mirror of
https://github.com/kennethreitz/tablib.git
synced 2026-06-05 06:56:13 +00:00
Merge branch 'master' into bugfix/invalid-ascii-csv
# Conflicts: # setup.py # tablib/compat.py # test_tablib.py
This commit is contained in:
Bruno Alla
+2
-3
@@ -1,11 +1,10 @@
|
||||
language: python
|
||||
cache: pip
|
||||
python:
|
||||
- 2.6
|
||||
- 2.7
|
||||
- 3.3
|
||||
- 3.4
|
||||
- 3.5
|
||||
- 3.6
|
||||
install:
|
||||
- python setup.py install
|
||||
- pip install -r requirements.txt
|
||||
script: python test_tablib.py
|
||||
|
||||
@@ -34,3 +34,5 @@ Patches and Suggestions
|
||||
- Mathias Loesch
|
||||
- Tushar Makkar
|
||||
- Andrii Soldatenko
|
||||
- Bruno Soares
|
||||
- Tsuyoshi Hombashi
|
||||
|
||||
@@ -1,6 +1,11 @@
|
||||
History
|
||||
-------
|
||||
|
||||
0.11.5 (2017-06-13)
|
||||
+++++++++++++++++++
|
||||
|
||||
- Use ``yaml.safe_load`` for importing yaml.
|
||||
|
||||
0.11.4 (2017-01-23)
|
||||
+++++++++++++++++++
|
||||
|
||||
|
||||
@@ -1,32 +1,6 @@
|
||||
Tablib includes some vendorized python libraries: ordereddict, markup.
|
||||
Tablib includes some vendorized Python libraries: markup.
|
||||
|
||||
Markup License
|
||||
==============
|
||||
|
||||
Markup is in the public domain.
|
||||
|
||||
|
||||
OrderedDict License
|
||||
===================
|
||||
|
||||
Copyright (c) 2009 Raymond Hettinger
|
||||
|
||||
Permission is hereby granted, free of charge, to any person
|
||||
obtaining a copy of this software and associated documentation files
|
||||
(the "Software"), to deal in the Software without restriction,
|
||||
including without limitation the rights to use, copy, modify, merge,
|
||||
publish, distribute, sublicense, and/or sell copies of the Software,
|
||||
and to permit persons to whom the Software is furnished to do so,
|
||||
subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be
|
||||
included in all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
|
||||
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
|
||||
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
|
||||
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
||||
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
||||
OTHER DEALINGS IN THE SOFTWARE.
|
||||
|
||||
+22
-9
@@ -21,14 +21,18 @@ Output formats supported:
|
||||
- Excel (Sets + Books)
|
||||
- JSON (Sets + Books)
|
||||
- YAML (Sets + Books)
|
||||
- Pandas DataFrames (Sets)
|
||||
- HTML (Sets)
|
||||
- Jira (Sets)
|
||||
- TSV (Sets)
|
||||
- OSD (Sets)
|
||||
- ODS (Sets)
|
||||
- CSV (Sets)
|
||||
- DBF (Sets)
|
||||
|
||||
Note that tablib *purposefully* excludes XML support. It always will. (Note: This is a joke. Pull requests are welcome.)
|
||||
|
||||
If you're interested in financially supporting Kenneth Reitz open source, consider `visiting this link <https://cash.me/$KennethReitz>`_. Your support helps tremendously with sustainability of motivation, as Open Source is no longer part of my day job.
|
||||
|
||||
Overview
|
||||
--------
|
||||
|
||||
@@ -64,13 +68,13 @@ Intelligently add new columns: ::
|
||||
|
||||
Slice rows: ::
|
||||
|
||||
>>> print data[:2]
|
||||
>>> print(data[:2])
|
||||
[('John', 'Adams', 90), ('George', 'Washington', 67)]
|
||||
|
||||
|
||||
Slice columns by header: ::
|
||||
|
||||
>>> print data['first_name']
|
||||
>>> print(data['first_name'])
|
||||
['John', 'George', 'Henry']
|
||||
|
||||
Easily delete rows: ::
|
||||
@@ -86,7 +90,7 @@ JSON!
|
||||
+++++
|
||||
::
|
||||
|
||||
>>> print data.json
|
||||
>>> print(data.export('json'))
|
||||
[
|
||||
{
|
||||
"last_name": "Adams",
|
||||
@@ -105,7 +109,7 @@ YAML!
|
||||
+++++
|
||||
::
|
||||
|
||||
>>> print data.yaml
|
||||
>>> print(data.export('yaml'))
|
||||
- {age: 90, first_name: John, last_name: Adams}
|
||||
- {age: 83, first_name: Henry, last_name: Ford}
|
||||
|
||||
@@ -113,7 +117,7 @@ CSV...
|
||||
++++++
|
||||
::
|
||||
|
||||
>>> print data.csv
|
||||
>>> print(data.export('csv'))
|
||||
first_name,last_name,age
|
||||
John,Adams,90
|
||||
Henry,Ford,83
|
||||
@@ -123,14 +127,23 @@ EXCEL!
|
||||
::
|
||||
|
||||
>>> with open('people.xls', 'wb') as f:
|
||||
... f.write(data.xls)
|
||||
... f.write(data.export('xls'))
|
||||
|
||||
DBF!
|
||||
++++
|
||||
::
|
||||
|
||||
>>> with open('people.dbf', 'wb') as f:
|
||||
... f.write(data.dbf)
|
||||
... f.write(data.export('dbf'))
|
||||
|
||||
Pandas DataFrame!
|
||||
+++++++++++++++++
|
||||
::
|
||||
|
||||
>>> print(data.export('df')):
|
||||
first_name last_name age
|
||||
0 John Adams 90
|
||||
1 Henry Ford 83
|
||||
|
||||
It's that easy.
|
||||
|
||||
@@ -140,7 +153,7 @@ Installation
|
||||
|
||||
To install tablib, simply: ::
|
||||
|
||||
$ pip install tablib
|
||||
$ pip install tablib[pandas]
|
||||
|
||||
Make sure to check out `Tablib on PyPi <https://pypi.python.org/pypi/tablib/>`_!
|
||||
|
||||
|
||||
+9
-4
@@ -29,18 +29,23 @@ Tablib is an :ref:`MIT Licensed <mit>` format-agnostic tabular dataset library,
|
||||
>>> data = tablib.Dataset(headers=['First Name', 'Last Name', 'Age'])
|
||||
>>> for i in [('Kenneth', 'Reitz', 22), ('Bessie', 'Monke', 21)]:
|
||||
... data.append(i)
|
||||
|
||||
|
||||
>>> print data.json
|
||||
|
||||
>>> print(data.export('json'))
|
||||
[{"Last Name": "Reitz", "First Name": "Kenneth", "Age": 22}, {"Last Name": "Monke", "First Name": "Bessie", "Age": 21}]
|
||||
|
||||
>>> print data.yaml
|
||||
>>> print(data.export('yaml'))
|
||||
- {Age: 22, First Name: Kenneth, Last Name: Reitz}
|
||||
- {Age: 21, First Name: Bessie, Last Name: Monke}
|
||||
|
||||
>>> data.xlsx
|
||||
>>> data.export('xlsx')
|
||||
<censored binary data>
|
||||
|
||||
>>> data.export('df')
|
||||
First Name Last Name Age
|
||||
0 Kenneth Reitz 22
|
||||
1 Bessie Monke 21
|
||||
|
||||
|
||||
Testimonials
|
||||
------------
|
||||
|
||||
+1
-11
@@ -16,7 +16,7 @@ Distribute & Pip
|
||||
|
||||
Of course, the recommended way to install Tablib is with `pip <http://www.pip-installer.org/>`_::
|
||||
|
||||
$ pip install tablib
|
||||
$ pip install tablib[pandas]
|
||||
|
||||
|
||||
-------------------
|
||||
@@ -40,16 +40,6 @@ To download the full source history from Git, see :ref:`Source Control <scm>`.
|
||||
.. _zipball: http://github.com/kennethreitz/tablib/zipball/master
|
||||
|
||||
|
||||
.. _speed-extensions:
|
||||
Speed Extensions
|
||||
----------------
|
||||
|
||||
You can gain some speed improvement by optionally installing the ujson_ library.
|
||||
Tablib will fallback to the standard `json` module if it doesn't find ``ujson``.
|
||||
|
||||
.. _ujson: https://pypi.python.org/pypi/ujson
|
||||
|
||||
|
||||
.. _updates:
|
||||
Staying Updated
|
||||
---------------
|
||||
|
||||
+1
-2
@@ -49,7 +49,7 @@ Tablib is released under terms of `The MIT License`_.
|
||||
Tablib License
|
||||
--------------
|
||||
|
||||
Copyright 2016 Kenneth Reitz
|
||||
Copyright 2017 Kenneth Reitz
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
@@ -77,7 +77,6 @@ Pythons Supported
|
||||
|
||||
At this time, the following Python platforms are officially supported:
|
||||
|
||||
* cPython 2.6
|
||||
* cPython 2.7
|
||||
* cPython 3.3
|
||||
* cPython 3.4
|
||||
|
||||
+23
-7
@@ -115,30 +115,38 @@ Tablib's killer feature is the ability to export your :class:`Dataset` objects i
|
||||
|
||||
**Comma-Separated Values** ::
|
||||
|
||||
>>> data.csv
|
||||
>>> data.export('csv')
|
||||
Last Name,First Name,Age
|
||||
Reitz,Kenneth,22
|
||||
Monke,Bessie,20
|
||||
|
||||
**JavaScript Object Notation** ::
|
||||
|
||||
>>> data.json
|
||||
>>> data.export('json')
|
||||
[{"Last Name": "Reitz", "First Name": "Kenneth", "Age": 22}, {"Last Name": "Monke", "First Name": "Bessie", "Age": 20}]
|
||||
|
||||
|
||||
**YAML Ain't Markup Language** ::
|
||||
|
||||
>>> data.yaml
|
||||
>>> data.export('yaml')
|
||||
- {Age: 22, First Name: Kenneth, Last Name: Reitz}
|
||||
- {Age: 20, First Name: Bessie, Last Name: Monke}
|
||||
|
||||
|
||||
**Microsoft Excel** ::
|
||||
|
||||
>>> data.xls
|
||||
>>> data.export('xls')
|
||||
<censored binary data>
|
||||
|
||||
|
||||
**Pandas DataFrame** ::
|
||||
|
||||
>>> data.export('df')
|
||||
First Name Last Name Age
|
||||
0 Kenneth Reitz 22
|
||||
1 Bessie Monke 21
|
||||
|
||||
|
||||
------------------------
|
||||
Selecting Rows & Columns
|
||||
------------------------
|
||||
@@ -216,7 +224,7 @@ Let's add a dynamic column to our :class:`Dataset` object. In this example, we h
|
||||
|
||||
Let's have a look at our data. ::
|
||||
|
||||
>>> data.yaml
|
||||
>>> data.export('yaml')
|
||||
- {Age: 22, First Name: Kenneth, Grade: 0.6, Last Name: Reitz}
|
||||
- {Age: 20, First Name: Bessie, Grade: 0.75, Last Name: Monke}
|
||||
|
||||
@@ -246,7 +254,7 @@ For example, we can use the data available in the row to guess the gender of a s
|
||||
|
||||
Adding this function to our dataset as a dynamic column would result in: ::
|
||||
|
||||
>>> data.yaml
|
||||
>>> data.export('yaml')
|
||||
- {Age: 22, First Name: Kenneth, Gender: Male, Last Name: Reitz}
|
||||
- {Age: 20, First Name: Bessie, Gender: Female, Last Name: Monke}
|
||||
|
||||
@@ -281,6 +289,14 @@ Now that we have extra meta-data on our rows, we can easily filter our :class:`D
|
||||
|
||||
It's that simple. The original :class:`Dataset` is untouched.
|
||||
|
||||
Open an Excel Workbook and read first sheet
|
||||
--------------------------------
|
||||
|
||||
To open an Excel 2007 and later workbook with a single sheet (or a workbook with multiple sheets but you just want the first sheet), use the following:
|
||||
|
||||
data = tablib.Dataset()
|
||||
data.xlsx = open('my_excel_file.xlsx', 'rb').read()
|
||||
print(data)
|
||||
|
||||
Excel Workbook With Multiple Sheets
|
||||
------------------------------------
|
||||
@@ -346,7 +362,7 @@ When, it's often useful to create a blank row containing information on the upco
|
||||
|
||||
# Write spreadsheet to disk
|
||||
with open('grades.xls', 'wb') as f:
|
||||
f.write(tests.xls)
|
||||
f.write(tests.export('xls'))
|
||||
|
||||
The resulting **tests.xls** will have the following layout:
|
||||
|
||||
|
||||
@@ -0,0 +1,21 @@
|
||||
certifi==2017.7.27.1
|
||||
chardet==3.0.4
|
||||
et-xmlfile==1.0.1
|
||||
idna==2.6
|
||||
jdcal==1.3
|
||||
numpy==1.13.1
|
||||
odfpy==1.3.5
|
||||
openpyxl==2.4.8
|
||||
pandas==0.20.3
|
||||
pkginfo==1.4.1
|
||||
python-dateutil==2.6.1
|
||||
pytz==2017.2
|
||||
PyYAML==3.12
|
||||
requests==2.18.4
|
||||
requests-toolbelt==0.8.0
|
||||
six==1.10.0
|
||||
tqdm==4.15.0
|
||||
unicodecsv==0.14.1
|
||||
urllib3==1.22
|
||||
xlrd==1.1.0
|
||||
xlwt==1.3.0
|
||||
@@ -14,15 +14,6 @@ if sys.argv[-1] == 'publish':
|
||||
os.system("python setup.py sdist upload")
|
||||
sys.exit()
|
||||
|
||||
if sys.argv[-1] == 'speedups':
|
||||
try:
|
||||
__import__('pip')
|
||||
except ImportError:
|
||||
print('Pip required.')
|
||||
sys.exit(1)
|
||||
|
||||
os.system('pip install ujson')
|
||||
sys.exit()
|
||||
|
||||
if sys.argv[-1] == 'test':
|
||||
try:
|
||||
@@ -43,13 +34,14 @@ packages = [
|
||||
|
||||
install = [
|
||||
'odfpy',
|
||||
'openpyxl',
|
||||
'openpyxl>=2.4.0',
|
||||
'backports.csv',
|
||||
'xlrd',
|
||||
'xlwt',
|
||||
'pyyaml',
|
||||
]
|
||||
|
||||
|
||||
with open('tablib/core.py', 'r') as fd:
|
||||
version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]',
|
||||
fd.read(), re.MULTILINE).group(1)
|
||||
@@ -71,13 +63,14 @@ setup(
|
||||
'Natural Language :: English',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
'Programming Language :: Python',
|
||||
'Programming Language :: Python :: 2.6',
|
||||
'Programming Language :: Python :: 2.7',
|
||||
'Programming Language :: Python :: 3.3',
|
||||
'Programming Language :: Python :: 3.4',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
],
|
||||
tests_require=['pytest'],
|
||||
install_requires=install,
|
||||
extras_require={
|
||||
'pandas': ['pandas'],
|
||||
},
|
||||
)
|
||||
|
||||
+6
-16
@@ -13,34 +13,24 @@ import sys
|
||||
is_py3 = (sys.version_info[0] > 2)
|
||||
|
||||
|
||||
|
||||
try:
|
||||
from collections import OrderedDict
|
||||
except ImportError:
|
||||
from tablib.packages.ordereddict import OrderedDict
|
||||
|
||||
|
||||
if is_py3:
|
||||
from io import BytesIO
|
||||
from io import StringIO
|
||||
from tablib.packages import markup3 as markup
|
||||
from statistics import median
|
||||
from itertools import zip_longest as izip_longest
|
||||
import csv
|
||||
import tablib.packages.dbfpy3 as dbfpy
|
||||
|
||||
import csv
|
||||
from io import StringIO
|
||||
# py3 mappings
|
||||
|
||||
ifilter = filter
|
||||
unicode = str
|
||||
bytes = bytes
|
||||
basestring = str
|
||||
xrange = range
|
||||
|
||||
else:
|
||||
from cStringIO import StringIO as BytesIO
|
||||
from StringIO import StringIO
|
||||
from tablib.packages import markup
|
||||
from itertools import ifilter
|
||||
|
||||
from tablib.packages.statistics import median
|
||||
from itertools import izip_longest
|
||||
from backports import csv
|
||||
import tablib.packages.dbfpy as dbfpy
|
||||
|
||||
|
||||
+38
-7
@@ -9,20 +9,21 @@
|
||||
:license: MIT, see LICENSE for more details.
|
||||
"""
|
||||
|
||||
from collections import OrderedDict
|
||||
from copy import copy
|
||||
from operator import itemgetter
|
||||
|
||||
from tablib import formats
|
||||
|
||||
from tablib.compat import OrderedDict, unicode
|
||||
from tablib.compat import unicode
|
||||
|
||||
|
||||
__title__ = 'tablib'
|
||||
__version__ = '0.11.4'
|
||||
__build__ = 0x001104
|
||||
__version__ = '0.12.1'
|
||||
__build__ = 0x001201
|
||||
__author__ = 'Kenneth Reitz'
|
||||
__license__ = 'MIT'
|
||||
__copyright__ = 'Copyright 2016 Kenneth Reitz'
|
||||
__copyright__ = 'Copyright 2017 Kenneth Reitz'
|
||||
__docformat__ = 'restructuredtext'
|
||||
|
||||
|
||||
@@ -526,9 +527,9 @@ class Dataset(object):
|
||||
|
||||
Import assumes (for now) that headers exist.
|
||||
|
||||
.. admonition:: Binary Warning
|
||||
.. admonition:: Binary Warning for Python 2
|
||||
|
||||
:class:`Dataset.csv` uses \\r\\n line endings by default, so make
|
||||
:class:`Dataset.csv` uses \\r\\n line endings by default so, in Python 2, make
|
||||
sure to write in binary mode::
|
||||
|
||||
with open('output.csv', 'wb') as f:
|
||||
@@ -536,6 +537,18 @@ class Dataset(object):
|
||||
|
||||
If you do not do this, and you export the file on Windows, your
|
||||
CSV file will open in Excel with a blank line between each row.
|
||||
|
||||
.. admonition:: Line endings for Python 3
|
||||
|
||||
:class:`Dataset.csv` uses \\r\\n line endings by default so, in Python 3, make
|
||||
sure to include newline='' otherwise you will get a blank line between each row
|
||||
when you open the file in Excel::
|
||||
|
||||
with open('output.csv', 'w', newline='') as f:
|
||||
f.write(data.csv)
|
||||
|
||||
If you do not do this, and you export the file on Windows, your
|
||||
CSV file will open in Excel with a blank line between each row.
|
||||
"""
|
||||
pass
|
||||
|
||||
@@ -570,6 +583,18 @@ class Dataset(object):
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
def df():
|
||||
"""A DataFrame representation of the :class:`Dataset` object.
|
||||
|
||||
A dataset object can also be imported by setting the :class:`Dataset.df` attribute: ::
|
||||
|
||||
data = tablib.Dataset()
|
||||
data.df = DataFrame(np.random.randn(6,4))
|
||||
|
||||
Import assumes (for now) that headers exist.
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
def json():
|
||||
@@ -619,7 +644,6 @@ class Dataset(object):
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
@property
|
||||
def latex():
|
||||
"""A LaTeX booktabs representation of the :class:`Dataset` object. If a
|
||||
@@ -629,6 +653,13 @@ class Dataset(object):
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
def jira():
|
||||
"""A Jira table representation of the :class:`Dataset` object.
|
||||
|
||||
.. note:: This method can be used for export only.
|
||||
"""
|
||||
pass
|
||||
|
||||
# ----
|
||||
# Rows
|
||||
|
||||
@@ -13,5 +13,8 @@ from . import _xlsx as xlsx
|
||||
from . import _ods as ods
|
||||
from . import _dbf as dbf
|
||||
from . import _latex as latex
|
||||
from . import _df as df
|
||||
from . import _rst as rst
|
||||
from . import _jira as jira
|
||||
|
||||
available = (json, xls, yaml, csv, dbf, tsv, html, latex, xlsx, ods)
|
||||
available = (json, xls, yaml, csv, dbf, tsv, html, jira, latex, xlsx, ods, df, rst)
|
||||
|
||||
@@ -39,7 +39,7 @@ def import_set(dset, in_stream, headers=True, **kwargs):
|
||||
|
||||
if (i == 0) and (headers):
|
||||
dset.headers = row
|
||||
else:
|
||||
elif row:
|
||||
dset.append(row)
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,49 @@
|
||||
""" Tablib - DataFrame Support.
|
||||
"""
|
||||
|
||||
|
||||
import sys
|
||||
|
||||
|
||||
if sys.version_info[0] > 2:
|
||||
from io import BytesIO
|
||||
else:
|
||||
from cStringIO import StringIO as BytesIO
|
||||
|
||||
try:
|
||||
from pandas import DataFrame
|
||||
except ImportError:
|
||||
DataFrame = None
|
||||
|
||||
import tablib
|
||||
|
||||
from tablib.compat import unicode
|
||||
|
||||
title = 'df'
|
||||
extensions = ('df', )
|
||||
|
||||
def detect(stream):
|
||||
"""Returns True if given stream is a DataFrame."""
|
||||
if DataFrame is None:
|
||||
return False
|
||||
try:
|
||||
DataFrame(stream)
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
|
||||
def export_set(dset, index=None):
|
||||
"""Returns DataFrame representation of DataBook."""
|
||||
if DataFrame is None:
|
||||
raise NotImplementedError(
|
||||
'DataFrame Format requires `pandas` to be installed.'
|
||||
' Try `pip install tablib[pandas]`.')
|
||||
dataframe = DataFrame(dset.dict, columns=dset.headers)
|
||||
return dataframe
|
||||
|
||||
|
||||
def import_set(dset, in_stream):
|
||||
"""Returns dataset from DataFrame."""
|
||||
dset.wipe()
|
||||
dset.dict = in_stream.to_dict(orient='records')
|
||||
@@ -0,0 +1,39 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""Tablib - Jira table export support.
|
||||
|
||||
Generates a Jira table from the dataset.
|
||||
"""
|
||||
from tablib.compat import unicode
|
||||
|
||||
title = 'jira'
|
||||
|
||||
|
||||
def export_set(dataset):
|
||||
"""Formats the dataset according to the Jira table syntax:
|
||||
|
||||
||heading 1||heading 2||heading 3||
|
||||
|col A1|col A2|col A3|
|
||||
|col B1|col B2|col B3|
|
||||
|
||||
:param dataset: dataset to serialize
|
||||
:type dataset: tablib.core.Dataset
|
||||
"""
|
||||
|
||||
header = _get_header(dataset.headers) if dataset.headers else ''
|
||||
body = _get_body(dataset)
|
||||
return '%s\n%s' % (header, body) if header else body
|
||||
|
||||
|
||||
def _get_body(dataset):
|
||||
return '\n'.join([_serialize_row(row) for row in dataset])
|
||||
|
||||
|
||||
def _get_header(headers):
|
||||
return _serialize_row(headers, delimiter='||')
|
||||
|
||||
|
||||
def _serialize_row(row, delimiter='|'):
|
||||
return '%s%s%s' % (delimiter,
|
||||
delimiter.join([unicode(item) if item else ' ' for item in row]),
|
||||
delimiter)
|
||||
@@ -3,36 +3,33 @@
|
||||
""" Tablib - JSON Support
|
||||
"""
|
||||
import decimal
|
||||
import json
|
||||
from uuid import UUID
|
||||
|
||||
import tablib
|
||||
|
||||
try:
|
||||
import ujson as json
|
||||
except ImportError:
|
||||
import json
|
||||
|
||||
title = 'json'
|
||||
extensions = ('json', 'jsn')
|
||||
|
||||
|
||||
def date_handler(obj):
|
||||
if isinstance(obj, decimal.Decimal):
|
||||
def serialize_objects_handler(obj):
|
||||
if isinstance(obj, decimal.Decimal) or isinstance(obj, UUID):
|
||||
return str(obj)
|
||||
elif hasattr(obj, 'isoformat'):
|
||||
return obj.isoformat()
|
||||
else:
|
||||
return obj
|
||||
# return obj.isoformat() if hasattr(obj, 'isoformat') else obj
|
||||
|
||||
|
||||
def export_set(dataset):
|
||||
"""Returns JSON representation of Dataset."""
|
||||
return json.dumps(dataset.dict, default=date_handler)
|
||||
return json.dumps(dataset.dict, default=serialize_objects_handler)
|
||||
|
||||
|
||||
def export_book(databook):
|
||||
"""Returns JSON representation of Databook."""
|
||||
return json.dumps(databook._package(), default=date_handler)
|
||||
return json.dumps(databook._package(), default=serialize_objects_handler)
|
||||
|
||||
|
||||
def import_set(dset, in_stream):
|
||||
|
||||
@@ -0,0 +1,273 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
""" Tablib - reStructuredText Support
|
||||
"""
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
from __future__ import unicode_literals
|
||||
|
||||
from textwrap import TextWrapper
|
||||
|
||||
from tablib.compat import (
|
||||
median,
|
||||
unicode,
|
||||
izip_longest,
|
||||
)
|
||||
|
||||
|
||||
title = 'rst'
|
||||
extensions = ('rst',)
|
||||
|
||||
|
||||
MAX_TABLE_WIDTH = 80 # Roughly. It may be wider to avoid breaking words.
|
||||
|
||||
|
||||
JUSTIFY_LEFT = 'left'
|
||||
JUSTIFY_CENTER = 'center'
|
||||
JUSTIFY_RIGHT = 'right'
|
||||
JUSTIFY_VALUES = (JUSTIFY_LEFT, JUSTIFY_CENTER, JUSTIFY_RIGHT)
|
||||
|
||||
|
||||
def to_unicode(value):
|
||||
if isinstance(value, bytes):
|
||||
return value.decode('utf-8')
|
||||
return unicode(value)
|
||||
|
||||
|
||||
def _max_word_len(text):
|
||||
"""
|
||||
Return the length of the longest word in `text`.
|
||||
|
||||
|
||||
>>> _max_word_len('Python Module for Tabular Datasets')
|
||||
8
|
||||
|
||||
"""
|
||||
return max((len(word) for word in text.split()))
|
||||
|
||||
|
||||
def _get_column_string_lengths(dataset):
|
||||
"""
|
||||
Returns a list of string lengths of each column, and a list of
|
||||
maximum word lengths.
|
||||
"""
|
||||
if dataset.headers:
|
||||
column_lengths = [[len(h)] for h in dataset.headers]
|
||||
word_lens = [_max_word_len(h) for h in dataset.headers]
|
||||
else:
|
||||
column_lengths = [[] for _ in range(dataset.width)]
|
||||
word_lens = [0 for _ in range(dataset.width)]
|
||||
for row in dataset.dict:
|
||||
values = iter(row.values() if hasattr(row, 'values') else row)
|
||||
for i, val in enumerate(values):
|
||||
text = to_unicode(val)
|
||||
column_lengths[i].append(len(text))
|
||||
word_lens[i] = max(word_lens[i], _max_word_len(text))
|
||||
return column_lengths, word_lens
|
||||
|
||||
|
||||
def _row_to_lines(values, widths, wrapper, sep='|', justify=JUSTIFY_LEFT):
|
||||
"""
|
||||
Returns a table row of wrapped values as a list of lines
|
||||
"""
|
||||
if justify not in JUSTIFY_VALUES:
|
||||
raise ValueError('Value of "justify" must be one of "{}"'.format(
|
||||
'", "'.join(JUSTIFY_VALUES)
|
||||
))
|
||||
if justify == JUSTIFY_LEFT:
|
||||
just = lambda text, width: text.ljust(width)
|
||||
elif justify == JUSTIFY_CENTER:
|
||||
just = lambda text, width: text.center(width)
|
||||
else:
|
||||
just = lambda text, width: text.rjust(width)
|
||||
lpad = sep + ' ' if sep else ''
|
||||
rpad = ' ' + sep if sep else ''
|
||||
pad = ' ' + sep + ' '
|
||||
cells = []
|
||||
for value, width in zip(values, widths):
|
||||
wrapper.width = width
|
||||
text = to_unicode(value)
|
||||
cell = wrapper.wrap(text)
|
||||
cells.append(cell)
|
||||
lines = izip_longest(*cells, fillvalue='')
|
||||
lines = (
|
||||
(just(cell_line, widths[i]) for i, cell_line in enumerate(line))
|
||||
for line in lines
|
||||
)
|
||||
lines = [''.join((lpad, pad.join(line), rpad)) for line in lines]
|
||||
return lines
|
||||
|
||||
|
||||
def _get_column_widths(dataset, max_table_width=MAX_TABLE_WIDTH, pad_len=3):
|
||||
"""
|
||||
Returns a list of column widths proportional to the median length
|
||||
of the text in their cells.
|
||||
"""
|
||||
str_lens, word_lens = _get_column_string_lengths(dataset)
|
||||
median_lens = [int(median(lens)) for lens in str_lens]
|
||||
total = sum(median_lens)
|
||||
if total > max_table_width - (pad_len * len(median_lens)):
|
||||
column_widths = (max_table_width * l // total for l in median_lens)
|
||||
else:
|
||||
column_widths = (l for l in median_lens)
|
||||
# Allow for separator and padding:
|
||||
column_widths = (w - pad_len if w > pad_len else w for w in column_widths)
|
||||
# Rather widen table than break words:
|
||||
column_widths = [max(w, l) for w, l in zip(column_widths, word_lens)]
|
||||
return column_widths
|
||||
|
||||
|
||||
def export_set_as_simple_table(dataset, column_widths=None):
|
||||
"""
|
||||
Returns reStructuredText grid table representation of dataset.
|
||||
"""
|
||||
lines = []
|
||||
wrapper = TextWrapper()
|
||||
if column_widths is None:
|
||||
column_widths = _get_column_widths(dataset, pad_len=2)
|
||||
border = ' '.join(['=' * w for w in column_widths])
|
||||
|
||||
lines.append(border)
|
||||
if dataset.headers:
|
||||
lines.extend(_row_to_lines(
|
||||
dataset.headers,
|
||||
column_widths,
|
||||
wrapper,
|
||||
sep='',
|
||||
justify=JUSTIFY_CENTER,
|
||||
))
|
||||
lines.append(border)
|
||||
for row in dataset.dict:
|
||||
values = iter(row.values() if hasattr(row, 'values') else row)
|
||||
lines.extend(_row_to_lines(values, column_widths, wrapper, ''))
|
||||
lines.append(border)
|
||||
return '\n'.join(lines)
|
||||
|
||||
|
||||
def export_set_as_grid_table(dataset, column_widths=None):
|
||||
"""
|
||||
Returns reStructuredText grid table representation of dataset.
|
||||
|
||||
|
||||
>>> from tablib import Dataset
|
||||
>>> from tablib.formats import rst
|
||||
>>> bits = ((0, 0), (1, 0), (0, 1), (1, 1))
|
||||
>>> data = Dataset()
|
||||
>>> data.headers = ['A', 'B', 'A and B']
|
||||
>>> for a, b in bits:
|
||||
... data.append([bool(a), bool(b), bool(a * b)])
|
||||
>>> print(rst.export_set(data, force_grid=True))
|
||||
+-------+-------+-------+
|
||||
| A | B | A and |
|
||||
| | | B |
|
||||
+=======+=======+=======+
|
||||
| False | False | False |
|
||||
+-------+-------+-------+
|
||||
| True | False | False |
|
||||
+-------+-------+-------+
|
||||
| False | True | False |
|
||||
+-------+-------+-------+
|
||||
| True | True | True |
|
||||
+-------+-------+-------+
|
||||
|
||||
"""
|
||||
lines = []
|
||||
wrapper = TextWrapper()
|
||||
if column_widths is None:
|
||||
column_widths = _get_column_widths(dataset)
|
||||
header_sep = '+=' + '=+='.join(['=' * w for w in column_widths]) + '=+'
|
||||
row_sep = '+-' + '-+-'.join(['-' * w for w in column_widths]) + '-+'
|
||||
|
||||
lines.append(row_sep)
|
||||
if dataset.headers:
|
||||
lines.extend(_row_to_lines(
|
||||
dataset.headers,
|
||||
column_widths,
|
||||
wrapper,
|
||||
justify=JUSTIFY_CENTER,
|
||||
))
|
||||
lines.append(header_sep)
|
||||
for row in dataset.dict:
|
||||
values = iter(row.values() if hasattr(row, 'values') else row)
|
||||
lines.extend(_row_to_lines(values, column_widths, wrapper))
|
||||
lines.append(row_sep)
|
||||
return '\n'.join(lines)
|
||||
|
||||
|
||||
def _use_simple_table(head0, col0, width0):
|
||||
"""
|
||||
Use a simple table if the text in the first column is never wrapped
|
||||
|
||||
|
||||
>>> _use_simple_table('menu', ['egg', 'bacon'], 10)
|
||||
True
|
||||
>>> _use_simple_table(None, ['lobster thermidor', 'spam'], 10)
|
||||
False
|
||||
|
||||
"""
|
||||
if head0 is not None:
|
||||
head0 = to_unicode(head0)
|
||||
if len(head0) > width0:
|
||||
return False
|
||||
for cell in col0:
|
||||
cell = to_unicode(cell)
|
||||
if len(cell) > width0:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def export_set(dataset, **kwargs):
|
||||
"""
|
||||
Returns reStructuredText table representation of dataset.
|
||||
|
||||
Returns a simple table if the text in the first column is never
|
||||
wrapped, otherwise returns a grid table.
|
||||
|
||||
|
||||
>>> from tablib import Dataset
|
||||
>>> bits = ((0, 0), (1, 0), (0, 1), (1, 1))
|
||||
>>> data = Dataset()
|
||||
>>> data.headers = ['A', 'B', 'A and B']
|
||||
>>> for a, b in bits:
|
||||
... data.append([bool(a), bool(b), bool(a * b)])
|
||||
>>> table = data.rst
|
||||
>>> table.split('\\n') == [
|
||||
... '===== ===== =====',
|
||||
... ' A B A and',
|
||||
... ' B ',
|
||||
... '===== ===== =====',
|
||||
... 'False False False',
|
||||
... 'True False False',
|
||||
... 'False True False',
|
||||
... 'True True True ',
|
||||
... '===== ===== =====',
|
||||
... ]
|
||||
True
|
||||
|
||||
"""
|
||||
if not dataset.dict:
|
||||
return ''
|
||||
force_grid = kwargs.get('force_grid', False)
|
||||
max_table_width = kwargs.get('max_table_width', MAX_TABLE_WIDTH)
|
||||
column_widths = _get_column_widths(dataset, max_table_width)
|
||||
|
||||
use_simple_table = _use_simple_table(
|
||||
dataset.headers[0] if dataset.headers else None,
|
||||
dataset.get_col(0),
|
||||
column_widths[0],
|
||||
)
|
||||
if use_simple_table and not force_grid:
|
||||
return export_set_as_simple_table(dataset, column_widths)
|
||||
else:
|
||||
return export_set_as_grid_table(dataset, column_widths)
|
||||
|
||||
|
||||
def export_book(databook):
|
||||
"""
|
||||
reStructuredText representation of a Databook.
|
||||
|
||||
Tables are separated by a blank line. All tables use the grid
|
||||
format.
|
||||
"""
|
||||
return '\n\n'.join(export_set(dataset, force_grid=True)
|
||||
for dataset in databook._datasets)
|
||||
@@ -52,7 +52,7 @@ def export_book(databook, freeze_panes=True):
|
||||
|
||||
wb = Workbook()
|
||||
for sheet in wb.worksheets:
|
||||
wb.remove_sheet(sheet)
|
||||
wb.remove(sheet)
|
||||
for i, dset in enumerate(databook._datasets):
|
||||
ws = wb.create_sheet()
|
||||
ws.title = dset.title if dset.title else 'Sheet%s' % (i)
|
||||
@@ -71,7 +71,7 @@ def import_set(dset, in_stream, headers=True):
|
||||
dset.wipe()
|
||||
|
||||
xls_book = openpyxl.reader.excel.load_workbook(BytesIO(in_stream))
|
||||
sheet = xls_book.get_active_sheet()
|
||||
sheet = xls_book.active
|
||||
|
||||
dset.title = sheet.title
|
||||
|
||||
@@ -119,7 +119,7 @@ def dset_sheet(dataset, ws, freeze_panes=True):
|
||||
row_number = i + 1
|
||||
for j, col in enumerate(row):
|
||||
col_idx = get_column_letter(j + 1)
|
||||
cell = ws.cell('%s%s' % (col_idx, row_number))
|
||||
cell = ws['%s%s' % (col_idx, row_number)]
|
||||
|
||||
# bold headers
|
||||
if (row_number == 1) and dataset.headers:
|
||||
|
||||
@@ -33,7 +33,7 @@ def import_book(dbook, in_stream):
|
||||
|
||||
dbook.wipe()
|
||||
|
||||
for sheet in yaml.load(in_stream):
|
||||
for sheet in yaml.safe_load(in_stream):
|
||||
data = tablib.Dataset()
|
||||
data.title = sheet['title']
|
||||
data.dict = sheet['data']
|
||||
|
||||
@@ -1,127 +0,0 @@
|
||||
# Copyright (c) 2009 Raymond Hettinger
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person
|
||||
# obtaining a copy of this software and associated documentation files
|
||||
# (the "Software"), to deal in the Software without restriction,
|
||||
# including without limitation the rights to use, copy, modify, merge,
|
||||
# publish, distribute, sublicense, and/or sell copies of the Software,
|
||||
# and to permit persons to whom the Software is furnished to do so,
|
||||
# subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be
|
||||
# included in all copies or substantial portions of the Software.
|
||||
#
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
|
||||
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
|
||||
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
|
||||
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
||||
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
||||
# OTHER DEALINGS IN THE SOFTWARE.
|
||||
|
||||
from UserDict import DictMixin
|
||||
|
||||
class OrderedDict(dict, DictMixin):
|
||||
|
||||
def __init__(self, *args, **kwds):
|
||||
if len(args) > 1:
|
||||
raise TypeError('expected at most 1 arguments, got %d' % len(args))
|
||||
try:
|
||||
self.__end
|
||||
except AttributeError:
|
||||
self.clear()
|
||||
self.update(*args, **kwds)
|
||||
|
||||
def clear(self):
|
||||
self.__end = end = []
|
||||
end += [None, end, end] # sentinel node for doubly linked list
|
||||
self.__map = {} # key --> [key, prev, next]
|
||||
dict.clear(self)
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
if key not in self:
|
||||
end = self.__end
|
||||
curr = end[1]
|
||||
curr[2] = end[1] = self.__map[key] = [key, curr, end]
|
||||
dict.__setitem__(self, key, value)
|
||||
|
||||
def __delitem__(self, key):
|
||||
dict.__delitem__(self, key)
|
||||
key, prev, next = self.__map.pop(key)
|
||||
prev[2] = next
|
||||
next[1] = prev
|
||||
|
||||
def __iter__(self):
|
||||
end = self.__end
|
||||
curr = end[2]
|
||||
while curr is not end:
|
||||
yield curr[0]
|
||||
curr = curr[2]
|
||||
|
||||
def __reversed__(self):
|
||||
end = self.__end
|
||||
curr = end[1]
|
||||
while curr is not end:
|
||||
yield curr[0]
|
||||
curr = curr[1]
|
||||
|
||||
def popitem(self, last=True):
|
||||
if not self:
|
||||
raise KeyError('dictionary is empty')
|
||||
if last:
|
||||
key = next(reversed(self))
|
||||
else:
|
||||
key = next(iter(self))
|
||||
value = self.pop(key)
|
||||
return key, value
|
||||
|
||||
def __reduce__(self):
|
||||
items = [[k, self[k]] for k in self]
|
||||
tmp = self.__map, self.__end
|
||||
del self.__map, self.__end
|
||||
inst_dict = vars(self).copy()
|
||||
self.__map, self.__end = tmp
|
||||
if inst_dict:
|
||||
return (self.__class__, (items,), inst_dict)
|
||||
return self.__class__, (items,)
|
||||
|
||||
def keys(self):
|
||||
return list(self)
|
||||
|
||||
setdefault = DictMixin.setdefault
|
||||
update = DictMixin.update
|
||||
pop = DictMixin.pop
|
||||
values = DictMixin.values
|
||||
items = DictMixin.items
|
||||
iterkeys = DictMixin.iterkeys
|
||||
itervalues = DictMixin.itervalues
|
||||
iteritems = DictMixin.iteritems
|
||||
|
||||
def __repr__(self):
|
||||
if not self:
|
||||
return '%s()' % (self.__class__.__name__,)
|
||||
return '%s(%r)' % (self.__class__.__name__, list(self.items()))
|
||||
|
||||
def copy(self):
|
||||
return self.__class__(self)
|
||||
|
||||
@classmethod
|
||||
def fromkeys(cls, iterable, value=None):
|
||||
d = cls()
|
||||
for key in iterable:
|
||||
d[key] = value
|
||||
return d
|
||||
|
||||
def __eq__(self, other):
|
||||
if isinstance(other, OrderedDict):
|
||||
if len(self) != len(other):
|
||||
return False
|
||||
for p, q in zip(list(self.items()), list(other.items())):
|
||||
if p != q:
|
||||
return False
|
||||
return True
|
||||
return dict.__eq__(self, other)
|
||||
|
||||
def __ne__(self, other):
|
||||
return not self == other
|
||||
@@ -0,0 +1,24 @@
|
||||
from __future__ import division
|
||||
|
||||
|
||||
def median(data):
|
||||
"""
|
||||
Return the median (middle value) of numeric data, using the common
|
||||
"mean of middle two" method. If data is empty, ValueError is raised.
|
||||
|
||||
Mimics the behaviour of Python3's statistics.median
|
||||
|
||||
>>> median([1, 3, 5])
|
||||
3
|
||||
>>> median([1, 3, 5, 7])
|
||||
4.0
|
||||
|
||||
"""
|
||||
data = sorted(data)
|
||||
n = len(data)
|
||||
if not n:
|
||||
raise ValueError("No median for empty data")
|
||||
i = n // 2
|
||||
if n % 2:
|
||||
return data[i]
|
||||
return (data[i - 1] + data[i]) / 2
|
||||
@@ -4,9 +4,11 @@
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import datetime
|
||||
import doctest
|
||||
import json
|
||||
import sys
|
||||
import unittest
|
||||
from uuid import uuid4
|
||||
|
||||
import tablib
|
||||
from tablib.compat import markup, unicode, is_py3
|
||||
@@ -227,6 +229,22 @@ class TablibTestCase(unittest.TestCase):
|
||||
# Delete from invalid index
|
||||
self.assertRaises(IndexError, self.founders.__delitem__, 3)
|
||||
|
||||
def test_json_export(self):
|
||||
"""Verify exporting dataset object as JSON"""
|
||||
|
||||
address_id = uuid4()
|
||||
headers = self.headers + ('address_id',)
|
||||
founders = tablib.Dataset(headers=headers, title='Founders')
|
||||
founders.append(('John', 'Adams', 90, address_id))
|
||||
founders_json = founders.export('json')
|
||||
|
||||
expected_json = (
|
||||
'[{"first_name": "John", "last_name": "Adams", "gpa": 90, '
|
||||
'"address_id": "%s"}]' % str(address_id)
|
||||
)
|
||||
|
||||
self.assertEqual(founders_json, expected_json)
|
||||
|
||||
def test_csv_export(self):
|
||||
"""Verify exporting dataset object as CSV."""
|
||||
|
||||
@@ -299,6 +317,23 @@ class TablibTestCase(unittest.TestCase):
|
||||
|
||||
self.assertEqual(html, d.html)
|
||||
|
||||
def test_jira_export(self):
|
||||
|
||||
expected = """||first_name||last_name||gpa||
|
||||
|John|Adams|90|
|
||||
|George|Washington|67|
|
||||
|Thomas|Jefferson|50|"""
|
||||
self.assertEqual(expected, self.founders.jira)
|
||||
|
||||
def test_jira_export_no_headers(self):
|
||||
self.assertEqual('|a|b|c|', tablib.Dataset(['a', 'b', 'c']).jira)
|
||||
|
||||
def test_jira_export_none_and_empty_values(self):
|
||||
self.assertEqual('| | |c|', tablib.Dataset(['', None, 'c']).jira)
|
||||
|
||||
def test_jira_export_empty_dataset(self):
|
||||
self.assertTrue(tablib.Dataset().jira is not None)
|
||||
|
||||
def test_latex_export(self):
|
||||
"""LaTeX export"""
|
||||
|
||||
@@ -382,7 +417,10 @@ class TablibTestCase(unittest.TestCase):
|
||||
data.xlsx
|
||||
data.ods
|
||||
data.html
|
||||
data.jira
|
||||
data.latex
|
||||
data.df
|
||||
data.rst
|
||||
|
||||
def test_datetime_append(self):
|
||||
"""Passes in a single datetime and a single date and exports."""
|
||||
@@ -402,7 +440,9 @@ class TablibTestCase(unittest.TestCase):
|
||||
data.xlsx
|
||||
data.ods
|
||||
data.html
|
||||
data.jira
|
||||
data.latex
|
||||
data.rst
|
||||
|
||||
def test_book_export_no_exceptions(self):
|
||||
"""Test that various exports don't error out."""
|
||||
@@ -416,6 +456,7 @@ class TablibTestCase(unittest.TestCase):
|
||||
book.xlsx
|
||||
book.ods
|
||||
book.html
|
||||
data.rst
|
||||
|
||||
def test_json_import_set(self):
|
||||
"""Generate and import JSON set serialization."""
|
||||
@@ -970,6 +1011,24 @@ class TablibTestCase(unittest.TestCase):
|
||||
self.founders.append(('First\nSecond', 'Name', 42))
|
||||
self.founders.export('xlsx')
|
||||
|
||||
def test_rst_force_grid(self):
|
||||
data.append(self.john)
|
||||
data.append(self.george)
|
||||
data.headers = self.headers
|
||||
|
||||
simple = tablib.formats._rst.export_set(data)
|
||||
grid = tablib.formats._rst.export_set(data, force_grid=True)
|
||||
self.assertNotEqual(simple, grid)
|
||||
self.assertNotIn('+', simple)
|
||||
self.assertIn('+', grid)
|
||||
|
||||
|
||||
class DocTests(unittest.TestCase):
|
||||
|
||||
def test_rst_formatter_doctests(self):
|
||||
results = doctest.testmod(tablib.formats._rst)
|
||||
self.assertEqual(results.failed, 0)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
@@ -1,11 +1,8 @@
|
||||
# Tox (http://tox.testrun.org/) is a tool for running tests
|
||||
# in multiple virtualenvs. This configuration file will run the
|
||||
# test suite on all supported python versions. To use it, "pip install tox"
|
||||
# and then run "tox" from this directory.
|
||||
|
||||
[tox]
|
||||
envlist = py26, py27, py33, py34, py35, py36, pypy
|
||||
minversion = 2.4
|
||||
envlist = py27, py34, py35, py36, pypy
|
||||
|
||||
[testenv]
|
||||
commands = python setup.py test
|
||||
deps = pytest
|
||||
extras = pandas
|
||||
commands = python setup.py test
|
||||
|
||||
Reference in New Issue
Block a user