next version

Make pandas optional

.travis.yml

@@ -1,11 +1,10 @@
 language: python
 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

README.rst

@@ -21,6 +21,7 @@ Output formats supported:
 - Excel (Sets + Books)
 - JSON (Sets + Books)
 - YAML (Sets + Books)
+- Pandas DataFrames (Sets)
 - HTML (Sets)
 - TSV (Sets)
 - OSD (Sets)
@@ -64,13 +65,13 @@ 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 +87,7 @@ JSON!
 +++++
 ::
 
-	>>> print data.json
+	>>> print(data.export('json'))
 	[
 	  {
 	    "last_name": "Adams",
@@ -105,7 +106,7 @@ 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 +114,7 @@ CSV...
 ++++++
 ::
 
-	>>> print data.csv
+	>>> print(data.export('csv'))
 	first_name,last_name,age
 	John,Adams,90
 	Henry,Ford,83
@@ -123,14 +124,23 @@ 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.
 

docs/index.rst

@@ -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
 ------------

docs/intro.rst

@@ -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

docs/tutorial.rst

@@ -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}
 
@@ -346,7 +354,7 @@ 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:
 

requirements.txt

@@ -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

setup.py

@@ -50,6 +50,7 @@ install = [
     'pyyaml',
 ]
 
+
 with open('tablib/core.py', 'r') as fd:
     version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]',
                         fd.read(), re.MULTILINE).group(1)
@@ -71,7 +72,6 @@ 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',
@@ -80,4 +80,7 @@ setup(
     ],
     tests_require=['pytest'],
     install_requires=install,
+    extras_require={
+        'pandas': ['pandas'],
+    },
 )

tablib/core.py

@@ -18,8 +18,8 @@ from tablib.compat import OrderedDict, unicode
 
 
 __title__ = 'tablib'
-__version__ = '0.11.5'
-__build__ = 0x001104
+__version__ = '0.12.1'
+__build__ = 0x001201
 __author__ = 'Kenneth Reitz'
 __license__ = 'MIT'
 __copyright__ = 'Copyright 2017 Kenneth Reitz'
@@ -570,6 +570,18 @@ 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():

tablib/formats/__init__.py

@@ -13,5 +13,6 @@ 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
 
-available = (json, xls, yaml, csv, dbf, tsv, html, latex, xlsx, ods)
+available = (json, xls, yaml, csv, dbf, tsv, html, latex, xlsx, ods, df)

tablib/formats/_df.py

@@ -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')

test_tablib.py

@@ -5,7 +5,6 @@
 import json
 import unittest
 import sys
-import os
 
 import datetime
 
@@ -383,6 +382,7 @@ class TablibTestCase(unittest.TestCase):
         data.ods
         data.html
         data.latex
+        data.df
 
     def test_datetime_append(self):
         """Passes in a single datetime and a single date and exports."""