TST: Fail on warning (#22699)

Author: TomAugspurger

.travis.yml

@@ -64,7 +64,7 @@ matrix:
     # In allow_failures
     - dist: trusty
       env:
-        - JOB="3.6, NumPy dev" ENV_FILE="ci/travis-36-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate"
+        - JOB="3.7, NumPy dev" ENV_FILE="ci/travis-37-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network -W error" PANDAS_TESTING_MODE="deprecate"
       addons:
         apt:
           packages:
@@ -79,7 +79,7 @@ matrix:
           - JOB="3.6, slow" ENV_FILE="ci/travis-36-slow.yaml" SLOW=true
       - dist: trusty
         env:
-          - JOB="3.6, NumPy dev" ENV_FILE="ci/travis-36-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate"
+          - JOB="3.7, NumPy dev" ENV_FILE="ci/travis-37-numpydev.yaml" TEST_ARGS="--skip-slow --skip-network -W error" PANDAS_TESTING_MODE="deprecate"
         addons:
           apt:
             packages:

ci/travis-36-numpydev.yaml renamed to ci/travis-37-numpydev.yaml

@@ -2,7 +2,7 @@ name: pandas
 channels:
   - defaults
 dependencies:
-  - python=3.6*
+  - python=3.7*
   - pytz
   - Cython>=0.28.2
   # universal

doc/source/contributing.rst

@@ -632,6 +632,14 @@ Otherwise, you need to do it manually:
         warnings.warn('Use new_func instead.', FutureWarning, stacklevel=2)
         new_func()
 
+You'll also need to
+
+1. write a new test that asserts a warning is issued when calling with the deprecated argument
+2. Update all of pandas existing tests and code to use the new argument
+
+See :ref:`contributing.warnings` for more.
+
+
 .. _contributing.ci:
 
 Testing With Continuous Integration
@@ -859,6 +867,55 @@ preferred if the inputs or logic are simple, with Hypothesis tests reserved
 for cases with complex logic or where there are too many combinations of
 options or subtle interactions to test (or think of!) all of them.
 
+.. _contributing.warnings:
+
+Testing Warnings
+~~~~~~~~~~~~~~~~
+
+By default, one of pandas CI workers will fail if any unhandled warnings are emitted.
+
+If your change involves checking that a warning is actually emitted, use
+``tm.assert_produces_warning(ExpectedWarning)``.
+
+
+.. code-block:: python
+
+   with tm.assert_prodcues_warning(FutureWarning):
+       df.some_operation()
+
+We prefer this to the ``pytest.warns`` context manager because ours checks that the warning's
+stacklevel is set correctly. The stacklevel is what ensure the *user's* file name and line number
+is printed in the warning, rather than something internal to pandas. It represents the number of
+function calls from user code (e.g. ``df.some_operation()``) to the function that actually emits
+the warning. Our linter will fail the build if you use ``pytest.warns`` in a test.
+
+If you have a test that would emit a warning, but you aren't actually testing the
+warning itself (say because it's going to be removed in the future, or because we're
+matching a 3rd-party library's behavior), then use ``pytest.mark.filterwarnings`` to
+ignore the error.
+
+.. code-block:: python
+
+   @pytest.mark.filterwarnings("ignore:msg:category")
+   def test_thing(self):
+       ...
+
+If the test generates a warning of class ``category`` whose message starts
+with ``msg``, the warning will be ignored and the test will pass.
+
+If you need finer-grained control, you can use Python's usual
+`warnings module <https://docs.python.org/3/library/warnings.html>`__
+to control whether a warning is ignored / raised at different places within
+a single test.
+
+.. code-block:: python
+
+   with warch.catch_warnings():
+       warnings.simplefilter("ignore", FutureWarning)
+       # Or use warnings.filterwarnings(...)
+
+Alternatively, consider breaking up the unit test.
+
 
 Running the test suite
 ----------------------

pandas/compat/__init__.py

@@ -38,6 +38,7 @@
 import struct
 import inspect
 from collections import namedtuple
+import collections
 
 PY2 = sys.version_info[0] == 2
 PY3 = sys.version_info[0] >= 3
@@ -135,6 +136,11 @@ def lfilter(*args, **kwargs):
 
     from importlib import reload
     reload = reload
+    Hashable = collections.abc.Hashable
+    Iterable = collections.abc.Iterable
+    Mapping = collections.abc.Mapping
+    Sequence = collections.abc.Sequence
+    Sized = collections.abc.Sized
 
 else:
     # Python 2
@@ -190,6 +196,12 @@ def get_range_parameters(data):
 
     reload = builtins.reload
 
+    Hashable = collections.Hashable
+    Iterable = collections.Iterable
+    Mapping = collections.Mapping
+    Sequence = collections.Sequence
+    Sized = collections.Sized
+
 if PY2:
     def iteritems(obj, **kw):
         return obj.iteritems(**kw)

pandas/compat/chainmap_impl.py

@@ -1,4 +1,11 @@
-from collections import MutableMapping
+import sys
+
+PY3 = sys.version_info[0] >= 3
+
+if PY3:
+    from collections.abc import MutableMapping
+else:
+    from collections import MutableMapping
 
 try:
     from thread import get_ident

pandas/core/algorithms.py

@@ -3,7 +3,7 @@
 intended for public consumption
 """
 from __future__ import division
-from warnings import warn, catch_warnings
+from warnings import warn, catch_warnings, simplefilter
 from textwrap import dedent
 
 import numpy as np
@@ -91,7 +91,8 @@ def _ensure_data(values, dtype=None):
 
             # ignore the fact that we are casting to float
             # which discards complex parts
-            with catch_warnings(record=True):
+            with catch_warnings():
+                simplefilter("ignore", np.ComplexWarning)
                 values = ensure_float64(values)
             return values, 'float64', 'float64'
 

pandas/core/arrays/datetimelike.py

@@ -59,6 +59,7 @@ def cmp_method(self, other):
         # numpy will show a DeprecationWarning on invalid elementwise
         # comparisons, this will raise in the future
         with warnings.catch_warnings(record=True):
+            warnings.filterwarnings("ignore", "elementwise", FutureWarning)
             with np.errstate(all='ignore'):
                 result = op(self.values, np.asarray(other))
 

pandas/core/arrays/integer.py

@@ -5,7 +5,7 @@
 
 from pandas._libs.lib import infer_dtype
 from pandas.util._decorators import cache_readonly
-from pandas.compat import u, range
+from pandas.compat import u, range, string_types
 from pandas.compat import set_function_name
 
 from pandas.core.dtypes.cast import astype_nansafe
@@ -147,6 +147,11 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
             dtype = values.dtype
 
     if dtype is not None:
+        if (isinstance(dtype, string_types) and
+                (dtype.startswith("Int") or dtype.startswith("UInt"))):
+            # Avoid DeprecationWarning from NumPy about np.dtype("Int64")
+            # https://github.com/numpy/numpy/pull/7476
+            dtype = dtype.lower()
         if not issubclass(type(dtype), _IntegerDtype):
             try:
                 dtype = _dtypes[str(np.dtype(dtype))]
@@ -507,7 +512,8 @@ def cmp_method(self, other):
 
             # numpy will show a DeprecationWarning on invalid elementwise
             # comparisons, this will raise in the future
-            with warnings.catch_warnings(record=True):
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", "elementwise", FutureWarning)
                 with np.errstate(all='ignore'):
                     result = op(self._data, other)
 

pandas/core/common.py

@@ -356,7 +356,7 @@ def standardize_mapping(into):
             return partial(
                 collections.defaultdict, into.default_factory)
         into = type(into)
-    if not issubclass(into, collections.Mapping):
+    if not issubclass(into, compat.Mapping):
         raise TypeError('unsupported type: {into}'.format(into=into))
     elif into == collections.defaultdict:
         raise TypeError(

pandas/core/computation/eval.py

@@ -323,6 +323,7 @@ def eval(expr, parser='pandas', engine=None, truediv=True,
             # to use a non-numeric indexer
             try:
                 with warnings.catch_warnings(record=True):
+                    # TODO: Filter the warnings we actually care about here.
                     target[assigner] = ret
             except (TypeError, IndexError):
                 raise ValueError("Cannot assign expression output to target")