Update formatting for mostly when building docs

Signed-off-by: James Campbell <[email protected]>
great-expectations · Oct 21, 2019 · 7514913 · 7514913
1 parent 04605a8
commit 7514913
Show file tree

Hide file tree

Showing 5 changed files with 16 additions and 4 deletions.
diff --git a/docs/changelog/changelog.rst b/docs/changelog/changelog.rst
@@ -2,6 +2,9 @@
 
 0.8.2__develop
 -----------------
+* Use higher precision for rendering 'mostly' parameter in data-docs
+* Documentation fixes (thanks @DanielOliver!)
+* Minor CLI wording fixes
 
 
 0.8.1

diff --git a/docs/features/data_context.rst b/docs/features/data_context.rst
@@ -36,6 +36,7 @@ option for selecting which expectations a DataContext returns.
 In many simple projects, the datasource or generator name may be omitted and the DataContext will infer
 the correct name when there is no ambiguity.
 
-Similarly, if no expectation suite name is provided, the DataContext will assume the name "default".
+The DataContext also provides other services, such as storing and substituting evaluation parameters during validation.
+See :ref:`data_context_evaluation_parameter_store` for more information.
 
 See the :ref:`data_context_reference` for more information.
diff --git a/docs/getting_started/pipeline_integration.rst b/docs/getting_started/pipeline_integration.rst
@@ -121,7 +121,7 @@ Finally, we can explicitly build batch kwargs, which can be particularly useful
   my_explicit_batch_kwargs = {
     "query": "select id, date, procedure_code from normalized_codes where date < '2019-07-01'"
   }
-  batch = context.get_batch("my_source/my_generator/my_asset","my_expectations", my_explicit_batch_kwargs)
+  batch = context.get_batch("my_source/my_generator/my_asset", "my_expectations", my_explicit_batch_kwargs)
 
 
 Validate

diff --git a/docs/reference/custom_expectations.rst b/docs/reference/custom_expectations.rst
@@ -421,7 +421,10 @@ structure below.
 
     >> import great_expectations as ge
     >> context = ge.DataContext()
-    >> my_df = context.get_batch("my_datasource/default/my_file")
+    >> my_df = context.get_batch(
+        "my_datasource/default/my_file",
+        "warning",
+        context.yield_batch_kwargs("my_datasource/default/my_file"))
 
     >> my_df.expect_column_values_to_equal_1("all_twos")
     {

diff --git a/docs/reference/data_docs_reference.rst b/docs/reference/data_docs_reference.rst
@@ -190,7 +190,12 @@ for how to profile a single batch of data and build documentation from the valid
   context = ge.data_context.DataContext()
 
   # load a batch from the data asset
-  batch = context.get_batch('ratings')
+  data_asset_name = context.normalize_data_asset_name('ratings')
+  context.creat_expectation_suite(data_asset_name, 'default'),
+  batch = context.get_batch(
+    data_asset_name=data_asset_name,
+    expectation_suite_name='default',
+    context.yield_batch_kwargs(data_asset_name))
 
   # run the profiler on the batch - this returns an expectation suite and validation results for this suite
   expectation_suite, validation_result = BasicDatasetProfiler().profile(batch)