Merge pull request #10 from jmenglund/return_type

Argument "return_type" to replace "return_values"
jmenglund · Jan 3, 2018 · ea1953f · ea1953f
2 parents c7b16ef + cc7b637
commit ea1953f
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diff --git a/.travis.yml b/.travis.yml
@@ -9,15 +9,16 @@ branches:
     only:
         - master
 
-before_install:
-    - pip install coverage
+install:
+    - pip install .
+    - pip install pycodestyle
     - pip install pytest
-    - pip install pytest-pep8
+    - pip install coverage
 
 script:
-    - py.test pandasvalidation.py --pep8
-    - coverage run -m py.test
-    - coverage report --include pandasvalidation.py -m
+    - pycodestyle pandasvalidation.py test_pandasvalidation.py setup.py
+    - coverage run -m pytest test_pandasvalidation.py
+    - coverage report -m pandasvalidation.py
 
 after_success:
     - codecov
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -4,6 +4,24 @@ Tracking changes in pandas-validation between versions.
 See also https://github.com/jmenglund/pandas-validation/releases.
 
 
+## 0.3.0 ##
+
+This minor release contains the following changes:
+
+* The validation functions now has a `return_type` argument that gives
+  the user control of the output. This replaces the `return_values` argument.
+* When returning values, the validation functions now filter out all invalid
+  values.
+* A few tests have been added to `test_pandasvalidation.py`. The code now has
+  full test coverage.
+* Updates to the documentation.
+* Removed use of the deprecated `pandas.tslib`
+
+Released: 2018-01-03
+
+[View commits](https://github.com/jmenglund/pandas-validation/compare/v0.2.0...v0.3.0)
+
+
 ## 0.2.0 ##
 
 This minor release contains the following changes:

diff --git a/README.rst b/README.rst
@@ -34,21 +34,35 @@ can also be installed using git:
 Running the tests
 -----------------
 
-Testing is carried out with `pytest <http://pytest.org>`_. The following
-example shows how you can run the test suite and generate a coverage report
-with `coverage <https://coverage.readthedocs.io/>`_:
+Testing is carried out with `pytest <https://docs.pytest.org/>`_:
 
 .. code-block::
 
-    $ py.test -v --pep8 pandasvalidation.py
-    $ coverage run -m py.test
-    $ coverage report --include pandasvalidation.py -m
+    $ pytest -v test_pandasvalidation.py
+
+Test coverage can be calculated with `Coverage.py
+<https://coverage.readthedocs.io/>`_ using the following commands:
+
+.. code-block::
+
+    $ coverage run -m pytest
+    $ coverage report -m pandasvalidation.py
+
+The code follow style conventions in `PEP8
+<https://www.python.org/dev/peps/pep-0008/>`_, which can be checked
+with `pycodestyle <http://pycodestyle.pycqa.org>`_:
+
+.. code-block::
+
+    $ pycodestyle pandasvalidation.py test_pandasvalidation.py setup.py
 
 
 Building the documentation
 --------------------------
 
-The documentation can be built with `Sphinx <http://www.sphinx-doc.org>`_:
+The documentation can be built with `Sphinx <http://www.sphinx-doc.org>`_
+and the `Read the Docs Sphinx Theme
+<https://sphinx-rtd-theme.readthedocs.io>`_:
 
 .. code-block::
 
@@ -59,13 +73,15 @@ The documentation can be built with `Sphinx <http://www.sphinx-doc.org>`_:
 License
 -------
 
-pandas-validation is distributed under the `MIT license <https://opensource.org/licenses/MIT>`_.
+pandas-validation is distributed under the `MIT license
+<https://opensource.org/licenses/MIT>`_.
 
 
 Author
 ------
 
-Markus Englund, `orcid.org/0000-0003-1688-7112 <http://orcid.org/0000-0003-1688-7112>`_
+Markus Englund, `orcid.org/0000-0003-1688-7112
+<http://orcid.org/0000-0003-1688-7112>`_
 
 
 .. |Build-Status| image:: https://api.travis-ci.org/jmenglund/pandas-validation.svg?branch=master

diff --git a/docs/source/pandas-validation/quickstart.rst b/docs/source/pandas-validation/quickstart.rst
@@ -17,11 +17,11 @@ know more, I suggest that you have a look at the :ref:`API reference<api>`.
 
 The code examples below assume that you first do the following imports:
 
-.. code-block:: pycon
+.. code-block:: python
 
-    >>> import numpy as np
-    >>> import pandas as pd
-    >>> import pandasvalidation as pv
+    import numpy as np
+    import pandas as pd
+    import pandasvalidation as pv
 
 
 .. _validate-dates:
@@ -37,18 +37,18 @@ pandas Series will be returned with the values converted to the datetime
 data type.
 
 
-.. code-block:: pycon
+.. code-block:: python
 
-    >>> s1 = pandas.Series(
-    ...     ['2014', '2014-01-07', '2014-02-28', np.nan],
-    ...     name='My dates')
-    >>> pv.validate_datetime(
-    ...     s1,
-    ...     nullable=False,
-    ...     unique=True,
-    ...     min_datetime='2014-01-05',
-    ...     max_datetime='2014-02-15',
-    ...     return_values=False)
+    s1 = pd.Series(
+        ['2014', '2014-01-07', '2014-02-28', np.nan],
+        name='My dates')
+    pv.validate_datetime(
+        s1,
+        nullable=False,
+        unique=True,
+        min_datetime='2014-01-05',
+        max_datetime='2014-02-15',
+        return_type=None)
 
 
 .. _validate-numbers:
@@ -61,19 +61,19 @@ Like in the example above, warnings will indicate invalid values to the user.
 If `return_values` is set to ``True``, a pandas Series will be returned with
 the values converted to a numeric data type.
 
-.. code-block:: pycon
+.. code-block:: python
 
-    >>> s2 = pandas.Series(
-    ...     [1, '1', '2.3', np.nan],
-    ...     name='My numeric values')
-    >>> pv.validate_numeric(
-    ...     s2,
-    ...     nullable=False,
-    ...     unique=True,
-    ...     integer=True,
-    ...     min_value=2,
-    ...     max_value=2,
-    ...     return_values=False)
+    s2 = pd.Series(
+        [1, '1', '2.3', np.nan],
+        name='My numeric values')
+    pv.validate_numeric(
+        s2,
+        nullable=False,
+        unique=True,
+        integer=True,
+        min_value=2,
+        max_value=2,
+        return_type=None)
 
 
 .. _validate-strings:
@@ -87,19 +87,19 @@ warnings then issued if there are invalid values. If `return_values` is
 set to ``True``, a pandas Series will be returned with the values rendered
 as strings.
 
-.. code-block:: pycon
-
-    >>> s3 = pandas.Series(
-    ...     [1, 1, 'ab\n', 'a b', 'Ab', 'AB', np.nan],
-    ...     name='My strings')
-    >>> pv.validate_string(
-    ...     s3,
-    ...     nullable=False,
-    ...     unique=True,
-    ...     min_length=2,
-    ...     max_length=2,
-    ...     case='lower',
-    ...     newlines=False,
-    ...     trailing_whitespace=False,
-    ...     whitespace=False,
-    ...     return_values=False)
+.. code-block:: python
+
+    s3 = pd.Series(
+        [1, 1, 'ab\n', 'a b', 'Ab', 'AB', np.nan],
+        name='My strings')
+    pv.validate_string(
+        s3,
+        nullable=False,
+        unique=True,
+        min_length=2,
+        max_length=2,
+        case='lower',
+        newlines=False,
+        trailing_whitespace=False,
+        whitespace=False,
+        return_type=False)