* [bugfix] convert metrics to numeric in dataframe
It appears sometimes the dbapi driver and pandas's read_sql fail at
returning the proper numeric types for metrics and they show up as
`object` in the dataframe. This results in "No numeric types to
aggregate" errors when trying to perform aggregations or pivoting in
pandas.
This PR looks for metrics in dataframes that are typed as "object"
and uses pandas' to_numeric to convert.
* Fix tests
* Remove all iteritems
* Allowing limit ordering by post-aggregation metrics
* don't overwrite og dictionaries
* update tests
* python3 compat
* code review comments, add tests, implement it in groupby as well
* python 3 compat for unittest
* more self
* Throw exception when get aggregations is called with postaggs
* Treat adhoc metrics as another aggregation
* move access permissions methods to security manager
* consolidate all security methods into SupersetSecurityManager
* update security method calls
* update calls from tests
* move get_or_create_main_db to utils
* raise if supersetsecuritymanager is not extended
* rename sm to security_manager
* [Explore] Save url parameters when user save slices
* remove print
(cherry picked from commit bd9ecbe)
* add unit test
(cherry picked from commit 0f350ad)
* wrapping all request params into url_params
(cherry picked from commit 17197c1)
* Remove comments from queries in SQL Lab that break Explore view
This fixes an issue where comments on the last line of the source query comment out the closing parenthesis of the subquery.
* Add test case for SqlaTable with query with comment
This test ensures that comments in the query are removed when calling SqlaTable.get_from_clause().
* Add missing blank line class definition (PEP8)
* [Explore view] Use POST method for charting requests
* fix per code review comments
* more code review fixes
* code review fix: remove duplicated calls for getting values from request
* [Explore view] Use POST method for charting requests
* fix per code review comments
* more code review fixes
* code review fix: remove duplicated calls for getting values from request
* Use the query_obj as the basis for the cache key
When we recently moved from hashing form_data to define the cache_key
towards using the rendered query instead,
it made is such that non deterministic form
control values like relative times specified in "from" and "until" time
bound resulted in making those miss cache 100% of the time.
Here we move away from using the rendered query and using the query_obj
instead.
* Deprecating using form_data in templates
* conditional check on datatype of results before converting to df
fix type checking
fix conditional checks
remove trailing whitespace and fix df_data fallback def
actually remove trailing whitespace
generalized type check to check all columns for dict
refactor dict col check
* move df conversion to helper and add unit test
add missing newlines
another missing newline
fix quotes
more quote fixes
* Fix USA's state geojson for 'Country Map' visualization
Turns out the ISO codes were missing from the geojson file, this adds it
and uses human-readable indents.
* using proper ISO codes
* Linting
New linting rules started applying, I'm guessing a new version of
pylint?
* Adding full Annotation Framework
* Viz types
* Re organizing native annotations
* liniting
* Bug fix
* Handle no data
* Cleanup
* Refactor slice form_data to data
Before this PR the only way to query lat/long is in the shape of 2
columns that contains lat and long.
Now we're adding 2 more options:
* a single column that has lat and long with a delimiter in between
* support for geohashes - geohashes are cool
* change reference for slices to chart
* change profile page reference
* change reference for Associated Slices
* change back to single quotes
* fix other single quotes
* linting
* last one
* fix test
A bug in to_dict(orient="records") in pandas/core/frame.py prevents
datetimes with time zones to be worked with. This works around the
issue in superset by re-implementing the logic of pandas in the
correct way. Until pandas fixes the issue this code should stay.
https://github.com/pandas-dev/pandas/issues/18372
This closes#1929
