Merge branch 'feature/cli_refactor_rebase' into feature/data_context_…

…namespace

Key conflicts came from need to harmonize profiling get_batch and get_expectations to new namespaced API.

Conflicts:
	docs/source/profiling.rst
	great_expectations/data_asset/data_asset.py
	great_expectations/util.py
	tests/conftest.py
	tests/test_autoinspect.py

docs/source/dataset_module.rst

@@ -79,11 +79,3 @@ great_expectations.dataset.util
     :undoc-members:
     :show-inheritance:
 
-
-great_expectations.dataset.autoinspect
---------------------------------------
-
-.. automodule:: great_expectations.dataset.autoinspect
-    :members:
-    :undoc-members:
-    :show-inheritance:

docs/source/index.rst

@@ -25,7 +25,7 @@ Advanced
 
    standard_arguments
    result_format
-   autoinspection
+   profiling
    evaluation_parameters
    custom_expectations
    conventions
@@ -40,6 +40,7 @@ Module Docs
    data_asset_module
    dataset_module
    data_context_module
+   profile_module
 
 Indices and tables
 ------------------

docs/source/profile.rst

@@ -0,0 +1,12 @@
+.. _profile_module:
+
+Profile Module
+==================================
+
+great_expectations.profile
+--------------------------------------
+
+.. automodule:: great_expectations.profile
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/source/autoinspection.rst → docs/source/profiling.rst

@@ -1,29 +1,21 @@
-.. _autoinspection:
+.. _profiling:
 
 ================================================================================
-Autoinspection
+Profiling
 ================================================================================
 
 It can be very convenient to have great expectations automatically review a \
 dataset and suggest expectations that may be appropriate. Currently, there's \
-a very basic, but easily extensible, autoinspection capability available.
-
-Dataset objects have an `autoinspect` method which allows you to provide a \
-function that will evaluate a dataset object and add expectations to it. \
-By default `autoinspect` will call the autoinspect function \
-:func:`columns_exist <great_expectations.dataset.autoinspect.columns_exist>` \
-which will add an `expect_column_to_exist` expectation for each column \
-currently present on the dataset.
-
-To implement additional autoinspection functions, you simply take a single \
-parameter, a Dataset, and evaluate and add expectations to that object.
+a very basic, but easily extensible, profiling capability available.
 
+Dataset objects have a `profile` method which allows you to provide a \
+profiler class that will evaluate a dataset object and add expectations to it.
 
 .. code-block:: python
 
     >> import great_expectations as ge
     >> df = ge.dataset.PandasDataset({"col": [1, 2, 3, 4, 5]})
-    >> df.autoinspect(ge.dataset.autoinspect.columns_exist)
+    >> df.profile(ge.profile.ColumnsExistProfiler)
     >> df.get_expectation_suite()
         {'dataset_name': None,
          'meta': {'great_expectations.__version__': '0.4.4__develop'},

docs/source/roadmap_changelog.rst

@@ -9,10 +9,16 @@ Planned Features
 * Improved variable typing
 * Support for non-tabular datasources (e.g. JSON, XML, AVRO)
 
-v.0.6.1__develop
+v.0.7.0__develop
 ________________
-
-
+* Name clarifications: we've stopped using the overloaded terms "expectations
+  config" and "config" and instead use "expectation suite" to refer to a
+  collection (or suite!) of expectations that can be used for validating a
+  data asset.
+* Major refactor of autoinspect. Autoinspect is now built around a module
+  called "profile" which provides a class-based structure for building
+  expectation suites. There is no longer a default  "autoinspect_func" --
+  calling autoinspect requires explicitly passing the desired profiler.
 
 v.0.6.1
 ________________