MAINT: Default inplace to False in pd.eval

Deprecated back in 0.18.0

xref gh-11149

[ci skip]

Author: gfyoung

Diff for: doc/source/whatsnew/v0.21.0.txt

@@ -76,6 +76,7 @@ Removal of prior version deprecations/changes
 
 - ``pd.read_excel()`` has dropped the ``has_index_names`` parameter (:issue:`10967`)
 - ``Categorical`` has dropped the ``.order()`` and ``.sort()`` methods in favor of ``.sort_values()`` (:issue:`12882`)
+- ``pd.eval`` and ``DataFrame.eval`` have changed the default of ``inplace`` from ``None`` to ``False`` (:issue:`11149`)
 
 
 .. _whatsnew_0210.performance:

Diff for: pandas/core/computation/eval.py

@@ -3,7 +3,6 @@
 """Top level ``eval`` module.
 """
 
-import warnings
 import tokenize
 from pandas.io.formats.printing import pprint_thing
 from pandas.core.computation import _NUMEXPR_INSTALLED
@@ -148,7 +147,7 @@ def _check_for_locals(expr, stack_level, parser):
 
 def eval(expr, parser='pandas', engine=None, truediv=True,
          local_dict=None, global_dict=None, resolvers=(), level=0,
-         target=None, inplace=None):
+         target=None, inplace=False):
     """Evaluate a Python expression as a string using various backends.
 
     The following arithmetic operations are supported: ``+``, ``-``, ``*``,
@@ -207,13 +206,10 @@ def eval(expr, parser='pandas', engine=None, truediv=True,
         scope. Most users will **not** need to change this parameter.
     target : a target object for assignment, optional, default is None
         essentially this is a passed in resolver
-    inplace : bool, default True
-        If expression mutates, whether to modify object inplace or return
-        copy with mutation.
-
-        WARNING: inplace=None currently falls back to to True, but
-        in a future version, will default to False.  Use inplace=True
-        explicitly rather than relying on the default.
+    inplace : bool, default False
+        If the expression mutates, whether to modify the expression inplace
+        or return a copy of it with mutation. In other words, it will return
+        the result of the expression if `inplace=True` and `target` otherwise.
 
     Returns
     -------
@@ -272,14 +268,6 @@ def eval(expr, parser='pandas', engine=None, truediv=True,
 
         # assign if needed
         if env.target is not None and parsed_expr.assigner is not None:
-            if inplace is None:
-                warnings.warn(
-                    "eval expressions containing an assignment currently"
-                    "default to operating inplace.\nThis will change in "
-                    "a future version of pandas, use inplace=True to "
-                    "avoid this warning.",
-                    FutureWarning, stacklevel=3)
-                inplace = True
 
             # if returning a copy, copy only on the first assignment
             if not inplace and first_expr:

Diff for: pandas/core/frame.py

@@ -2224,7 +2224,7 @@ def query(self, expr, inplace=False, **kwargs):
         else:
             return new_data
 
-    def eval(self, expr, inplace=None, **kwargs):
+    def eval(self, expr, inplace=False, **kwargs):
         """Evaluate an expression in the context of the calling DataFrame
         instance.
 
@@ -2236,10 +2236,6 @@ def eval(self, expr, inplace=None, **kwargs):
             If the expression contains an assignment, whether to return a new
             DataFrame or mutate the existing.
 
-            WARNING: inplace=None currently falls back to to True, but
-            in a future version, will default to False.  Use inplace=True
-            explicitly rather than relying on the default.
-
             .. versionadded:: 0.18.0
 
         kwargs : dict

Diff for: pandas/tests/computation/test_eval.py

@@ -1311,14 +1311,6 @@ def assignment_not_inplace(self):
         expected['c'] = expected['a'] + expected['b']
         tm.assert_frame_equal(df, expected)
 
-        # Default for inplace will change
-        with tm.assert_produces_warnings(FutureWarning):
-            df.eval('c = a + b')
-
-        # but don't warn without assignment
-        with tm.assert_produces_warnings(None):
-            df.eval('a + b')
-
     def test_multi_line_expression(self):
         # GH 11149
         df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})