to dicts, assuming there is at most one row for each key. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 2],
...           ['b', 3]]
>>> # if the specified key is not unique and strict=False (default),
... # the first value wins
... lkp = etl.dictlookupone(table1, 'foo')
>>> lkp['a']
{'foo': 'a', 'bar': 1}
>>> lkp['b']
{'foo': 'b', 'bar': 2}
>>> # if the specified key is not unique and strict=True, will raise
... # DuplicateKeyError
... try:
...     lkp = etl.dictlookupone(table1, 'foo', strict=True)
... except etl.errors.DuplicateKeyError as e:
...     print(e)
...
duplicate key: 'b'
>>> # compound keys are supported
... table2 = [['foo', 'bar', 'baz'],
...           ['a', 1, True],
...           ['b', 2, False],
...           ['b', 3, True],
...           ['b', 3, False]]
>>> lkp = etl.dictlookupone(table2, ('foo', 'bar'))
>>> lkp[('a', 1)]
{'foo': 'a', 'baz': True, 'bar': 1}
>>> lkp[('b', 2)]
{'foo': 'b', 'baz': False, 'bar': 2}
>>> lkp[('b', 3)]
{'foo': 'b', 'baz': True, 'bar': 3}
>>> # data can be loaded into an existing dictionary-like
... # object, including persistent dictionaries created via the
... # shelve module
... import shelve
>>> lkp = shelve.open('example.dat', flag='n')
>>> lkp = etl.dictlookupone(table1, 'foo', lkp)
>>> lkp.close()
>>> lkp = shelve.open('example.dat', flag='r')
>>> lkp['a']
{'foo': 'a', 'bar': 1}
>>> lkp['b']
{'foo': 'b', 'bar': 2}

petl.util.lookups.recordlookup(table, key, dictionary=None)[source]¶: Load a dictionary with data from the given table, mapping to record objects.

petl.util.lookups.recordlookupone(table, key, dictionary=None, strict=False)[source]¶: Load a dictionary with data from the given table, mapping to record objects, assuming there is at most one row for each key.

Parsing string/text values¶

petl.util.parsers.dateparser(fmt, strict=True)[source]¶

Return a function to parse strings as datetime.date objects using a given format. E.g.:

>>> from petl import dateparser
>>> isodate = dateparser('%Y-%m-%d')
>>> isodate('2002-12-25')
datetime.date(2002, 12, 25)
>>> try:
...     isodate('2002-02-30')
... except ValueError as e:
...     print(e)
...
day is out of range for month

If strict=False then if an error occurs when parsing, the original value will be returned as-is, and no error will be raised.

petl.util.parsers.timeparser(fmt, strict=True)[source]¶

Return a function to parse strings as datetime.time objects using a given format. E.g.:

>>> from petl import timeparser
>>> isotime = timeparser('%H:%M:%S')
>>> isotime('00:00:00')
datetime.time(0, 0)
>>> isotime('13:00:00')
datetime.time(13, 0)
>>> try:
...     isotime('12:00:99')
... except ValueError as e:
...     print(e)
...
unconverted data remains: 9
>>> try:
...     isotime('25:00:00')
... except ValueError as e:
...     print(e)
...
time data '25:00:00' does not match format '%H:%M:%S'

If strict=False then if an error occurs when parsing, the original value will be returned as-is, and no error will be raised.

petl.util.parsers.datetimeparser(fmt, strict=True)[source]¶

Return a function to parse strings as datetime.datetime objects using a given format. E.g.:

>>> from petl import datetimeparser
>>> isodatetime = datetimeparser('%Y-%m-%dT%H:%M:%S')
>>> isodatetime('2002-12-25T00:00:00')
datetime.datetime(2002, 12, 25, 0, 0)
>>> try:
...     isodatetime('2002-12-25T00:00:99')
... except ValueError as e:
...     print(e)
...
unconverted data remains: 9

If strict=False then if an error occurs when parsing, the original value will be returned as-is, and no error will be raised.

petl.util.parsers.boolparser(true_strings=('true', 't', 'yes', 'y', '1'), false_strings=('false', 'f', 'no', 'n', '0'), case_sensitive=False, strict=True)[source]¶

Return a function to parse strings as bool objects using a given set of string representations for True and False. E.g.:

>>> from petl import boolparser
>>> mybool = boolparser(true_strings=['yes', 'y'], false_strings=['no', 'n'])
>>> mybool('y')
True
>>> mybool('yes')
True
>>> mybool('Y')
True
>>> mybool('No')
False
>>> try:
...     mybool('foo')
... except ValueError as e:
...     print(e)
...
value is not one of recognised boolean strings: 'foo'
>>> try:
...     mybool('True')
... except ValueError as e:
...     print(e)
...
value is not one of recognised boolean strings: 'true'

If strict=False then if an error occurs when parsing, the original value will be returned as-is, and no error will be raised.

petl.util.parsers.numparser(strict=False)[source]¶: Return a function that will attempt to parse the value as a number, trying int(), long(), float() and complex() in that order. If all fail, return the value as-is, unless strict=True, in which case raise the underlying exception.

Counting¶

petl.util.counting.nrows(table)[source]¶

Count the number of data rows in a table. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2]]
>>> etl.nrows(table)
2

petl.util.counting.valuecount(table, field, value, missing=None)[source]¶

Count the number of occurrences of value under the given field. Returns the absolute count and relative frequency as a pair. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'],
...          ['a', 1],
...          ['b', 2],
...          ['b', 7]]
>>> etl.valuecount(table, 'foo', 'b')
(2, 0.6666666666666666)

The field argument can be a single field name or index (starting from zero) or a tuple of field names and/or indexes.

petl.util.counting.valuecounter(table, *field, **kwargs)[source]¶

Find distinct values for the given field and count the number of occurrences. Returns a dict mapping values to counts. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'],
...          ['a', True],
...          ['b'],
...          ['b', True],
...          ['c', False]]
>>> etl.valuecounter(table, 'foo')
Counter({'b': 2, 'c': 1, 'a': 1})

The field argument can be a single field name or index (starting from zero) or a tuple of field names and/or indexes.

petl.util.counting.valuecounts(table, *field, **kwargs)[source]¶

Find distinct values for the given field and count the number and relative frequency of occurrences. Returns a table mapping values to counts, with most common values first. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['a', True, 0.12],
...          ['a', True, 0.17],
...          ['b', False, 0.34],
...          ['b', False, 0.44],
...          ['b']]
>>> etl.valuecounts(table, 'foo')
+-----+-------+-----------+
| foo | count | frequency |
+=====+=======+===========+
| 'b' |     3 |       0.6 |
+-----+-------+-----------+
| 'a' |     2 |       0.4 |
+-----+-------+-----------+

>>> etl.valuecounts(table, 'foo', 'bar')
+-----+-------+-------+-----------+
| foo | bar   | count | frequency |
+=====+=======+=======+===========+
| 'b' | False |     2 |       0.4 |
+-----+-------+-------+-----------+
| 'a' | True  |     2 |       0.4 |
+-----+-------+-------+-----------+
| 'b' | None  |     1 |       0.2 |
+-----+-------+-------+-----------+

If rows are short, the value of the keyword argument missing is counted.

Multiple fields can be given as positional arguments. If multiple fields are given, these are treated as a compound key.

petl.util.counting.stringpatterncounter(table, field)[source]¶: Profile string patterns in the given field, returning a dict mapping patterns to counts.

petl.util.counting.stringpatterns(table, field)[source]¶

Profile string patterns in the given field, returning a table of patterns, counts and frequencies. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'],
...          ['Mr. Foo', '123-1254'],
...          ['Mrs. Bar', '234-1123'],
...          ['Mr. Spo', '123-1254'],
...          [u'Mr. Baz', u'321 1434'],
...          [u'Mrs. Baz', u'321 1434'],
...          ['Mr. Quux', '123-1254-XX']]
>>> etl.stringpatterns(table, 'foo')
+------------+-------+---------------------+
| pattern    | count | frequency           |
+============+=======+=====================+
| 'Aa. Aaa'  |     3 |                 0.5 |
+------------+-------+---------------------+
| 'Aaa. Aaa' |     2 |  0.3333333333333333 |
+------------+-------+---------------------+
| 'Aa. Aaaa' |     1 | 0.16666666666666666 |
+------------+-------+---------------------+

>>> etl.stringpatterns(table, 'bar')
+---------------+-------+---------------------+
| pattern       | count | frequency           |
+===============+=======+=====================+
| '999-9999'    |     3 |                 0.5 |
+---------------+-------+---------------------+
| '999 9999'    |     2 |  0.3333333333333333 |
+---------------+-------+---------------------+
| '999-9999-AA' |     1 | 0.16666666666666666 |
+---------------+-------+---------------------+

petl.util.counting.rowlengths(table)[source]¶

Report on row lengths found in the table. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['A', 1, 2],
...          ['B', '2', '3.4'],
...          [u'B', u'3', u'7.8', True],
...          ['D', 'xyz', 9.0],
...          ['E', None],
...          ['F', 9]]
>>> etl.rowlengths(table)
+--------+-------+
| length | count |
+========+=======+
|      3 |     3 |
+--------+-------+
|      2 |     2 |
+--------+-------+
|      4 |     1 |
+--------+-------+

Useful for finding potential problems in data files.

petl.util.counting.typecounter(table, field)[source]¶

Count the number of values found for each Python type.

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['A', 1, 2],
...          ['B', u'2', '3.4'],
...          [u'B', u'3', u'7.8', True],
...          ['D', u'xyz', 9.0],
...          ['E', 42]]
>>> etl.typecounter(table, 'foo')
Counter({'str': 5})
>>> etl.typecounter(table, 'bar')
Counter({'str': 3, 'int': 2})
>>> etl.typecounter(table, 'baz')
Counter({'str': 2, 'int': 1, 'NoneType': 1, 'float': 1})

The field argument can be a field name or index (starting from zero).

petl.util.counting.typecounts(table, field)[source]¶

Count the number of values found for each Python type and return a table mapping class names to counts and frequencies. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          [b'A', 1, 2],
...          [b'B', '2', b'3.4'],
...          ['B', '3', '7.8', True],
...          ['D', u'xyz', 9.0],
...          ['E', 42]]
>>> etl.typecounts(table, 'foo')
+---------+-------+-----------+
| type    | count | frequency |
+=========+=======+===========+
| 'str'   |     3 |       0.6 |
+---------+-------+-----------+
| 'bytes' |     2 |       0.4 |
+---------+-------+-----------+

>>> etl.typecounts(table, 'bar')
+-------+-------+-----------+
| type  | count | frequency |
+=======+=======+===========+
| 'str' |     3 |       0.6 |
+-------+-------+-----------+
| 'int' |     2 |       0.4 |
+-------+-------+-----------+

>>> etl.typecounts(table, 'baz')
+------------+-------+-----------+
| type       | count | frequency |
+============+=======+===========+
| 'int'      |     1 |       0.2 |
+------------+-------+-----------+
| 'NoneType' |     1 |       0.2 |
+------------+-------+-----------+
| 'bytes'    |     1 |       0.2 |
+------------+-------+-----------+
| 'float'    |     1 |       0.2 |
+------------+-------+-----------+
| 'str'      |     1 |       0.2 |
+------------+-------+-----------+

The field argument can be a field name or index (starting from zero).

petl.util.counting.parsecounter(table, field, parsers=(('int', <type 'int'>), ('float', <type 'float'>)))[source]¶

Count the number of str or unicode values under the given fields that can be parsed as ints, floats or via custom parser functions. Return a pair of Counter objects, the first mapping parser names to the number of strings successfully parsed, the second mapping parser names to the number of errors. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['A', 'aaa', 2],
...          ['B', u'2', '3.4'],
...          [u'B', u'3', u'7.8', True],
...          ['D', '3.7', 9.0],
...          ['E', 42]]
>>> counter, errors = etl.parsecounter(table, 'bar')
>>> counter
Counter({'float': 3, 'int': 2})
>>> errors
Counter({'int': 2, 'float': 1})

The field argument can be a field name or index (starting from zero).

petl.util.counting.parsecounts(table, field, parsers=(('int', <type 'int'>), ('float', <type 'float'>)))[source]¶

Count the number of str or unicode values that can be parsed as ints, floats or via custom parser functions. Return a table mapping parser names to the number of values successfully parsed and the number of errors. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['A', 'aaa', 2],
...          ['B', u'2', '3.4'],
...          [u'B', u'3', u'7.8', True],
...          ['D', '3.7', 9.0],
...          ['E', 42]]
>>> etl.parsecounts(table, 'bar')
+---------+-------+--------+
| type    | count | errors |
+=========+=======+========+
| 'float' |     3 |      1 |
+---------+-------+--------+
| 'int'   |     2 |      2 |
+---------+-------+--------+

The field argument can be a field name or index (starting from zero).

Timing¶

petl.util.timing.progress(table, batchsize=1000, prefix='', out=<open file '<stderr>', mode 'w' at 0x7f1bdd9421e0>)[source]¶

Report progress on rows passing through. E.g.:

>>> import petl as etl
>>> table = etl.dummytable(100000)
>>> table.progress(10000).tocsv('example.csv')
10000 rows in 0.13s (78363 row/s); batch in 0.13s (78363 row/s)
20000 rows in 0.22s (91679 row/s); batch in 0.09s (110448 row/s)
30000 rows in 0.31s (96573 row/s); batch in 0.09s (108114 row/s)
40000 rows in 0.40s (99535 row/s); batch in 0.09s (109625 row/s)
50000 rows in 0.49s (101396 row/s); batch in 0.09s (109591 row/s)
60000 rows in 0.59s (102245 row/s); batch in 0.09s (106709 row/s)
70000 rows in 0.68s (103221 row/s); batch in 0.09s (109498 row/s)
80000 rows in 0.77s (103810 row/s); batch in 0.09s (108126 row/s)
90000 rows in 0.90s (99465 row/s); batch in 0.13s (74516 row/s)
100000 rows in 1.02s (98409 row/s); batch in 0.11s (89821 row/s)
100000 rows in 1.02s (98402 row/s); batches in 0.10 +/- 0.02s [0.09-0.13] (100481 +/- 13340 rows/s [74516-110448])

Statistics¶

petl.util.statistics.limits(table, field)[source]¶

Find minimum and maximum values under the given field. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2], ['b', 3]]
>>> minv, maxv = etl.limits(table, 'bar')
>>> minv
1
>>> maxv
3

The field argument can be a field name or index (starting from zero).

petl.util.statistics.stats(table, field)[source]¶

Calculate basic descriptive statistics on a given field. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['A', 1, 2],
...          ['B', '2', '3.4'],
...          [u'B', u'3', u'7.8', True],
...          ['D', 'xyz', 9.0],
...          ['E', None]]
>>> etl.stats(table, 'bar')
stats(count=3, errors=2, sum=6.0, min=1.0, max=3.0, mean=2.0, pvariance=0.6666666666666666, pstdev=0.816496580927726)

The field argument can be a field name or index (starting from zero).

Materialising tables¶

petl.util.materialise.columns(table, missing=None)[source]¶

Construct a dict mapping field names to lists of values. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar'], ['a', 1], ['b', 2], ['b', 3]]
>>> cols = etl.columns(table)
>>> cols['foo']
['a', 'b', 'b']
>>> cols['bar']
[1, 2, 3]

petl.util.materialise.facetcolumns(table, key, missing=None)[source]¶

Like petl.util.materialise.columns() but stratified by values of the given key field. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['a', 1, True],
...          ['b', 2, True],
...          ['b', 3]]
>>> fc = etl.facetcolumns(table, 'foo')
>>> fc['a']
{'foo': ['a'], 'baz': [True], 'bar': [1]}
>>> fc['b']
{'foo': ['b', 'b'], 'baz': [True, None], 'bar': [2, 3]}

petl.util.materialise.listoflists(tbl)[source]¶

petl.util.materialise.listoftuples(tbl)[source]¶

petl.util.materialise.tupleoflists(tbl)[source]¶

petl.util.materialise.tupleoftuples(tbl)[source]¶

petl.util.materialise.cache(table, n=None)[source]¶: Wrap the table with a cache that caches up to n rows as they are initially requested via iteration (cache all rows be default).

Randomly generated tables¶

petl.util.random.randomtable(numflds=5, numrows=100, wait=0, seed=None)[source]¶

Construct a table with random numerical data. Use numflds and numrows to specify the number of fields and rows respectively. Set wait to a float greater than zero to simulate a delay on each row generation (number of seconds per row). E.g.:

>>> import petl as etl
>>> table = etl.randomtable(3, 100, seed=42)
>>> table
+----------------------+----------------------+---------------------+
| f0                   | f1                   | f2                  |
+======================+======================+=====================+
|   0.6394267984578837 | 0.025010755222666936 | 0.27502931836911926 |
+----------------------+----------------------+---------------------+
|  0.22321073814882275 |   0.7364712141640124 |  0.6766994874229113 |
+----------------------+----------------------+---------------------+
|   0.8921795677048454 |  0.08693883262941615 |  0.4219218196852704 |
+----------------------+----------------------+---------------------+
| 0.029797219438070344 |  0.21863797480360336 |  0.5053552881033624 |
+----------------------+----------------------+---------------------+
| 0.026535969683863625 |   0.1988376506866485 |  0.6498844377795232 |
+----------------------+----------------------+---------------------+
...

Note that the data are generated on the fly and are not stored in memory, so this function can be used to simulate very large tables.

petl.util.random.dummytable(numrows=100, fields=(('foo', <functools.partial object at 0x7f1bd897e520>), ('bar', <functools.partial object at 0x7f1bd897e578>), ('baz', <built-in method random of Random object at 0x1f0db90>)), wait=0, seed=None)[source]¶

Construct a table with dummy data. Use numrows to specify the number of rows. Set wait to a float greater than zero to simulate a delay on each row generation (number of seconds per row). E.g.:

>>> import petl as etl
>>> table1 = etl.dummytable(100, seed=42)
>>> table1
+-----+----------+----------------------+
| foo | bar      | baz                  |
+=====+==========+======================+
|  81 | 'apples' | 0.025010755222666936 |
+-----+----------+----------------------+
|  35 | 'pears'  |  0.22321073814882275 |
+-----+----------+----------------------+
|  94 | 'apples' |   0.6766994874229113 |
+-----+----------+----------------------+
|  69 | 'apples' |   0.5904925124490397 |
+-----+----------+----------------------+
|   4 | 'apples' |  0.09369523986159245 |
+-----+----------+----------------------+
...

>>> # customise fields
... import random
>>> from functools import partial
>>> fields = [('foo', random.random),
...           ('bar', partial(random.randint, 0, 500)),
...           ('baz', partial(random.choice,
...                           ['chocolate', 'strawberry', 'vanilla']))]
>>> table2 = etl.dummytable(100, fields=fields, seed=42)
>>> table2
+---------------------+-----+-------------+
| foo                 | bar | baz         |
+=====================+=====+=============+
|  0.6394267984578837 |  12 | 'vanilla'   |
+---------------------+-----+-------------+
| 0.27502931836911926 | 114 | 'chocolate' |
+---------------------+-----+-------------+
|  0.7364712141640124 | 346 | 'vanilla'   |
+---------------------+-----+-------------+
|  0.8921795677048454 |  44 | 'vanilla'   |
+---------------------+-----+-------------+
|  0.4219218196852704 |  15 | 'chocolate' |
+---------------------+-----+-------------+
...

Data generation functions can be specified via the fields keyword argument.

Note that the data are generated on the fly and are not stored in memory, so this function can be used to simulate very large tables.

Miscellaneous¶

petl.util.misc.typeset(table, field)[source]¶

Return a set containing all Python types found for values in the given field. E.g.:

>>> import petl as etl
>>> table = [['foo', 'bar', 'baz'],
...          ['A', 1, '2'],
...          ['B', u'2', '3.4'],
...          [u'B', u'3', '7.8', True],
...          ['D', u'xyz', 9.0],
...          ['E', 42]]
>>> sorted(etl.typeset(table, 'foo'))
['str']
>>> sorted(etl.typeset(table, 'bar'))
['int', 'str']
>>> sorted(etl.typeset(table, 'baz'))
['NoneType', 'float', 'str']

The field argument can be a field name or index (starting from zero).

petl.util.misc.diffheaders(t1, t2)[source]¶

Return the difference between the headers of the two tables as a pair of sets. E.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
...           ['a', 1, .3]]
>>> table2 = [['baz', 'bar', 'quux'],
...           ['a', 1, .3]]
>>> add, sub = etl.diffheaders(table1, table2)
>>> add
{'quux'}
>>> sub
{'foo'}

petl.util.misc.diffvalues(t1, t2, f)[source]¶

Return the difference between the values under the given field in the two tables, e.g.:

>>> import petl as etl
>>> table1 = [['foo', 'bar'],
...           ['a', 1],
...           ['b', 3]]
>>> table2 = [['bar', 'foo'],
...           [1, 'a'],
...           [3, 'c']]
>>> add, sub = etl.diffvalues(table1, table2, 'foo')
>>> add
{'c'}
>>> sub
{'b'}

petl.util.misc.strjoin(s)[source]¶: Return a function to join sequences using s as the separator. Intended for use with petl.transform.conversions.convert().

petl.util.misc.nthword(n, sep=None)[source]¶

Construct a function to return the nth word in a string. E.g.:

>>> import petl as etl
>>> s = 'foo bar'
>>> f = etl.nthword(0)
>>> f(s)
'foo'
>>> g = etl.nthword(1)
>>> g(s)
'bar'

Intended for use with petl.transform.conversions.convert().

petl.util.misc.coalesce(*fields, **kwargs)[source]¶: Return a function which accepts a row and returns the first non-missing value from the specified fields. Intended for use with petl.transform.basics.addfield().

Changes¶

Version 1.0¶

Version 1.0 is a new major release of petl. The main purpose of version 1.0 is to introduce support for Python 3.4, in addition to the existing support for Python 2.6 and 2.7. Much of the functionality available in petl versions 0.x has remained unchanged in version 1.0, and most existing code that uses petl should work unchanged with version 1.0 or with minor changes. However there have been a number of API changes, and some functionality has been migrated from the petlx package, described below.

If you have any questions about migrating to version 1.0 or find any problems or issues please email python-etl@googlegroups.com.

Text file encoding¶

Version 1.0 unifies the API for working with ASCII and non-ASCII encoded text files, including CSV and HTML.

The following functions now accept an ‘encoding’ argument, which defaults to the value of locale.getpreferredencoding() (usually ‘utf-8’): fromcsv, tocsv, appendcsv, teecsv, fromtsv, totsv, appendtsv, teetsv, fromtext, totext, appendtext, tohtml, teehtml.

The following functions have been removed as they are now redundant: fromucsv, toucsv, appenducsv, teeucsv, fromutsv, toutsv, appendutsv, teeutsv, fromutext, toutext, appendutext, touhtml, teeuhtml.

To migrate code, in most cases it should be possible to simply replace ‘fromucsv’ with ‘fromcsv’, etc.

pelt.fluent and petl.interactive¶

The functionality previously available through the petl.fluent and petl.interactive modules is now available through the root petl module.

This means two things.

First, is is now possible to use either functional or fluent (i.e., object-oriented) styles of programming with the root petl module, as described in introductory section on Functional and object-oriented programming styles.

Second, the default representation of table objects uses the petl.util.vis.look() function, so you can simply return a table from the prompt to inspect it, as described in the introductory section on Interactive use.

The petl.fluent and petl.interactive modules have been removed as they are now redundant.

To migrate code, it should be possible to simply replace “import petl.fluent as etl” or “import petl.interactive as etl” with “import petl as etl”.

Note that the automatic caching behaviour of the petl.interactive module has not been retained. If you want to enable caching behaviour for a particular table, make an explicit call to the petl.util.materialise.cache() function. See also Caching.

IPython notebook integration¶

In version 1.0 petl table container objects implement _repr_html_() so can be returned from a cell in an IPython notebook and will automatically format as an HTML table.

Also, the petl.util.vis.display() and petl.util.vis.displayall() functions have been migrated across from the petlx.ipython package. If you are working within the IPython notebook these functions give greater control over how tables are rendered. For some examples, see:

http://nbviewer.ipython.org/github/alimanfoo/petl/blob/v1.0/repr_html.ipynb

Database extract/load functions¶

The petl.io.db.todb() function now supports automatic table creation, inferring a schema from data in the table to be loaded. This functionality has been migrated across from the petlx package, and requires SQLAlchemy to be installed.

The functions fromsqlite3, tosqlite3 and appendsqlite3 have been removed as they duplicate functionality available from the existing functions petl.io.db.fromdb(), petl.io.db.todb() and petl.io.db.appenddb(). These existing functions have been modified so that if a string is provided as the dbo argument it is interpreted as the name of an sqlite3 file. It should be possible to migrate code by simply replacing ‘fromsqlite3’ with ‘fromdb’, etc.

Other functions removed or renamed¶

The following functions have been removed because they are overly complicated and/or hardly ever used. If you use any of these functions and would like to see them re-instated then please email python-etl@googlegroups.com: rangefacet, rangerowreduce, rangeaggregate, rangecounts, multirangeaggregate, lenstats.

The following functions were marked as deprecated in petl 0.x and have been removed in version 1.0: dataslice (use data instead), fieldconvert (use convert instead), fieldselect (use select instead), parsenumber (use numparser instead), recordmap (use rowmap instead), recordmapmany (use rowmapmany instead), recordreduce (use rowreduce instead), recordselect (use rowselect instead), valueset (use table.values(‘foo’).set() instead).

The following functions are no longer available in the root petl namespace, but are still available from a subpackage if you really need them: iterdata (use data instead), iterdicts (use dicts instead), iternamedtuples (use namedtuples instead), iterrecords (use records instead), itervalues (use values instead).

The following functions have been renamed: isordered (renamed to issorted), StringSource (renamed to MemorySource).

The function selectre has been removed as it duplicates functionality, use search instead.

Sorting and comparison¶

A major difference between Python 2 and Python 3 involves comparison and sorting of objects of different types. Python 3 is a lot stricter about what you can compare with what, e.g., None < 1 < 'foo' works in Python 2.x but raises an exception in Python 3. The strict comparison behaviour of Python 3 is generally a problem for typical usages of petl, where data can be highly heterogeneous and a column in a table may have a mixture of values of many different types, including None for missing.

To maintain the usability of petl in this type of scenario, and to ensure that the behaviour of petl is as consistent as possible across different Python versions, the petl.transform.sorts.sort() function and anything that depends on it (as well as any other functions making use of rich comparisons) emulate the relaxed comparison behaviour that is available under Python 2.x. In fact petl goes further than this, allowing comparison of a wider range of types than is possible under Python 2.x (e.g., datetime with None).

As the underlying code to achieve this has been completely reworked, there may be inconsistencies or unexpected behaviour, so it’s worth testing carefully the results of any code previously run using petl 0.x, especially if you are also migrating from Python 2 to Python 3.

The different comparison behaviour under different Python versions may also give unexpected results when selecting rows of a table. E.g., the following will work under Python 2.x but raise an exception under Python 3.4:

>>> import petl as etl
>>> table = [['foo', 'bar'],
...          ['a', 1],
...          ['b', None]]
>>> # raises exception under Python 3
... etl.select(table, 'bar', lambda v: v > 0)

To get the more relaxed behaviour under Python 3.4, use the petl.transform.selects.selectgt function, or wrap values with petl.comparison.Comparable, e.g.:

>>> # works under Python 3
... etl.selectgt(table, 'bar', 0)
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+

>>> # or ...
... etl.select(table, 'bar', lambda v: v > etl.Comparable(0))
+-----+-----+
| foo | bar |
+=====+=====+
| 'a' |   1 |
+-----+-----+

New extract/load modules¶

Several new extract/load modules have been added, migrating functionality previously available from the petlx package:

These modules all have dependencies on third party packages, but these have been kept as optional dependencies so are not required for installing petl.

New validate function¶

A new petl.transform.validation.validate() function has been added to provide a convenient interface when validating a table against a set of constraints.

New intervals module¶

A new module has been added providing transformation functions based on intervals, migrating functionality previously available from the petlx package:

Intervals (intervaltree)

This module requires the intervaltree module.

New configuration module¶

All configuration variables have been brought together into a new petl.config module. See the source code for the variables available, they should be self-explanatory.

`petl.push` moved to `petlx`¶

The petl.push module remains in an experimental state and has been moved to the petlx extensions project.

Argument names and other minor changes¶

Argument names for a small number of functions have been changed to create consistency across the API.

There are some other minor changes as well. If you are migrating from petl version 0.x the best thing is to run your code and inspect any errors. Email python-etl@googlegroups.com if you have any questions.

Source code reorganisation¶

The source code has been substantially reorganised. This should not affect users of the petl package however as all functions in the public API are available through the root petl namespace.

Contributing¶

Contributions to petl are welcome in any form, please feel free to email the python-etl@googlegroups.com mailing list if you have some code or ideas you’d like to discuss.

Please note that the petl package is intended as a stable, general purpose library for ETL work. If you would like to extend petl with functionality that is domain-specific, or if you have an experimental or tentative feature that is not yet ready for inclusion in the core petl package but you would like to distribute it, please contribute to the petlx project instead, or distribute your code as a separate package.

If you are thinking of developing or modifying the petl code base in any way, here is some information on how to set up your development environment to run tests etc.

Running the test suite¶

The main petl test suite can be run with nose. E.g., assuming you have the source code repository cloned to the current working directory, you can run the test suite with:

$ pip install nose
$ nosetests -v

Currently petl supports Python 2.6, Python 2.7 and Python 3.4, so the tests should pass under all three Python versions.

Dependencies¶

To keep installation as simple as possible on different platforms, petl has no installation dependencies. Most functionality also depends only on the Python core libraries.

Some petl functions depend on third party packages, however these should be kept as optional requirements. Any tests for modules requiring third party packages should be written so that they are skipped if the packages are not available. See the existing tests for examples of how to do this.

Running database tests¶

There are some additional tests within the test suite that require database servers to be setup correctly on the local host. To run these additional tests, make sure you have both MySQL and PostgreSQL servers running locally, and have created a user “petl” with password “test” and all permissions granted on a database called “petl”. Install dependencies:

$ pip install pymysql psycopg2 sqlalchemy

If these dependencies are not installed, or if a local database server is not found, these tests are skipped.

Running doctests¶

Doctests in docstrings should (almost) all be runnable, and should pass if run with Python 3.4. Doctests can be run with nose. See the tox.ini file for example doctest commands.

Building the documentation¶

Documentation is built with sphinx. To build:

$ pip install sphinx
$ cd docs
$ make html

Built docs can then be found in the docs/_build/html/ directory.

Automatically running all tests¶

All of the above tests can be run automatically using tox. You will need binaries for Python 2.6, Python 2.7 and Python 3.4 available on your system.

To run all tests without installing any of the optional dependencies, do:

$ tox -e py26,py27,py34,doctests

To run the entire test suite, including installation of all optional dependencies, do:

$ tox

The first time you run this it will take some while all the optional dependencies are installed in each environment.

Contributing code via GitHub¶

The best way to contribute code is via a GitHub pull request.

Please include unit tests with any code contributed.

If you are able, please run tox and ensure that all the above tests pass before making a pull request.

Thanks!

petl - Extract, Transform and Load (Tables of Data)¶

Introduction¶

Installation¶

Dependencies and extensions¶

Design goals¶

ETL pipelines¶

Functional and object-oriented programming styles¶

Interactive use¶

IPython notebook integration¶

petl executable¶

Conventions - table containers and table iterators¶

Caching¶

Acknowledgments¶

Extract/Load - reading/writing tables from files, databases and other sources¶

Extract (read)¶

Load (write)¶

Delimited files¶

Pickle files¶

Text files¶

XML files¶

HTML files¶

JSON files¶

Databases¶

Excel .xls files (xlrd/xlwt)¶

Excel .xlsx files (openpyxl)¶

Arrays (NumPy)¶

DataFrames (pandas)¶

HDF5 files (PyTables)¶

Text indexes (Whoosh)¶

I/O helper classes¶

Transform - transforming tables¶

Basic transformations¶

Header manipulations¶

Converting values¶

Selecting rows¶

Regular expressions¶

Unpacking compound values¶

Transforming rows¶

Sorting¶

Joins¶

Set operations¶

Deduplicating rows¶

Reducing rows¶

Reshaping tables¶

Filling missing values¶

Validation¶

Intervals (intervaltree)¶

Utility functions¶

Basic utilities¶

Visualising tables¶

Lookup data structures¶

Parsing string/text values¶

Counting¶

Timing¶

Statistics¶

Materialising tables¶

Randomly generated tables¶

Miscellaneous¶

Changes¶

Version 1.0¶

Text file encoding¶

pelt.fluent and petl.interactive¶

IPython notebook integration¶

Database extract/load functions¶

Other functions removed or renamed¶

Sorting and comparison¶

New extract/load modules¶

New validate function¶

New intervals module¶

New configuration module¶

petl.push moved to petlx¶

Argument names and other minor changes¶

Source code reorganisation¶

Contributing¶

Running the test suite¶

Dependencies¶

Running database tests¶

Running doctests¶

Building the documentation¶

Automatically running all tests¶

`petl` executable¶

`petl.push` moved to `petlx`¶