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Add script to automatically download wheels
diff --git a/doc/source/development/maintaining.rst b/doc/source/development/maintaining.rst index 6e9f622e18eea..23eb5d7b18c7d 100644 --- a/doc/source/development/maintaining.rst +++ b/doc/source/development/maintaining.rst @@ -465,7 +465,8 @@ which will be triggered when the tag is pushed. 7. Download all wheels from the Anaconda repository where MacPython uploads them: https://anaconda.org/multibuild-wheels-staging/pandas/files?version=<version> - to the ``dist/`` directory in the local pandas copy. + to the ``dist/`` directory in the local pandas copy. You can use the script + ``scripts/download_wheels.sh`` to download all wheels at once. 8. Upload wheels to PyPI: diff --git a/scripts/download_wheels.sh b/scripts/download_wheels.sh new file mode 100755 index 0000000000000..0b92e83113f5f --- /dev/null +++ b/scripts/download_wheels.sh @@ -0,0 +1,28 @@ +#!/bin/sh +# +# Download all wheels for a pandas version. +# +# This script is mostly useful during the release process, when wheels +# generated by the MacPython repo need to be downloaded locally to then +# be uploaded to the PyPI. +# +# There is no API to access the wheel files, so the script downloads the +# website, extracts the file urls from the html, and then downloads it +# one by one to the dist/ directory where they would be generated. + +VERSION=$1 +DIST_DIR="$(realpath $(dirname -- $0)/../dist)" + +if [ -z $VERSION ]; then + printf "Usage:\n\t$0 <version>\n\nWhere <version> is for example 1.5.3" + exit 1 +fi + +curl "https://anaconda.org/multibuild-wheels-staging/pandas/files?version=${VERSION}" | \ + grep "href=\"/multibuild-wheels-staging/pandas/${VERSION}" | \ + sed -r 's/.*<a href="([^"]+\.whl)">.*/\1/g' | \ + awk '{print "https://anaconda.org" $0 }' | \ + xargs wget -P $DIST_DIR + +printf "\nWheels downloaded to $DIST_DIR\nYou can upload them to PyPI using:\n\n" +printf "\ttwine upload ${DIST_DIR}/pandas-${VERSION}*.{whl,tar.gz} --skip-existing"
There is a similar script in the pandas-release repo, but that repo is not needed anyomore for the release, and it makes more sense to have it here. Also, the original script is in Python, which makes it much more complex. Here I use a shell script with a single command.
https://api.github.com/repos/pandas-dev/pandas/pulls/50859
2023-01-19T08:02:53Z
2023-01-26T19:02:21Z
2023-01-26T19:02:21Z
2023-01-26T19:02:22Z
BUG/PERF: Series(category).replace
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index dc05745c8c0e5..873c7aabde785 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -883,6 +883,7 @@ Performance improvements - Performance improvement in :func:`merge` and :meth:`DataFrame.join` when joining on a sorted :class:`MultiIndex` (:issue:`48504`) - Performance improvement in :func:`to_datetime` when parsing strings with timezone offsets (:issue:`50107`) - Performance improvement in :meth:`DataFrame.loc` and :meth:`Series.loc` for tuple-based indexing of a :class:`MultiIndex` (:issue:`48384`) +- Performance improvement for :meth:`Series.replace` with categorical dtype (:issue:`49404`) - Performance improvement for :meth:`MultiIndex.unique` (:issue:`48335`) - Performance improvement for :func:`concat` with extension array backed indexes (:issue:`49128`, :issue:`49178`) - Reduce memory usage of :meth:`DataFrame.to_pickle`/:meth:`Series.to_pickle` when using BZ2 or LZMA (:issue:`49068`) @@ -927,6 +928,8 @@ Bug fixes Categorical ^^^^^^^^^^^ - Bug in :meth:`Categorical.set_categories` losing dtype information (:issue:`48812`) +- Bug in :meth:`Series.replace` with categorical dtype when ``to_replace`` values overlap with new values (:issue:`49404`) +- Bug in :meth:`Series.replace` with categorical dtype losing nullable dtypes of underlying categories (:issue:`49404`) - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` would reorder categories when used as a grouper (:issue:`48749`) - Bug in :class:`Categorical` constructor when constructing from a :class:`Categorical` object and ``dtype="category"`` losing ordered-ness (:issue:`49309`) - diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py index 14f334d72dbb1..5b61695410474 100644 --- a/pandas/core/arrays/categorical.py +++ b/pandas/core/arrays/categorical.py @@ -1137,14 +1137,9 @@ def remove_categories(self, removals): if not is_list_like(removals): removals = [removals] - removal_set = set(removals) - not_included = removal_set - set(self.dtype.categories) - new_categories = [c for c in self.dtype.categories if c not in removal_set] - - # GH 10156 - if any(isna(removals)): - not_included = {x for x in not_included if notna(x)} - new_categories = [x for x in new_categories if notna(x)] + removals = {x for x in set(removals) if notna(x)} + new_categories = self.dtype.categories.difference(removals) + not_included = removals.difference(self.dtype.categories) if len(not_included) != 0: raise ValueError(f"removals must all be in old categories: {not_included}") @@ -2273,42 +2268,28 @@ def isin(self, values) -> npt.NDArray[np.bool_]: return algorithms.isin(self.codes, code_values) def _replace(self, *, to_replace, value, inplace: bool = False): + from pandas import Index + inplace = validate_bool_kwarg(inplace, "inplace") cat = self if inplace else self.copy() - # other cases, like if both to_replace and value are list-like or if - # to_replace is a dict, are handled separately in NDFrame - if not is_list_like(to_replace): - to_replace = [to_replace] - - categories = cat.categories.tolist() - removals = set() - for replace_value in to_replace: - if value == replace_value: - continue - if replace_value not in cat.categories: - continue - if isna(value): - removals.add(replace_value) - continue - - index = categories.index(replace_value) - - if value in cat.categories: - value_index = categories.index(value) - cat._codes[cat._codes == index] = value_index - removals.add(replace_value) - else: - categories[index] = value - cat._set_categories(categories) + mask = isna(np.asarray(value)) + if mask.any(): + removals = np.asarray(to_replace)[mask] + removals = cat.categories[cat.categories.isin(removals)] + new_cat = cat.remove_categories(removals) + NDArrayBacked.__init__(cat, new_cat.codes, new_cat.dtype) - if len(removals): - new_categories = [c for c in categories if c not in removals] - new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered) - codes = recode_for_categories( - cat.codes, cat.categories, new_dtype.categories - ) - NDArrayBacked.__init__(cat, codes, new_dtype) + ser = cat.categories.to_series() + ser = ser.replace(to_replace=to_replace, value=value) + + all_values = Index(ser) + new_categories = Index(ser.drop_duplicates(keep="first")) + new_codes = recode_for_categories( + cat._codes, all_values, new_categories, copy=False + ) + new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered) + NDArrayBacked.__init__(cat, new_codes, new_dtype) if not inplace: return cat diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py index 00ab9d02cee00..8fb6a18ca137a 100644 --- a/pandas/core/internals/blocks.py +++ b/pandas/core/internals/blocks.py @@ -536,12 +536,10 @@ def replace( if isinstance(values, Categorical): # TODO: avoid special-casing + # GH49404 blk = self if inplace else self.copy() - # error: Item "ExtensionArray" of "Union[ndarray[Any, Any], - # ExtensionArray]" has no attribute "_replace" - blk.values._replace( # type: ignore[union-attr] - to_replace=to_replace, value=value, inplace=True - ) + values = cast(Categorical, blk.values) + values._replace(to_replace=to_replace, value=value, inplace=True) return [blk] if not self._can_hold_element(to_replace): @@ -651,6 +649,14 @@ def replace_list( """ values = self.values + if isinstance(values, Categorical): + # TODO: avoid special-casing + # GH49404 + blk = self if inplace else self.copy() + values = cast(Categorical, blk.values) + values._replace(to_replace=src_list, value=dest_list, inplace=True) + return [blk] + # Exclude anything that we know we won't contain pairs = [ (x, y) for x, y in zip(src_list, dest_list) if self._can_hold_element(x) diff --git a/pandas/tests/arrays/categorical/test_replace.py b/pandas/tests/arrays/categorical/test_replace.py index a3ba420c84a17..c25f1d9c9feac 100644 --- a/pandas/tests/arrays/categorical/test_replace.py +++ b/pandas/tests/arrays/categorical/test_replace.py @@ -21,6 +21,11 @@ ((5, 6), 2, [1, 2, 3], False), ([1], [2], [2, 2, 3], False), ([1, 4], [5, 2], [5, 2, 3], False), + # GH49404: overlap between to_replace and value + ([1, 2, 3], [2, 3, 4], [2, 3, 4], False), + # GH50872, GH46884: replace with null + (1, None, [None, 2, 3], False), + (1, pd.NA, [None, 2, 3], False), # check_categorical sorts categories, which crashes on mixed dtypes (3, "4", [1, 2, "4"], False), ([1, 2, "3"], "5", ["5", "5", 3], True), @@ -65,3 +70,11 @@ def test_replace_categorical(to_replace, value, result, expected_error_msg): pd.Series(cat).replace(to_replace, value, inplace=True) tm.assert_categorical_equal(cat, expected) + + +def test_replace_categorical_ea_dtype(): + # GH49404 + cat = Categorical(pd.array(["a", "b"], dtype="string")) + result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values + expected = Categorical(pd.array(["c", pd.NA], dtype="string")) + tm.assert_categorical_equal(result, expected)
- [x] closes #50872 - [x] closes #46884 - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/v2.0.0.rst` file if fixing a bug or adding a new feature. Redo of #49404 with fix that was reverted in #50848
https://api.github.com/repos/pandas-dev/pandas/pulls/50857
2023-01-19T04:52:44Z
2023-01-24T19:02:33Z
2023-01-24T19:02:33Z
2023-02-23T01:38:38Z
RLS/DOC: Adding release notes for 1.5.4
diff --git a/doc/source/whatsnew/index.rst b/doc/source/whatsnew/index.rst index 821f77dbba3e2..69f845096ea24 100644 --- a/doc/source/whatsnew/index.rst +++ b/doc/source/whatsnew/index.rst @@ -24,6 +24,7 @@ Version 1.5 .. toctree:: :maxdepth: 2 + v1.5.4 v1.5.3 v1.5.2 v1.5.1 diff --git a/doc/source/whatsnew/v1.5.2.rst b/doc/source/whatsnew/v1.5.2.rst index 6397016d827f2..efc8ca6afa342 100644 --- a/doc/source/whatsnew/v1.5.2.rst +++ b/doc/source/whatsnew/v1.5.2.rst @@ -43,4 +43,4 @@ Other Contributors ~~~~~~~~~~~~ -.. contributors:: v1.5.1..v1.5.2|HEAD +.. contributors:: v1.5.1..v1.5.2 diff --git a/doc/source/whatsnew/v1.5.3.rst b/doc/source/whatsnew/v1.5.3.rst index 67c2347fe53ac..5358d45048af8 100644 --- a/doc/source/whatsnew/v1.5.3.rst +++ b/doc/source/whatsnew/v1.5.3.rst @@ -55,4 +55,4 @@ Other Contributors ~~~~~~~~~~~~ -.. contributors:: v1.5.2..v1.5.3|HEAD +.. contributors:: v1.5.2..v1.5.3 diff --git a/doc/source/whatsnew/v1.5.4.rst b/doc/source/whatsnew/v1.5.4.rst new file mode 100644 index 0000000000000..0d91424eb65ac --- /dev/null +++ b/doc/source/whatsnew/v1.5.4.rst @@ -0,0 +1,38 @@ +.. _whatsnew_154: + +What's new in 1.5.4 (March XX, 2023) +-------------------------------------- + +These are the changes in pandas 1.5.4. See :ref:`release` for a full changelog +including other versions of pandas. + +{{ header }} + +.. --------------------------------------------------------------------------- +.. _whatsnew_154.regressions: + +Fixed regressions +~~~~~~~~~~~~~~~~~ +- + +.. --------------------------------------------------------------------------- +.. _whatsnew_154.bug_fixes: + +Bug fixes +~~~~~~~~~ +- + +.. --------------------------------------------------------------------------- +.. _whatsnew_154.other: + +Other +~~~~~ +- + +.. --------------------------------------------------------------------------- +.. _whatsnew_154.contributors: + +Contributors +~~~~~~~~~~~~ + +.. contributors:: v1.5.3..v1.5.4|HEAD
Adding release notes for 1.5.4.
https://api.github.com/repos/pandas-dev/pandas/pulls/50855
2023-01-19T04:08:53Z
2023-01-19T08:49:51Z
2023-01-19T08:49:51Z
2023-01-19T08:49:58Z
DOC: Fix typo in sed command in the release instructions
diff --git a/doc/source/development/maintaining.rst b/doc/source/development/maintaining.rst index 6e9f622e18eea..e55ab0d124c45 100644 --- a/doc/source/development/maintaining.rst +++ b/doc/source/development/maintaining.rst @@ -458,8 +458,8 @@ which will be triggered when the tag is pushed. git checkout master git pull --ff-only upstream master git checkout -B RLS-<version> - sed -i 's/BUILD_COMMIT: "v.*/BUILD_COMMIT: "'<version>'"/' azure/windows.yml azure/posix.yml - sed -i 's/BUILD_COMMIT="v.*/BUILD_COMMIT="'<version>'"/' .travis.yml + sed -i 's/BUILD_COMMIT: "v.*/BUILD_COMMIT: "'v<version>'"/' azure/windows.yml azure/posix.yml + sed -i 's/BUILD_COMMIT="v.*/BUILD_COMMIT="'v<version>'"/' .travis.yml git commit -am "RLS <version>" git push -u origin RLS-<version>
The `BUILD_COMMIT` in the MacPython CI is the tag name (e.g. `v1.5.3`) and not the version name (e.g. `1.5.3`). The release instructions have it wrong, fixing it here.
https://api.github.com/repos/pandas-dev/pandas/pulls/50854
2023-01-19T04:01:42Z
2023-01-19T17:45:34Z
2023-01-19T17:45:33Z
2023-01-19T17:45:41Z
REF: Use * syntax to make reindex kwargs keyword only
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index dc05745c8c0e5..7e8b640a4e0f3 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -736,7 +736,7 @@ Removal of prior version deprecations/changes - Disallow passing non-keyword arguments to :meth:`DataFrame.replace`, :meth:`Series.replace` except for ``to_replace`` and ``value`` (:issue:`47587`) - Disallow passing non-keyword arguments to :meth:`DataFrame.sort_values` except for ``by`` (:issue:`41505`) - Disallow passing non-keyword arguments to :meth:`Series.sort_values` (:issue:`41505`) -- Disallow passing 2 non-keyword arguments to :meth:`DataFrame.reindex` (:issue:`17966`) +- Disallow passing non-keyword arguments to :meth:`DataFrame.reindex` except for ``labels`` (:issue:`17966`) - Disallow :meth:`Index.reindex` with non-unique :class:`Index` objects (:issue:`42568`) - Disallowed constructing :class:`Categorical` with scalar ``data`` (:issue:`38433`) - Disallowed constructing :class:`CategoricalIndex` without passing ``data`` (:issue:`38944`) diff --git a/pandas/conftest.py b/pandas/conftest.py index 2e9638036eec5..92624ca54e5db 100644 --- a/pandas/conftest.py +++ b/pandas/conftest.py @@ -149,8 +149,6 @@ def pytest_collection_modifyitems(items, config) -> None: ignored_doctest_warnings = [ # Docstring divides by zero to show behavior difference ("missing.mask_zero_div_zero", "divide by zero encountered"), - # Docstring demonstrates the call raises a warning - ("_validators.validate_axis_style_args", "Use named arguments"), ] for item in items: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 9efc07628cccd..f8fdcbdfd34d4 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -101,12 +101,10 @@ Appender, Substitution, doc, - rewrite_axis_style_signature, ) from pandas.util._exceptions import find_stack_level from pandas.util._validators import ( validate_ascending, - validate_axis_style_args, validate_bool_kwarg, validate_percentile, ) @@ -260,11 +258,18 @@ levels and/or column labels. - if `axis` is 1 or `'columns'` then `by` may contain column levels and/or index labels.""", - "optional_labels": """labels : array-like, optional - New labels / index to conform the axis specified by 'axis' to.""", - "optional_axis": """axis : int or str, optional - Axis to target. Can be either the axis name ('index', 'columns') - or number (0, 1).""", + "optional_reindex": """ +labels : array-like, optional + New labels / index to conform the axis specified by 'axis' to. +index : array-like, optional + New labels for the index. Preferably an Index object to avoid + duplicating data. +columns : array-like, optional + New labels for the columns. Preferably an Index object to avoid + duplicating data. +axis : int or str, optional + Axis to target. Can be either the axis name ('index', 'columns') + or number (0, 1).""", "replace_iloc": """ This differs from updating with ``.loc`` or ``.iloc``, which require you to specify a location to update with some value.""", @@ -4990,26 +4995,37 @@ def set_axis( ) -> DataFrame: return super().set_axis(labels, axis=axis, copy=copy) - @Substitution(**_shared_doc_kwargs) - @Appender(NDFrame.reindex.__doc__) - @rewrite_axis_style_signature( - "labels", - [ - ("method", None), - ("copy", None), - ("level", None), - ("fill_value", np.nan), - ("limit", None), - ("tolerance", None), - ], + @doc( + NDFrame.reindex, + klass=_shared_doc_kwargs["klass"], + optional_reindex=_shared_doc_kwargs["optional_reindex"], ) - def reindex(self, *args, **kwargs) -> DataFrame: - axes = validate_axis_style_args(self, args, kwargs, "labels", "reindex") - kwargs.update(axes) - # Pop these, since the values are in `kwargs` under different names - kwargs.pop("axis", None) - kwargs.pop("labels", None) - return super().reindex(**kwargs) + def reindex( # type: ignore[override] + self, + labels=None, + *, + index=None, + columns=None, + axis: Axis | None = None, + method: str | None = None, + copy: bool | None = None, + level: Level | None = None, + fill_value: Scalar | None = np.nan, + limit: int | None = None, + tolerance=None, + ) -> DataFrame: + return super().reindex( + labels=labels, + index=index, + columns=columns, + axis=axis, + method=method, + copy=copy, + level=level, + fill_value=fill_value, + limit=limit, + tolerance=tolerance, + ) @overload def drop( diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 998c57b66509d..c06f6d7c85673 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -69,6 +69,7 @@ NDFrameT, RandomState, Renamer, + Scalar, SortKind, StorageOptions, Suffixes, @@ -5104,11 +5105,21 @@ def sort_index( @doc( klass=_shared_doc_kwargs["klass"], - axes=_shared_doc_kwargs["axes"], - optional_labels="", - optional_axis="", + optional_reindex="", ) - def reindex(self: NDFrameT, *args, **kwargs) -> NDFrameT: + def reindex( + self: NDFrameT, + labels=None, + index=None, + columns=None, + axis: Axis | None = None, + method: str | None = None, + copy: bool_t | None = None, + level: Level | None = None, + fill_value: Scalar | None = np.nan, + limit: int | None = None, + tolerance=None, + ) -> NDFrameT: """ Conform {klass} to new index with optional filling logic. @@ -5118,11 +5129,7 @@ def reindex(self: NDFrameT, *args, **kwargs) -> NDFrameT: Parameters ---------- - {optional_labels} - {axes} : array-like, optional - New labels / index to conform to, should be specified using - keywords. Preferably an Index object to avoid duplicating data. - {optional_axis} + {optional_reindex} method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a @@ -5311,31 +5318,34 @@ def reindex(self: NDFrameT, *args, **kwargs) -> NDFrameT: # TODO: Decide if we care about having different examples for different # kinds - # construct the args - axes, kwargs = self._construct_axes_from_arguments(args, kwargs) - method = clean_reindex_fill_method(kwargs.pop("method", None)) - level = kwargs.pop("level", None) - copy = kwargs.pop("copy", None) - limit = kwargs.pop("limit", None) - tolerance = kwargs.pop("tolerance", None) - fill_value = kwargs.pop("fill_value", None) - - # Series.reindex doesn't use / need the axis kwarg - # We pop and ignore it here, to make writing Series/Frame generic code - # easier - kwargs.pop("axis", None) - - if kwargs: - raise TypeError( - "reindex() got an unexpected keyword " - f'argument "{list(kwargs.keys())[0]}"' - ) + if index is not None and columns is not None and labels is not None: + raise TypeError("Cannot specify all of 'labels', 'index', 'columns'.") + elif index is not None or columns is not None: + if axis is not None: + raise TypeError( + "Cannot specify both 'axis' and any of 'index' or 'columns'" + ) + if labels is not None: + if index is not None: + columns = labels + else: + index = labels + else: + if axis and self._get_axis_number(axis) == 1: + columns = labels + else: + index = labels + axes: dict[Literal["index", "columns"], Any] = { + "index": index, + "columns": columns, + } + method = clean_reindex_fill_method(method) # if all axes that are requested to reindex are equal, then only copy # if indicated must have index names equal here as well as values if all( - self._get_axis(axis).identical(ax) - for axis, ax in axes.items() + self._get_axis(axis_name).identical(ax) + for axis_name, ax in axes.items() if ax is not None ): return self.copy(deep=copy) @@ -5517,7 +5527,7 @@ def filter( name = self._get_axis_name(axis) # error: Keywords must be strings return self.reindex( # type: ignore[misc] - **{name: [r for r in items if r in labels]} + **{name: [r for r in items if r in labels]} # type: ignore[arg-type] ) elif like: diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index c15948ce877a8..c9bfa18e48a60 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -4098,7 +4098,7 @@ def _reindex_output( "copy": False, "fill_value": fill_value, } - return output.reindex(**d) + return output.reindex(**d) # type: ignore[arg-type] # GH 13204 # Here, the categorical in-axis groupers, which need to be fully diff --git a/pandas/core/series.py b/pandas/core/series.py index c6ba217042353..3d5b44ba52594 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -58,6 +58,7 @@ NaPosition, QuantileInterpolation, Renamer, + Scalar, SingleManager, SortKind, StorageOptions, @@ -190,8 +191,12 @@ "duplicated": "Series", "optional_by": "", "optional_mapper": "", - "optional_labels": "", - "optional_axis": "", + "optional_reindex": """ +index : array-like, optional + New labels for the index. Preferably an Index object to avoid + duplicating data. +axis : int or str, optional + Unused.""", "replace_iloc": """ This differs from updating with ``.loc`` or ``.iloc``, which require you to specify a location to update with some value.""", @@ -4862,21 +4867,29 @@ def set_axis( @doc( NDFrame.reindex, # type: ignore[has-type] klass=_shared_doc_kwargs["klass"], - axes=_shared_doc_kwargs["axes"], - optional_labels=_shared_doc_kwargs["optional_labels"], - optional_axis=_shared_doc_kwargs["optional_axis"], + optional_reindex=_shared_doc_kwargs["optional_reindex"], ) - def reindex(self, *args, **kwargs) -> Series: - if len(args) > 1: - raise TypeError("Only one positional argument ('index') is allowed") - if args: - (index,) = args - if "index" in kwargs: - raise TypeError( - "'index' passed as both positional and keyword argument" - ) - kwargs.update({"index": index}) - return super().reindex(**kwargs) + def reindex( # type: ignore[override] + self, + index=None, + *, + axis: Axis | None = None, + method: str | None = None, + copy: bool | None = None, + level: Level | None = None, + fill_value: Scalar | None = None, + limit: int | None = None, + tolerance=None, + ) -> Series: + return super().reindex( + index=index, + method=method, + copy=copy, + level=level, + fill_value=fill_value, + limit=limit, + tolerance=tolerance, + ) @overload def drop( diff --git a/pandas/tests/frame/methods/test_reindex.py b/pandas/tests/frame/methods/test_reindex.py index a627d0fbb4c7a..f455213bd436b 100644 --- a/pandas/tests/frame/methods/test_reindex.py +++ b/pandas/tests/frame/methods/test_reindex.py @@ -841,17 +841,18 @@ def test_reindex_positional_raises(self): # https://github.com/pandas-dev/pandas/issues/12392 # Enforced in 2.0 df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) - with pytest.raises(TypeError, match=r".* is ambiguous."): + msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given" + with pytest.raises(TypeError, match=msg): df.reindex([0, 1], ["A", "B", "C"]) def test_reindex_axis_style_raises(self): # https://github.com/pandas-dev/pandas/issues/12392 df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) with pytest.raises(TypeError, match="Cannot specify both 'axis'"): - df.reindex([0, 1], ["A"], axis=1) + df.reindex([0, 1], columns=["A"], axis=1) with pytest.raises(TypeError, match="Cannot specify both 'axis'"): - df.reindex([0, 1], ["A"], axis="index") + df.reindex([0, 1], columns=["A"], axis="index") with pytest.raises(TypeError, match="Cannot specify both 'axis'"): df.reindex(index=[0, 1], axis="index") @@ -866,7 +867,7 @@ def test_reindex_axis_style_raises(self): df.reindex(index=[0, 1], columns=[0, 1], axis="columns") with pytest.raises(TypeError, match="Cannot specify all"): - df.reindex([0, 1], [0], ["A"]) + df.reindex(labels=[0, 1], index=[0], columns=["A"]) # Mixing styles with pytest.raises(TypeError, match="Cannot specify both 'axis'"): diff --git a/pandas/tests/series/methods/test_reindex.py b/pandas/tests/series/methods/test_reindex.py index b00858d2779bc..2c427399c9cd5 100644 --- a/pandas/tests/series/methods/test_reindex.py +++ b/pandas/tests/series/methods/test_reindex.py @@ -369,16 +369,15 @@ def test_reindex_periodindex_with_object(p_values, o_values, values, expected_va def test_reindex_too_many_args(): # GH 40980 ser = Series([1, 2]) - with pytest.raises( - TypeError, match=r"Only one positional argument \('index'\) is allowed" - ): + msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given" + with pytest.raises(TypeError, match=msg): ser.reindex([2, 3], False) def test_reindex_double_index(): # GH 40980 ser = Series([1, 2]) - msg = r"'index' passed as both positional and keyword argument" + msg = r"reindex\(\) got multiple values for argument 'index'" with pytest.raises(TypeError, match=msg): ser.reindex([2, 3], index=[3, 4]) diff --git a/pandas/util/_validators.py b/pandas/util/_validators.py index 943d0ef3c1332..b60169f8364da 100644 --- a/pandas/util/_validators.py +++ b/pandas/util/_validators.py @@ -5,7 +5,6 @@ from __future__ import annotations from typing import ( - Any, Iterable, Sequence, TypeVar, @@ -262,94 +261,6 @@ def validate_bool_kwarg( return value -def validate_axis_style_args( - data, args, kwargs, arg_name, method_name -) -> dict[str, Any]: - """ - Argument handler for mixed index, columns / axis functions - - In an attempt to handle both `.method(index, columns)`, and - `.method(arg, axis=.)`, we have to do some bad things to argument - parsing. This translates all arguments to `{index=., columns=.}` style. - - Parameters - ---------- - data : DataFrame - args : tuple - All positional arguments from the user - kwargs : dict - All keyword arguments from the user - arg_name, method_name : str - Used for better error messages - - Returns - ------- - kwargs : dict - A dictionary of keyword arguments. Doesn't modify ``kwargs`` - inplace, so update them with the return value here. - - Examples - -------- - >>> df = pd.DataFrame(range(2)) - >>> validate_axis_style_args(df, (str.upper,), {'columns': id}, - ... 'mapper', 'rename') - {'columns': <built-in function id>, 'index': <method 'upper' of 'str' objects>} - """ - # TODO: Change to keyword-only args and remove all this - - out = {} - # Goal: fill 'out' with index/columns-style arguments - # like out = {'index': foo, 'columns': bar} - - # Start by validating for consistency - if "axis" in kwargs and any(x in kwargs for x in data._AXIS_TO_AXIS_NUMBER): - msg = "Cannot specify both 'axis' and any of 'index' or 'columns'." - raise TypeError(msg) - - # First fill with explicit values provided by the user... - if arg_name in kwargs: - if args: - msg = f"{method_name} got multiple values for argument '{arg_name}'" - raise TypeError(msg) - - axis = data._get_axis_name(kwargs.get("axis", 0)) - out[axis] = kwargs[arg_name] - - # More user-provided arguments, now from kwargs - for k, v in kwargs.items(): - try: - ax = data._get_axis_name(k) - except ValueError: - pass - else: - out[ax] = v - - # All user-provided kwargs have been handled now. - # Now we supplement with positional arguments, emitting warnings - # when there's ambiguity and raising when there's conflicts - - if len(args) == 0: - pass # It's up to the function to decide if this is valid - elif len(args) == 1: - axis = data._get_axis_name(kwargs.get("axis", 0)) - out[axis] = args[0] - elif len(args) == 2: - if "axis" in kwargs: - # Unambiguously wrong - msg = "Cannot specify both 'axis' and any of 'index' or 'columns'" - raise TypeError(msg) - - msg = ( - f"'.{method_name}(a, b)' is ambiguous. Use named keyword arguments" - "for 'index' or 'columns'." - ) - raise TypeError(msg) - else: - msg = f"Cannot specify all of '{arg_name}', 'index', 'columns'." - raise TypeError(msg) - return out - - def validate_fillna_kwargs(value, method, validate_scalar_dict_value: bool = True): """ Validate the keyword arguments to 'fillna'.
After enforcing #17966, this makes `DataFrame.reindex` effectively keyword only except for `labels`, so removed ad-hoc logic to enforce that. `Series.reindex` also has similar ad-hoc logic which can be removed. Also did some docstring cleanup for both functions.
https://api.github.com/repos/pandas-dev/pandas/pulls/50853
2023-01-19T02:18:10Z
2023-01-30T20:07:45Z
2023-01-30T20:07:45Z
2023-01-30T20:07:50Z
REF: consolidate cast_from_unit checks
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx index df6204d61f983..5f7fb05876b35 100644 --- a/pandas/_libs/tslib.pyx +++ b/pandas/_libs/tslib.pyx @@ -220,6 +220,19 @@ def format_array_from_datetime( return result +cdef int64_t _wrapped_cast_from_unit(object val, str unit) except? -1: + """ + Call cast_from_unit and re-raise OverflowError as OutOfBoundsDatetime + """ + # See also timedeltas._maybe_cast_from_unit + try: + return cast_from_unit(val, unit) + except OverflowError as err: + raise OutOfBoundsDatetime( + f"cannot convert input {val} with the unit '{unit}'" + ) from err + + def array_with_unit_to_datetime( ndarray[object] values, str unit, @@ -261,13 +274,10 @@ def array_with_unit_to_datetime( bint is_raise = errors=="raise" ndarray[int64_t] iresult object tz = None - bint is_ym float fval assert is_ignore or is_coerce or is_raise - is_ym = unit in "YM" - if unit == "ns": result, tz = array_to_datetime( values.astype(object, copy=False), @@ -292,19 +302,7 @@ def array_with_unit_to_datetime( if val != val or val == NPY_NAT: iresult[i] = NPY_NAT else: - if is_ym and is_float_object(val) and not val.is_integer(): - # Analogous to GH#47266 for Timestamp - raise ValueError( - f"Conversion of non-round float with unit={unit} " - "is ambiguous" - ) - - try: - iresult[i] = cast_from_unit(val, unit) - except OverflowError: - raise OutOfBoundsDatetime( - f"cannot convert input {val} with the unit '{unit}'" - ) + iresult[i] = _wrapped_cast_from_unit(val, unit) elif isinstance(val, str): if len(val) == 0 or val in nat_strings: @@ -319,23 +317,7 @@ def array_with_unit_to_datetime( f"non convertible value {val} with the unit '{unit}'" ) - if is_ym and not fval.is_integer(): - # Analogous to GH#47266 for Timestamp - raise ValueError( - f"Conversion of non-round float with unit={unit} " - "is ambiguous" - ) - - try: - iresult[i] = cast_from_unit(fval, unit) - except ValueError: - raise ValueError( - f"non convertible value {val} with the unit '{unit}'" - ) - except OverflowError: - raise OutOfBoundsDatetime( - f"cannot convert input {val} with the unit '{unit}'" - ) + iresult[i] = _wrapped_cast_from_unit(fval, unit) else: # TODO: makes more sense as TypeError, but that would be an diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx index 8cdad5c79bc1b..d862b5bb606cc 100644 --- a/pandas/_libs/tslibs/conversion.pyx +++ b/pandas/_libs/tslibs/conversion.pyx @@ -108,6 +108,15 @@ cdef int64_t cast_from_unit(object ts, str unit) except? -1: if ts is None: return m + if unit in ["Y", "M"] and is_float_object(ts) and not ts.is_integer(): + # GH#47267 it is clear that 2 "M" corresponds to 1970-02-01, + # but not clear what 2.5 "M" corresponds to, so we will + # disallow that case. + raise ValueError( + f"Conversion of non-round float with unit={unit} " + "is ambiguous" + ) + # cast the unit, multiply base/frace separately # to avoid precision issues from float -> int base = <int64_t>ts @@ -287,13 +296,6 @@ cdef _TSObject convert_to_tsobject(object ts, tzinfo tz, str unit, # GH#47266 Avoid cast_from_unit, which would give weird results # e.g. with "Y" and 150.0 we'd get 2120-01-01 09:00:00 return convert_to_tsobject(int(ts), tz, unit, False, False) - else: - # GH#47267 it is clear that 2 "M" corresponds to 1970-02-01, - # but not clear what 2.5 "M" corresponds to, so we will - # disallow that case. - raise ValueError( - f"Conversion of non-round float with unit={unit} is ambiguous." - ) ts = cast_from_unit(ts, unit) obj.value = ts
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50852
2023-01-19T01:59:51Z
2023-01-19T17:50:01Z
2023-01-19T17:50:01Z
2023-01-19T17:50:55Z
REF: tighter typing in parsing.pyx
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx index 5f7fb05876b35..0575ac69ca452 100644 --- a/pandas/_libs/tslib.pyx +++ b/pandas/_libs/tslib.pyx @@ -273,7 +273,7 @@ def array_with_unit_to_datetime( bint is_coerce = errors=="coerce" bint is_raise = errors=="raise" ndarray[int64_t] iresult - object tz = None + tzinfo tz = None float fval assert is_ignore or is_coerce or is_raise @@ -346,7 +346,7 @@ cdef _array_with_unit_to_datetime_object_fallback(ndarray[object] values, str un cdef: Py_ssize_t i, n = len(values) ndarray[object] oresult - object tz = None + tzinfo tz = None # TODO: fix subtle differences between this and no-unit code oresult = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0) diff --git a/pandas/_libs/tslibs/dtypes.pxd b/pandas/_libs/tslibs/dtypes.pxd index 3e3f206685d37..c0b0db1336d14 100644 --- a/pandas/_libs/tslibs/dtypes.pxd +++ b/pandas/_libs/tslibs/dtypes.pxd @@ -11,6 +11,7 @@ cpdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1 cpdef NPY_DATETIMEUNIT get_supported_reso(NPY_DATETIMEUNIT reso) cdef dict attrname_to_abbrevs +cdef dict npy_unit_to_attrname cdef enum c_FreqGroup: # Mirrors FreqGroup in the .pyx file diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx index 2df5349f45272..928f620b5e7c6 100644 --- a/pandas/_libs/tslibs/dtypes.pyx +++ b/pandas/_libs/tslibs/dtypes.pyx @@ -423,3 +423,15 @@ cdef dict _reso_str_map = { } cdef dict _str_reso_map = {v: k for k, v in _reso_str_map.items()} + +cdef dict npy_unit_to_attrname = { + NPY_DATETIMEUNIT.NPY_FR_Y: "year", + NPY_DATETIMEUNIT.NPY_FR_M: "month", + NPY_DATETIMEUNIT.NPY_FR_D: "day", + NPY_DATETIMEUNIT.NPY_FR_h: "hour", + NPY_DATETIMEUNIT.NPY_FR_m: "minute", + NPY_DATETIMEUNIT.NPY_FR_s: "second", + NPY_DATETIMEUNIT.NPY_FR_ms: "millisecond", + NPY_DATETIMEUNIT.NPY_FR_us: "microsecond", + NPY_DATETIMEUNIT.NPY_FR_ns: "nanosecond", +} diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx index 98667436915f3..8a22f8d45dac9 100644 --- a/pandas/_libs/tslibs/parsing.pyx +++ b/pandas/_libs/tslibs/parsing.pyx @@ -12,7 +12,10 @@ from cpython.datetime cimport ( datetime, datetime_new, import_datetime, + timedelta, + tzinfo, ) +from datetime import timezone from cpython.object cimport PyObject_Str from cython cimport Py_ssize_t from libc.string cimport strchr @@ -49,6 +52,7 @@ from dateutil.tz import ( from pandas._config import get_option from pandas._libs.tslibs.ccalendar cimport c_MONTH_NUMBERS +from pandas._libs.tslibs.dtypes cimport npy_unit_to_attrname from pandas._libs.tslibs.nattype cimport ( c_NaT as NaT, c_nat_strings as nat_strings, @@ -120,7 +124,9 @@ cdef int _parse_4digit(const char* s): return result -cdef object _parse_delimited_date(str date_string, bint dayfirst): +cdef datetime _parse_delimited_date( + str date_string, bint dayfirst, NPY_DATETIMEUNIT* creso +): """ Parse special cases of dates: MM/DD/YYYY, DD/MM/YYYY, MM/YYYY. @@ -138,12 +144,12 @@ cdef object _parse_delimited_date(str date_string, bint dayfirst): ---------- date_string : str dayfirst : bool + creso : NPY_DATETIMEUNIT* + For specifying identified resolution. Returns: -------- datetime or None - str or None - Describing resolution of the parsed string. """ cdef: const char* buf @@ -157,45 +163,45 @@ cdef object _parse_delimited_date(str date_string, bint dayfirst): month = _parse_2digit(buf) day = _parse_2digit(buf + 3) year = _parse_4digit(buf + 6) - reso = "day" + creso[0] = NPY_DATETIMEUNIT.NPY_FR_D can_swap = 1 elif length == 9 and _is_delimiter(buf[1]) and _is_delimiter(buf[4]): # parsing M?DD?YYYY and D?MM?YYYY dates month = _parse_1digit(buf) day = _parse_2digit(buf + 2) year = _parse_4digit(buf + 5) - reso = "day" + creso[0] = NPY_DATETIMEUNIT.NPY_FR_D can_swap = 1 elif length == 9 and _is_delimiter(buf[2]) and _is_delimiter(buf[4]): # parsing MM?D?YYYY and DD?M?YYYY dates month = _parse_2digit(buf) day = _parse_1digit(buf + 3) year = _parse_4digit(buf + 5) - reso = "day" + creso[0] = NPY_DATETIMEUNIT.NPY_FR_D can_swap = 1 elif length == 8 and _is_delimiter(buf[1]) and _is_delimiter(buf[3]): # parsing M?D?YYYY and D?M?YYYY dates month = _parse_1digit(buf) day = _parse_1digit(buf + 2) year = _parse_4digit(buf + 4) - reso = "day" + creso[0] = NPY_DATETIMEUNIT.NPY_FR_D can_swap = 1 elif length == 7 and _is_delimiter(buf[2]): # parsing MM?YYYY dates if buf[2] == b".": # we cannot reliably tell whether e.g. 10.2010 is a float # or a date, thus we refuse to parse it here - return None, None + return None month = _parse_2digit(buf) year = _parse_4digit(buf + 3) - reso = "month" + creso[0] = NPY_DATETIMEUNIT.NPY_FR_M else: - return None, None + return None if month < 0 or day < 0 or year < 1000: # some part is not an integer, so # date_string can't be converted to date, above format - return None, None + return None if 1 <= month <= MAX_DAYS_IN_MONTH and 1 <= day <= MAX_DAYS_IN_MONTH \ and (month <= MAX_MONTH or day <= MAX_MONTH): @@ -203,7 +209,7 @@ cdef object _parse_delimited_date(str date_string, bint dayfirst): day, month = month, day # In Python <= 3.6.0 there is no range checking for invalid dates # in C api, thus we call faster C version for 3.6.1 or newer - return datetime_new(year, month, day, 0, 0, 0, 0, None), reso + return datetime_new(year, month, day, 0, 0, 0, 0, None) raise DateParseError(f"Invalid date specified ({month}/{day})") @@ -264,6 +270,7 @@ def parse_datetime_string( cdef: datetime dt + NPY_DATETIMEUNIT creso if not _does_string_look_like_datetime(date_string): raise ValueError(f'Given date string "{date_string}" not likely a datetime') @@ -274,7 +281,7 @@ def parse_datetime_string( yearfirst=yearfirst) return dt - dt, _ = _parse_delimited_date(date_string, dayfirst) + dt = _parse_delimited_date(date_string, dayfirst, &creso) if dt is not None: return dt @@ -360,18 +367,19 @@ def parse_datetime_string_with_reso( bint string_to_dts_failed npy_datetimestruct dts NPY_DATETIMEUNIT out_bestunit - int out_local + int out_local = 0 int out_tzoffset + tzinfo tz if not _does_string_look_like_datetime(date_string): raise ValueError(f'Given date string "{date_string}" not likely a datetime') - parsed, reso = _parse_delimited_date(date_string, dayfirst) + parsed = _parse_delimited_date(date_string, dayfirst, &out_bestunit) if parsed is not None: + reso = npy_unit_to_attrname[out_bestunit] return parsed, reso # Try iso8601 first, as it handles nanoseconds - # TODO: does this render some/all of parse_delimited_date redundant? string_to_dts_failed = string_to_dts( date_string, &dts, &out_bestunit, &out_local, &out_tzoffset, False @@ -381,31 +389,25 @@ def parse_datetime_string_with_reso( NPY_DATETIMEUNIT.NPY_FR_ps, NPY_DATETIMEUNIT.NPY_FR_fs, NPY_DATETIMEUNIT.NPY_FR_as} - if out_bestunit in timestamp_units or out_local: - # TODO: the not-out_local case we could do without Timestamp; - # avoid circular import + if out_bestunit in timestamp_units: + # TODO: avoid circular import from pandas import Timestamp parsed = Timestamp(date_string) else: - parsed = datetime( - dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us + if out_local: + tz = timezone(timedelta(minutes=out_tzoffset)) + else: + tz = None + parsed = datetime_new( + dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz ) # Match Timestamp and drop picoseconds, femtoseconds, attoseconds # The new resolution will just be nano # GH 50417 if out_bestunit in timestamp_units: out_bestunit = NPY_DATETIMEUNIT.NPY_FR_ns - reso = { - NPY_DATETIMEUNIT.NPY_FR_Y: "year", - NPY_DATETIMEUNIT.NPY_FR_M: "month", - NPY_DATETIMEUNIT.NPY_FR_D: "day", - NPY_DATETIMEUNIT.NPY_FR_h: "hour", - NPY_DATETIMEUNIT.NPY_FR_m: "minute", - NPY_DATETIMEUNIT.NPY_FR_s: "second", - NPY_DATETIMEUNIT.NPY_FR_ms: "millisecond", - NPY_DATETIMEUNIT.NPY_FR_us: "microsecond", - NPY_DATETIMEUNIT.NPY_FR_ns: "nanosecond", - }[out_bestunit] + + reso = npy_unit_to_attrname[out_bestunit] return parsed, reso try:
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50851
2023-01-19T01:57:16Z
2023-01-20T18:25:46Z
2023-01-20T18:25:46Z
2023-01-20T18:27:32Z
PERF: Improve performance for array equal fast
diff --git a/pandas/_libs/lib.pyi b/pandas/_libs/lib.pyi index 2439082bf7413..72b46d9e30684 100644 --- a/pandas/_libs/lib.pyi +++ b/pandas/_libs/lib.pyi @@ -240,6 +240,6 @@ def get_reverse_indexer( ) -> npt.NDArray[np.intp]: ... def is_bool_list(obj: list) -> bool: ... def dtypes_all_equal(types: list[DtypeObj]) -> bool: ... -def array_equal_fast( - left: np.ndarray, right: np.ndarray # np.ndarray[np.int64, ndim=1] +def is_range_indexer( + left: np.ndarray, n: int # np.ndarray[np.int64, ndim=1] ) -> bool: ... diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx index 176307ef27cff..16d5bbaad9de9 100644 --- a/pandas/_libs/lib.pyx +++ b/pandas/_libs/lib.pyx @@ -650,22 +650,20 @@ ctypedef fused int6432_t: @cython.wraparound(False) @cython.boundscheck(False) -def array_equal_fast( - ndarray[int6432_t, ndim=1] left, ndarray[int6432_t, ndim=1] right, -) -> bool: +def is_range_indexer(ndarray[int6432_t, ndim=1] left, int n) -> bool: """ Perform an element by element comparison on 1-d integer arrays, meant for indexer comparisons """ cdef: - Py_ssize_t i, n = left.size + Py_ssize_t i - if left.size != right.size: + if left.size != n: return False for i in range(n): - if left[i] != right[i]: + if left[i] != i: return False return True diff --git a/pandas/core/frame.py b/pandas/core/frame.py index eb0eb34dbefc4..3b122eaa814e5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -48,7 +48,7 @@ from pandas._libs.hashtable import duplicated from pandas._libs.lib import ( NoDefault, - array_equal_fast, + is_range_indexer, no_default, ) from pandas._typing import ( @@ -6724,7 +6724,7 @@ def sort_values( else: return self.copy(deep=None) - if array_equal_fast(indexer, np.arange(0, len(indexer), dtype=indexer.dtype)): + if is_range_indexer(indexer, len(indexer)): if inplace: return self._update_inplace(self) else: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index ab9b76fbdf712..a91c46d7d06c4 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -35,7 +35,7 @@ ) from pandas._libs import lib -from pandas._libs.lib import array_equal_fast +from pandas._libs.lib import is_range_indexer from pandas._libs.tslibs import ( Period, Tick, @@ -3780,10 +3780,7 @@ def _take( axis == 0 and indices.ndim == 1 and using_copy_on_write() - and array_equal_fast( - indices, - np.arange(0, len(self), dtype=np.intp), - ) + and is_range_indexer(indices, len(self)) ): return self.copy(deep=None) diff --git a/pandas/core/series.py b/pandas/core/series.py index 91f7095e59db5..2849b009cf72c 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -33,7 +33,7 @@ reshape, ) from pandas._libs.lib import ( - array_equal_fast, + is_range_indexer, no_default, ) from pandas._typing import ( @@ -891,7 +891,7 @@ def take(self, indices, axis: Axis = 0, **kwargs) -> Series: if ( indices.ndim == 1 and using_copy_on_write() - and array_equal_fast(indices, np.arange(0, len(self), dtype=indices.dtype)) + and is_range_indexer(indices, len(self)) ): return self.copy(deep=None) @@ -3566,9 +3566,7 @@ def sort_values( values_to_sort = ensure_key_mapped(self, key)._values if key else self._values sorted_index = nargsort(values_to_sort, kind, bool(ascending), na_position) - if array_equal_fast( - sorted_index, np.arange(0, len(sorted_index), dtype=sorted_index.dtype) - ): + if is_range_indexer(sorted_index, len(sorted_index)): if inplace: return self._update_inplace(self) return self.copy(deep=None) diff --git a/pandas/tests/libs/test_lib.py b/pandas/tests/libs/test_lib.py index e352250dc748d..302dc21ec997c 100644 --- a/pandas/tests/libs/test_lib.py +++ b/pandas/tests/libs/test_lib.py @@ -244,25 +244,22 @@ def test_get_reverse_indexer(self): tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize("dtype", ["int64", "int32"]) - def test_array_equal_fast(self, dtype): + def test_is_range_indexer(self, dtype): # GH#50592 - left = np.arange(1, 100, dtype=dtype) - right = np.arange(1, 100, dtype=dtype) - assert lib.array_equal_fast(left, right) + left = np.arange(0, 100, dtype=dtype) + assert lib.is_range_indexer(left, 100) @pytest.mark.parametrize("dtype", ["int64", "int32"]) - def test_array_equal_fast_not_equal(self, dtype): + def test_is_range_indexer_not_equal(self, dtype): # GH#50592 left = np.array([1, 2], dtype=dtype) - right = np.array([2, 2], dtype=dtype) - assert not lib.array_equal_fast(left, right) + assert not lib.is_range_indexer(left, 2) @pytest.mark.parametrize("dtype", ["int64", "int32"]) - def test_array_equal_fast_not_equal_shape(self, dtype): + def test_is_range_indexer_not_equal_shape(self, dtype): # GH#50592 - left = np.array([1, 2, 3], dtype=dtype) - right = np.array([2, 2], dtype=dtype) - assert not lib.array_equal_fast(left, right) + left = np.array([0, 1, 2], dtype=dtype) + assert not lib.is_range_indexer(left, 2) def test_cache_readonly_preserve_docstrings():
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. gives us a 50% boost if everything is equal and reduces it to nanoseconds if the first non-equal element is within the first couple elements compared to 500 microseconds
https://api.github.com/repos/pandas-dev/pandas/pulls/50850
2023-01-18T23:18:25Z
2023-01-20T17:03:03Z
2023-01-20T17:03:02Z
2023-01-20T17:03:06Z
ENH: Add ea support to get_dummies
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index bbecf3fee01f3..ff21a68d31f92 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -158,6 +158,7 @@ Other enhancements - Added ``name`` parameter to :meth:`IntervalIndex.from_breaks`, :meth:`IntervalIndex.from_arrays` and :meth:`IntervalIndex.from_tuples` (:issue:`48911`) - Improve exception message when using :func:`assert_frame_equal` on a :class:`DataFrame` to include the column that is compared (:issue:`50323`) - Improved error message for :func:`merge_asof` when join-columns were duplicated (:issue:`50102`) +- Added support for extension array dtypes to :func:`get_dummies` (:func:`32430`) - Added :meth:`Index.infer_objects` analogous to :meth:`Series.infer_objects` (:issue:`50034`) - Added ``copy`` parameter to :meth:`Series.infer_objects` and :meth:`DataFrame.infer_objects`, passing ``False`` will avoid making copies for series or columns that are already non-object or where no better dtype can be inferred (:issue:`50096`) - :meth:`DataFrame.plot.hist` now recognizes ``xlabel`` and ``ylabel`` arguments (:issue:`49793`) diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py index 7e45e587ca84a..2aa1a3001fb6b 100644 --- a/pandas/core/reshape/encoding.py +++ b/pandas/core/reshape/encoding.py @@ -16,6 +16,7 @@ is_integer_dtype, is_list_like, is_object_dtype, + pandas_dtype, ) from pandas.core.arrays import SparseArray @@ -240,9 +241,9 @@ def _get_dummies_1d( if dtype is None: dtype = np.dtype(bool) - dtype = np.dtype(dtype) + _dtype = pandas_dtype(dtype) - if is_object_dtype(dtype): + if is_object_dtype(_dtype): raise ValueError("dtype=object is not a valid dtype for get_dummies") def get_empty_frame(data) -> DataFrame: @@ -317,7 +318,12 @@ def get_empty_frame(data) -> DataFrame: else: # take on axis=1 + transpose to ensure ndarray layout is column-major - dummy_mat = np.eye(number_of_cols, dtype=dtype).take(codes, axis=1).T + eye_dtype: NpDtype + if isinstance(_dtype, np.dtype): + eye_dtype = _dtype + else: + eye_dtype = np.bool_ + dummy_mat = np.eye(number_of_cols, dtype=eye_dtype).take(codes, axis=1).T if not dummy_na: # reset NaN GH4446 @@ -327,7 +333,7 @@ def get_empty_frame(data) -> DataFrame: # remove first GH12042 dummy_mat = dummy_mat[:, 1:] dummy_cols = dummy_cols[1:] - return DataFrame(dummy_mat, index=index, columns=dummy_cols) + return DataFrame(dummy_mat, index=index, columns=dummy_cols, dtype=_dtype) def from_dummies( diff --git a/pandas/tests/reshape/test_get_dummies.py b/pandas/tests/reshape/test_get_dummies.py index 8a7985280eff4..ed4da9562aeee 100644 --- a/pandas/tests/reshape/test_get_dummies.py +++ b/pandas/tests/reshape/test_get_dummies.py @@ -657,3 +657,23 @@ def test_get_dummies_with_string_values(self, values): with pytest.raises(TypeError, match=msg): get_dummies(df, columns=values) + + def test_get_dummies_ea_dtype_series(self, any_numeric_ea_dtype): + # GH#32430 + ser = Series(list("abca")) + result = get_dummies(ser, dtype=any_numeric_ea_dtype) + expected = DataFrame( + {"a": [1, 0, 0, 1], "b": [0, 1, 0, 0], "c": [0, 0, 1, 0]}, + dtype=any_numeric_ea_dtype, + ) + tm.assert_frame_equal(result, expected) + + def test_get_dummies_ea_dtype_dataframe(self, any_numeric_ea_dtype): + # GH#32430 + df = DataFrame({"x": list("abca")}) + result = get_dummies(df, dtype=any_numeric_ea_dtype) + expected = DataFrame( + {"x_a": [1, 0, 0, 1], "x_b": [0, 1, 0, 0], "x_c": [0, 0, 1, 0]}, + dtype=any_numeric_ea_dtype, + ) + tm.assert_frame_equal(result, expected)
- [x] closes #32430 (Replace xxxx with the GitHub issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50849
2023-01-18T23:02:43Z
2023-01-23T18:14:11Z
2023-01-23T18:14:11Z
2023-04-18T01:52:35Z
Revert "BUG/PERF: Series.replace with dtype="category""
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index 614832c5acd1b..7054d93457264 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -844,7 +844,6 @@ Performance improvements - Performance improvement in :func:`merge` and :meth:`DataFrame.join` when joining on a sorted :class:`MultiIndex` (:issue:`48504`) - Performance improvement in :func:`to_datetime` when parsing strings with timezone offsets (:issue:`50107`) - Performance improvement in :meth:`DataFrame.loc` and :meth:`Series.loc` for tuple-based indexing of a :class:`MultiIndex` (:issue:`48384`) -- Performance improvement for :meth:`Series.replace` with categorical dtype (:issue:`49404`) - Performance improvement for :meth:`MultiIndex.unique` (:issue:`48335`) - Performance improvement for :func:`concat` with extension array backed indexes (:issue:`49128`, :issue:`49178`) - Reduce memory usage of :meth:`DataFrame.to_pickle`/:meth:`Series.to_pickle` when using BZ2 or LZMA (:issue:`49068`) @@ -887,8 +886,6 @@ Bug fixes Categorical ^^^^^^^^^^^ - Bug in :meth:`Categorical.set_categories` losing dtype information (:issue:`48812`) -- Bug in :meth:`Series.replace` with categorical dtype when ``to_replace`` values overlap with new values (:issue:`49404`) -- Bug in :meth:`Series.replace` with categorical dtype losing nullable dtypes of underlying categories (:issue:`49404`) - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` would reorder categories when used as a grouper (:issue:`48749`) - Bug in :class:`Categorical` constructor when constructing from a :class:`Categorical` object and ``dtype="category"`` losing ordered-ness (:issue:`49309`) - diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py index 64fdc7949f96b..14f334d72dbb1 100644 --- a/pandas/core/arrays/categorical.py +++ b/pandas/core/arrays/categorical.py @@ -2273,24 +2273,42 @@ def isin(self, values) -> npt.NDArray[np.bool_]: return algorithms.isin(self.codes, code_values) def _replace(self, *, to_replace, value, inplace: bool = False): - from pandas import ( - Index, - Series, - ) - inplace = validate_bool_kwarg(inplace, "inplace") cat = self if inplace else self.copy() - ser = Series(cat.categories, copy=True) - ser = ser.replace(to_replace=to_replace, value=value) + # other cases, like if both to_replace and value are list-like or if + # to_replace is a dict, are handled separately in NDFrame + if not is_list_like(to_replace): + to_replace = [to_replace] + + categories = cat.categories.tolist() + removals = set() + for replace_value in to_replace: + if value == replace_value: + continue + if replace_value not in cat.categories: + continue + if isna(value): + removals.add(replace_value) + continue + + index = categories.index(replace_value) + + if value in cat.categories: + value_index = categories.index(value) + cat._codes[cat._codes == index] = value_index + removals.add(replace_value) + else: + categories[index] = value + cat._set_categories(categories) - all_values = Index(ser) - new_categories = Index(ser.dropna().drop_duplicates(keep="first")) - new_codes = recode_for_categories( - cat._codes, all_values, new_categories, copy=False - ) - new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered) - NDArrayBacked.__init__(cat, new_codes, new_dtype) + if len(removals): + new_categories = [c for c in categories if c not in removals] + new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered) + codes = recode_for_categories( + cat.codes, cat.categories, new_dtype.categories + ) + NDArrayBacked.__init__(cat, codes, new_dtype) if not inplace: return cat diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py index 6056cada27069..4bb4882574228 100644 --- a/pandas/core/internals/blocks.py +++ b/pandas/core/internals/blocks.py @@ -536,10 +536,12 @@ def replace( if isinstance(values, Categorical): # TODO: avoid special-casing - # GH49404 blk = self if inplace else self.copy() - values = cast(Categorical, blk.values) - values._replace(to_replace=to_replace, value=value, inplace=True) + # error: Item "ExtensionArray" of "Union[ndarray[Any, Any], + # ExtensionArray]" has no attribute "_replace" + blk.values._replace( # type: ignore[union-attr] + to_replace=to_replace, value=value, inplace=True + ) return [blk] if not self._can_hold_element(to_replace): @@ -649,14 +651,6 @@ def replace_list( """ values = self.values - if isinstance(values, Categorical): - # TODO: avoid special-casing - # GH49404 - blk = self if inplace else self.copy() - values = cast(Categorical, blk.values) - values._replace(to_replace=src_list, value=dest_list, inplace=True) - return [blk] - # Exclude anything that we know we won't contain pairs = [ (x, y) for x, y in zip(src_list, dest_list) if self._can_hold_element(x) diff --git a/pandas/tests/arrays/categorical/test_replace.py b/pandas/tests/arrays/categorical/test_replace.py index 62a7bf0673a16..a3ba420c84a17 100644 --- a/pandas/tests/arrays/categorical/test_replace.py +++ b/pandas/tests/arrays/categorical/test_replace.py @@ -21,8 +21,6 @@ ((5, 6), 2, [1, 2, 3], False), ([1], [2], [2, 2, 3], False), ([1, 4], [5, 2], [5, 2, 3], False), - # GH49404 - ([1, 2, 3], [2, 3, 4], [2, 3, 4], False), # check_categorical sorts categories, which crashes on mixed dtypes (3, "4", [1, 2, "4"], False), ([1, 2, "3"], "5", ["5", "5", 3], True), @@ -67,11 +65,3 @@ def test_replace_categorical(to_replace, value, result, expected_error_msg): pd.Series(cat).replace(to_replace, value, inplace=True) tm.assert_categorical_equal(cat, expected) - - -def test_replace_categorical_ea_dtype(): - # GH49404 - cat = Categorical(pd.array(["a", "b"], dtype="string")) - result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values - expected = Categorical(pd.array(["c", pd.NA], dtype="string")) - tm.assert_categorical_equal(result, expected)
Reverts pandas-dev/pandas#49404
https://api.github.com/repos/pandas-dev/pandas/pulls/50848
2023-01-18T22:36:40Z
2023-01-18T23:24:04Z
2023-01-18T23:24:04Z
2023-01-19T00:10:08Z
BUG: read_csv overflowing for ea int with nulls
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index 7054d93457264..b95f799750367 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -1023,6 +1023,7 @@ I/O - Bug in :meth:`DataFrame.to_string` ignoring float formatter for extension arrays (:issue:`39336`) - Fixed memory leak which stemmed from the initialization of the internal JSON module (:issue:`49222`) - Fixed issue where :func:`json_normalize` would incorrectly remove leading characters from column names that matched the ``sep`` argument (:issue:`49861`) +- Bug in :func:`read_csv` unnecessarily overflowing for extension array dtype when containing ``NA`` (:issue:`32134`) - Bug in :meth:`DataFrame.to_dict` not converting ``NA`` to ``None`` (:issue:`50795`) - Bug in :meth:`DataFrame.to_json` where it would segfault when failing to encode a string (:issue:`50307`) - Bug in :func:`read_xml` where file-like objects failed when iterparse is used (:issue:`50641`) diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py index 6ae2c3a2e2749..2d9a3ae63259d 100644 --- a/pandas/core/arrays/numeric.py +++ b/pandas/core/arrays/numeric.py @@ -285,7 +285,7 @@ def _from_sequence_of_strings( ) -> T: from pandas.core.tools.numeric import to_numeric - scalars = to_numeric(strings, errors="raise") + scalars = to_numeric(strings, errors="raise", use_nullable_dtypes=True) return cls._from_sequence(scalars, dtype=dtype, copy=copy) _HANDLED_TYPES = (np.ndarray, numbers.Number) diff --git a/pandas/tests/io/parser/dtypes/test_dtypes_basic.py b/pandas/tests/io/parser/dtypes/test_dtypes_basic.py index 8fd08122f0834..52b142d81cd5e 100644 --- a/pandas/tests/io/parser/dtypes/test_dtypes_basic.py +++ b/pandas/tests/io/parser/dtypes/test_dtypes_basic.py @@ -18,6 +18,7 @@ import pandas._testing as tm from pandas.core.arrays import ( ArrowStringArray, + IntegerArray, StringArray, ) @@ -527,3 +528,23 @@ def test_use_nullable_dtypes_pyarrow_backend(all_parsers, request): } ) tm.assert_frame_equal(result, expected) + + +def test_ea_int_avoid_overflow(all_parsers): + # GH#32134 + parser = all_parsers + data = """a,b +1,1 +,1 +1582218195625938945,1 +""" + result = parser.read_csv(StringIO(data), dtype={"a": "Int64"}) + expected = DataFrame( + { + "a": IntegerArray( + np.array([1, 1, 1582218195625938945]), np.array([False, True, False]) + ), + "b": 1, + } + ) + tm.assert_frame_equal(result, expected)
- [x] closes #32134 (Replace xxxx with the GitHub issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50847
2023-01-18T22:33:00Z
2023-01-19T18:04:50Z
2023-01-19T18:04:50Z
2023-01-19T20:21:26Z
BUG: groupby.describe on a frame with duplicate column names
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index dacf333e0fcc0..07524fbf49652 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -1263,6 +1263,7 @@ Groupby/resample/rolling - Bug in :meth:`.SeriesGroupBy.value_counts` did not respect ``sort=False`` (:issue:`50482`) - Bug in :meth:`.DataFrameGroupBy.resample` raises ``KeyError`` when getting the result from a key list when resampling on time index (:issue:`50840`) - Bug in :meth:`.DataFrameGroupBy.transform` and :meth:`.SeriesGroupBy.transform` would raise incorrectly when grouper had ``axis=1`` for ``"ngroup"`` argument (:issue:`45986`) +- Bug in :meth:`.DataFrameGroupBy.describe` produced incorrect results when data had duplicate columns (:issue:`50806`) - Reshaping diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py index 1ef97fed7ba05..1fcbc7c305a06 100644 --- a/pandas/tests/groupby/test_function.py +++ b/pandas/tests/groupby/test_function.py @@ -1256,6 +1256,27 @@ def test_describe_with_duplicate_output_column_names(as_index, keys): tm.assert_frame_equal(result, expected) +def test_describe_duplicate_columns(): + # GH#50806 + df = DataFrame([[0, 1, 2, 3]]) + df.columns = [0, 1, 2, 0] + gb = df.groupby(df[1]) + result = gb.describe(percentiles=[]) + + columns = ["count", "mean", "std", "min", "50%", "max"] + frames = [ + DataFrame([[1.0, val, np.nan, val, val, val]], index=[1], columns=columns) + for val in (0.0, 2.0, 3.0) + ] + expected = pd.concat(frames, axis=1) + expected.columns = MultiIndex( + levels=[[0, 2], columns], + codes=[6 * [0] + 6 * [1] + 6 * [0], 3 * list(range(6))], + ) + expected.index.names = [1] + tm.assert_frame_equal(result, expected) + + def test_groupby_mean_no_overflow(): # Regression test for (#22487) df = DataFrame( @@ -1596,3 +1617,22 @@ def test_multiindex_group_all_columns_when_empty(groupby_func): result = method(*args).index expected = df.index tm.assert_index_equal(result, expected) + + +def test_duplicate_columns(request, groupby_func, as_index): + # GH#50806 + if groupby_func == "corrwith": + msg = "GH#50845 - corrwith fails when there are duplicate columns" + request.node.add_marker(pytest.mark.xfail(reason=msg)) + df = DataFrame([[1, 3, 6], [1, 4, 7], [2, 5, 8]], columns=list("abb")) + args = get_groupby_method_args(groupby_func, df) + gb = df.groupby("a", as_index=as_index) + result = getattr(gb, groupby_func)(*args) + + expected_df = df.set_axis(["a", "b", "c"], axis=1) + expected_args = get_groupby_method_args(groupby_func, expected_df) + expected_gb = expected_df.groupby("a", as_index=as_index) + expected = getattr(expected_gb, groupby_func)(*expected_args) + if groupby_func not in ("size", "ngroup", "cumcount"): + expected = expected.rename(columns={"c": "b"}) + tm.assert_equal(result, expected) diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py index 4861b7c90d1bb..d7b015fa7104a 100644 --- a/pandas/tests/groupby/test_groupby.py +++ b/pandas/tests/groupby/test_groupby.py @@ -2828,3 +2828,13 @@ def test_groupby_reduce_period(): expected = ser[:10] expected.index = Index(range(10), dtype=np.int_) tm.assert_series_equal(res, expected) + + +def test_obj_with_exclusions_duplicate_columns(): + # GH#50806 + df = DataFrame([[0, 1, 2, 3]]) + df.columns = [0, 1, 2, 0] + gb = df.groupby(df[1]) + result = gb._obj_with_exclusions + expected = df.take([0, 2, 3], axis=1) + tm.assert_frame_equal(result, expected)
- [x] closes #50806 (Replace xxxx with the GitHub issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. ASVs are below. ``` before after ratio [a063af0e] [185e4f8e] <groupby_select_obj_dup_cols~2> <groupby_select_obj_dup_cols> - 769±10μs 694±6μs 0.90 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'transformation', 5) - 758±3μs 683±9μs 0.90 groupby.GroupByMethods.time_dtype_as_group('int16', 'last', 'transformation', 5) - 11.9±0.04ms 10.7±0.2ms 0.90 groupby.AggEngine.time_dataframe_cython(True) - 846±4μs 759±6μs 0.90 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation', 5) - 857±5μs 769±3μs 0.90 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'transformation', 5) - 758±3μs 678±2μs 0.89 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'transformation', 5) - 763±3μs 682±8μs 0.89 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'transformation', 5) - 766±1μs 683±4μs 0.89 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'transformation', 5) - 775±2μs 691±7μs 0.89 groupby.GroupByMethods.time_dtype_as_group('int16', 'min', 'transformation', 5) - 860±8μs 766±7μs 0.89 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation', 5) - 775±3μs 689±8μs 0.89 groupby.GroupByMethods.time_dtype_as_group('int16', 'first', 'transformation', 5) - 847±4μs 752±9μs 0.89 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation', 5) - 759±2μs 674±2μs 0.89 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'transformation', 5) - 709±3μs 627±10μs 0.88 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation', 5) - 765±2μs 676±4μs 0.88 groupby.GroupByMethods.time_dtype_as_group('int16', 'prod', 'transformation', 5) - 854±10μs 754±10μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'transformation', 5) - 766±4μs 675±20μs 0.88 groupby.GroupByMethods.time_dtype_as_group('uint', 'max', 'transformation', 5) - 775±3μs 683±6μs 0.88 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'transformation', 5) - 670±1μs 590±9μs 0.88 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumcount', 'transformation', 5) - 767±4μs 674±20μs 0.88 groupby.GroupByMethods.time_dtype_as_group('int16', 'sum', 'transformation', 5) - 777±2μs 683±10μs 0.88 groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'transformation', 5) - 858±7μs 753±7μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'transformation', 5) - 772±5μs 678±1μs 0.88 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'transformation', 5) - 661±2μs 579±1μs 0.88 groupby.GroupByMethods.time_dtype_as_group('int16', 'cumcount', 'transformation', 5) - 588±2μs 513±1μs 0.87 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'transformation', 5) - 779±3μs 680±9μs 0.87 groupby.GroupByMethods.time_dtype_as_group('int16', 'max', 'transformation', 5) - 781±3μs 677±20μs 0.87 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'transformation', 5) - 772±10μs 669±9μs 0.87 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'transformation', 5) - 663±3μs 574±9μs 0.87 groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'transformation', 5) - 769±7μs 665±20μs 0.86 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'transformation', 5) - 770±7μs 666±20μs 0.86 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'transformation', 5) - 645±2μs 557±1μs 0.86 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'transformation', 5) - 729±4μs 629±10μs 0.86 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'transformation', 5) - 779±2μs 673±20μs 0.86 groupby.GroupByMethods.time_dtype_as_group('uint', 'min', 'transformation', 5) - 717±3μs 618±20μs 0.86 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'transformation', 5) - 781±2μs 665±20μs 0.85 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'transformation', 5) - 782±20μs 665±20μs 0.85 groupby.GroupByMethods.time_dtype_as_group('uint', 'last', 'transformation', 5) - 580±6μs 491±7μs 0.85 groupby.TransformNaN.time_first - 448±2μs 353±10μs 0.79 groupby.SumBools.time_groupby_sum_booleans - 374±1μs 292±1μs 0.78 groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'direct', 5) - 372±3μs 284±2μs 0.76 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'direct', 5) - 358±3μs 272±1μs 0.76 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'direct', 5) - 376±7μs 283±0.7μs 0.75 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumcount', 'direct', 5) - 374±2μs 279±3μs 0.75 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'direct', 5) - 382±7μs 283±7μs 0.74 groupby.GroupByMethods.time_dtype_as_group('int16', 'cumcount', 'direct', 5) - 181±2μs 85.9±1μs 0.47 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct', 5) - 175±3μs 82.9±0.3μs 0.47 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct', 5) - 175±2μs 82.9±0.2μs 0.47 groupby.GroupByMethods.time_dtype_as_group('int16', 'first', 'direct', 5) - 178±0.2μs 84.1±0.9μs 0.47 groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'direct', 5) - 180±3μs 83.8±2μs 0.47 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'direct', 5) - 177±0.9μs 82.3±0.2μs 0.47 groupby.GroupByMethods.time_dtype_as_group('uint', 'min', 'direct', 5) - 175±1μs 81.4±1μs 0.47 groupby.GroupByMethods.time_dtype_as_group('int16', 'sum', 'direct', 5) - 179±1μs 83.2±0.9μs 0.46 groupby.GroupByMethods.time_dtype_as_group('int16', 'max', 'direct', 5) - 179±3μs 82.7±1μs 0.46 groupby.GroupByMethods.time_dtype_as_group('uint', 'max', 'direct', 5) - 181±2μs 83.6±0.6μs 0.46 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct', 5) - 180±1μs 82.9±0.6μs 0.46 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'direct', 5) - 178±1μs 81.9±0.3μs 0.46 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'direct', 5) - 178±1μs 81.4±0.7μs 0.46 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'direct', 5) - 177±1μs 81.3±0.5μs 0.46 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'direct', 5) - 180±2μs 82.4±0.1μs 0.46 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'direct', 5) - 176±1μs 80.6±2μs 0.46 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'direct', 5) - 170±0.4μs 77.5±1μs 0.46 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'direct', 5) - 175±3μs 79.7±0.2μs 0.46 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 5) - 179±1μs 81.5±0.3μs 0.46 groupby.GroupByMethods.time_dtype_as_group('int16', 'min', 'direct', 5) - 179±4μs 81.5±2μs 0.45 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'direct', 5) - 174±0.5μs 79.0±3μs 0.45 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'direct', 5) - 175±1μs 79.4±2μs 0.45 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'direct', 5) - 172±2μs 77.2±1μs 0.45 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'direct', 5) - 170±2μs 75.6±0.3μs 0.44 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'direct', 5) - 170±0.9μs 75.5±0.3μs 0.44 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct', 5) - 170±2μs 75.5±0.7μs 0.44 groupby.GroupByMethods.time_dtype_as_group('int16', 'prod', 'direct', 5) - 172±4μs 76.2±0.2μs 0.44 groupby.GroupByMethods.time_dtype_as_group('int16', 'last', 'direct', 5) - 175±0.6μs 75.7±2μs 0.43 groupby.GroupByMethods.time_dtype_as_group('uint', 'last', 'direct', 5) - 158±1μs 66.0±2μs 0.42 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'direct', 5) - 160±2μs 65.5±1μs 0.41 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct', 5) SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY. PERFORMANCE INCREASED. ```
https://api.github.com/repos/pandas-dev/pandas/pulls/50846
2023-01-18T22:30:27Z
2023-02-03T18:12:48Z
2023-02-03T18:12:48Z
2023-04-02T14:21:55Z
ENH: Add ignore_index to Series.drop_duplicates
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index 7054d93457264..b5b466edaccbe 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -161,6 +161,7 @@ Other enhancements - Added :meth:`Index.infer_objects` analogous to :meth:`Series.infer_objects` (:issue:`50034`) - Added ``copy`` parameter to :meth:`Series.infer_objects` and :meth:`DataFrame.infer_objects`, passing ``False`` will avoid making copies for series or columns that are already non-object or where no better dtype can be inferred (:issue:`50096`) - :meth:`DataFrame.plot.hist` now recognizes ``xlabel`` and ``ylabel`` arguments (:issue:`49793`) +- :meth:`Series.drop_duplicates` has gained ``ignore_index`` keyword to reset index (:issue:`48304`) - Improved error message in :func:`to_datetime` for non-ISO8601 formats, informing users about the position of the first error (:issue:`50361`) - Improved error message when trying to align :class:`DataFrame` objects (for example, in :func:`DataFrame.compare`) to clarify that "identically labelled" refers to both index and columns (:issue:`50083`) - Added :meth:`DatetimeIndex.as_unit` and :meth:`TimedeltaIndex.as_unit` to convert to different resolutions; supported resolutions are "s", "ms", "us", and "ns" (:issue:`50616`) diff --git a/pandas/core/series.py b/pandas/core/series.py index 91f7095e59db5..465cc3ce41fad 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2131,22 +2131,32 @@ def unique(self) -> ArrayLike: # pylint: disable=useless-parent-delegation @overload def drop_duplicates( - self, *, keep: DropKeep = ..., inplace: Literal[False] = ... + self, + *, + keep: DropKeep = ..., + inplace: Literal[False] = ..., + ignore_index: bool = ..., ) -> Series: ... @overload - def drop_duplicates(self, *, keep: DropKeep = ..., inplace: Literal[True]) -> None: + def drop_duplicates( + self, *, keep: DropKeep = ..., inplace: Literal[True], ignore_index: bool = ... + ) -> None: ... @overload def drop_duplicates( - self, *, keep: DropKeep = ..., inplace: bool = ... + self, *, keep: DropKeep = ..., inplace: bool = ..., ignore_index: bool = ... ) -> Series | None: ... def drop_duplicates( - self, *, keep: DropKeep = "first", inplace: bool = False + self, + *, + keep: DropKeep = "first", + inplace: bool = False, + ignore_index: bool = False, ) -> Series | None: """ Return Series with duplicate values removed. @@ -2163,6 +2173,11 @@ def drop_duplicates( inplace : bool, default ``False`` If ``True``, performs operation inplace and returns None. + ignore_index : bool, default ``False`` + If ``True``, the resulting axis will be labeled 0, 1, …, n - 1. + + .. versionadded:: 2.0.0 + Returns ------- Series or None @@ -2225,6 +2240,10 @@ def drop_duplicates( """ inplace = validate_bool_kwarg(inplace, "inplace") result = super().drop_duplicates(keep=keep) + + if ignore_index: + result.index = default_index(len(result)) + if inplace: self._update_inplace(result) return None diff --git a/pandas/tests/series/methods/test_drop_duplicates.py b/pandas/tests/series/methods/test_drop_duplicates.py index 698430095b453..7e4503be2ec47 100644 --- a/pandas/tests/series/methods/test_drop_duplicates.py +++ b/pandas/tests/series/methods/test_drop_duplicates.py @@ -242,3 +242,10 @@ def test_drop_duplicates_categorical_bool_na(self, nulls_fixture): index=[0, 1, 4], ) tm.assert_series_equal(result, expected) + + def test_drop_duplicates_ignore_index(self): + # GH#48304 + ser = Series([1, 2, 2, 3]) + result = ser.drop_duplicates(ignore_index=True) + expected = Series([1, 2, 3]) + tm.assert_series_equal(result, expected)
- [x] closes #48304 (Replace xxxx with the GitHub issue number) - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50844
2023-01-18T21:47:34Z
2023-01-19T18:20:25Z
2023-01-19T18:20:25Z
2023-01-19T20:21:42Z
PERF: Avoid re-computing mask in nanmedian
diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst index 1a071ab978de9..9366404445fb2 100644 --- a/doc/source/whatsnew/v2.0.0.rst +++ b/doc/source/whatsnew/v2.0.0.rst @@ -870,6 +870,7 @@ Performance improvements - Performance improvement in :func:`read_html` when there are multiple tables (:issue:`49929`) - Performance improvement in :func:`to_datetime` when using ``'%Y%m%d'`` format (:issue:`17410`) - Performance improvement in :func:`to_datetime` when format is given or can be inferred (:issue:`50465`) +- Performance improvement in :meth:`Series.median` for nullable dtypes (:issue:`50838`) - Performance improvement in :func:`read_csv` when passing :func:`to_datetime` lambda-function to ``date_parser`` and inputs have mixed timezone offsetes (:issue:`35296`) - Performance improvement in :meth:`.SeriesGroupBy.value_counts` with categorical dtype (:issue:`46202`) - Fixed a reference leak in :func:`read_hdf` (:issue:`37441`) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 02372356d3fe4..61f1bcdef9568 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -746,16 +746,19 @@ def nanmedian(values, *, axis: AxisInt | None = None, skipna: bool = True, mask= 2.0 """ - def get_median(x): - mask = notna(x) - if not skipna and not mask.all(): + def get_median(x, _mask=None): + if _mask is None: + _mask = notna(x) + else: + _mask = ~_mask + if not skipna and not _mask.all(): return np.nan with warnings.catch_warnings(): # Suppress RuntimeWarning about All-NaN slice warnings.filterwarnings( "ignore", "All-NaN slice encountered", RuntimeWarning ) - res = np.nanmedian(x[mask]) + res = np.nanmedian(x[_mask]) return res values, mask, dtype, _, _ = _get_values(values, skipna, mask=mask) @@ -796,7 +799,7 @@ def get_median(x): else: # otherwise return a scalar value - res = get_median(values) if notempty else np.nan + res = get_median(values, mask) if notempty else np.nan return _wrap_results(res, dtype)
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50838
2023-01-18T18:57:20Z
2023-01-19T18:37:14Z
2023-01-19T18:37:14Z
2023-01-19T20:21:58Z
WEB/CI: Use GITHUB_TOKEN when making Github API calls
diff --git a/web/pandas_web.py b/web/pandas_web.py index e4568136edece..8c508a15f9a2b 100755 --- a/web/pandas_web.py +++ b/web/pandas_web.py @@ -43,6 +43,12 @@ import requests import yaml +api_token = os.environ.get("GITHUB_TOKEN") +if api_token is not None: + GITHUB_API_HEADERS = {"Authorization": f"Bearer {api_token}"} +else: + GITHUB_API_HEADERS = {} + class Preprocessors: """ @@ -168,7 +174,9 @@ def maintainers_add_info(context): for user in ( context["maintainers"]["active"] + context["maintainers"]["inactive"] ): - resp = requests.get(f"https://api.github.com/users/{user}") + resp = requests.get( + f"https://api.github.com/users/{user}", headers=GITHUB_API_HEADERS + ) if resp.status_code == 403: sys.stderr.write( "WARN: GitHub API quota exceeded when fetching maintainers\n" @@ -199,7 +207,10 @@ def home_add_releases(context): context["releases"] = [] github_repo_url = context["main"]["github_repo_url"] - resp = requests.get(f"https://api.github.com/repos/{github_repo_url}/releases") + resp = requests.get( + f"https://api.github.com/repos/{github_repo_url}/releases", + headers=GITHUB_API_HEADERS, + ) if resp.status_code == 403: sys.stderr.write("WARN: GitHub API quota exceeded when fetching releases\n") resp_bkp = requests.get(context["main"]["production_url"] + "releases.json") @@ -275,7 +286,8 @@ def roadmap_pdeps(context): github_repo_url = context["main"]["github_repo_url"] resp = requests.get( "https://api.github.com/search/issues?" - f"q=is:pr is:open label:PDEP repo:{github_repo_url}" + f"q=is:pr is:open label:PDEP repo:{github_repo_url}", + headers=GITHUB_API_HEADERS, ) if resp.status_code == 403: sys.stderr.write("WARN: GitHub API quota exceeded when fetching pdeps\n")
Follow up to https://github.com/pandas-dev/pandas/pull/50811, might as well still make authenticated API calls to Github
https://api.github.com/repos/pandas-dev/pandas/pulls/50837
2023-01-18T18:47:23Z
2023-01-19T02:20:50Z
2023-01-19T02:20:50Z
2023-01-20T03:25:11Z
BUG: merge_asof with non-nano
diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py index ac4e8934570ce..1aedc3a31e3e7 100644 --- a/pandas/core/arrays/timedeltas.py +++ b/pandas/core/arrays/timedeltas.py @@ -316,7 +316,7 @@ def _unbox_scalar(self, value) -> np.timedelta64: raise ValueError("'value' should be a Timedelta.") self._check_compatible_with(value) if value is NaT: - return np.timedelta64(value.value, "ns") + return np.timedelta64(value.value, self.unit) else: return value.as_unit(self.unit).asm8 diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py index 8d009d25a66ba..7d8d7a37ff7e7 100644 --- a/pandas/core/reshape/merge.py +++ b/pandas/core/reshape/merge.py @@ -89,7 +89,10 @@ ) from pandas.core.arrays._mixins import NDArrayBackedExtensionArray import pandas.core.common as com -from pandas.core.construction import extract_array +from pandas.core.construction import ( + ensure_wrapped_if_datetimelike, + extract_array, +) from pandas.core.frame import _merge_doc from pandas.core.indexes.api import default_index from pandas.core.sorting import is_int64_overflow_possible @@ -2109,12 +2112,24 @@ def injection(obj): # initial type conversion as needed if needs_i8_conversion(left_values): - left_values = left_values.view("i8") - right_values = right_values.view("i8") if tolerance is not None: tolerance = Timedelta(tolerance) + + # TODO: we have no test cases with PeriodDtype here; probably + # need to adjust tolerance for that case. + if left_values.dtype.kind in ["m", "M"]: + # Make sure the i8 representation for tolerance + # matches that for left_values/right_values. + lvs = ensure_wrapped_if_datetimelike(left_values) + tolerance = tolerance.as_unit(lvs.unit) + tolerance = tolerance.value + # TODO: require left_values.dtype == right_values.dtype, or at least + # comparable for e.g. dt64tz + left_values = left_values.view("i8") + right_values = right_values.view("i8") + # a "by" parameter requires special handling if self.left_by is not None: # remove 'on' parameter from values if one existed diff --git a/pandas/tests/reshape/merge/test_merge_asof.py b/pandas/tests/reshape/merge/test_merge_asof.py index 4123f686163d4..3b522eaa075f0 100644 --- a/pandas/tests/reshape/merge/test_merge_asof.py +++ b/pandas/tests/reshape/merge/test_merge_asof.py @@ -16,6 +16,14 @@ from pandas.core.reshape.merge import MergeError +@pytest.fixture(params=["s", "ms", "us", "ns"]) +def unit(request): + """ + Resolution for datetimelike dtypes. + """ + return request.param + + class TestAsOfMerge: def read_data(self, datapath, name, dedupe=False): path = datapath("reshape", "merge", "data", name) @@ -63,8 +71,13 @@ def test_examples1(self): result = merge_asof(left, right, on="a") tm.assert_frame_equal(result, expected) - def test_examples2(self): + def test_examples2(self, unit): """doc-string examples""" + if unit == "s": + pytest.skip( + "This test is invalid for unit='s' because that would " + "round the trades['time']]" + ) trades = pd.DataFrame( { "time": to_datetime( @@ -75,7 +88,7 @@ def test_examples2(self): "20160525 13:30:00.048", "20160525 13:30:00.048", ] - ), + ).astype(f"M8[{unit}]"), "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"], "price": [51.95, 51.95, 720.77, 720.92, 98.00], "quantity": [75, 155, 100, 100, 100], @@ -96,7 +109,7 @@ def test_examples2(self): "20160525 13:30:00.072", "20160525 13:30:00.075", ] - ), + ).astype(f"M8[{unit}]"), "ticker": [ "GOOG", "MSFT", @@ -127,7 +140,7 @@ def test_examples2(self): "20160525 13:30:00.048", "20160525 13:30:00.048", ] - ), + ).astype(f"M8[{unit}]"), "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"], "price": [51.95, 51.95, 720.77, 720.92, 98.00], "quantity": [75, 155, 100, 100, 100], @@ -639,7 +652,7 @@ def test_tolerance_nearest(self): result = merge_asof(left, right, on="a", direction="nearest", tolerance=1) tm.assert_frame_equal(result, expected) - def test_tolerance_tz(self): + def test_tolerance_tz(self, unit): # GH 14844 left = pd.DataFrame( { @@ -648,6 +661,7 @@ def test_tolerance_tz(self): freq="D", periods=5, tz=pytz.timezone("UTC"), + unit=unit, ), "value1": np.arange(5), } @@ -659,6 +673,7 @@ def test_tolerance_tz(self): freq="D", periods=5, tz=pytz.timezone("UTC"), + unit=unit, ), "value2": list("ABCDE"), } @@ -672,6 +687,7 @@ def test_tolerance_tz(self): freq="D", periods=5, tz=pytz.timezone("UTC"), + unit=unit, ), "value1": np.arange(5), "value2": list("BCDEE"), @@ -1314,22 +1330,27 @@ def test_by_mixed_tz_aware(self): expected["value_y"] = np.array([np.nan], dtype=object) tm.assert_frame_equal(result, expected) - def test_timedelta_tolerance_nearest(self): + def test_timedelta_tolerance_nearest(self, unit): # GH 27642 + if unit == "s": + pytest.skip( + "This test is invalid with unit='s' because that would " + "round left['time']" + ) left = pd.DataFrame( list(zip([0, 5, 10, 15, 20, 25], [0, 1, 2, 3, 4, 5])), columns=["time", "left"], ) - left["time"] = pd.to_timedelta(left["time"], "ms") + left["time"] = pd.to_timedelta(left["time"], "ms").astype(f"m8[{unit}]") right = pd.DataFrame( list(zip([0, 3, 9, 12, 15, 18], [0, 1, 2, 3, 4, 5])), columns=["time", "right"], ) - right["time"] = pd.to_timedelta(right["time"], "ms") + right["time"] = pd.to_timedelta(right["time"], "ms").astype(f"m8[{unit}]") expected = pd.DataFrame( list( @@ -1342,7 +1363,7 @@ def test_timedelta_tolerance_nearest(self): columns=["time", "left", "right"], ) - expected["time"] = pd.to_timedelta(expected["time"], "ms") + expected["time"] = pd.to_timedelta(expected["time"], "ms").astype(f"m8[{unit}]") result = merge_asof( left, right, on="time", tolerance=Timedelta("1ms"), direction="nearest" @@ -1400,12 +1421,17 @@ def test_merge_index_column_tz(self): ) tm.assert_frame_equal(result, expected) - def test_left_index_right_index_tolerance(self): + def test_left_index_right_index_tolerance(self, unit): # https://github.com/pandas-dev/pandas/issues/35558 - dr1 = pd.date_range(start="1/1/2020", end="1/20/2020", freq="2D") + Timedelta( - seconds=0.4 - ) - dr2 = pd.date_range(start="1/1/2020", end="2/1/2020") + if unit == "s": + pytest.skip( + "This test is invalid with unit='s' because that would round dr1" + ) + + dr1 = pd.date_range( + start="1/1/2020", end="1/20/2020", freq="2D", unit=unit + ) + Timedelta(seconds=0.4).as_unit(unit) + dr2 = pd.date_range(start="1/1/2020", end="2/1/2020", unit=unit) df1 = pd.DataFrame({"val1": "foo"}, index=pd.DatetimeIndex(dr1)) df2 = pd.DataFrame({"val2": "bar"}, index=pd.DatetimeIndex(dr2))
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50835
2023-01-18T18:33:22Z
2023-01-23T19:41:26Z
2023-01-23T19:41:26Z
2023-05-13T15:07:15Z
ENH: Get rid of float cast in masked reduction ops
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py index 77735add89bf7..d45fe05d52937 100644 --- a/pandas/core/arrays/masked.py +++ b/pandas/core/arrays/masked.py @@ -1081,12 +1081,7 @@ def _reduce(self, name: str, *, skipna: bool = True, **kwargs): data = self._data mask = self._mask - # coerce to a nan-aware float if needed - # (we explicitly use NaN within reductions) - if self._hasna: - data = self.to_numpy("float64", na_value=np.nan) - - # median, skew, kurt, idxmin, idxmax + # median, skew, kurt, sem op = getattr(nanops, f"nan{name}") result = op(data, axis=0, skipna=skipna, mask=mask, **kwargs) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 02372356d3fe4..c22af960927f6 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -758,15 +758,15 @@ def get_median(x): res = np.nanmedian(x[mask]) return res - values, mask, dtype, _, _ = _get_values(values, skipna, mask=mask) + values, mask, dtype, _, _ = _get_values(values, skipna, mask=mask, fill_value=0) if not is_float_dtype(values.dtype): try: values = values.astype("f8") except ValueError as err: # e.g. "could not convert string to float: 'a'" raise TypeError(str(err)) from err - if mask is not None: - values[mask] = np.nan + if mask is not None: + values[mask] = np.nan notempty = values.size @@ -1040,8 +1040,11 @@ def nansem( if not is_float_dtype(values.dtype): values = values.astype("f8") + if not skipna and mask is not None and mask.any(): + return np.nan + count, _ = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype) - var = nanvar(values, axis=axis, skipna=skipna, ddof=ddof) + var = nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask) return np.sqrt(var) / np.sqrt(count) @@ -1222,6 +1225,8 @@ def nanskew( if skipna and mask is not None: values = values.copy() np.putmask(values, mask, 0) + elif not skipna and mask is not None and mask.any(): + return np.nan mean = values.sum(axis, dtype=np.float64) / count if axis is not None: @@ -1310,6 +1315,8 @@ def nankurt( if skipna and mask is not None: values = values.copy() np.putmask(values, mask, 0) + elif not skipna and mask is not None and mask.any(): + return np.nan mean = values.sum(axis, dtype=np.float64) / count if axis is not None:
- [x] closes #30436 (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature. Shouldn't be user visible
https://api.github.com/repos/pandas-dev/pandas/pulls/50833
2023-01-18T18:08:31Z
2023-01-19T20:40:19Z
2023-01-19T20:40:19Z
2023-01-19T20:41:06Z
DOC fix link in bug report
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yaml b/.github/ISSUE_TEMPLATE/bug_report.yaml index d9330e396ed12..4e1bc8f61d04e 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yaml +++ b/.github/ISSUE_TEMPLATE/bug_report.yaml @@ -17,8 +17,8 @@ body: [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. required: true - label: > - I have confirmed this bug exists on the [main branch] - (https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) + I have confirmed this bug exists on the + [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas. - type: textarea id: example
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50831
2023-01-18T16:32:56Z
2023-01-18T16:48:20Z
2023-01-18T16:48:20Z
2023-01-18T18:08:18Z
CLN remove unneccessary code from array_strptime which doesn't spark joy
diff --git a/pandas/_libs/tslibs/strptime.pyx b/pandas/_libs/tslibs/strptime.pyx index 69878625295d6..3ca87f8680b53 100644 --- a/pandas/_libs/tslibs/strptime.pyx +++ b/pandas/_libs/tslibs/strptime.pyx @@ -183,52 +183,51 @@ def array_strptime( bint found_naive = False bint found_tz = False tzinfo tz_out = None - bint iso_format = fmt is not None and format_is_iso(fmt) + bint iso_format = format_is_iso(fmt) NPY_DATETIMEUNIT out_bestunit int out_local = 0, out_tzoffset = 0 assert is_raise or is_ignore or is_coerce - if fmt is not None: - if "%W" in fmt or "%U" in fmt: - if "%Y" not in fmt and "%y" not in fmt: - raise ValueError("Cannot use '%W' or '%U' without day and year") - if "%A" not in fmt and "%a" not in fmt and "%w" not in fmt: - raise ValueError("Cannot use '%W' or '%U' without day and year") - elif "%Z" in fmt and "%z" in fmt: - raise ValueError("Cannot parse both %Z and %z") - elif "%j" in fmt and "%G" in fmt: - raise ValueError("Day of the year directive '%j' is not " - "compatible with ISO year directive '%G'. " - "Use '%Y' instead.") - elif "%G" in fmt and ( - "%V" not in fmt - or not ( - "%A" in fmt - or "%a" in fmt - or "%w" in fmt - or "%u" in fmt - ) - ): - raise ValueError("ISO year directive '%G' must be used with " - "the ISO week directive '%V' and a weekday " - "directive '%A', '%a', '%w', or '%u'.") - elif "%V" in fmt and "%Y" in fmt: - raise ValueError("ISO week directive '%V' is incompatible with " - "the year directive '%Y'. Use the ISO year " - "'%G' instead.") - elif "%V" in fmt and ( - "%G" not in fmt - or not ( - "%A" in fmt - or "%a" in fmt - or "%w" in fmt - or "%u" in fmt - ) - ): - raise ValueError("ISO week directive '%V' must be used with " - "the ISO year directive '%G' and a weekday " - "directive '%A', '%a', '%w', or '%u'.") + if "%W" in fmt or "%U" in fmt: + if "%Y" not in fmt and "%y" not in fmt: + raise ValueError("Cannot use '%W' or '%U' without day and year") + if "%A" not in fmt and "%a" not in fmt and "%w" not in fmt: + raise ValueError("Cannot use '%W' or '%U' without day and year") + elif "%Z" in fmt and "%z" in fmt: + raise ValueError("Cannot parse both %Z and %z") + elif "%j" in fmt and "%G" in fmt: + raise ValueError("Day of the year directive '%j' is not " + "compatible with ISO year directive '%G'. " + "Use '%Y' instead.") + elif "%G" in fmt and ( + "%V" not in fmt + or not ( + "%A" in fmt + or "%a" in fmt + or "%w" in fmt + or "%u" in fmt + ) + ): + raise ValueError("ISO year directive '%G' must be used with " + "the ISO week directive '%V' and a weekday " + "directive '%A', '%a', '%w', or '%u'.") + elif "%V" in fmt and "%Y" in fmt: + raise ValueError("ISO week directive '%V' is incompatible with " + "the year directive '%Y'. Use the ISO year " + "'%G' instead.") + elif "%V" in fmt and ( + "%G" not in fmt + or not ( + "%A" in fmt + or "%a" in fmt + or "%w" in fmt + or "%u" in fmt + ) + ): + raise ValueError("ISO week directive '%V' must be used with " + "the ISO year directive '%G' and a weekday " + "directive '%A', '%a', '%w', or '%u'.") global _TimeRE_cache, _regex_cache with _cache_lock:
`fmt` can't be `None` so there's more complexity here than need be If reviewing, I'd suggest using the "hide whitespace" option
https://api.github.com/repos/pandas-dev/pandas/pulls/50830
2023-01-18T16:28:31Z
2023-01-18T19:23:20Z
2023-01-18T19:23:20Z
2023-01-18T19:23:28Z
PERF: datetime parsing with Q
diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx index 9e593ec64f7d2..1b81d53c09e7e 100644 --- a/pandas/_libs/tslibs/parsing.pyx +++ b/pandas/_libs/tslibs/parsing.pyx @@ -472,16 +472,15 @@ cpdef bint _does_string_look_like_datetime(str py_string): return True -cdef object _parse_dateabbr_string(object date_string, datetime default, +cdef object _parse_dateabbr_string(str date_string, datetime default, str freq=None): + # special handling for possibilities eg, 2Q2005, 2Q05, 2005Q1, 05Q1 cdef: datetime ret # year initialized to prevent compiler warnings int year = -1, quarter = -1, month Py_ssize_t date_len - - # special handling for possibilities eg, 2Q2005, 2Q05, 2005Q1, 05Q1 - assert isinstance(date_string, str) + const char* buf if date_string in nat_strings: return NaT, "" @@ -498,10 +497,11 @@ cdef object _parse_dateabbr_string(object date_string, datetime default, pass if 4 <= date_len <= 7: + buf = get_c_string_buf_and_size(date_string, &date_len) try: i = date_string.index("Q", 1, 6) if i == 1: - quarter = int(date_string[0]) + quarter = _parse_1digit(buf) # i.e. int(date_string[0]) if date_len == 4 or (date_len == 5 and date_string[i + 1] == "-"): # r'(\d)Q-?(\d\d)') @@ -516,7 +516,8 @@ cdef object _parse_dateabbr_string(object date_string, datetime default, # r'(\d\d)-?Q(\d)' if date_len == 4 or (date_len == 5 and date_string[i - 1] == "-"): - quarter = int(date_string[-1]) + # i.e. quarter = int(date_string[-1]) + quarter = _parse_1digit(buf + date_len - 1) year = 2000 + int(date_string[:2]) else: raise ValueError @@ -524,7 +525,8 @@ cdef object _parse_dateabbr_string(object date_string, datetime default, if date_len == 6 or (date_len == 7 and date_string[i - 1] == "-"): # r'(\d\d\d\d)-?Q(\d)' - quarter = int(date_string[-1]) + # i.e. quarter = int(date_string[-1]) + quarter = _parse_1digit(buf + date_len - 1) year = int(date_string[:4]) else: raise ValueError
``` In [3]: %timeit pd.Timestamp("2014Q1") 3.04 µs ± 63.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each) # <- main 2.8 µs ± 19.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each) # <- PR ```
https://api.github.com/repos/pandas-dev/pandas/pulls/50828
2023-01-18T16:02:50Z
2023-01-18T19:19:53Z
2023-01-18T19:19:53Z
2023-01-18T19:21:05Z
Manual backport fix github quota
diff --git a/.github/workflows/docbuild-and-upload.yml b/.github/workflows/docbuild-and-upload.yml index 17ebed5a8c1e6..13da56806de6e 100644 --- a/.github/workflows/docbuild-and-upload.yml +++ b/.github/workflows/docbuild-and-upload.yml @@ -45,12 +45,6 @@ jobs: - name: Build Pandas uses: ./.github/actions/build_pandas - - name: Set up maintainers cache - uses: actions/cache@v3 - with: - path: maintainers.json - key: maintainers - - name: Build website run: python web/pandas_web.py web/pandas --target-path=web/build diff --git a/web/pandas/about/team.md b/web/pandas/about/team.md index 261d577b2abc1..bdd5d5d2b2468 100644 --- a/web/pandas/about/team.md +++ b/web/pandas/about/team.md @@ -9,7 +9,8 @@ If you want to support pandas development, you can find information in the [dona ## Active maintainers <div class="card-group maintainers"> - {% for person in maintainers.active_with_github_info %} + {% for username in maintainers.active %} + {% set person = maintainers.github_info.get(username) %} <div class="card"> <img class="card-img-top" alt="" src="{{ person.avatar_url }}"/> <div class="card-body"> @@ -63,7 +64,8 @@ The project governance is available in the [project governance page](governance. ## Inactive maintainers <ul> - {% for person in maintainers.inactive_with_github_info %} + {% for username in maintainers.inactive %} + {% set person = maintainers.github_info.get(username) %} <li> <a href="{{ person.blog or person.html_url }}"> {{ person.name or person.login }} diff --git a/web/pandas/config.yml b/web/pandas/config.yml index 16e1357d405a0..85dee1d114800 100644 --- a/web/pandas/config.yml +++ b/web/pandas/config.yml @@ -1,10 +1,10 @@ main: templates_path: _templates base_template: "layout.html" + production_url: "https://pandas.pydata.org/" ignore: - _templates/layout.html - config.yml - - try.md # the binder page will be added later github_repo_url: pandas-dev/pandas context_preprocessors: - pandas_web.Preprocessors.current_year diff --git a/web/pandas_web.py b/web/pandas_web.py index 3e5b089cab64a..1a2bc45bd87e0 100755 --- a/web/pandas_web.py +++ b/web/pandas_web.py @@ -158,35 +158,39 @@ def maintainers_add_info(context): Given the active maintainers defined in the yaml file, it fetches the GitHub user information for them. """ - timestamp = time.time() - - cache_file = pathlib.Path("maintainers.json") - if cache_file.is_file(): - with open(cache_file) as f: - context["maintainers"] = json.load(f) - # refresh cache after 1 hour - if (timestamp - context["maintainers"]["timestamp"]) < 3_600: - return context - - context["maintainers"]["timestamp"] = timestamp - repeated = set(context["maintainers"]["active"]) & set( context["maintainers"]["inactive"] ) if repeated: raise ValueError(f"Maintainers {repeated} are both active and inactive") - for kind in ("active", "inactive"): - context["maintainers"][f"{kind}_with_github_info"] = [] - for user in context["maintainers"][kind]: - resp = requests.get(f"https://api.github.com/users/{user}") - if context["ignore_io_errors"] and resp.status_code == 403: - return context - resp.raise_for_status() - context["maintainers"][f"{kind}_with_github_info"].append(resp.json()) + maintainers_info = {} + for user in ( + context["maintainers"]["active"] + context["maintainers"]["inactive"] + ): + resp = requests.get(f"https://api.github.com/users/{user}") + if resp.status_code == 403: + sys.stderr.write( + "WARN: GitHub API quota exceeded when fetching maintainers\n" + ) + # if we exceed github api quota, we use the github info + # of maintainers saved with the website + resp_bkp = requests.get( + context["main"]["production_url"] + "maintainers.json" + ) + resp_bkp.raise_for_status() + maintainers_info = resp_bkp.json() + break + + resp.raise_for_status() + maintainers_info[user] = resp.json() - with open(cache_file, "w") as f: - json.dump(context["maintainers"], f) + context["maintainers"]["github_info"] = maintainers_info + + # save the data fetched from github to use it in case we exceed + # git github api quota in the future + with open(pathlib.Path(context["target_path"]) / "maintainers.json", "w") as f: + json.dump(maintainers_info, f) return context @@ -196,11 +200,19 @@ def home_add_releases(context): github_repo_url = context["main"]["github_repo_url"] resp = requests.get(f"https://api.github.com/repos/{github_repo_url}/releases") - if context["ignore_io_errors"] and resp.status_code == 403: - return context - resp.raise_for_status() + if resp.status_code == 403: + sys.stderr.write("WARN: GitHub API quota exceeded when fetching releases\n") + resp_bkp = requests.get(context["main"]["production_url"] + "releases.json") + resp_bkp.raise_for_status() + releases = resp_bkp.json() + else: + resp.raise_for_status() + releases = resp.json() - for release in resp.json(): + with open(pathlib.Path(context["target_path"]) / "releases.json", "w") as f: + json.dump(releases, f, default=datetime.datetime.isoformat) + + for release in releases: if release["prerelease"]: continue published = datetime.datetime.strptime( @@ -218,6 +230,7 @@ def home_add_releases(context): ), } ) + return context @staticmethod @@ -264,12 +277,20 @@ def roadmap_pdeps(context): "https://api.github.com/search/issues?" f"q=is:pr is:open label:PDEP repo:{github_repo_url}" ) - if context["ignore_io_errors"] and resp.status_code == 403: - return context - resp.raise_for_status() + if resp.status_code == 403: + sys.stderr.write("WARN: GitHub API quota exceeded when fetching pdeps\n") + resp_bkp = requests.get(context["main"]["production_url"] + "pdeps.json") + resp_bkp.raise_for_status() + pdeps = resp_bkp.json() + else: + resp.raise_for_status() + pdeps = resp.json() + + with open(pathlib.Path(context["target_path"]) / "pdeps.json", "w") as f: + json.dump(pdeps, f) - for pdep in resp.json()["items"]: - context["pdeps"]["under_discussion"].append( + for pdep in pdeps["items"]: + context["pdeps"]["Under discussion"].append( {"title": pdep["title"], "url": pdep["url"]} ) @@ -302,7 +323,7 @@ def get_callable(obj_as_str: str) -> object: return obj -def get_context(config_fname: str, ignore_io_errors: bool, **kwargs): +def get_context(config_fname: str, **kwargs): """ Load the config yaml as the base context, and enrich it with the information added by the context preprocessors defined in the file. @@ -311,7 +332,6 @@ def get_context(config_fname: str, ignore_io_errors: bool, **kwargs): context = yaml.safe_load(f) context["source_path"] = os.path.dirname(config_fname) - context["ignore_io_errors"] = ignore_io_errors context.update(kwargs) preprocessors = ( @@ -349,7 +369,9 @@ def extend_base_template(content: str, base_template: str) -> str: def main( - source_path: str, target_path: str, base_url: str, ignore_io_errors: bool + source_path: str, + target_path: str, + base_url: str, ) -> int: """ Copy every file in the source directory to the target directory. @@ -363,7 +385,7 @@ def main( os.makedirs(target_path, exist_ok=True) sys.stderr.write("Generating context...\n") - context = get_context(config_fname, ignore_io_errors, base_url=base_url) + context = get_context(config_fname, base_url=base_url, target_path=target_path) sys.stderr.write("Context generated\n") templates_path = os.path.join(source_path, context["main"]["templates_path"]) @@ -407,15 +429,5 @@ def main( parser.add_argument( "--base-url", default="", help="base url where the website is served from" ) - parser.add_argument( - "--ignore-io-errors", - action="store_true", - help="do not fail if errors happen when fetching " - "data from http sources, and those fail " - "(mostly useful to allow github quota errors " - "when running the script locally)", - ) args = parser.parse_args() - sys.exit( - main(args.source_path, args.target_path, args.base_url, args.ignore_io_errors) - ) + sys.exit(main(args.source_path, args.target_path, args.base_url))
xref #50811
https://api.github.com/repos/pandas-dev/pandas/pulls/50827
2023-01-18T15:26:01Z
2023-01-18T17:48:10Z
2023-01-18T17:48:10Z
2023-01-18T17:48:11Z
DEPR: remove Int/Uint/Float64Index from pandas/tests/indexes/ranges
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py index 73e4a51ca3e7c..0ff733ab51b85 100644 --- a/pandas/core/indexes/range.py +++ b/pandas/core/indexes/range.py @@ -47,11 +47,7 @@ from pandas.core.construction import extract_array import pandas.core.indexes.base as ibase from pandas.core.indexes.base import maybe_extract_name -from pandas.core.indexes.numeric import ( - Float64Index, - Int64Index, - NumericIndex, -) +from pandas.core.indexes.numeric import NumericIndex from pandas.core.ops.common import unpack_zerodim_and_defer if TYPE_CHECKING: @@ -64,8 +60,8 @@ class RangeIndex(NumericIndex): """ Immutable Index implementing a monotonic integer range. - RangeIndex is a memory-saving special case of Int64Index limited to - representing monotonic ranges. Using RangeIndex may in some instances + RangeIndex is a memory-saving special case of an Index limited to representing + monotonic ranges with a 64-bit dtype. Using RangeIndex may in some instances improve computing speed. This is the default index type used @@ -97,7 +93,6 @@ class RangeIndex(NumericIndex): See Also -------- Index : The base pandas Index type. - Int64Index : Index of int64 data. """ _typ = "rangeindex" @@ -185,7 +180,7 @@ def _simple_new( # type: ignore[override] # -------------------------------------------------------------------- - # error: Return type "Type[Int64Index]" of "_constructor" incompatible with return + # error: Return type "Type[NumericIndex]" of "_constructor" incompatible with return # type "Type[RangeIndex]" in supertype "Index" @cache_readonly def _constructor(self) -> type[NumericIndex]: # type: ignore[override] @@ -331,7 +326,7 @@ def inferred_type(self) -> str: # -------------------------------------------------------------------- # Indexing Methods - @doc(Int64Index.get_loc) + @doc(NumericIndex.get_loc) def get_loc(self, key): if is_integer(key) or (is_float(key) and key.is_integer()): new_key = int(key) @@ -377,32 +372,32 @@ def _get_indexer( def tolist(self) -> list[int]: return list(self._range) - @doc(Int64Index.__iter__) + @doc(NumericIndex.__iter__) def __iter__(self) -> Iterator[int]: yield from self._range - @doc(Int64Index._shallow_copy) + @doc(NumericIndex._shallow_copy) def _shallow_copy(self, values, name: Hashable = no_default): name = self.name if name is no_default else name if values.dtype.kind == "f": - return Float64Index(values, name=name) + return NumericIndex(values, name=name, dtype=np.float64) # GH 46675 & 43885: If values is equally spaced, return a - # more memory-compact RangeIndex instead of Int64Index + # more memory-compact RangeIndex instead of Index with 64-bit dtype unique_diffs = unique_deltas(values) if len(unique_diffs) == 1 and unique_diffs[0] != 0: diff = unique_diffs[0] new_range = range(values[0], values[-1] + diff, diff) return type(self)._simple_new(new_range, name=name) else: - return Int64Index._simple_new(values, name=name) + return NumericIndex._simple_new(values, name=name) def _view(self: RangeIndex) -> RangeIndex: result = type(self)._simple_new(self._range, name=self._name) result._cache = self._cache return result - @doc(Int64Index.copy) + @doc(NumericIndex.copy) def copy(self, name: Hashable = None, deep: bool = False): name = self._validate_names(name=name, deep=deep)[0] new_index = self._rename(name=name) @@ -517,7 +512,6 @@ def _intersection(self, other: Index, sort: bool = False): # caller is responsible for checking self and other are both non-empty if not isinstance(other, RangeIndex): - # Int64Index return super()._intersection(other, sort=sort) first = self._range[::-1] if self.step < 0 else self._range @@ -604,10 +598,10 @@ def _union(self, other: Index, sort): sort : False or None, default None Whether to sort (monotonically increasing) the resulting index. ``sort=None`` returns a ``RangeIndex`` if possible or a sorted - ``Int64Index`` if not. + ``Index`` with a int64 dtype if not. ``sort=False`` can return a ``RangeIndex`` if self is monotonically increasing and other is fully contained in self. Otherwise, returns - an unsorted ``Int64Index`` + an unsorted ``Index`` with an int64 dtype. Returns ------- @@ -819,9 +813,9 @@ def _concat(self, indexes: list[Index], name: Hashable) -> Index: Overriding parent method for the case of all RangeIndex instances. When all members of "indexes" are of type RangeIndex: result will be - RangeIndex if possible, Int64Index otherwise. E.g.: + RangeIndex if possible, Index with a int64 dtype otherwise. E.g.: indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6) - indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Int64Index([0,1,2,4,5]) + indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Index([0,1,2,4,5], dtype='int64') """ if not all(isinstance(x, RangeIndex) for x in indexes): return super()._concat(indexes, name) @@ -848,7 +842,7 @@ def _concat(self, indexes: list[Index], name: Hashable) -> Index: # First non-empty index had only one element if rng.start == start: values = np.concatenate([x._values for x in rng_indexes]) - result = Int64Index(values) + result = self._constructor(values) return result.rename(name) step = rng.start - start @@ -857,7 +851,9 @@ def _concat(self, indexes: list[Index], name: Hashable) -> Index: next_ is not None and rng.start != next_ ) if non_consecutive: - result = Int64Index(np.concatenate([x._values for x in rng_indexes])) + result = self._constructor( + np.concatenate([x._values for x in rng_indexes]) + ) return result.rename(name) if step is not None: @@ -905,7 +901,6 @@ def __getitem__(self, key): "and integer or boolean " "arrays are valid indices" ) - # fall back to Int64Index return super().__getitem__(key) def _getitem_slice(self: RangeIndex, slobj: slice) -> RangeIndex: @@ -1010,15 +1005,14 @@ def _arith_method(self, other, op): res_name = ops.get_op_result_name(self, other) result = type(self)(rstart, rstop, rstep, name=res_name) - # for compat with numpy / Int64Index + # for compat with numpy / Index with int64 dtype # even if we can represent as a RangeIndex, return - # as a Float64Index if we have float-like descriptors + # as a float64 Index if we have float-like descriptors if not all(is_integer(x) for x in [rstart, rstop, rstep]): result = result.astype("float64") return result except (ValueError, TypeError, ZeroDivisionError): - # Defer to Int64Index implementation # test_arithmetic_explicit_conversions return super()._arith_method(other, op) diff --git a/pandas/tests/indexes/ranges/test_join.py b/pandas/tests/indexes/ranges/test_join.py index c3c2560693d3d..958dd6aa9a563 100644 --- a/pandas/tests/indexes/ranges/test_join.py +++ b/pandas/tests/indexes/ranges/test_join.py @@ -1,24 +1,25 @@ import numpy as np +from pandas.core.dtypes.common import is_int64_dtype + from pandas import ( Index, RangeIndex, ) import pandas._testing as tm -from pandas.core.indexes.api import Int64Index class TestJoin: def test_join_outer(self): - # join with Int64Index + # join with Index[int64] index = RangeIndex(start=0, stop=20, step=2) - other = Int64Index(np.arange(25, 14, -1)) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) res, lidx, ridx = index.join(other, how="outer", return_indexers=True) noidx_res = index.join(other, how="outer") tm.assert_index_equal(res, noidx_res) - eres = Int64Index( + eres = Index( [0, 2, 4, 6, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] ) elidx = np.array( @@ -30,9 +31,9 @@ def test_join_outer(self): dtype=np.intp, ) - assert isinstance(res, Int64Index) + assert isinstance(res, Index) and is_int64_dtype(res.dtype) assert not isinstance(res, RangeIndex) - tm.assert_index_equal(res, eres) + tm.assert_index_equal(res, eres, exact=True) tm.assert_numpy_array_equal(lidx, elidx) tm.assert_numpy_array_equal(ridx, eridx) @@ -43,7 +44,7 @@ def test_join_outer(self): noidx_res = index.join(other, how="outer") tm.assert_index_equal(res, noidx_res) - assert isinstance(res, Int64Index) + assert isinstance(res, Index) and res.dtype == np.int64 assert not isinstance(res, RangeIndex) tm.assert_index_equal(res, eres) tm.assert_numpy_array_equal(lidx, elidx) @@ -52,7 +53,7 @@ def test_join_outer(self): def test_join_inner(self): # Join with non-RangeIndex index = RangeIndex(start=0, stop=20, step=2) - other = Int64Index(np.arange(25, 14, -1)) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) res, lidx, ridx = index.join(other, how="inner", return_indexers=True) @@ -62,7 +63,7 @@ def test_join_inner(self): lidx = lidx.take(ind) ridx = ridx.take(ind) - eres = Int64Index([16, 18]) + eres = Index([16, 18]) elidx = np.array([8, 9], dtype=np.intp) eridx = np.array([9, 7], dtype=np.intp) @@ -82,9 +83,9 @@ def test_join_inner(self): tm.assert_numpy_array_equal(ridx, eridx) def test_join_left(self): - # Join with Int64Index + # Join with Index[int64] index = RangeIndex(start=0, stop=20, step=2) - other = Int64Index(np.arange(25, 14, -1)) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) res, lidx, ridx = index.join(other, how="left", return_indexers=True) eres = index @@ -96,7 +97,7 @@ def test_join_left(self): tm.assert_numpy_array_equal(ridx, eridx) # Join withRangeIndex - other = Int64Index(np.arange(25, 14, -1)) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) res, lidx, ridx = index.join(other, how="left", return_indexers=True) @@ -106,15 +107,15 @@ def test_join_left(self): tm.assert_numpy_array_equal(ridx, eridx) def test_join_right(self): - # Join with Int64Index + # Join with Index[int64] index = RangeIndex(start=0, stop=20, step=2) - other = Int64Index(np.arange(25, 14, -1)) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) res, lidx, ridx = index.join(other, how="right", return_indexers=True) eres = other elidx = np.array([-1, -1, -1, -1, -1, -1, -1, 9, -1, 8, -1], dtype=np.intp) - assert isinstance(other, Int64Index) + assert isinstance(other, Index) and other.dtype == np.int64 tm.assert_index_equal(res, eres) tm.assert_numpy_array_equal(lidx, elidx) assert ridx is None @@ -164,7 +165,7 @@ def test_join_non_unique(self): res, lidx, ridx = index.join(other, return_indexers=True) - eres = Int64Index([0, 2, 4, 4, 6, 8, 10, 12, 14, 16, 18]) + eres = Index([0, 2, 4, 4, 6, 8, 10, 12, 14, 16, 18]) elidx = np.array([0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.intp) eridx = np.array([-1, -1, 0, 1, -1, -1, -1, -1, -1, -1, -1], dtype=np.intp) diff --git a/pandas/tests/indexes/ranges/test_range.py b/pandas/tests/indexes/ranges/test_range.py index e534147c891d2..d255dc748b7dc 100644 --- a/pandas/tests/indexes/ranges/test_range.py +++ b/pandas/tests/indexes/ranges/test_range.py @@ -4,20 +4,15 @@ from pandas.core.dtypes.common import ensure_platform_int import pandas as pd -import pandas._testing as tm -from pandas.core.indexes.api import ( - Float64Index, +from pandas import ( Index, - Int64Index, RangeIndex, ) +import pandas._testing as tm from pandas.tests.indexes.common import NumericBase # aliases to make some tests easier to read RI = RangeIndex -I64 = Int64Index -F64 = Float64Index -OI = Index class TestRangeIndex(NumericBase): @@ -111,7 +106,7 @@ def test_insert(self): tm.assert_index_equal(idx[0:4], result.insert(0, idx[0]), exact="equiv") # GH 18295 (test missing) - expected = Float64Index([0, np.nan, 1, 2, 3, 4]) + expected = Index([0, np.nan, 1, 2, 3, 4], dtype=np.float64) for na in [np.nan, None, pd.NA]: result = RangeIndex(5).insert(1, na) tm.assert_index_equal(result, expected) @@ -379,7 +374,7 @@ def test_nbytes(self): # memory savings vs int index idx = RangeIndex(0, 1000) - assert idx.nbytes < Int64Index(idx._values).nbytes / 10 + assert idx.nbytes < Index(idx._values).nbytes / 10 # constant memory usage i2 = RangeIndex(0, 10) @@ -530,16 +525,16 @@ def test_len_specialised(self, step): ([RI(-4, -8), RI(-8, -12)], RI(0, 0)), ([RI(-4, -8), RI(3, -4)], RI(0, 0)), ([RI(-4, -8), RI(3, 5)], RI(3, 5)), - ([RI(-4, -2), RI(3, 5)], I64([-4, -3, 3, 4])), + ([RI(-4, -2), RI(3, 5)], Index([-4, -3, 3, 4])), ([RI(-2), RI(3, 5)], RI(3, 5)), - ([RI(2), RI(2)], I64([0, 1, 0, 1])), + ([RI(2), RI(2)], Index([0, 1, 0, 1])), ([RI(2), RI(2, 5), RI(5, 8, 4)], RI(0, 6)), - ([RI(2), RI(3, 5), RI(5, 8, 4)], I64([0, 1, 3, 4, 5])), + ([RI(2), RI(3, 5), RI(5, 8, 4)], Index([0, 1, 3, 4, 5])), ([RI(-2, 2), RI(2, 5), RI(5, 8, 4)], RI(-2, 6)), - ([RI(3), OI([-1, 3, 15])], OI([0, 1, 2, -1, 3, 15])), - ([RI(3), OI([-1, 3.1, 15.0])], OI([0, 1, 2, -1, 3.1, 15.0])), - ([RI(3), OI(["a", None, 14])], OI([0, 1, 2, "a", None, 14])), - ([RI(3, 1), OI(["a", None, 14])], OI(["a", None, 14])), + ([RI(3), Index([-1, 3, 15])], Index([0, 1, 2, -1, 3, 15])), + ([RI(3), Index([-1, 3.1, 15.0])], Index([0, 1, 2, -1, 3.1, 15.0])), + ([RI(3), Index(["a", None, 14])], Index([0, 1, 2, "a", None, 14])), + ([RI(3, 1), Index(["a", None, 14])], Index(["a", None, 14])), ] ) def appends(self, request): diff --git a/pandas/tests/indexes/ranges/test_setops.py b/pandas/tests/indexes/ranges/test_setops.py index 29ba5a91498db..d417b8b743dc5 100644 --- a/pandas/tests/indexes/ranges/test_setops.py +++ b/pandas/tests/indexes/ranges/test_setops.py @@ -11,12 +11,11 @@ import numpy as np import pytest -import pandas._testing as tm -from pandas.core.indexes.api import ( +from pandas import ( Index, - Int64Index, RangeIndex, ) +import pandas._testing as tm class TestRangeIndexSetOps: @@ -62,7 +61,7 @@ def test_intersection_empty(self, sort, names): tm.assert_index_equal(result, index[:0].rename(names[2]), exact=True) def test_intersection(self, sort): - # intersect with Int64Index + # intersect with Index with dtype int64 index = RangeIndex(start=0, stop=20, step=2) other = Index(np.arange(1, 6)) result = index.intersection(other, sort=sort) @@ -133,7 +132,7 @@ def test_intersection_non_overlapping_gcd(self, sort, names): tm.assert_index_equal(result, expected) def test_union_noncomparable(self, sort): - # corner case, non-Int64Index + # corner case, Index with non-int64 dtype index = RangeIndex(start=0, stop=20, step=2) other = Index([datetime.now() + timedelta(i) for i in range(4)], dtype=object) result = index.union(other, sort=sort) @@ -181,25 +180,25 @@ def test_union_noncomparable(self, sort): RangeIndex(0, 10, 2), RangeIndex(1, 10, 2), RangeIndex(0, 10, 1), - Int64Index(list(range(0, 10, 2)) + list(range(1, 10, 2))), + Index(list(range(0, 10, 2)) + list(range(1, 10, 2))), ), ( RangeIndex(0, 11, 2), RangeIndex(1, 12, 2), RangeIndex(0, 12, 1), - Int64Index(list(range(0, 11, 2)) + list(range(1, 12, 2))), + Index(list(range(0, 11, 2)) + list(range(1, 12, 2))), ), ( RangeIndex(0, 21, 4), RangeIndex(-2, 24, 4), RangeIndex(-2, 24, 2), - Int64Index(list(range(0, 21, 4)) + list(range(-2, 24, 4))), + Index(list(range(0, 21, 4)) + list(range(-2, 24, 4))), ), ( RangeIndex(0, -20, -2), RangeIndex(-1, -21, -2), RangeIndex(-19, 1, 1), - Int64Index(list(range(0, -20, -2)) + list(range(-1, -21, -2))), + Index(list(range(0, -20, -2)) + list(range(-1, -21, -2))), ), ( RangeIndex(0, 100, 5), @@ -211,13 +210,13 @@ def test_union_noncomparable(self, sort): RangeIndex(0, -100, -5), RangeIndex(5, -100, -20), RangeIndex(-95, 10, 5), - Int64Index(list(range(0, -100, -5)) + [5]), + Index(list(range(0, -100, -5)) + [5]), ), ( RangeIndex(0, -11, -1), RangeIndex(1, -12, -4), RangeIndex(-11, 2, 1), - Int64Index(list(range(0, -11, -1)) + [1, -11]), + Index(list(range(0, -11, -1)) + [1, -11]), ), (RangeIndex(0), RangeIndex(0), RangeIndex(0), RangeIndex(0)), ( @@ -236,7 +235,7 @@ def test_union_noncomparable(self, sort): RangeIndex(0, -100, -2), RangeIndex(-100, 50, 102), RangeIndex(-100, 4, 2), - Int64Index(list(range(0, -100, -2)) + [-100, 2]), + Index(list(range(0, -100, -2)) + [-100, 2]), ), ( RangeIndex(0, -100, -1), @@ -254,25 +253,25 @@ def test_union_noncomparable(self, sort): RangeIndex(0, 10, 5), RangeIndex(-5, -6, -20), RangeIndex(-5, 10, 5), - Int64Index([0, 5, -5]), + Index([0, 5, -5]), ), ( RangeIndex(0, 3, 1), RangeIndex(4, 5, 1), - Int64Index([0, 1, 2, 4]), - Int64Index([0, 1, 2, 4]), + Index([0, 1, 2, 4]), + Index([0, 1, 2, 4]), ), ( RangeIndex(0, 10, 1), - Int64Index([]), + Index([], dtype=np.int64), RangeIndex(0, 10, 1), RangeIndex(0, 10, 1), ), ( RangeIndex(0), - Int64Index([1, 5, 6]), - Int64Index([1, 5, 6]), - Int64Index([1, 5, 6]), + Index([1, 5, 6]), + Index([1, 5, 6]), + Index([1, 5, 6]), ), # GH 43885 ( @@ -292,7 +291,7 @@ def test_union_sorted(self, idx1, idx2, expected_sorted, expected_notsorted): tm.assert_index_equal(res1, expected_notsorted, exact=True) res2 = idx2.union(idx1, sort=None) - res3 = Int64Index(idx1._values, name=idx1.name).union(idx2, sort=None) + res3 = Index(idx1._values, name=idx1.name).union(idx2, sort=None) tm.assert_index_equal(res2, expected_sorted, exact=True) tm.assert_index_equal(res3, expected_sorted, exact="equiv") @@ -302,7 +301,7 @@ def test_union_same_step_misaligned(self): right = RangeIndex(range(1, 21, 4)) result = left.union(right) - expected = Int64Index([0, 1, 4, 5, 8, 9, 12, 13, 16, 17]) + expected = Index([0, 1, 4, 5, 8, 9, 12, 13, 16, 17]) tm.assert_index_equal(result, expected, exact=True) def test_difference(self): @@ -338,8 +337,8 @@ def test_difference(self): tm.assert_index_equal(result, obj[:-3][::-1], exact=True) result = obj.difference(obj[2:6]) - expected = Int64Index([1, 2, 7, 8, 9], name="foo") - tm.assert_index_equal(result, expected) + expected = Index([1, 2, 7, 8, 9], name="foo") + tm.assert_index_equal(result, expected, exact=True) def test_difference_sort(self): # GH#44085 ensure we respect the sort keyword @@ -402,25 +401,25 @@ def test_difference_interior_non_preserving(self): other = idx[3:4] result = idx.difference(other) - expected = Int64Index([0, 1, 2, 4, 5, 6, 7, 8, 9]) + expected = Index([0, 1, 2, 4, 5, 6, 7, 8, 9]) tm.assert_index_equal(result, expected, exact=True) # case with other.step / self.step > 2 other = idx[::3] result = idx.difference(other) - expected = Int64Index([1, 2, 4, 5, 7, 8]) + expected = Index([1, 2, 4, 5, 7, 8]) tm.assert_index_equal(result, expected, exact=True) # cases with only reaching one end of left obj = Index(range(20)) other = obj[:10:2] result = obj.difference(other) - expected = Int64Index([1, 3, 5, 7, 9] + list(range(10, 20))) + expected = Index([1, 3, 5, 7, 9] + list(range(10, 20))) tm.assert_index_equal(result, expected, exact=True) other = obj[1:11:2] result = obj.difference(other) - expected = Int64Index([0, 2, 4, 6, 8, 10] + list(range(11, 20))) + expected = Index([0, 2, 4, 6, 8, 10] + list(range(11, 20))) tm.assert_index_equal(result, expected, exact=True) def test_symmetric_difference(self): @@ -436,8 +435,8 @@ def test_symmetric_difference(self): tm.assert_index_equal(result, left.rename(None)) result = left[:-2].symmetric_difference(left[2:]) - expected = Int64Index([1, 2, 8, 9], name="foo") - tm.assert_index_equal(result, expected) + expected = Index([1, 2, 8, 9], name="foo") + tm.assert_index_equal(result, expected, exact=True) right = RangeIndex.from_range(range(10, 15)) @@ -446,8 +445,8 @@ def test_symmetric_difference(self): tm.assert_index_equal(result, expected) result = left.symmetric_difference(right[1:]) - expected = Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14]) - tm.assert_index_equal(result, expected) + expected = Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14]) + tm.assert_index_equal(result, expected, exact=True) def assert_range_or_not_is_rangelike(index): @@ -470,7 +469,7 @@ def assert_range_or_not_is_rangelike(index): ) def test_range_difference(start1, stop1, step1, start2, stop2, step2): # test that - # a) we match Int64Index.difference and + # a) we match Index[int64].difference and # b) we return RangeIndex whenever it is possible to do so. assume(step1 != 0) assume(step2 != 0) @@ -481,11 +480,14 @@ def test_range_difference(start1, stop1, step1, start2, stop2, step2): result = left.difference(right, sort=None) assert_range_or_not_is_rangelike(result) - alt = Int64Index(left).difference(Int64Index(right), sort=None) + left_int64 = Index(left.to_numpy()) + right_int64 = Index(right.to_numpy()) + + alt = left_int64.difference(right_int64, sort=None) tm.assert_index_equal(result, alt, exact="equiv") result = left.difference(right, sort=False) assert_range_or_not_is_rangelike(result) - alt = Int64Index(left).difference(Int64Index(right), sort=False) + alt = left_int64.difference(right_int64, sort=False) tm.assert_index_equal(result, alt, exact="equiv")
Extracted from #50479 to make it more manageable. Progress towards #42717.
https://api.github.com/repos/pandas-dev/pandas/pulls/50826
2023-01-18T15:19:36Z
2023-01-19T21:12:32Z
2023-01-19T21:12:32Z
2023-01-19T21:26:11Z
remove Int/Uint/Float64Index from pandas/tests/frame
diff --git a/pandas/tests/frame/methods/test_astype.py b/pandas/tests/frame/methods/test_astype.py index 2d3d576154740..e9f6371239b4a 100644 --- a/pandas/tests/frame/methods/test_astype.py +++ b/pandas/tests/frame/methods/test_astype.py @@ -11,6 +11,7 @@ CategoricalDtype, DataFrame, DatetimeTZDtype, + Index, Interval, IntervalDtype, NaT, @@ -22,7 +23,6 @@ option_context, ) import pandas._testing as tm -from pandas.core.api import UInt64Index def _check_cast(df, v): @@ -372,7 +372,7 @@ def test_astype_extension_dtypes_duplicate_col(self, dtype): ) def test_astype_column_metadata(self, dtype): # GH#19920 - columns = UInt64Index([100, 200, 300], name="foo") + columns = Index([100, 200, 300], dtype=np.uint64, name="foo") df = DataFrame(np.arange(15).reshape(5, 3), columns=columns) df = df.astype(dtype) tm.assert_index_equal(df.columns, columns) @@ -441,7 +441,7 @@ def test_astype_to_datetime_unit(self, unit): arr = np.array([[1, 2, 3]], dtype=dtype) df = DataFrame(arr) ser = df.iloc[:, 0] - idx = pd.Index(ser) + idx = Index(ser) dta = ser._values if unit in ["ns", "us", "ms", "s"]: @@ -476,7 +476,7 @@ def test_astype_to_datetime_unit(self, unit): exp_dta = exp_ser._values res_index = idx.astype(dtype) - exp_index = pd.Index(exp_ser) + exp_index = Index(exp_ser) assert exp_index.dtype == dtype tm.assert_index_equal(res_index, exp_index) @@ -504,7 +504,7 @@ def test_astype_to_timedelta_unit(self, unit): arr = np.array([[1, 2, 3]], dtype=dtype) df = DataFrame(arr) ser = df.iloc[:, 0] - tdi = pd.Index(ser) + tdi = Index(ser) tda = tdi._values if unit in ["us", "ms", "s"]: diff --git a/pandas/tests/frame/methods/test_truncate.py b/pandas/tests/frame/methods/test_truncate.py index 21f0664707ebe..149fcfb35f44d 100644 --- a/pandas/tests/frame/methods/test_truncate.py +++ b/pandas/tests/frame/methods/test_truncate.py @@ -5,11 +5,11 @@ from pandas import ( DataFrame, DatetimeIndex, + Index, Series, date_range, ) import pandas._testing as tm -from pandas.core.api import Int64Index class TestDataFrameTruncate: @@ -108,13 +108,13 @@ def test_truncate_nonsortedindex_axis1(self): "before, after, indices", [(1, 2, [2, 1]), (None, 2, [2, 1, 0]), (1, None, [3, 2, 1])], ) - @pytest.mark.parametrize("klass", [Int64Index, DatetimeIndex]) + @pytest.mark.parametrize("dtyp", [*tm.ALL_REAL_NUMPY_DTYPES, "datetime64[ns]"]) def test_truncate_decreasing_index( - self, before, after, indices, klass, frame_or_series + self, before, after, indices, dtyp, frame_or_series ): # https://github.com/pandas-dev/pandas/issues/33756 - idx = klass([3, 2, 1, 0]) - if klass is DatetimeIndex: + idx = Index([3, 2, 1, 0], dtype=dtyp) + if isinstance(idx, DatetimeIndex): before = pd.Timestamp(before) if before is not None else None after = pd.Timestamp(after) if after is not None else None indices = [pd.Timestamp(i) for i in indices]
Extracted from #50479 to make it more manageable. Progress towards #42717.
https://api.github.com/repos/pandas-dev/pandas/pulls/50825
2023-01-18T15:07:23Z
2023-01-18T18:35:40Z
2023-01-18T18:35:40Z
2023-01-18T21:41:24Z
Remove int64/uint64/float64 indexes from tests/resample & tests/reshape
diff --git a/pandas/tests/resample/test_resampler_grouper.py b/pandas/tests/resample/test_resampler_grouper.py index 0c8e303b4ac56..3ab57e137f1c1 100644 --- a/pandas/tests/resample/test_resampler_grouper.py +++ b/pandas/tests/resample/test_resampler_grouper.py @@ -15,7 +15,6 @@ Timestamp, ) import pandas._testing as tm -from pandas.core.api import Int64Index from pandas.core.indexes.datetimes import date_range test_frame = DataFrame( @@ -333,7 +332,7 @@ def test_consistency_with_window(): # consistent return values with window df = test_frame - expected = Int64Index([1, 2, 3], name="A") + expected = Index([1, 2, 3], name="A") result = df.groupby("A").resample("2s").mean() assert result.index.nlevels == 2 tm.assert_index_equal(result.index.levels[0], expected) diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py index 35d10eafb5ba7..e52687e3cfbc2 100644 --- a/pandas/tests/reshape/merge/test_merge.py +++ b/pandas/tests/reshape/merge/test_merge.py @@ -20,6 +20,7 @@ CategoricalIndex, DataFrame, DatetimeIndex, + Index, IntervalIndex, MultiIndex, PeriodIndex, @@ -29,11 +30,6 @@ ) import pandas._testing as tm from pandas.api.types import CategoricalDtype as CDT -from pandas.core.api import ( - Float64Index, - Int64Index, - UInt64Index, -) from pandas.core.reshape.concat import concat from pandas.core.reshape.merge import ( MergeError, @@ -1324,8 +1320,13 @@ def test_merge_two_empty_df_no_division_error(self): ["2001-01-01", "2002-02-02", "2003-03-03", pd.NaT, pd.NaT, pd.NaT] ), ), - (Float64Index([1, 2, 3]), Float64Index([1, 2, 3, None, None, None])), - (Int64Index([1, 2, 3]), Float64Index([1, 2, 3, None, None, None])), + *[ + ( + Index([1, 2, 3], dtype=dtyp), + Index([1, 2, 3, None, None, None], dtype=np.float64), + ) + for dtyp in tm.ALL_REAL_NUMPY_DTYPES + ], ( IntervalIndex.from_tuples([(1, 2), (2, 3), (3, 4)]), IntervalIndex.from_tuples( @@ -2142,15 +2143,13 @@ def test_merge_on_indexes(self, left_df, right_df, how, sort, expected): @pytest.mark.parametrize( "index", - [ + [Index([1, 2], dtype=dtyp, name="index_col") for dtyp in tm.ALL_REAL_NUMPY_DTYPES] + + [ CategoricalIndex(["A", "B"], categories=["A", "B"], name="index_col"), - Float64Index([1.0, 2.0], name="index_col"), - Int64Index([1, 2], name="index_col"), - UInt64Index([1, 2], name="index_col"), RangeIndex(start=0, stop=2, name="index_col"), DatetimeIndex(["2018-01-01", "2018-01-02"], name="index_col"), ], - ids=lambda x: type(x).__name__, + ids=lambda x: f"{type(x).__name__}[{x.dtype}]", ) def test_merge_index_types(index): # gh-20777 @@ -2652,11 +2651,11 @@ def test_merge_duplicate_columns_with_suffix_causing_another_duplicate_raises(): def test_merge_string_float_column_result(): # GH 13353 - df1 = DataFrame([[1, 2], [3, 4]], columns=pd.Index(["a", 114.0])) + df1 = DataFrame([[1, 2], [3, 4]], columns=Index(["a", 114.0])) df2 = DataFrame([[9, 10], [11, 12]], columns=["x", "y"]) result = merge(df2, df1, how="inner", left_index=True, right_index=True) expected = DataFrame( - [[9, 10, 1, 2], [11, 12, 3, 4]], columns=pd.Index(["x", "y", "a", 114.0]) + [[9, 10, 1, 2], [11, 12, 3, 4]], columns=Index(["x", "y", "a", 114.0]) ) tm.assert_frame_equal(result, expected) @@ -2712,8 +2711,8 @@ def test_merge_outer_with_NaN(dtype): def test_merge_different_index_names(): # GH#45094 - left = DataFrame({"a": [1]}, index=pd.Index([1], name="c")) - right = DataFrame({"a": [1]}, index=pd.Index([1], name="d")) + left = DataFrame({"a": [1]}, index=Index([1], name="c")) + right = DataFrame({"a": [1]}, index=Index([1], name="d")) result = merge(left, right, left_on="c", right_on="d") expected = DataFrame({"a_x": [1], "a_y": 1}) tm.assert_frame_equal(result, expected)
Extracted from #50479 to make it more manageable. Progress towards #42717.
https://api.github.com/repos/pandas-dev/pandas/pulls/50823
2023-01-18T15:03:25Z
2023-01-18T18:31:51Z
2023-01-18T18:31:51Z
2023-01-18T21:40:18Z
DOC: Add 1.5.x note to main
diff --git a/doc/source/whatsnew/v1.5.3.rst b/doc/source/whatsnew/v1.5.3.rst index 489a9b47c11e1..67c2347fe53ac 100644 --- a/doc/source/whatsnew/v1.5.3.rst +++ b/doc/source/whatsnew/v1.5.3.rst @@ -47,6 +47,7 @@ Other as pandas works toward compatibility with SQLAlchemy 2.0. - Reverted deprecation (:issue:`45324`) of behavior of :meth:`Series.__getitem__` and :meth:`Series.__setitem__` slicing with an integer :class:`Index`; this will remain positional (:issue:`49612`) +- A ``FutureWarning`` raised when attempting to set values inplace with :meth:`DataFrame.loc` or :meth:`DataFrame.iloc` has been changed to a ``DeprecationWarning`` (:issue:`48673`) .. --------------------------------------------------------------------------- .. _whatsnew_153.contributors:
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature - [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). - [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. - [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
https://api.github.com/repos/pandas-dev/pandas/pulls/50822
2023-01-18T14:40:08Z
2023-01-18T16:06:01Z
2023-01-18T16:06:01Z
2023-01-18T18:10:05Z
DOC: Fix whatsnew
diff --git a/doc/source/whatsnew/v1.5.3.rst b/doc/source/whatsnew/v1.5.3.rst index 7488f5f755bf4..97c4c73f08c37 100644 --- a/doc/source/whatsnew/v1.5.3.rst +++ b/doc/source/whatsnew/v1.5.3.rst @@ -48,7 +48,7 @@ Other as pandas works toward compatibility with SQLAlchemy 2.0. - Reverted deprecation (:issue:`45324`) of behavior of :meth:`Series.__getitem__` and :meth:`Series.__setitem__` slicing with an integer :class:`Index`; this will remain positional (:issue:`49612`) -- A ``FutureWarning`` raised when attempting to set values inplace with :meth:`DataFrame.loc` or :meth:`DataFrame.loc` has been changed to a ``DeprecationWarning`` (:issue:`48673`) +- A ``FutureWarning`` raised when attempting to set values inplace with :meth:`DataFrame.loc` or :meth:`DataFrame.iloc` has been changed to a ``DeprecationWarning`` (:issue:`48673`) .. --------------------------------------------------------------------------- .. _whatsnew_153.contributors:
cc @datapythonista
https://api.github.com/repos/pandas-dev/pandas/pulls/50821
2023-01-18T14:39:08Z
2023-01-18T18:09:00Z
2023-01-18T18:09:00Z
2023-01-18T18:09:05Z
update dev meeting frequency
diff --git a/doc/source/development/community.rst b/doc/source/development/community.rst index c321c9b0cccf6..c536cafce3367 100644 --- a/doc/source/development/community.rst +++ b/doc/source/development/community.rst @@ -22,7 +22,7 @@ The pandas Community Meeting is a regular sync meeting for the project's maintainers which is open to the community. Everyone is welcome to attend and contribute to conversations. -The meetings take place on the second Wednesday of each month at 18:00 UTC. +The meetings take place on the second and fourth Wednesdays of each month at 18:00 UTC. The minutes of past meetings are available in `this Google Document <https://docs.google.com/document/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?usp=sharing>`__.
Updated the dev meeting description to include the 2nd monthly meeting (4th Wednesday)
https://api.github.com/repos/pandas-dev/pandas/pulls/50820
2023-01-18T13:49:05Z
2023-01-18T17:53:14Z
2023-01-18T17:53:14Z
2023-01-18T17:53:14Z
DOC: Fix formatting issue with DataFrame.info docs
diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py index 27b61d502e9de..9a93724ca8f37 100644 --- a/pandas/io/formats/info.py +++ b/pandas/io/formats/info.py @@ -254,7 +254,7 @@ buf : writable buffer, defaults to sys.stdout Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process - the output.\ + the output. {max_cols_sub} memory_usage : bool, str, optional Specifies whether total memory usage of the {klass}
Removed a backslash that was causing the description of the **max_cols** argument from the [DataFrme.info](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html#pandas.DataFrame.info) method to be merged with the description of the **buf** argument. ![image](https://user-images.githubusercontent.com/32521936/213172824-a61ba4d6-6449-4ce2-a6f2-6d16ce62b381.png)
https://api.github.com/repos/pandas-dev/pandas/pulls/50816
2023-01-18T12:37:28Z
2023-01-18T19:24:17Z
2023-01-18T19:24:17Z
2023-01-18T19:33:17Z
Manually Backport PR #50809
diff --git a/doc/source/whatsnew/v1.5.3.rst b/doc/source/whatsnew/v1.5.3.rst index 88ae0df704e69..7488f5f755bf4 100644 --- a/doc/source/whatsnew/v1.5.3.rst +++ b/doc/source/whatsnew/v1.5.3.rst @@ -1,7 +1,7 @@ .. _whatsnew_153: -What's new in 1.5.3 (December ??, 2022) ---------------------------------------- +What's new in 1.5.3 (January 18, 2023) +-------------------------------------- These are the changes in pandas 1.5.3. See :ref:`release` for a full changelog including other versions of pandas. @@ -20,7 +20,7 @@ Fixed regressions - Fixed regression in :meth:`.SeriesGroupBy.apply` setting a ``name`` attribute on the result if the result was a :class:`DataFrame` (:issue:`49907`) - Fixed performance regression in setting with the :meth:`~DataFrame.at` indexer (:issue:`49771`) - Fixed regression in the methods ``apply``, ``agg``, and ``transform`` when used with NumPy functions that informed users to supply ``numeric_only=True`` if the operation failed on non-numeric dtypes; such columns must be dropped prior to using these methods (:issue:`50538`) -- +- Fixed regression in :func:`to_datetime` raising ``ValueError`` when parsing array of ``float`` containing ``np.nan`` (:issue:`50237`) .. --------------------------------------------------------------------------- .. _whatsnew_153.bug_fixes: @@ -49,7 +49,6 @@ Other - Reverted deprecation (:issue:`45324`) of behavior of :meth:`Series.__getitem__` and :meth:`Series.__setitem__` slicing with an integer :class:`Index`; this will remain positional (:issue:`49612`) - A ``FutureWarning`` raised when attempting to set values inplace with :meth:`DataFrame.loc` or :meth:`DataFrame.loc` has been changed to a ``DeprecationWarning`` (:issue:`48673`) -- .. --------------------------------------------------------------------------- .. _whatsnew_153.contributors:
xref #50809
https://api.github.com/repos/pandas-dev/pandas/pulls/50813
2023-01-18T10:15:26Z
2023-01-18T14:45:59Z
2023-01-18T14:45:59Z
2023-01-18T14:45:59Z
CI/WEB: Fix github quota errors by using website as cache
diff --git a/.github/workflows/docbuild-and-upload.yml b/.github/workflows/docbuild-and-upload.yml index 7a9f491228a83..908259597cafb 100644 --- a/.github/workflows/docbuild-and-upload.yml +++ b/.github/workflows/docbuild-and-upload.yml @@ -46,12 +46,6 @@ jobs: - name: Build Pandas uses: ./.github/actions/build_pandas - - name: Set up maintainers cache - uses: actions/cache@v3 - with: - path: maintainers.json - key: maintainers - - name: Build website run: python web/pandas_web.py web/pandas --target-path=web/build diff --git a/web/pandas/about/team.md b/web/pandas/about/team.md index c3e5ef0a968eb..5229201ca7d36 100644 --- a/web/pandas/about/team.md +++ b/web/pandas/about/team.md @@ -9,7 +9,8 @@ If you want to support pandas development, you can find information in the [dona ## Active maintainers <div class="card-group maintainers"> - {% for person in maintainers.active_with_github_info %} + {% for username in maintainers.active %} + {% set person = maintainers.github_info.get(username) %} <div class="card"> <img class="card-img-top" alt="" src="{{ person.avatar_url }}"/> <div class="card-body"> @@ -67,7 +68,8 @@ The project governance is available in the [project governance page](governance. ## Inactive maintainers <ul> - {% for person in maintainers.inactive_with_github_info %} + {% for username in maintainers.inactive %} + {% set person = maintainers.github_info.get(username) %} <li> <a href="{{ person.blog or person.html_url }}"> {{ person.name or person.login }} diff --git a/web/pandas/config.yml b/web/pandas/config.yml index 77dfac41ba4d7..816eb6ab296c1 100644 --- a/web/pandas/config.yml +++ b/web/pandas/config.yml @@ -1,10 +1,10 @@ main: templates_path: _templates base_template: "layout.html" + production_url: "https://pandas.pydata.org/" ignore: - _templates/layout.html - config.yml - - try.md # the binder page will be added later github_repo_url: pandas-dev/pandas context_preprocessors: - pandas_web.Preprocessors.current_year diff --git a/web/pandas_web.py b/web/pandas_web.py index 4c30e1959fdff..e4568136edece 100755 --- a/web/pandas_web.py +++ b/web/pandas_web.py @@ -43,12 +43,6 @@ import requests import yaml -api_token = os.environ.get("GITHUB_TOKEN") -if api_token is not None: - GITHUB_API_HEADERS = {"Authorization": f"Bearer {api_token}"} -else: - GITHUB_API_HEADERS = {} - class Preprocessors: """ @@ -164,37 +158,39 @@ def maintainers_add_info(context): Given the active maintainers defined in the yaml file, it fetches the GitHub user information for them. """ - timestamp = time.time() - - cache_file = pathlib.Path("maintainers.json") - if cache_file.is_file(): - with open(cache_file) as f: - context["maintainers"] = json.load(f) - # refresh cache after 1 hour - if (timestamp - context["maintainers"]["timestamp"]) < 3_600: - return context - - context["maintainers"]["timestamp"] = timestamp - repeated = set(context["maintainers"]["active"]) & set( context["maintainers"]["inactive"] ) if repeated: raise ValueError(f"Maintainers {repeated} are both active and inactive") - for kind in ("active", "inactive"): - context["maintainers"][f"{kind}_with_github_info"] = [] - for user in context["maintainers"][kind]: - resp = requests.get( - f"https://api.github.com/users/{user}", headers=GITHUB_API_HEADERS + maintainers_info = {} + for user in ( + context["maintainers"]["active"] + context["maintainers"]["inactive"] + ): + resp = requests.get(f"https://api.github.com/users/{user}") + if resp.status_code == 403: + sys.stderr.write( + "WARN: GitHub API quota exceeded when fetching maintainers\n" + ) + # if we exceed github api quota, we use the github info + # of maintainers saved with the website + resp_bkp = requests.get( + context["main"]["production_url"] + "maintainers.json" ) - if context["ignore_io_errors"] and resp.status_code == 403: - return context - resp.raise_for_status() - context["maintainers"][f"{kind}_with_github_info"].append(resp.json()) + resp_bkp.raise_for_status() + maintainers_info = resp_bkp.json() + break - with open(cache_file, "w") as f: - json.dump(context["maintainers"], f) + resp.raise_for_status() + maintainers_info[user] = resp.json() + + context["maintainers"]["github_info"] = maintainers_info + + # save the data fetched from github to use it in case we exceed + # git github api quota in the future + with open(pathlib.Path(context["target_path"]) / "maintainers.json", "w") as f: + json.dump(maintainers_info, f) return context @@ -203,15 +199,20 @@ def home_add_releases(context): context["releases"] = [] github_repo_url = context["main"]["github_repo_url"] - resp = requests.get( - f"https://api.github.com/repos/{github_repo_url}/releases", - headers=GITHUB_API_HEADERS, - ) - if context["ignore_io_errors"] and resp.status_code == 403: - return context - resp.raise_for_status() + resp = requests.get(f"https://api.github.com/repos/{github_repo_url}/releases") + if resp.status_code == 403: + sys.stderr.write("WARN: GitHub API quota exceeded when fetching releases\n") + resp_bkp = requests.get(context["main"]["production_url"] + "releases.json") + resp_bkp.raise_for_status() + releases = resp_bkp.json() + else: + resp.raise_for_status() + releases = resp.json() + + with open(pathlib.Path(context["target_path"]) / "releases.json", "w") as f: + json.dump(releases, f, default=datetime.datetime.isoformat) - for release in resp.json(): + for release in releases: if release["prerelease"]: continue published = datetime.datetime.strptime( @@ -229,6 +230,7 @@ def home_add_releases(context): ), } ) + return context @staticmethod @@ -273,15 +275,22 @@ def roadmap_pdeps(context): github_repo_url = context["main"]["github_repo_url"] resp = requests.get( "https://api.github.com/search/issues?" - f"q=is:pr is:open label:PDEP repo:{github_repo_url}", - headers=GITHUB_API_HEADERS, + f"q=is:pr is:open label:PDEP repo:{github_repo_url}" ) - if context["ignore_io_errors"] and resp.status_code == 403: - return context - resp.raise_for_status() + if resp.status_code == 403: + sys.stderr.write("WARN: GitHub API quota exceeded when fetching pdeps\n") + resp_bkp = requests.get(context["main"]["production_url"] + "pdeps.json") + resp_bkp.raise_for_status() + pdeps = resp_bkp.json() + else: + resp.raise_for_status() + pdeps = resp.json() - for pdep in resp.json()["items"]: - context["pdeps"]["under_discussion"].append( + with open(pathlib.Path(context["target_path"]) / "pdeps.json", "w") as f: + json.dump(pdeps, f) + + for pdep in pdeps["items"]: + context["pdeps"]["Under discussion"].append( {"title": pdep["title"], "url": pdep["url"]} ) @@ -314,7 +323,7 @@ def get_callable(obj_as_str: str) -> object: return obj -def get_context(config_fname: str, ignore_io_errors: bool, **kwargs): +def get_context(config_fname: str, **kwargs): """ Load the config yaml as the base context, and enrich it with the information added by the context preprocessors defined in the file. @@ -323,7 +332,6 @@ def get_context(config_fname: str, ignore_io_errors: bool, **kwargs): context = yaml.safe_load(f) context["source_path"] = os.path.dirname(config_fname) - context["ignore_io_errors"] = ignore_io_errors context.update(kwargs) preprocessors = ( @@ -361,7 +369,9 @@ def extend_base_template(content: str, base_template: str) -> str: def main( - source_path: str, target_path: str, base_url: str, ignore_io_errors: bool + source_path: str, + target_path: str, + base_url: str, ) -> int: """ Copy every file in the source directory to the target directory. @@ -375,7 +385,7 @@ def main( os.makedirs(target_path, exist_ok=True) sys.stderr.write("Generating context...\n") - context = get_context(config_fname, ignore_io_errors, base_url=base_url) + context = get_context(config_fname, base_url=base_url, target_path=target_path) sys.stderr.write("Context generated\n") templates_path = os.path.join(source_path, context["main"]["templates_path"]) @@ -419,15 +429,5 @@ def main( parser.add_argument( "--base-url", default="", help="base url where the website is served from" ) - parser.add_argument( - "--ignore-io-errors", - action="store_true", - help="do not fail if errors happen when fetching " - "data from http sources, and those fail " - "(mostly useful to allow GitHub quota errors " - "when running the script locally)", - ) args = parser.parse_args() - sys.exit( - main(args.source_path, args.target_path, args.base_url, args.ignore_io_errors) - ) + sys.exit(main(args.source_path, args.target_path, args.base_url))
xref #50485 Looks like the previous approach didn't fix the github quota errors. @mroeschke I saw you also added a github token, but that didn't seem to work either. What I'm doing here is to save the data we fetch from github in the website. For example https://pandas.pydata.org/maintainers.json (also for releases and pdeps). The, we request data from github normally, but if the quota is exceeded, if fallbacks to that json file with the data of the previous build. In the worst case scenario we'll have some delay updating the info in the website (we use the quota on PRs, and it can happen that for commits to main that update the cache files, we don't have quota for a while). But we shouldn't see any other CI failure caused by the github quota. Since building the website shouldn't fail anymore, in the CI or locally, I remove the `--ignore-io-errors` parameter of the script, which doesn't make sense anymore. Also the previous cache, and the token, which I think they didn't help much. I also found a typo when fetching the PDEPs under discussion, that was preventing to render then in the roadmap page. It's also being fixed here.
https://api.github.com/repos/pandas-dev/pandas/pulls/50811
2023-01-18T08:31:44Z
2023-01-18T15:12:58Z
2023-01-18T15:12:58Z
2023-03-15T15:49:24Z
TYP: fixed io.html._read typing
diff --git a/pandas/io/html.py b/pandas/io/html.py index 7dcbd76b77b28..f025e12bd0f55 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -19,6 +19,7 @@ ) from pandas._typing import ( + BaseBuffer, FilePath, ReadBuffer, ) @@ -130,9 +131,7 @@ def _get_skiprows(skiprows: int | Sequence[int] | slice | None) -> int | Sequenc raise TypeError(f"{type(skiprows).__name__} is not a valid type for skipping rows") -def _read( - obj: bytes | FilePath | ReadBuffer[str] | ReadBuffer[bytes], encoding: str | None -) -> str | bytes: +def _read(obj: FilePath | BaseBuffer, encoding: str | None) -> str | bytes: """ Try to read from a url, file or string. @@ -150,13 +149,7 @@ def _read( or hasattr(obj, "read") or (isinstance(obj, str) and file_exists(obj)) ): - # error: Argument 1 to "get_handle" has incompatible type "Union[str, bytes, - # Union[IO[Any], RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]]"; - # expected "Union[PathLike[str], Union[str, Union[IO[Any], RawIOBase, - # BufferedIOBase, TextIOBase, TextIOWrapper, mmap]]]" - with get_handle( - obj, "r", encoding=encoding # type: ignore[arg-type] - ) as handles: + with get_handle(obj, "r", encoding=encoding) as handles: text = handles.handle.read() elif isinstance(obj, (str, bytes)): text = obj
xref https://github.com/pandas-dev/pandas/issues/37715
https://api.github.com/repos/pandas-dev/pandas/pulls/50810
2023-01-18T06:50:58Z
2023-01-18T19:22:31Z
2023-01-18T19:22:31Z
2023-01-18T20:05:44Z
RLS: first release candidate for v0.13
diff --git a/setup.py b/setup.py index 44229fa64d4cd..cf9d036f1b1ff 100755 --- a/setup.py +++ b/setup.py @@ -189,11 +189,11 @@ def build_extensions(self): ] MAJOR = 0 -MINOR = 12 +MINOR = 13 MICRO = 0 -ISRELEASED = False +ISRELEASED = True VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO) -QUALIFIER = '' +QUALIFIER = 'rc1' FULLVERSION = VERSION if not ISRELEASED:
https://api.github.com/repos/pandas-dev/pandas/pulls/5617
2013-11-29T18:10:35Z
2013-11-29T20:53:06Z
2013-11-29T20:53:06Z
2014-07-16T08:42:08Z
DOC: SQL to pandas comparison (#4524)
diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst new file mode 100644 index 0000000000000..3d8b85e9460c4 --- /dev/null +++ b/doc/source/comparison_with_sql.rst @@ -0,0 +1,380 @@ +.. currentmodule:: pandas +.. _compare_with_sql: + +Comparison with SQL +******************** +Since many potential pandas users have some familiarity with +`SQL <http://en.wikipedia.org/wiki/SQL>`_, this page is meant to provide some examples of how +various SQL operations would be performed using pandas. + +If you're new to pandas, you might want to first read through :ref:`10 Minutes to Pandas<10min>` +to familiarize yourself with the library. + +As is customary, we import pandas and numpy as follows: + +.. ipython:: python + + import pandas as pd + import numpy as np + +Most of the examples will utilize the ``tips`` dataset found within pandas tests. We'll read +the data into a DataFrame called `tips` and assume we have a database table of the same name and +structure. + +.. ipython:: python + + url = 'https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv' + tips = pd.read_csv(url) + tips.head() + +SELECT +------ +In SQL, selection is done using a comma-separated list of columns you'd like to select (or a ``*`` +to select all columns): + +.. code-block:: sql + + SELECT total_bill, tip, smoker, time + FROM tips + LIMIT 5; + +With pandas, column selection is done by passing a list of column names to your DataFrame: + +.. ipython:: python + + tips[['total_bill', 'tip', 'smoker', 'time']].head(5) + +Calling the DataFrame without the list of column names would display all columns (akin to SQL's +``*``). + +WHERE +----- +Filtering in SQL is done via a WHERE clause. + +.. code-block:: sql + + SELECT * + FROM tips + WHERE time = 'Dinner' + LIMIT 5; + +DataFrames can be filtered in multiple ways; the most intuitive of which is using +`boolean indexing <http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing>`_. + +.. ipython:: python + + tips[tips['time'] == 'Dinner'].head(5) + +The above statement is simply passing a ``Series`` of True/False objects to the DataFrame, +returning all rows with True. + +.. ipython:: python + + is_dinner = tips['time'] == 'Dinner' + is_dinner.value_counts() + tips[is_dinner].head(5) + +Just like SQL's OR and AND, multiple conditions can be passed to a DataFrame using | (OR) and & +(AND). + +.. code-block:: sql + + -- tips of more than $5.00 at Dinner meals + SELECT * + FROM tips + WHERE time = 'Dinner' AND tip > 5.00; + +.. ipython:: python + + # tips of more than $5.00 at Dinner meals + tips[(tips['time'] == 'Dinner') & (tips['tip'] > 5.00)] + +.. code-block:: sql + + -- tips by parties of at least 5 diners OR bill total was more than $45 + SELECT * + FROM tips + WHERE size >= 5 OR total_bill > 45; + +.. ipython:: python + + # tips by parties of at least 5 diners OR bill total was more than $45 + tips[(tips['size'] >= 5) | (tips['total_bill'] > 45)] + +NULL checking is done using the :meth:`~pandas.Series.notnull` and :meth:`~pandas.Series.isnull` +methods. + +.. ipython:: python + + frame = pd.DataFrame({'col1': ['A', 'B', np.NaN, 'C', 'D'], + 'col2': ['F', np.NaN, 'G', 'H', 'I']}) + frame + +Assume we have a table of the same structure as our DataFrame above. We can see only the records +where ``col2`` IS NULL with the following query: + +.. code-block:: sql + + SELECT * + FROM frame + WHERE col2 IS NULL; + +.. ipython:: python + + frame[frame['col2'].isnull()] + +Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.notnull`. + +.. code-block:: sql + + SELECT * + FROM frame + WHERE col1 IS NOT NULL; + +.. ipython:: python + + frame[frame['col1'].notnull()] + + +GROUP BY +-------- +In pandas, SQL's GROUP BY operations performed using the similarly named +:meth:`~pandas.DataFrame.groupby` method. :meth:`~pandas.DataFrame.groupby` typically refers to a +process where we'd like to split a dataset into groups, apply some function (typically aggregation) +, and then combine the groups together. + +A common SQL operation would be getting the count of records in each group throughout a dataset. +For instance, a query getting us the number of tips left by sex: + +.. code-block:: sql + + SELECT sex, count(*) + FROM tips + GROUP BY sex; + /* + Female 87 + Male 157 + */ + + +The pandas equivalent would be: + +.. ipython:: python + + tips.groupby('sex').size() + +Notice that in the pandas code we used :meth:`~pandas.DataFrameGroupBy.size` and not +:meth:`~pandas.DataFrameGroupBy.count`. This is because :meth:`~pandas.DataFrameGroupBy.count` +applies the function to each column, returning the number of ``not null`` records within each. + +.. ipython:: python + + tips.groupby('sex').count() + +Alternatively, we could have applied the :meth:`~pandas.DataFrameGroupBy.count` method to an +individual column: + +.. ipython:: python + + tips.groupby('sex')['total_bill'].count() + +Multiple functions can also be applied at once. For instance, say we'd like to see how tip amount +differs by day of the week - :meth:`~pandas.DataFrameGroupBy.agg` allows you to pass a dictionary +to your grouped DataFrame, indicating which functions to apply to specific columns. + +.. code-block:: sql + + SELECT day, AVG(tip), COUNT(*) + FROM tips + GROUP BY day; + /* + Fri 2.734737 19 + Sat 2.993103 87 + Sun 3.255132 76 + Thur 2.771452 62 + */ + +.. ipython:: python + + tips.groupby('day').agg({'tip': np.mean, 'day': np.size}) + +Grouping by more than one column is done by passing a list of columns to the +:meth:`~pandas.DataFrame.groupby` method. + +.. code-block:: sql + + SELECT smoker, day, COUNT(*), AVG(tip) + FROM tip + GROUP BY smoker, day; + /* + smoker day + No Fri 4 2.812500 + Sat 45 3.102889 + Sun 57 3.167895 + Thur 45 2.673778 + Yes Fri 15 2.714000 + Sat 42 2.875476 + Sun 19 3.516842 + Thur 17 3.030000 + */ + +.. ipython:: python + + tips.groupby(['smoker', 'day']).agg({'tip': [np.size, np.mean]}) + +.. _compare_with_sql.join: + +JOIN +---- +JOINs can be performed with :meth:`~pandas.DataFrame.join` or :meth:`~pandas.merge`. By default, +:meth:`~pandas.DataFrame.join` will join the DataFrames on their indices. Each method has +parameters allowing you to specify the type of join to perform (LEFT, RIGHT, INNER, FULL) or the +columns to join on (column names or indices). + +.. ipython:: python + + df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], + 'value': np.random.randn(4)}) + df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'], + 'value': np.random.randn(4)}) + +Assume we have two database tables of the same name and structure as our DataFrames. + +Now let's go over the various types of JOINs. + +INNER JOIN +~~~~~~~~~~ +.. code-block:: sql + + SELECT * + FROM df1 + INNER JOIN df2 + ON df1.key = df2.key; + +.. ipython:: python + + # merge performs an INNER JOIN by default + pd.merge(df1, df2, on='key') + +:meth:`~pandas.merge` also offers parameters for cases when you'd like to join one DataFrame's +column with another DataFrame's index. + +.. ipython:: python + + indexed_df2 = df2.set_index('key') + pd.merge(df1, indexed_df2, left_on='key', right_index=True) + +LEFT OUTER JOIN +~~~~~~~~~~~~~~~ +.. code-block:: sql + + -- show all records from df1 + SELECT * + FROM df1 + LEFT OUTER JOIN df2 + ON df1.key = df2.key; + +.. ipython:: python + + # show all records from df1 + pd.merge(df1, df2, on='key', how='left') + +RIGHT JOIN +~~~~~~~~~~ +.. code-block:: sql + + -- show all records from df2 + SELECT * + FROM df1 + RIGHT OUTER JOIN df2 + ON df1.key = df2.key; + +.. ipython:: python + + # show all records from df2 + pd.merge(df1, df2, on='key', how='right') + +FULL JOIN +~~~~~~~~~ +pandas also allows for FULL JOINs, which display both sides of the dataset, whether or not the +joined columns find a match. As of writing, FULL JOINs are not supported in all RDBMS (MySQL). + +.. code-block:: sql + + -- show all records from both tables + SELECT * + FROM df1 + FULL OUTER JOIN df2 + ON df1.key = df2.key; + +.. ipython:: python + + # show all records from both frames + pd.merge(df1, df2, on='key', how='outer') + + +UNION +----- +UNION ALL can be performed using :meth:`~pandas.concat`. + +.. ipython:: python + + df1 = pd.DataFrame({'city': ['Chicago', 'San Francisco', 'New York City'], + 'rank': range(1, 4)}) + df2 = pd.DataFrame({'city': ['Chicago', 'Boston', 'Los Angeles'], + 'rank': [1, 4, 5]}) + +.. code-block:: sql + + SELECT city, rank + FROM df1 + UNION ALL + SELECT city, rank + FROM df2; + /* + city rank + Chicago 1 + San Francisco 2 + New York City 3 + Chicago 1 + Boston 4 + Los Angeles 5 + */ + +.. ipython:: python + + pd.concat([df1, df2]) + +SQL's UNION is similar to UNION ALL, however UNION will remove duplicate rows. + +.. code-block:: sql + + SELECT city, rank + FROM df1 + UNION + SELECT city, rank + FROM df2; + -- notice that there is only one Chicago record this time + /* + city rank + Chicago 1 + San Francisco 2 + New York City 3 + Boston 4 + Los Angeles 5 + */ + +In pandas, you can use :meth:`~pandas.concat` in conjunction with +:meth:`~pandas.DataFrame.drop_duplicates`. + +.. ipython:: python + + pd.concat([df1, df2]).drop_duplicates() + + +UPDATE +------ + + +DELETE +------ \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index 02e193cdb6938..c406c4f2cfa27 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -132,5 +132,6 @@ See the package overview for more detail about what's in the library. r_interface related comparison_with_r + comparison_with_sql api release diff --git a/doc/source/merging.rst b/doc/source/merging.rst index a68fc6e0739d5..8f1eb0dc779be 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -305,7 +305,10 @@ better) than other open source implementations (like ``base::merge.data.frame`` in R). The reason for this is careful algorithmic design and internal layout of the data in DataFrame. -See the :ref:`cookbook<cookbook.merge>` for some advanced strategies +See the :ref:`cookbook<cookbook.merge>` for some advanced strategies. + +Users who are familiar with SQL but new to pandas might be interested in a +:ref:`comparison with SQL<compare_with_sql.join>`. pandas provides a single function, ``merge``, as the entry point for all standard database join operations between DataFrame objects: diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index a39e415abe519..9f410cf7b4f8b 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -10,6 +10,9 @@ Highlights include support for a new index type ``Float64Index``, support for ne Several experimental features are added, including new ``eval/query`` methods for expression evaluation, support for ``msgpack`` serialization, and an io interface to Google's ``BigQuery``. +The docs also received a new section, :ref:`Comparison with SQL<compare_with_sql>`, which should +be useful for those familiar with SQL but still learning pandas. + .. warning:: In 0.13.0 ``Series`` has internally been refactored to no longer sub-class ``ndarray``
@jtratner I was a little slower moving on this than I'd hoped, but it's a pretty good start.
https://api.github.com/repos/pandas-dev/pandas/pulls/5615
2013-11-29T06:15:33Z
2013-12-07T00:40:20Z
2013-12-07T00:40:20Z
2014-06-17T19:04:21Z
BUG: repr formating to use iloc rather than getitem for element access (GH5605)
diff --git a/pandas/core/format.py b/pandas/core/format.py index cc6f5bd516a19..8bc74f2ff4c08 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -284,6 +284,7 @@ def __init__(self, frame, buf=None, columns=None, col_space=None, self.line_width = line_width self.max_rows = max_rows self.max_cols = max_cols + self.max_rows_displayed = min(max_rows or len(self.frame),len(self.frame)) self.show_dimensions = show_dimensions if justify is None: @@ -483,7 +484,7 @@ def write(buf, frame, column_format, strcols): def _format_col(self, i): formatter = self._get_formatter(i) - return format_array(self.frame.icol(i)[:self.max_rows].get_values(), + return format_array((self.frame.iloc[:self.max_rows_displayed,i]).get_values(), formatter, float_format=self.float_format, na_rep=self.na_rep, space=self.col_space) diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 8e23176e9d005..fce2bdceba570 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -1455,6 +1455,22 @@ def test_repr_html_long(self): assert u('%d rows ') % h in long_repr assert u('2 columns') in long_repr + def test_repr_html_float(self): + max_rows = get_option('display.max_rows') + h = max_rows - 1 + df = pandas.DataFrame({'idx':np.linspace(-10,10,h), 'A':np.arange(1,1+h), 'B': np.arange(41, 41+h) }).set_index('idx') + reg_repr = df._repr_html_() + assert '...' not in reg_repr + assert str(40 + h) in reg_repr + + h = max_rows + 1 + df = pandas.DataFrame({'idx':np.linspace(-10,10,h), 'A':np.arange(1,1+h), 'B': np.arange(41, 41+h) }).set_index('idx') + long_repr = df._repr_html_() + assert '...' in long_repr + assert str(40 + h) not in long_repr + assert u('%d rows ') % h in long_repr + assert u('2 columns') in long_repr + def test_repr_html_long_multiindex(self): max_rows = get_option('display.max_rows') max_L1 = max_rows//2
closes #5605
https://api.github.com/repos/pandas-dev/pandas/pulls/5606
2013-11-28T02:33:38Z
2013-11-28T03:19:25Z
2013-11-28T03:19:24Z
2014-07-04T06:16:27Z
Expand groupby dispatch whitelist (GH5480)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index c635baa0e2739..7a7fe32963457 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -50,13 +50,30 @@ # forwarding methods from NDFrames _plotting_methods = frozenset(['plot', 'boxplot', 'hist']) -_apply_whitelist = frozenset(['last', 'first', - 'mean', 'sum', 'min', 'max', - 'cumsum', 'cumprod', 'cummin', 'cummax', - 'resample', - 'describe', - 'rank', 'quantile', 'count', - 'fillna', 'dtype']) | _plotting_methods +_common_apply_whitelist = frozenset([ + 'last', 'first', + 'head', 'tail', 'median', + 'mean', 'sum', 'min', 'max', + 'cumsum', 'cumprod', 'cummin', 'cummax', 'cumcount', + 'resample', + 'describe', + 'rank', 'quantile', 'count', + 'fillna', + 'mad', + 'any', 'all', + 'irow', 'take', + 'shift', 'tshift', + 'ffill', 'bfill', + 'pct_change', 'skew', + 'corr', 'cov', +]) | _plotting_methods + +_series_apply_whitelist = \ + (_common_apply_whitelist - set(['boxplot'])) | \ + frozenset(['dtype', 'value_counts']) + +_dataframe_apply_whitelist = \ + _common_apply_whitelist | frozenset(['dtypes', 'corrwith']) class GroupByError(Exception): @@ -185,6 +202,7 @@ class GroupBy(PandasObject): len(grouped) : int Number of groups """ + _apply_whitelist = _common_apply_whitelist def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, @@ -252,7 +270,7 @@ def _selection_list(self): return self._selection def _local_dir(self): - return sorted(set(self.obj._local_dir() + list(_apply_whitelist))) + return sorted(set(self.obj._local_dir() + list(self._apply_whitelist))) def __getattr__(self, attr): if attr in self.obj: @@ -268,7 +286,7 @@ def __getitem__(self, key): raise NotImplementedError def _make_wrapper(self, name): - if name not in _apply_whitelist: + if name not in self._apply_whitelist: is_callable = callable(getattr(self.obj, name, None)) kind = ' callable ' if is_callable else ' ' msg = ("Cannot access{0}attribute {1!r} of {2!r} objects, try " @@ -1605,6 +1623,7 @@ def _convert_grouper(axis, grouper): class SeriesGroupBy(GroupBy): + _apply_whitelist = _series_apply_whitelist def aggregate(self, func_or_funcs, *args, **kwargs): """ @@ -2401,6 +2420,7 @@ def add_indices(): class DataFrameGroupBy(NDFrameGroupBy): + _apply_whitelist = _dataframe_apply_whitelist _block_agg_axis = 1 diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 76fee1702d64a..6802b57bc39d1 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -3221,10 +3221,67 @@ def test_groupby_whitelist(self): 'letters': Series(random_letters)}) s = df.floats - blacklist = ['eval', 'query', 'abs', 'shift', 'tshift', 'where', - 'mask', 'align', 'groupby', 'clip', 'astype', - 'at', 'combine', 'consolidate', 'convert_objects', - 'corr', 'corr_with', 'cov'] + df_whitelist = frozenset([ + 'last', 'first', + 'mean', 'sum', 'min', 'max', + 'head', 'tail', + 'cumsum', 'cumprod', 'cummin', 'cummax', 'cumcount', + 'resample', + 'describe', + 'rank', 'quantile', 'count', + 'fillna', + 'mad', + 'any', 'all', + 'irow', 'take', + 'shift', 'tshift', + 'ffill', 'bfill', + 'pct_change', 'skew', + 'plot', 'boxplot', 'hist', + 'median', 'dtypes', + 'corrwith', 'corr', 'cov', + ]) + s_whitelist = frozenset([ + 'last', 'first', + 'mean', 'sum', 'min', 'max', + 'head', 'tail', + 'cumsum', 'cumprod', 'cummin', 'cummax', 'cumcount', + 'resample', + 'describe', + 'rank', 'quantile', 'count', + 'fillna', + 'mad', + 'any', 'all', + 'irow', 'take', + 'shift', 'tshift', + 'ffill', 'bfill', + 'pct_change', 'skew', + 'plot', 'hist', + 'median', 'dtype', + 'corr', 'cov', + 'value_counts', + ]) + + for obj, whitelist in zip((df, s), + (df_whitelist, s_whitelist)): + gb = obj.groupby(df.letters) + self.assertEqual(whitelist, gb._apply_whitelist) + for m in whitelist: + getattr(gb, m) + + def test_groupby_blacklist(self): + from string import ascii_lowercase + letters = np.array(list(ascii_lowercase)) + N = 10 + random_letters = letters.take(np.random.randint(0, 26, N)) + df = DataFrame({'floats': N / 10 * Series(np.random.random(N)), + 'letters': Series(random_letters)}) + s = df.floats + + blacklist = [ + 'eval', 'query', 'abs', 'where', + 'mask', 'align', 'groupby', 'clip', 'astype', + 'at', 'combine', 'consolidate', 'convert_objects', + ] to_methods = [method for method in dir(df) if method.startswith('to_')] blacklist.extend(to_methods) @@ -3319,8 +3376,12 @@ def test_tab_completion(self): 'groups','hist','indices','last','max','mean','median', 'min','name','ngroups','nth','ohlc','plot', 'prod', 'size','std','sum','transform','var', 'count', 'head', 'describe', - 'cummax', 'dtype', 'quantile', 'rank', 'cumprod', 'tail', - 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount']) + 'cummax', 'quantile', 'rank', 'cumprod', 'tail', + 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount', + 'all', 'shift', 'skew', 'bfill', 'irow', 'ffill', + 'take', 'tshift', 'pct_change', 'any', 'mad', 'corr', 'corrwith', + 'cov', 'dtypes', + ]) self.assertEqual(results, expected) def assert_fp_equal(a, b):
(and create a groupby dispatch blacklist) closes #5480.
https://api.github.com/repos/pandas-dev/pandas/pulls/5604
2013-11-28T00:56:41Z
2013-12-07T14:16:56Z
2013-12-07T14:16:56Z
2014-07-06T11:29:07Z
ENH: Added method to pandas.data.Options to download all option data for...
diff --git a/doc/source/release.rst b/doc/source/release.rst index fb2c24acff30d..da6c46ce37a94 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -55,6 +55,10 @@ performance improvements along with a large number of bug fixes. Highlights include: +Experimental Features +~~~~~~~~~~~~~~~~~~~~~ +- ``pandas.io.data.Options`` has a get_all_data method and now consistently returns a multi-indexed ''DataFrame'' (:issue:`5602`) + See the :ref:`v0.14.1 Whatsnew <whatsnew_0141>` overview or the issue tracker on GitHub for an extensive list of all API changes, enhancements and bugs that have been fixed in 0.14.1. diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index b0cd96cac6f5f..aae36ee1d54b3 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -52,6 +52,43 @@ Yahoo! Finance f=web.DataReader("F", 'yahoo', start, end) f.ix['2010-01-04'] +.. _remote_data.yahoo_Options: + +Yahoo! Finance Options +---------------------- +***Experimental*** + +The Options class allows the download of options data from Yahoo! Finance. + +The ''get_all_data'' method downloads and caches option data for all expiry months +and provides a formatted ''DataFrame'' with a hierarchical index, so its easy to get +to the specific option you want. + +.. ipython:: python + + from pandas.io.data import Options + aapl = Options('aapl', 'yahoo') + data = aapl.get_all_data() + data.head() + + #Show the $600 strike puts at all expiry dates: + data.loc[(600, slice(None), 'put'),:].head() + + #Show the volume traded of $600 strike puts at all expiry dates: + data.loc[(600, slice(None), 'put'),'Vol'].head() + +If you don't want to download all the data, more specific requests can be made. + +.. ipython:: python + + import datetime + expiry = datetime.date(2016, 1, 1) + data = aapl.get_call_data(expiry=expiry) + data.head() + +Note that if you call ''get_all_data'' first, this second call will happen much faster, as the data is cached. + + .. _remote_data.google: Google Finance diff --git a/doc/source/v0.14.1.txt b/doc/source/v0.14.1.txt index 4959f52183a92..b72d48735d39a 100644 --- a/doc/source/v0.14.1.txt +++ b/doc/source/v0.14.1.txt @@ -148,7 +148,23 @@ Performance Experimental ~~~~~~~~~~~~ -There are no experimental changes in 0.14.1 +``pandas.io.data.Options`` has a get_all_data method and now consistently returns a multi-indexed ''DataFrame'' (PR `#5602`) + See :ref:`the docs<remote_data.yahoo_Options>` ***Experimental*** + + .. ipython:: python + + from pandas.io.data import Options + aapl = Options('aapl', 'yahoo') + data = aapl.get_all_data() + data.head() + + .. ipython:: python + + from pandas.io.data import Options + aapl = Options('aapl', 'yahoo') + data = aapl.get_all_data() + data.head() + .. _whatsnew_0141.bug_fixes: diff --git a/pandas/io/data.py b/pandas/io/data.py index 525a7ce64f0c2..fe87c0d9fb5e7 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -16,10 +16,11 @@ StringIO, bytes_to_str, range, lrange, lmap, zip ) import pandas.compat as compat -from pandas import Panel, DataFrame, Series, read_csv, concat +from pandas import Panel, DataFrame, Series, read_csv, concat, to_datetime from pandas.core.common import is_list_like, PandasError from pandas.io.parsers import TextParser from pandas.io.common import urlopen, ZipFile, urlencode +from pandas.tseries.offsets import MonthBegin from pandas.util.testing import _network_error_classes @@ -518,12 +519,21 @@ def get_data_famafrench(name): # Items needed for options class CUR_MONTH = dt.datetime.now().month CUR_YEAR = dt.datetime.now().year - +CUR_DAY = dt.datetime.now().day def _unpack(row, kind): - els = row.xpath('.//%s' % kind) - return [val.text_content() for val in els] + def _parse_row_values(val): + ret = val.text_content() + if 'neg_arrow' in val.xpath('.//@class'): + try: + ret = float(ret.replace(',', ''))*(-1.0) + except ValueError: + ret = np.nan + + return ret + els = row.xpath('.//%s' % kind) + return [_parse_row_values(val) for val in els] def _parse_options_data(table): rows = table.xpath('.//tr') @@ -540,42 +550,49 @@ def _two_char_month(s): class Options(object): """ + ***Experimental*** This class fetches call/put data for a given stock/expiry month. It is instantiated with a string representing the ticker symbol. The class has the following methods: - get_options:(month, year) - get_calls:(month, year) - get_puts: (month, year) + get_options_data:(month, year, expiry) + get_call_data:(month, year, expiry) + get_put_data: (month, year, expiry) get_near_stock_price(opt_frame, above_below) - get_forward(months, call, put) + get_all_data(call, put) + get_forward_data(months, call, put) (deprecated) Examples -------- # Instantiate object with ticker >>> aapl = Options('aapl', 'yahoo') - # Fetch September 2012 call data - >>> calls = aapl.get_calls(9, 2012) + # Fetch May 2014 call data + >>> expiry = datetime.date(2014, 5, 1) + >>> calls = aapl.get_call_data(expiry=expiry) # Can now access aapl.calls instance variable >>> aapl.calls - # Fetch September 2012 put data - >>> puts = aapl.get_puts(9, 2012) + # Fetch May 2014 put data + >>> puts = aapl.get_put_data(expiry=expiry) # Can now access aapl.puts instance variable >>> aapl.puts # cut down the call data to be 3 below and 3 above the stock price. - >>> cut_calls = aapl.get_near_stock_price(calls, above_below=3) + >>> cut_calls = aapl.get_near_stock_price(call=True, above_below=3) # Fetch call and put data with expiry from now to 8 months out - >>> forward_calls, forward_puts = aapl.get_forward_data(8, - ... call=True, put=True) + >>> forward_data = aapl.get_forward_data(8, call=True, put=True) + # Fetch all call and put data + >>> all_data = aapl.get_all_data() """ + + _TABLE_LOC = {'calls': 9, 'puts': 13} + def __init__(self, symbol, data_source=None): """ Instantiates options_data with a ticker saved as symbol """ self.symbol = symbol.upper() @@ -588,6 +605,7 @@ def __init__(self, symbol, data_source=None): def get_options_data(self, month=None, year=None, expiry=None): """ + ***Experimental*** Gets call/put data for the stock with the expiration data in the given month and year @@ -598,15 +616,30 @@ def get_options_data(self, month=None, year=None, expiry=None): Returns ------- - call_data: pandas.DataFrame - A DataFrame with call options data. - - put_data: pandas.DataFrame - A DataFrame with call options data. - + pandas.DataFrame + A DataFrame with requested options data. + + Index: + Strike: Option strike, int + Expiry: Option expiry, datetime.date + Type: Call or Put, string + Symbol: Option symbol as reported on Yahoo, string + Columns: + Last: Last option price, float + Chg: Change from prior day, float + Bid: Bid price, float + Ask: Ask price, float + Vol: Volume traded, int64 + Open_Int: Open interest, int64 + IsNonstandard: True if the the deliverable is not 100 shares, otherwise false + Underlying: Ticker of the underlying security, string + Underlying_Price: Price of the underlying security, float64 + Quote_Time: Time of the quote, Timestamp Notes ----- + Note: Format of returned data frame is dependent on Yahoo and may change. + When called, this function will add instance variables named calls and puts. See the following example: @@ -618,67 +651,96 @@ def get_options_data(self, month=None, year=None, expiry=None): Also note that aapl.calls and appl.puts will always be the calls and puts for the next expiry. If the user calls this method with a different month or year, the ivar will be named callsMMYY or - putsMMYY where MM and YY are, repsectively, two digit + putsMMYY where MM and YY are, respectively, two digit representations of the month and year for the expiry of the options. """ - return [f(month, year, expiry) for f in (self.get_put_data, - self.get_call_data)] + return concat([f(month, year, expiry) + for f in (self.get_put_data, + self.get_call_data)]).sortlevel() _OPTIONS_BASE_URL = 'http://finance.yahoo.com/q/op?s={sym}' - def _get_option_data(self, month, year, expiry, table_loc, name): - if (month is None or year is None) and expiry is None: - msg = "You must specify either (`year` and `month`) or `expiry`." - raise ValueError(msg) + def _get_option_tables(self, month, year, expiry): - year, month = self._try_parse_dates(year, month, expiry) + year, month, expiry = self._try_parse_dates(year, month, expiry) url = self._OPTIONS_BASE_URL.format(sym=self.symbol) if month and year: # try to get specified month from yahoo finance - m1, m2 = _two_char_month(month), month + m1 = _two_char_month(month) # if this month use other url - if m1 != CUR_MONTH and m2 != CUR_MONTH: - url += '&m={year}-{m1}'.format(year=year, m1=m1) - else: + if month == CUR_MONTH and year == CUR_YEAR: url += '+Options' + else: + url += '&m={year}-{m1}'.format(year=year, m1=m1) else: # Default to current month url += '+Options' - try: - from lxml.html import parse - except ImportError: - raise ImportError("Please install lxml if you want to use the " - "{0!r} class".format(self.__class__.__name__)) - try: - doc = parse(url) - except _network_error_classes: - raise RemoteDataError("Unable to parse tables from URL " - "{0!r}".format(url)) + root = self._parse_url(url) + tables = root.xpath('.//table') + table_name = '_tables' + m1 + str(year)[-2:] + setattr(self, table_name, tables) + + self.underlying_price, self.quote_time = self._get_underlying_price(root) + + return tables + + def _get_underlying_price(self, root): + underlying_price = float(root.xpath('.//*[@class="time_rtq_ticker"]')[0]\ + .getchildren()[0].text) + + #Gets the time of the quote, note this is actually the time of the underlying price. + quote_time_text = root.xpath('.//*[@class="time_rtq"]')[0].getchildren()[0].text + if quote_time_text: + #weekend and prior to market open time format + split = quote_time_text.split(",") + timesplit = split[1].strip().split(":") + timestring = split[0] + ", " + timesplit[0].zfill(2) + ":" + timesplit[1] + quote_time = dt.datetime.strptime(timestring, "%b %d, %H:%M%p EDT") + quote_time = quote_time.replace(year=CUR_YEAR) else: - root = doc.getroot() - if root is None: - raise RemoteDataError("Parsed URL {0!r} has no root" - "element".format(url)) - tables = root.xpath('.//table') - ntables = len(tables) - if ntables == 0: - raise RemoteDataError("No tables found at {0!r}".format(url)) - elif table_loc - 1 > ntables: - raise IndexError("Table location {0} invalid, {1} tables" - " found".format(table_loc, ntables)) + quote_time_text = root.xpath('.//*[@class="time_rtq"]')[0].getchildren()[0].getchildren()[0].text + quote_time = dt.datetime.strptime(quote_time_text, "%H:%M%p EDT") + quote_time = quote_time.replace(year=CUR_YEAR, month=CUR_MONTH, day=CUR_DAY) + + return underlying_price, quote_time + + + def _get_option_data(self, month, year, expiry, name): + year, month, expiry = self._try_parse_dates(year, month, expiry) + m1 = _two_char_month(month) + table_name = '_tables' + m1 + str(year)[-2:] + + try: + tables = getattr(self, table_name) + except AttributeError: + tables = self._get_option_tables(month, year, expiry) + + ntables = len(tables) + table_loc = self._TABLE_LOC[name] + if ntables == 0: + raise RemoteDataError("No tables found at {0!r}".format(url)) + elif table_loc - 1 > ntables: + raise IndexError("Table location {0} invalid, {1} tables" + " found".format(table_loc, ntables)) option_data = _parse_options_data(tables[table_loc]) + option_data = self._process_data(option_data) + option_data['Type'] = name[:-1] + option_data.set_index(['Strike', 'Expiry', 'Type', 'Symbol'], inplace=True) - if month: - name += m1 + str(year)[-2:] + if month == CUR_MONTH and year == CUR_YEAR: + setattr(self, name, option_data) + + name += m1 + str(year)[-2:] setattr(self, name, option_data) return option_data def get_call_data(self, month=None, year=None, expiry=None): """ + ***Experimental*** Gets call/put data for the stock with the expiration data in the given month and year @@ -690,10 +752,29 @@ def get_call_data(self, month=None, year=None, expiry=None): Returns ------- call_data: pandas.DataFrame - A DataFrame with call options data. + A DataFrame with requested options data. + + Index: + Strike: Option strike, int + Expiry: Option expiry, datetime.date + Type: Call or Put, string + Symbol: Option symbol as reported on Yahoo, string + Columns: + Last: Last option price, float + Chg: Change from prior day, float + Bid: Bid price, float + Ask: Ask price, float + Vol: Volume traded, int64 + Open_Int: Open interest, int64 + IsNonstandard: True if the the deliverable is not 100 shares, otherwise false + Underlying: Ticker of the underlying security, string + Underlying_Price: Price of the underlying security, float64 + Quote_Time: Time of the quote, Timestamp Notes ----- + Note: Format of returned data frame is dependent on Yahoo and may change. + When called, this function will add instance variables named calls and puts. See the following example: @@ -705,13 +786,14 @@ def get_call_data(self, month=None, year=None, expiry=None): Also note that aapl.calls will always be the calls for the next expiry. If the user calls this method with a different month or year, the ivar will be named callsMMYY where MM and YY are, - repsectively, two digit representations of the month and year + respectively, two digit representations of the month and year for the expiry of the options. """ - return self._get_option_data(month, year, expiry, 9, 'calls') + return self._get_option_data(month, year, expiry, 'calls').sortlevel() def get_put_data(self, month=None, year=None, expiry=None): """ + ***Experimental*** Gets put data for the stock with the expiration data in the given month and year @@ -723,10 +805,29 @@ def get_put_data(self, month=None, year=None, expiry=None): Returns ------- put_data: pandas.DataFrame - A DataFrame with call options data. + A DataFrame with requested options data. + + Index: + Strike: Option strike, int + Expiry: Option expiry, datetime.date + Type: Call or Put, string + Symbol: Option symbol as reported on Yahoo, string + Columns: + Last: Last option price, float + Chg: Change from prior day, float + Bid: Bid price, float + Ask: Ask price, float + Vol: Volume traded, int64 + Open_Int: Open interest, int64 + IsNonstandard: True if the the deliverable is not 100 shares, otherwise false + Underlying: Ticker of the underlying security, string + Underlying_Price: Price of the underlying security, float64 + Quote_Time: Time of the quote, Timestamp Notes ----- + Note: Format of returned data frame is dependent on Yahoo and may change. + When called, this function will add instance variables named puts. See the following example: @@ -743,11 +844,12 @@ def get_put_data(self, month=None, year=None, expiry=None): repsectively, two digit representations of the month and year for the expiry of the options. """ - return self._get_option_data(month, year, expiry, 13, 'puts') + return self._get_option_data(month, year, expiry, 'puts').sortlevel() def get_near_stock_price(self, above_below=2, call=True, put=False, month=None, year=None, expiry=None): """ + ***Experimental*** Cuts the data frame opt_df that is passed in to only take options that are near the current stock price. @@ -774,9 +876,11 @@ def get_near_stock_price(self, above_below=2, call=True, put=False, The resultant DataFrame chopped down to be 2 * above_below + 1 rows desired. If there isn't data as far out as the user has asked for then + + Note: Format of returned data frame is dependent on Yahoo and may change. + """ - year, month = self._try_parse_dates(year, month, expiry) - price = float(get_quote_yahoo([self.symbol])['last']) + year, month, expiry = self._try_parse_dates(year, month, expiry) to_ret = Series({'calls': call, 'puts': put}) to_ret = to_ret[to_ret].index @@ -792,30 +896,60 @@ def get_near_stock_price(self, above_below=2, call=True, put=False, df = getattr(self, name) except AttributeError: meth_name = 'get_{0}_data'.format(nam[:-1]) - df = getattr(self, meth_name)(month, year) + df = getattr(self, meth_name)(expiry=expiry) - start_index = np.where(df['Strike'] > price)[0][0] + start_index = np.where(df.index.get_level_values('Strike') + > self.underlying_price)[0][0] get_range = slice(start_index - above_below, start_index + above_below + 1) chop = df[get_range].dropna(how='all') - chop.reset_index(inplace=True) data[nam] = chop - return [data[nam] for nam in to_ret] - def _try_parse_dates(self, year, month, expiry): - if year is not None or month is not None: + return concat([data[nam] for nam in to_ret]).sortlevel() + + @staticmethod + def _try_parse_dates(year, month, expiry): + """ + Validates dates provided by user. Ensures the user either provided both a month and a year or an expiry. + + Parameters + ---------- + year: Calendar year, int (deprecated) + + month: Calendar month, int (deprecated) + + expiry: Expiry date (month and year), datetime.date, (preferred) + + Returns + ------- + Tuple of year (int), month (int), expiry (datetime.date) + """ + + #Checks if the user gave one of the month or the year but not both and did not provide an expiry: + if (month is not None and year is None) or (month is None and year is not None) and expiry is None: + msg = "You must specify either (`year` and `month`) or `expiry` " \ + "or none of these options for the current month." + raise ValueError(msg) + + if (year is not None or month is not None) and expiry is None: warnings.warn("month, year arguments are deprecated, use expiry" " instead", FutureWarning) if expiry is not None: year = expiry.year month = expiry.month - return year, month + elif year is None and month is None: + year = CUR_YEAR + month = CUR_MONTH + expiry = dt.date(year, month, 1) + + return year, month, expiry def get_forward_data(self, months, call=True, put=False, near=False, above_below=2): """ + ***Experimental*** Gets either call, put, or both data for months starting in the current month and going out in the future a specified amount of time. @@ -841,7 +975,28 @@ def get_forward_data(self, months, call=True, put=False, near=False, Returns ------- - data : dict of str, DataFrame + pandas.DataFrame + A DataFrame with requested options data. + + Index: + Strike: Option strike, int + Expiry: Option expiry, datetime.date + Type: Call or Put, string + Symbol: Option symbol as reported on Yahoo, string + Columns: + Last: Last option price, float + Chg: Change from prior day, float + Bid: Bid price, float + Ask: Ask price, float + Vol: Volume traded, int64 + Open_Int: Open interest, int64 + IsNonstandard: True if the the deliverable is not 100 shares, otherwise false + Underlying: Ticker of the underlying security, string + Underlying_Price: Price of the underlying security, float64 + Quote_Time: Time of the quote, Timestamp + + Note: Format of returned data frame is dependent on Yahoo and may change. + """ warnings.warn("get_forward_data() is deprecated", FutureWarning) in_months = lrange(CUR_MONTH, CUR_MONTH + months + 1) @@ -860,10 +1015,9 @@ def get_forward_data(self, months, call=True, put=False, near=False, to_ret = Series({'calls': call, 'puts': put}) to_ret = to_ret[to_ret].index - data = {} + all_data = [] for name in to_ret: - all_data = DataFrame() for mon in range(months): m2 = in_months[mon] @@ -882,22 +1036,148 @@ def get_forward_data(self, months, call=True, put=False, near=False, frame = self.get_near_stock_price(call=call, put=put, above_below=above_below, month=m2, year=y2) - tick = str(frame.Symbol[0]) - start = len(self.symbol) - year = tick[start:start + 2] - month = tick[start + 2:start + 4] - day = tick[start + 4:start + 6] - expiry = month + '-' + day + '-' + year - frame['Expiry'] = expiry - - if not mon: - all_data = all_data.join(frame, how='right') - else: - all_data = concat([all_data, frame]) - data[name] = all_data - ret = [data[k] for k in to_ret] - if len(ret) == 1: - return ret.pop() - if len(ret) != 2: - raise AssertionError("should be len 2") - return ret + frame = self._process_data(frame) + + all_data.append(frame) + + return concat(all_data).sortlevel() + + def get_all_data(self, call=True, put=True): + """ + ***Experimental*** + Gets either call, put, or both data for all available months starting + in the current month. + + Parameters + ---------- + call: bool, optional (default=True) + Whether or not to collect data for call options + + put: bool, optional (default=True) + Whether or not to collect data for put options. + + Returns + ------- + pandas.DataFrame + A DataFrame with requested options data. + + Index: + Strike: Option strike, int + Expiry: Option expiry, datetime.date + Type: Call or Put, string + Symbol: Option symbol as reported on Yahoo, string + Columns: + Last: Last option price, float + Chg: Change from prior day, float + Bid: Bid price, float + Ask: Ask price, float + Vol: Volume traded, int64 + Open_Int: Open interest, int64 + IsNonstandard: True if the the deliverable is not 100 shares, otherwise false + Underlying: Ticker of the underlying security, string + Underlying_Price: Price of the underlying security, float64 + Quote_Time: Time of the quote, Timestamp + + Note: Format of returned data frame is dependent on Yahoo and may change. + + """ + to_ret = Series({'calls': call, 'puts': put}) + to_ret = to_ret[to_ret].index + + try: + months = self.months + except AttributeError: + months = self._get_expiry_months() + + all_data = [] + + for name in to_ret: + + for month in months: + m2 = month.month + y2 = month.year + + m1 = _two_char_month(m2) + nam = name + str(m1) + str(y2)[2:] + + try: # Try to access on the instance + frame = getattr(self, nam) + except AttributeError: + meth_name = 'get_{0}_data'.format(name[:-1]) + frame = getattr(self, meth_name)(expiry=month) + + all_data.append(frame) + + return concat(all_data).sortlevel() + + def _get_expiry_months(self): + """ + Gets available expiry months. + + Returns + ------- + months : List of datetime objects + """ + + url = 'http://finance.yahoo.com/q/op?s={sym}'.format(sym=self.symbol) + root = self._parse_url(url) + + links = root.xpath('.//*[@id="yfncsumtab"]')[0].xpath('.//a') + month_gen = (element.attrib['href'].split('=')[-1] + for element in links + if '/q/op?s=' in element.attrib['href'] + and '&m=' in element.attrib['href']) + + months = [dt.date(int(month.split('-')[0]), + int(month.split('-')[1]), 1) + for month in month_gen] + + current_month_text = root.xpath('.//*[@id="yfncsumtab"]')[0].xpath('.//strong')[0].text + current_month = dt.datetime.strptime(current_month_text, '%b %y') + months.insert(0, current_month) + self.months = months + + return months + + def _parse_url(self, url): + """ + Downloads and parses a URL, returns xml root. + + """ + try: + from lxml.html import parse + except ImportError: + raise ImportError("Please install lxml if you want to use the " + "{0!r} class".format(self.__class__.__name__)) + try: + doc = parse(url) + except _network_error_classes: + raise RemoteDataError("Unable to parse URL " + "{0!r}".format(url)) + else: + root = doc.getroot() + if root is None: + raise RemoteDataError("Parsed URL {0!r} has no root" + "element".format(url)) + return root + + + def _process_data(self, frame): + """ + Adds columns for Expiry, IsNonstandard (ie: deliverable is not 100 shares) + and Tag (the tag indicating what is actually deliverable, None if standard). + + """ + frame["Rootexp"] = frame.Symbol.str[0:-9] + frame["Root"] = frame.Rootexp.str[0:-6] + frame["Expiry"] = to_datetime(frame.Rootexp.str[-6:]) + #Removes dashes in equity ticker to map to option ticker. + #Ex: BRK-B to BRKB140517C00100000 + frame["IsNonstandard"] = frame['Root'] != self.symbol.replace('-','') + del frame["Rootexp"] + frame["Underlying"] = self.symbol + frame['Underlying_Price'] = self.underlying_price + frame["Quote_Time"] = self.quote_time + frame.rename(columns={'Open Int': 'Open_Int'}, inplace=True) + + return frame diff --git a/pandas/io/tests/data/yahoo_options1.html b/pandas/io/tests/data/yahoo_options1.html new file mode 100644 index 0000000000000..987072b15e280 --- /dev/null +++ b/pandas/io/tests/data/yahoo_options1.html @@ -0,0 +1,329 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> +<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><title>AAPL Options | Apple Inc. 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Analysis</a></li></ul><h3>News &amp; Info</h3><ul><li><a href="/q/h?s=AAPL+Headlines">Headlines</a></li><li><a href="/q/p?s=AAPL+Press+Releases">Press Releases</a></li><li><a href="/q/ce?s=AAPL+Company+Events">Company Events</a></li><li><a href="/mb/AAPL/">Message Boards</a></li><li><a href="/marketpulse/AAPL">Market Pulse</a></li></ul><h3>Company</h3><ul><li><a href="/q/pr?s=AAPL+Profile">Profile</a></li><li><a href="/q/ks?s=AAPL+Key+Statistics">Key Statistics</a></li><li><a href="/q/sec?s=AAPL+SEC+Filings">SEC Filings</a></li><li><a href="/q/co?s=AAPL+Competitors">Competitors</a></li><li><a href="/q/in?s=AAPL+Industry">Industry</a></li><li><a href="/q/ct?s=AAPL+Components">Components</a></li></ul><h3>Analyst Coverage</h3><ul><li><a href="/q/ao?s=AAPL+Analyst+Opinion">Analyst Opinion</a></li><li><a href="/q/ae?s=AAPL+Analyst+Estimates">Analyst Estimates</a></li></ul><h3>Ownership</h3><ul><li><a href="/q/mh?s=AAPL+Major+Holders">Major Holders</a></li><li><a href="/q/it?s=AAPL+Insider+Transactions">Insider Transactions</a></li><li><a href="/q/ir?s=AAPL+Insider+Roster">Insider Roster</a></li></ul><h3>Financials</h3><ul><li><a href="/q/is?s=AAPL+Income+Statement&amp;annual">Income Statement</a></li><li><a href="/q/bs?s=AAPL+Balance+Sheet&amp;annual">Balance Sheet</a></li><li><a href="/q/cf?s=AAPL+Cash+Flow&amp;annual">Cash Flow</a></li></ul></div><div class="ft"></div></div></div><div id="rightcol"><table border="0" cellspacing="0" cellpadding="0" width="580" id="yfncbrobtn" style="padding-top:1px;"><tr><td align="center"><!--ADS:LOCATION=FB2--><span id="yfs_ad_n4FB2" ><!-- APT Vendor: WSOD, Format: Polite in Page --> +<script type="text/javascript" src="https://ad.wsod.com/embed/8bec9b10877d5d7fd7c0fb6e6a631357/1542.0.js.120x60/1399845989.435266?yud=smpv%3d3%26ed%3dKfb2BHkzObF0OPI9Q.6FtZbkSliqfEhrwqW60UZSoR3xvucF8txzwcjUIdM1FNLOlDMJNO3VKY3lwvdYEY3fDwfAWZ7CQCWWuATZjY6FR39ApR6n7HML9c5BGa46lM3IFeXC791QmLU_5P8ySRjZU52HE7FMYjYP89t3Fl69NTaCpC370qQC2YY8QLoGEerBJanAt.Jr1zL4bn5POCOF9fQ.xA3qWqWdGH8Zek_s9EU.2dd.BOCKiClmLlOPKDp.PZj.Y0en_piXkeV6Sl0hZBuvmlSj7dz6pspxUV98vAz3rpjQFOZCqCQbdzA5VVs2THJ_AIY6drRj34eS0EDA6HzpCAXXrkHF6qybNpcxQXaDCi.VPZjhiQsYZKK1iiD1h_SndNjUeZqQXVboOx1f5toCNPHZyUIFLcwomgdL3cMh9sSwbykyh_5zFQK4iqzEItM3DoaCkVzR_qrtGvh7fDdF6a8OfSRLU24QG2hGdH93EfjtIubjS3eZtf.a_S_59t65mJpmMK3.Z9CNEU6ejIYAOO_8fbHbGVTyz6If.Fg2wA1u1srk8qpNr_Akp306F2aCfDCgZe.Ay7HDimV4_UBmANLJpHwXuO7fzmJ5gwxqgjqXcH6_Rc8dUv3LKv0Nsve1ZrNxpaD.xfVFtbA_VWCM9Q1lbRNn9jTjN6Kmq2cwo_XJLCHNPEw59fQr7_EyXmyw&encver=1&encalgo=3DES-CFB-SHA1&app=apt&intf=1&click=http://clicks.beap.bc.yahoo.com/yc/YnY9MS4wLjAmYnM9KDE3NWVkZnVtcihnaWQkbFljcXlqSXdOaTZ5UmhzbVVwVHlvUU93TVRBNExsTnY5R1BfX3hNQSxzdCQxMzk5ODQ1OTg5MzYyOTU2LHNpJDQ0NTEwNTEsc3AkMjg5NTE0MTIsY3IkNDAwNjQ5MDA1MSx2JDIuMCxhaWQkOVBzTWZHS0xjNkUtLGN0JDI1LHlieCRhdjhyZklraWRLU2FCa1hGLnVub3RRLGJpJDIwOTQ5OTU1NTEsbW1lJDg4MzIxNzA5NjY5NDM0ODk0NDIsciQwLHlvbyQxLGFncCQzMTk1OTM2NTUxLGFwJEZCMikp/0/*"></script><NOSCRIPT><a href="http://clicks.beap.bc.yahoo.com/yc/YnY9MS4wLjAmYnM9KDE3aXZsbjhqbChnaWQkbFljcXlqSXdOaTZ5UmhzbVVwVHlvUU93TVRBNExsTnY5R1BfX3hNQSxzdCQxMzk5ODQ1OTg5MzYyOTU2LHNpJDQ0NTEwNTEsc3AkMjg5NTE0MTIsY3IkNDAwNjQ5MDA1MSx2JDIuMCxhaWQkOVBzTWZHS0xjNkUtLGN0JDI1LHlieCRhdjhyZklraWRLU2FCa1hGLnVub3RRLGJpJDIwOTQ5OTU1NTEsbW1lJDg4MzIxNzA5NjY5NDM0ODk0NDIsciQxLHJkJDE0YnQ3b2dwYix5b28kMSxhZ3AkMzE5NTkzNjU1MSxhcCRGQjIpKQ/2/*https://ad.wsod.com/click/8bec9b10877d5d7fd7c0fb6e6a631357/1542.0.img.120x60/?yud=&encver=${ENC_VERSION}&encalgo=${ENC_ALGO}&app=apt&intf=1" target="_blank"><img width="120" height="60" border="0" src="https://ad.wsod.com/embed/8bec9b10877d5d7fd7c0fb6e6a631357/1542.0.img.120x60/1399845989.435266?yud=smpv%3d3%26ed%3dKfb2BHkzObF0OPI9Q.6FtZbkSliqfEhrwqW60UZSoR3xvucF8txzwcjUIdM1FNLOlDMJNO3VKY3lwvdYEY3fDwfAWZ7CQCWWuATZjY6FR39ApR6n7HML9c5BGa46lM3IFeXC791QmLU_5P8ySRjZU52HE7FMYjYP89t3Fl69NTaCpC370qQC2YY8QLoGEerBJanAt.Jr1zL4bn5POCOF9fQ.xA3qWqWdGH8Zek_s9EU.2dd.BOCKiClmLlOPKDp.PZj.Y0en_piXkeV6Sl0hZBuvmlSj7dz6pspxUV98vAz3rpjQFOZCqCQbdzA5VVs2THJ_AIY6drRj34eS0EDA6HzpCAXXrkHF6qybNpcxQXaDCi.VPZjhiQsYZKK1iiD1h_SndNjUeZqQXVboOx1f5toCNPHZyUIFLcwomgdL3cMh9sSwbykyh_5zFQK4iqzEItM3DoaCkVzR_qrtGvh7fDdF6a8OfSRLU24QG2hGdH93EfjtIubjS3eZtf.a_S_59t65mJpmMK3.Z9CNEU6ejIYAOO_8fbHbGVTyz6If.Fg2wA1u1srk8qpNr_Akp306F2aCfDCgZe.Ay7HDimV4_UBmANLJpHwXuO7fzmJ5gwxqgjqXcH6_Rc8dUv3LKv0Nsve1ZrNxpaD.xfVFtbA_VWCM9Q1lbRNn9jTjN6Kmq2cwo_XJLCHNPEw59fQr7_EyXmyw&encver=1&encalgo=3DES-CFB-SHA1&app=apt&intf=1&" /></a></NOSCRIPT><!--QYZ 2094995551,4006490051,98.139.115.226;;FB2;28951412;1;--><script language=javascript> +if(window.xzq_d==null)window.xzq_d=new Object(); 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+window.xzq_d['tj8NfGKLc6E-']='(as$12r4964np,aid$tj8NfGKLc6E-,bi$2094999551,cr$4006494551,ct$25,at$H,eob$gd1_match_id=-1:ypos=FB2)'; +</script><noscript><img width=1 height=1 alt="" src="http://csc.beap.bc.yahoo.com/yi?bv=1.0.0&bs=(134q4e8mq(gid$lYcqyjIwNi6yRhsmUpTyoQOwMTA4LlNv9GP__xMA,st$1399845989362956,si$4451051,sp$28951412,pv$1,v$2.0))&t=J_3-D_3&al=(as$12r4964np,aid$tj8NfGKLc6E-,bi$2094999551,cr$4006494551,ct$25,at$H,eob$gd1_match_id=-1:ypos=FB2)"></noscript></span></td></tr></table><br><div class="rtq_leaf"><div class="rtq_div"><div class="yui-g"><div class="yfi_rt_quote_summary" id="yfi_rt_quote_summary"><div class="hd"><div class="title"><h2>Apple Inc. (AAPL)</h2> <span class="rtq_exch"><span class="rtq_dash">-</span>NasdaqGS </span><span class="wl_sign"></span></div></div><div class="yfi_rt_quote_summary_rt_top sigfig_promo_1"><div> <span class="time_rtq_ticker"><span id="yfs_l84_aapl">585.54</span></span> <span class="down_r time_rtq_content"><span id="yfs_c63_aapl"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> 2.45</span><span id="yfs_p43_aapl">(0.42%)</span> </span><span class="time_rtq"> <span id="yfs_t53_aapl">May 9, 4:00PM EDT</span></span></div><div><span class="rtq_separator">|</span>After Hours + : + <span class="yfs_rtq_quote"><span id="yfs_l86_aapl">585.73</span></span> <span class="up_g"><span id="yfs_c85_aapl"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> 0.19</span> <span id="yfs_c86_aapl">(0.03%)</span></span><span class="time_rtq"> <span id="yfs_t54_aapl">May 9, 7:59PM EDT</span></span></div></div><style> + #yfi_toolbox_mini_rtq.sigfig_promo { + bottom:45px !important; + } + </style><div class="sigfig_promo" id="yfi_toolbox_mini_rtq"><div class="promo_text">Get the big picture on all your investments.</div><div class="promo_link"><a href="https://finance.yahoo.com/portfolio/ytosv2">Sync your Yahoo portfolio now</a></div></div></div></div></div></div><table width="580" id="yfncsumtab" cellpadding="0" cellspacing="0" border="0"><tr><td colspan="3"><table border="0" cellspacing="0" class="yfnc_modtitle1" style="border-bottom:1px solid #dcdcdc; margin-bottom:10px; width:100%;"><form action="/q/op" accept-charset="utf-8"><tr><td><big><b>Options</b></big></td><td align="right"><small><b>Get Options for:</b> <input name="s" id="pageTicker" size="10" /></small><input id="get_quote_logic_opt" name="ql" value="1" type="hidden" autocomplete="off"><input value="GO" type="submit" style="margin-left:3px;" class="rapid-nf"></td></tr></form></table></td></tr><tr valign="top"><td>View By Expiration: <strong>May 14</strong> | <a href="/q/op?s=AAPL&amp;m=2014-06">Jun 14</a> | <a href="/q/op?s=AAPL&amp;m=2014-07">Jul 14</a> | <a href="/q/op?s=AAPL&amp;m=2014-08">Aug 14</a> | <a href="/q/op?s=AAPL&amp;m=2014-10">Oct 14</a> | <a href="/q/op?s=AAPL&amp;m=2015-01">Jan 15</a> | <a href="/q/op?s=AAPL&amp;m=2016-01">Jan 16</a><table cellpadding="0" cellspacing="0" border="0"><tr><td height="2"></td></tr></table><table class="yfnc_mod_table_title1" width="100%" cellpadding="2" cellspacing="0" border="0"><tr valign="top"><td><small><b><strong class="yfi-module-title">Call Options</strong></b></small></td><td align="right">Expire at close Friday, May 30, 2014</td></tr></table><table class="yfnc_datamodoutline1" width="100%" cellpadding="0" cellspacing="0" border="0"><tr valign="top"><td><table border="0" cellpadding="3" cellspacing="1" width="100%"><tr><th scope="col" class="yfnc_tablehead1" width="12%" align="left">Strike</th><th scope="col" class="yfnc_tablehead1" width="12%">Symbol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Last</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Chg</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Bid</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Ask</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Vol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Open Int</th></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=330.000000"><strong>330.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00330000">AAPL140517C00330000</a></td><td class="yfnc_h" align="right"><b>263.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00330000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">254.30</td><td class="yfnc_h" align="right">256.90</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=400.000000"><strong>400.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00400000">AAPL140517C00400000</a></td><td class="yfnc_h" align="right"><b>190.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00400000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">184.35</td><td class="yfnc_h" align="right">186.40</td><td class="yfnc_h" align="right">873</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=410.000000"><strong>410.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00410000">AAPL140517C00410000</a></td><td class="yfnc_h" align="right"><b>181.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00410000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">174.40</td><td class="yfnc_h" align="right">176.45</td><td class="yfnc_h" align="right">68</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=420.000000"><strong>420.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00420000">AAPL140517C00420000</a></td><td class="yfnc_h" align="right"><b>170.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00420000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">164.35</td><td class="yfnc_h" align="right">166.50</td><td class="yfnc_h" align="right">124</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=430.000000"><strong>430.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00430000">AAPL140517C00430000</a></td><td class="yfnc_h" align="right"><b>160.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00430000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">154.40</td><td class="yfnc_h" align="right">156.50</td><td class="yfnc_h" align="right">376</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=440.000000"><strong>440.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00440000">AAPL140517C00440000</a></td><td class="yfnc_h" align="right"><b>149.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00440000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">144.40</td><td class="yfnc_h" align="right">146.60</td><td class="yfnc_h" align="right">90</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=445.000000"><strong>445.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00445000">AAPL140517C00445000</a></td><td class="yfnc_h" align="right"><b>145.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00445000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">139.40</td><td class="yfnc_h" align="right">141.45</td><td class="yfnc_h" align="right">106</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00450000">AAPL140517C00450000</a></td><td class="yfnc_h" align="right"><b>131.27</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00450000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.18</b></span></td><td class="yfnc_h" align="right">134.40</td><td class="yfnc_h" align="right">136.90</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">52</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00450000">AAPL7140517C00450000</a></td><td class="yfnc_h" align="right"><b>117.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00450000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">133.40</td><td class="yfnc_h" align="right">137.80</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=455.000000"><strong>455.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00455000">AAPL140517C00455000</a></td><td class="yfnc_h" align="right"><b>134.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00455000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">129.40</td><td class="yfnc_h" align="right">131.40</td><td class="yfnc_h" align="right">90</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00460000">AAPL140517C00460000</a></td><td class="yfnc_h" align="right"><b>130.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00460000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">124.40</td><td class="yfnc_h" align="right">126.90</td><td class="yfnc_h" align="right">309</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00460000">AAPL7140517C00460000</a></td><td class="yfnc_h" align="right"><b>122.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00460000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">17.50</b></span></td><td class="yfnc_h" align="right">123.30</td><td class="yfnc_h" align="right">127.55</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=465.000000"><strong>465.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00465000">AAPL140517C00465000</a></td><td class="yfnc_h" align="right"><b>125.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00465000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">119.45</td><td class="yfnc_h" align="right">121.70</td><td class="yfnc_h" align="right">172</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00470000">AAPL140517C00470000</a></td><td class="yfnc_h" align="right"><b>111.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00470000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.76</b></span></td><td class="yfnc_h" align="right">114.45</td><td class="yfnc_h" align="right">116.10</td><td class="yfnc_h" align="right">15</td><td class="yfnc_h" align="right">19</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00470000">AAPL7140517C00470000</a></td><td class="yfnc_h" align="right"><b>62.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">113.40</td><td class="yfnc_h" align="right">116.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00470000">AAPL140523C00470000</a></td><td class="yfnc_h" align="right"><b>122.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">114.50</td><td class="yfnc_h" align="right">116.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00475000">AAPL140517C00475000</a></td><td class="yfnc_h" align="right"><b>108.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00475000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.23</b></span></td><td class="yfnc_h" align="right">109.40</td><td class="yfnc_h" align="right">111.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=480.000000"><strong>480.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00480000">AAPL140517C00480000</a></td><td class="yfnc_h" align="right"><b>102.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00480000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">8.60</b></span></td><td class="yfnc_h" align="right">104.40</td><td class="yfnc_h" align="right">106.80</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">31</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00485000">AAPL140517C00485000</a></td><td class="yfnc_h" align="right"><b>107.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00485000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">99.35</td><td class="yfnc_h" align="right">101.85</td><td class="yfnc_h" align="right">302</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00490000">AAPL140517C00490000</a></td><td class="yfnc_h" align="right"><b>91.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00490000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">8.55</b></span></td><td class="yfnc_h" align="right">94.35</td><td class="yfnc_h" align="right">96.90</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">18</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00490000">AAPL7140517C00490000</a></td><td class="yfnc_h" align="right"><b>97.53</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">93.40</td><td class="yfnc_h" align="right">97.35</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00495000">AAPL140517C00495000</a></td><td class="yfnc_h" align="right"><b>89.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00495000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.01</b></span></td><td class="yfnc_h" align="right">89.40</td><td class="yfnc_h" align="right">91.85</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00500000">AAPL140517C00500000</a></td><td class="yfnc_h" align="right"><b>85.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.75</b></span></td><td class="yfnc_h" align="right">84.45</td><td class="yfnc_h" align="right">85.95</td><td class="yfnc_h" align="right">7</td><td class="yfnc_h" align="right">518</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00500000">AAPL7140517C00500000</a></td><td class="yfnc_h" align="right"><b>95.89</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">83.40</td><td class="yfnc_h" align="right">86.90</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">41</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00500000">AAPL140523C00500000</a></td><td class="yfnc_h" align="right"><b>85.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.15</b></span></td><td class="yfnc_h" align="right">85.25</td><td class="yfnc_h" align="right">86.00</td><td class="yfnc_h" align="right">103</td><td class="yfnc_h" align="right">260</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00500000">AAPL140530C00500000</a></td><td class="yfnc_h" align="right"><b>91.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">84.55</td><td class="yfnc_h" align="right">87.00</td><td class="yfnc_h" align="right">85</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=502.500000"><strong>502.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00502500">AAPL140530C00502500</a></td><td class="yfnc_h" align="right"><b>80.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00502500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">6.52</b></span></td><td class="yfnc_h" align="right">82.05</td><td class="yfnc_h" align="right">83.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00505000">AAPL140517C00505000</a></td><td class="yfnc_h" align="right"><b>87.73</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">79.35</td><td class="yfnc_h" align="right">81.25</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">43</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00505000">AAPL7140517C00505000</a></td><td class="yfnc_h" align="right"><b>92.71</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">78.40</td><td class="yfnc_h" align="right">81.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00505000">AAPL140523C00505000</a></td><td class="yfnc_h" align="right"><b>28.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">79.50</td><td class="yfnc_h" align="right">81.90</td><td class="yfnc_h" align="right">385</td><td class="yfnc_h" align="right">20</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00510000">AAPL140517C00510000</a></td><td class="yfnc_h" align="right"><b>71.58</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.95</b></span></td><td class="yfnc_h" align="right">74.45</td><td class="yfnc_h" align="right">76.00</td><td class="yfnc_h" align="right">28</td><td class="yfnc_h" align="right">104</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00515000">AAPL140517C00515000</a></td><td class="yfnc_h" align="right"><b>67.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00515000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.05</b></span></td><td class="yfnc_h" align="right">69.40</td><td class="yfnc_h" align="right">71.00</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">50</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00515000">AAPL140523C00515000</a></td><td class="yfnc_h" align="right"><b>73.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">69.50</td><td class="yfnc_h" align="right">71.80</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00515000">AAPL140530C00515000</a></td><td class="yfnc_h" align="right"><b>74.24</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">69.50</td><td class="yfnc_h" align="right">71.50</td><td class="yfnc_h" align="right">63</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00520000">AAPL140517C00520000</a></td><td class="yfnc_h" align="right"><b>62.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.19</b></span></td><td class="yfnc_h" align="right">64.45</td><td class="yfnc_h" align="right">66.00</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">116</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00520000">AAPL7140517C00520000</a></td><td class="yfnc_h" align="right"><b>72.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">63.45</td><td class="yfnc_h" align="right">67.00</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">27</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00520000">AAPL140530C00520000</a></td><td class="yfnc_h" align="right"><b>72.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">64.65</td><td class="yfnc_h" align="right">66.30</td><td class="yfnc_h" align="right">351</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=522.500000"><strong>522.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00522500">AAPL140523C00522500</a></td><td class="yfnc_h" align="right"><b>60.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00522500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.95</b></span></td><td class="yfnc_h" align="right">62.05</td><td class="yfnc_h" align="right">64.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00525000">AAPL140517C00525000</a></td><td class="yfnc_h" align="right"><b>59.87</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.68</b></span></td><td class="yfnc_h" align="right">59.70</td><td class="yfnc_h" align="right">61.00</td><td class="yfnc_h" align="right">15</td><td class="yfnc_h" align="right">380</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00525000">AAPL7140517C00525000</a></td><td class="yfnc_h" align="right"><b>60.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.35</b></span></td><td class="yfnc_h" align="right">58.45</td><td class="yfnc_h" align="right">61.85</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">27</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00525000">AAPL140523C00525000</a></td><td class="yfnc_h" align="right"><b>57.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">8.60</b></span></td><td class="yfnc_h" align="right">59.55</td><td class="yfnc_h" align="right">61.15</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">17</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00525000">AAPL7140523C00525000</a></td><td class="yfnc_h" align="right"><b>60.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">46.00</b></span></td><td class="yfnc_h" align="right">58.45</td><td class="yfnc_h" align="right">61.40</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00525000">AAPL140530C00525000</a></td><td class="yfnc_h" align="right"><b>57.02</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">8.64</b></span></td><td class="yfnc_h" align="right">59.65</td><td class="yfnc_h" align="right">61.10</td><td class="yfnc_h" align="right">33</td><td class="yfnc_h" align="right">43</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00525000">AAPL7140530C00525000</a></td><td class="yfnc_h" align="right"><b>56.64</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">11.64</b></span></td><td class="yfnc_h" align="right">58.60</td><td class="yfnc_h" align="right">62.90</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00530000">AAPL140517C00530000</a></td><td class="yfnc_h" align="right"><b>54.86</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.44</b></span></td><td class="yfnc_h" align="right">55.15</td><td class="yfnc_h" align="right">55.90</td><td class="yfnc_h" align="right">21</td><td class="yfnc_h" align="right">326</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00530000">AAPL7140517C00530000</a></td><td class="yfnc_h" align="right"><b>55.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.00</b></span></td><td class="yfnc_h" align="right">53.40</td><td class="yfnc_h" align="right">56.45</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00530000">AAPL140523C00530000</a></td><td class="yfnc_h" align="right"><b>57.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">54.50</td><td class="yfnc_h" align="right">56.35</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">81</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00530000">AAPL7140523C00530000</a></td><td class="yfnc_h" align="right"><b>63.18</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">53.55</td><td class="yfnc_h" align="right">57.40</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00530000">AAPL140530C00530000</a></td><td class="yfnc_h" align="right"><b>52.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.95</b></span></td><td class="yfnc_h" align="right">54.65</td><td class="yfnc_h" align="right">56.90</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">14</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=532.500000"><strong>532.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00532500">AAPL140523C00532500</a></td><td class="yfnc_h" align="right"><b>57.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00532500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">52.00</td><td class="yfnc_h" align="right">54.15</td><td class="yfnc_h" align="right">165</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=532.500000"><strong>532.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00532500">AAPL140530C00532500</a></td><td class="yfnc_h" align="right"><b>57.86</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00532500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">52.25</td><td class="yfnc_h" align="right">54.50</td><td class="yfnc_h" align="right">66</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00535000">AAPL140517C00535000</a></td><td class="yfnc_h" align="right"><b>50.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.75</b></span></td><td class="yfnc_h" align="right">50.10</td><td class="yfnc_h" align="right">51.05</td><td class="yfnc_h" align="right">228</td><td class="yfnc_h" align="right">769</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00535000">AAPL7140517C00535000</a></td><td class="yfnc_h" align="right"><b>57.36</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">48.50</td><td class="yfnc_h" align="right">51.25</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">17</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00535000">AAPL140523C00535000</a></td><td class="yfnc_h" align="right"><b>57.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">49.60</td><td class="yfnc_h" align="right">51.40</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00535000">AAPL140530C00535000</a></td><td class="yfnc_h" align="right"><b>47.52</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.23</b></span></td><td class="yfnc_h" align="right">49.75</td><td class="yfnc_h" align="right">51.90</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">58</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00540000">AAPL140517C00540000</a></td><td class="yfnc_h" align="right"><b>41.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.89</b></span></td><td class="yfnc_h" align="right">44.45</td><td class="yfnc_h" align="right">46.00</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">199</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00540000">AAPL7140517C00540000</a></td><td class="yfnc_h" align="right"><b>43.37</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">6.83</b></span></td><td class="yfnc_h" align="right">43.50</td><td class="yfnc_h" align="right">46.35</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">15</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00540000">AAPL140523C00540000</a></td><td class="yfnc_h" align="right"><b>42.29</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.36</b></span></td><td class="yfnc_h" align="right">44.60</td><td class="yfnc_h" align="right">46.30</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">33</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00540000">AAPL140530C00540000</a></td><td class="yfnc_h" align="right"><b>42.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.20</b></span></td><td class="yfnc_h" align="right">44.75</td><td class="yfnc_h" align="right">46.95</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00542500">AAPL140523C00542500</a></td><td class="yfnc_h" align="right"><b>48.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00542500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">42.15</td><td class="yfnc_h" align="right">43.90</td><td class="yfnc_h" align="right">156</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00542500">AAPL140530C00542500</a></td><td class="yfnc_h" align="right"><b>50.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00542500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">42.45</td><td class="yfnc_h" align="right">44.35</td><td class="yfnc_h" align="right">7</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00545000">AAPL140517C00545000</a></td><td class="yfnc_h" align="right"><b>36.72</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.68</b></span></td><td class="yfnc_h" align="right">40.25</td><td class="yfnc_h" align="right">41.05</td><td class="yfnc_h" align="right">177</td><td class="yfnc_h" align="right">901</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00545000">AAPL7140517C00545000</a></td><td class="yfnc_h" align="right"><b>42.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">38.50</td><td class="yfnc_h" align="right">41.40</td><td class="yfnc_h" align="right">144</td><td class="yfnc_h" align="right">150</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00545000">AAPL140530C00545000</a></td><td class="yfnc_h" align="right"><b>45.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">40.00</td><td class="yfnc_h" align="right">41.90</td><td class="yfnc_h" align="right">125</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00550000">AAPL140517C00550000</a></td><td class="yfnc_h" align="right"><b>35.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.00</b></span></td><td class="yfnc_h" align="right">35.65</td><td class="yfnc_h" align="right">36.00</td><td class="yfnc_h" align="right">131</td><td class="yfnc_h" align="right">701</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00550000">AAPL7140517C00550000</a></td><td class="yfnc_h" align="right"><b>30.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">11.50</b></span></td><td class="yfnc_h" align="right">33.55</td><td class="yfnc_h" align="right">36.35</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00550000">AAPL140523C00550000</a></td><td class="yfnc_h" align="right"><b>34.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.60</b></span></td><td class="yfnc_h" align="right">34.80</td><td class="yfnc_h" align="right">36.30</td><td class="yfnc_h" align="right">19</td><td class="yfnc_h" align="right">20</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00550000">AAPL140530C00550000</a></td><td class="yfnc_h" align="right"><b>32.83</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.82</b></span></td><td class="yfnc_h" align="right">35.50</td><td class="yfnc_h" align="right">36.80</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">42</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=552.500000"><strong>552.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00552500">AAPL140523C00552500</a></td><td class="yfnc_h" align="right"><b>38.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00552500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">32.40</td><td class="yfnc_h" align="right">34.20</td><td class="yfnc_h" align="right">214</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=552.500000"><strong>552.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00552500">AAPL140530C00552500</a></td><td class="yfnc_h" align="right"><b>37.64</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00552500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">32.95</td><td class="yfnc_h" align="right">34.80</td><td class="yfnc_h" align="right">69</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00555000">AAPL140517C00555000</a></td><td class="yfnc_h" align="right"><b>30.67</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.66</b></span></td><td class="yfnc_h" align="right">30.45</td><td class="yfnc_h" align="right">31.05</td><td class="yfnc_h" align="right">44</td><td class="yfnc_h" align="right">132</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00555000">AAPL7140517C00555000</a></td><td class="yfnc_h" align="right"><b>36.65</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">28.50</td><td class="yfnc_h" align="right">32.55</td><td class="yfnc_h" align="right">9</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00555000">AAPL140523C00555000</a></td><td class="yfnc_h" align="right"><b>32.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">29.95</td><td class="yfnc_h" align="right">31.65</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00555000">AAPL140530C00555000</a></td><td class="yfnc_h" align="right"><b>38.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">30.65</td><td class="yfnc_h" align="right">32.25</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">11</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00557500">AAPL140530C00557500</a></td><td class="yfnc_h" align="right"><b>26.52</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.48</b></span></td><td class="yfnc_h" align="right">28.40</td><td class="yfnc_h" align="right">29.85</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00560000">AAPL140517C00560000</a></td><td class="yfnc_h" align="right"><b>26.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.30</b></span></td><td class="yfnc_h" align="right">25.65</td><td class="yfnc_h" align="right">26.15</td><td class="yfnc_h" align="right">142</td><td class="yfnc_h" align="right">439</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00560000">AAPL7140517C00560000</a></td><td class="yfnc_h" align="right"><b>25.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">6.67</b></span></td><td class="yfnc_h" align="right">23.60</td><td class="yfnc_h" align="right">27.65</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">26</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00560000">AAPL140523C00560000</a></td><td class="yfnc_h" align="right"><b>23.11</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.89</b></span></td><td class="yfnc_h" align="right">25.35</td><td class="yfnc_h" align="right">26.85</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">127</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00560000">AAPL7140523C00560000</a></td><td class="yfnc_h" align="right"><b>42.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00560000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">24.30</td><td class="yfnc_h" align="right">28.20</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00560000">AAPL140530C00560000</a></td><td class="yfnc_h" align="right"><b>24.12</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.63</b></span></td><td class="yfnc_h" align="right">26.20</td><td class="yfnc_h" align="right">27.65</td><td class="yfnc_h" align="right">8</td><td class="yfnc_h" align="right">102</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00562500">AAPL140517C00562500</a></td><td class="yfnc_h" align="right"><b>19.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.35</b></span></td><td class="yfnc_h" align="right">23.10</td><td class="yfnc_h" align="right">23.80</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00562500">AAPL140523C00562500</a></td><td class="yfnc_h" align="right"><b>24.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.75</b></span></td><td class="yfnc_h" align="right">23.20</td><td class="yfnc_h" align="right">24.55</td><td class="yfnc_h" align="right">29</td><td class="yfnc_h" align="right">6</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00562500">AAPL140530C00562500</a></td><td class="yfnc_h" align="right"><b>31.29</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">24.10</td><td class="yfnc_h" align="right">25.50</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00565000">AAPL140517C00565000</a></td><td class="yfnc_h" align="right"><b>21.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.40</b></span></td><td class="yfnc_h" align="right">20.75</td><td class="yfnc_h" align="right">21.35</td><td class="yfnc_h" align="right">64</td><td class="yfnc_h" align="right">247</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00565000">AAPL7140517C00565000</a></td><td class="yfnc_h" align="right"><b>17.01</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.02</b></span></td><td class="yfnc_h" align="right">18.75</td><td class="yfnc_h" align="right">22.80</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00565000">AAPL140523C00565000</a></td><td class="yfnc_h" align="right"><b>20.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.40</b></span></td><td class="yfnc_h" align="right">21.45</td><td class="yfnc_h" align="right">22.35</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">145</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00565000">AAPL7140523C00565000</a></td><td class="yfnc_h" align="right"><b>29.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">19.80</td><td class="yfnc_h" align="right">22.85</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00565000">AAPL140530C00565000</a></td><td class="yfnc_h" align="right"><b>23.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.60</b></span></td><td class="yfnc_h" align="right">22.85</td><td class="yfnc_h" align="right">23.40</td><td class="yfnc_h" align="right">15</td><td class="yfnc_h" align="right">119</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00565000">AAPL7140530C00565000</a></td><td class="yfnc_h" align="right"><b>33.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">21.20</td><td class="yfnc_h" align="right">23.85</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00567500">AAPL140517C00567500</a></td><td class="yfnc_h" align="right"><b>16.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">33.50</b></span></td><td class="yfnc_h" align="right">18.30</td><td class="yfnc_h" align="right">18.95</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00567500">AAPL140523C00567500</a></td><td class="yfnc_h" align="right"><b>20.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.85</b></span></td><td class="yfnc_h" align="right">19.50</td><td class="yfnc_h" align="right">20.20</td><td class="yfnc_h" align="right">8</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00570000">AAPL140517C00570000</a></td><td class="yfnc_h" align="right"><b>16.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.64</b></span></td><td class="yfnc_h" align="right">16.10</td><td class="yfnc_h" align="right">16.65</td><td class="yfnc_h" align="right">475</td><td class="yfnc_h" align="right">581</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00570000">AAPL7140517C00570000</a></td><td class="yfnc_h" align="right"><b>12.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.38</b></span></td><td class="yfnc_h" align="right">14.45</td><td class="yfnc_h" align="right">17.05</td><td class="yfnc_h" align="right">9</td><td class="yfnc_h" align="right">84</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00570000">AAPL140523C00570000</a></td><td class="yfnc_h" align="right"><b>15.21</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.64</b></span></td><td class="yfnc_h" align="right">17.50</td><td class="yfnc_h" align="right">18.10</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">146</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00570000">AAPL7140523C00570000</a></td><td class="yfnc_h" align="right"><b>16.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.03</b></span></td><td class="yfnc_h" align="right">16.00</td><td class="yfnc_h" align="right">18.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00570000">AAPL140530C00570000</a></td><td class="yfnc_h" align="right"><b>18.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.20</b></span></td><td class="yfnc_h" align="right">18.90</td><td class="yfnc_h" align="right">19.35</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">295</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00570000">AAPL7140530C00570000</a></td><td class="yfnc_h" align="right"><b>18.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.50</b></span></td><td class="yfnc_h" align="right">16.80</td><td class="yfnc_h" align="right">20.05</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">63</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00572500">AAPL140517C00572500</a></td><td class="yfnc_h" align="right"><b>14.15</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">7.15</b></span></td><td class="yfnc_h" align="right">13.90</td><td class="yfnc_h" align="right">14.40</td><td class="yfnc_h" align="right">222</td><td class="yfnc_h" align="right">40</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00572500">AAPL140523C00572500</a></td><td class="yfnc_h" align="right"><b>16.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.10</b></span></td><td class="yfnc_h" align="right">15.55</td><td class="yfnc_h" align="right">16.10</td><td class="yfnc_h" align="right">79</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00572500">AAPL140530C00572500</a></td><td class="yfnc_h" align="right"><b>17.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.25</b></span></td><td class="yfnc_h" align="right">16.85</td><td class="yfnc_h" align="right">17.50</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">44</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00572500">AAPL7140530C00572500</a></td><td class="yfnc_h" align="right"><b>8.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00572500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">15.35</td><td class="yfnc_h" align="right">17.85</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00575000">AAPL140517C00575000</a></td><td class="yfnc_h" align="right"><b>12.15</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.08</b></span></td><td class="yfnc_h" align="right">12.00</td><td class="yfnc_h" align="right">12.30</td><td class="yfnc_h" align="right">705</td><td class="yfnc_h" align="right">678</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00575000">AAPL7140517C00575000</a></td><td class="yfnc_h" align="right"><b>13.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00575000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">9.95</td><td class="yfnc_h" align="right">12.55</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00575000">AAPL140523C00575000</a></td><td class="yfnc_h" align="right"><b>14.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.30</b></span></td><td class="yfnc_h" align="right">13.85</td><td class="yfnc_h" align="right">14.25</td><td class="yfnc_h" align="right">101</td><td class="yfnc_h" align="right">277</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00575000">AAPL7140523C00575000</a></td><td class="yfnc_h" align="right"><b>14.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.12</b></span></td><td class="yfnc_h" align="right">11.75</td><td class="yfnc_h" align="right">14.55</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">17</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00575000">AAPL140530C00575000</a></td><td class="yfnc_h" align="right"><b>15.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.90</b></span></td><td class="yfnc_h" align="right">15.30</td><td class="yfnc_h" align="right">15.70</td><td class="yfnc_h" align="right">84</td><td class="yfnc_h" align="right">449</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00575000">AAPL7140530C00575000</a></td><td class="yfnc_h" align="right"><b>14.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.58</b></span></td><td class="yfnc_h" align="right">13.60</td><td class="yfnc_h" align="right">15.95</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">179</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00577500">AAPL140517C00577500</a></td><td class="yfnc_h" align="right"><b>10.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00577500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">21.85</b></span></td><td class="yfnc_h" align="right">9.95</td><td class="yfnc_h" align="right">10.35</td><td class="yfnc_h" align="right">1,154</td><td class="yfnc_h" align="right">46</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00577500">AAPL140523C00577500</a></td><td class="yfnc_h" align="right"><b>10.99</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00577500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.76</b></span></td><td class="yfnc_h" align="right">12.10</td><td class="yfnc_h" align="right">12.45</td><td class="yfnc_h" align="right">125</td><td class="yfnc_h" align="right">130</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00577500">AAPL7140523C00577500</a></td><td class="yfnc_h" align="right"><b>15.07</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00577500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">10.45</td><td class="yfnc_h" align="right">13.40</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">15</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00580000">AAPL140517C00580000</a></td><td class="yfnc_h" align="right"><b>8.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.05</b></span></td><td class="yfnc_h" align="right">8.25</td><td class="yfnc_h" align="right">8.45</td><td class="yfnc_h" align="right">5,097</td><td class="yfnc_h" align="right">2,008</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00580000">AAPL7140517C00580000</a></td><td class="yfnc_h" align="right"><b>8.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.80</b></span></td><td class="yfnc_h" align="right">7.70</td><td class="yfnc_h" align="right">8.80</td><td class="yfnc_h" align="right">50</td><td class="yfnc_h" align="right">597</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00580000">AAPL140523C00580000</a></td><td class="yfnc_h" align="right"><b>10.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.45</b></span></td><td class="yfnc_h" align="right">10.50</td><td class="yfnc_h" align="right">10.80</td><td class="yfnc_h" align="right">395</td><td class="yfnc_h" align="right">418</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00580000">AAPL7140523C00580000</a></td><td class="yfnc_h" align="right"><b>10.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.90</b></span></td><td class="yfnc_h" align="right">8.90</td><td class="yfnc_h" align="right">11.65</td><td class="yfnc_h" align="right">21</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00580000">AAPL140530C00580000</a></td><td class="yfnc_h" align="right"><b>12.21</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.59</b></span></td><td class="yfnc_h" align="right">12.15</td><td class="yfnc_h" align="right">12.45</td><td class="yfnc_h" align="right">224</td><td class="yfnc_h" align="right">544</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00580000">AAPL7140530C00580000</a></td><td class="yfnc_h" align="right"><b>9.96</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.99</b></span></td><td class="yfnc_h" align="right">10.50</td><td class="yfnc_h" align="right">12.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">188</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00582500">AAPL140517C00582500</a></td><td class="yfnc_h" align="right"><b>6.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00582500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.40</b></span></td><td class="yfnc_h" align="right">6.70</td><td class="yfnc_h" align="right">6.85</td><td class="yfnc_h" align="right">3,528</td><td class="yfnc_h" align="right">98</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00582500">AAPL7140523C00582500</a></td><td class="yfnc_h" align="right"><b>5.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00582500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">5.60</b></span></td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">0</td><td class="yfnc_h" align="right">82</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00582500">AAPL7140530C00582500</a></td><td class="yfnc_h" align="right"><b>41.32</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00582500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">41.32</b></span></td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">0</td><td class="yfnc_h" align="right">14</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517C00585000">AAPL140517C00585000</a></td><td class="yfnc_h" align="right"><b>5.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.84</b></span></td><td class="yfnc_h" align="right">5.25</td><td class="yfnc_h" align="right">5.45</td><td class="yfnc_h" align="right">11,785</td><td class="yfnc_h" align="right">4,386</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517C00585000">AAPL7140517C00585000</a></td><td class="yfnc_h" align="right"><b>5.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.82</b></span></td><td class="yfnc_h" align="right">4.70</td><td class="yfnc_h" align="right">5.65</td><td class="yfnc_h" align="right">39</td><td class="yfnc_h" align="right">208</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523C00585000">AAPL140523C00585000</a></td><td class="yfnc_h" align="right"><b>7.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.40</b></span></td><td class="yfnc_h" align="right">7.65</td><td class="yfnc_h" align="right">7.95</td><td class="yfnc_h" align="right">854</td><td class="yfnc_h" align="right">484</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523C00585000">AAPL7140523C00585000</a></td><td class="yfnc_h" align="right"><b>6.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.43</b></span></td><td class="yfnc_h" align="right">5.65</td><td class="yfnc_h" align="right">9.00</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">535</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530C00585000">AAPL140530C00585000</a></td><td class="yfnc_h" align="right"><b>9.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.30</b></span></td><td class="yfnc_h" align="right">9.40</td><td class="yfnc_h" align="right">9.70</td><td class="yfnc_h" align="right">348</td><td class="yfnc_h" align="right">234</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530C00585000">AAPL7140530C00585000</a></td><td class="yfnc_h" align="right"><b>8.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.25</b></span></td><td class="yfnc_h" align="right">7.50</td><td class="yfnc_h" align="right">9.85</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">26</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00587500">AAPL140517C00587500</a></td><td class="yfnc_tabledata1" align="right"><b>4.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.53</b></span></td><td class="yfnc_tabledata1" align="right">4.05</td><td class="yfnc_tabledata1" align="right">4.20</td><td class="yfnc_tabledata1" align="right">2,245</td><td class="yfnc_tabledata1" align="right">305</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00587500">AAPL7140517C00587500</a></td><td class="yfnc_tabledata1" align="right"><b>3.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">3.35</b></span></td><td class="yfnc_tabledata1" align="right">3.50</td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523C00587500">AAPL7140523C00587500</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523c00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">159</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00590000">AAPL140517C00590000</a></td><td class="yfnc_tabledata1" align="right"><b>3.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.35</b></span></td><td class="yfnc_tabledata1" align="right">3.10</td><td class="yfnc_tabledata1" align="right">3.20</td><td class="yfnc_tabledata1" align="right">8,579</td><td class="yfnc_tabledata1" align="right">6,343</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00590000">AAPL7140517C00590000</a></td><td class="yfnc_tabledata1" align="right"><b>3.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.10</b></span></td><td class="yfnc_tabledata1" align="right">2.00</td><td class="yfnc_tabledata1" align="right">3.25</td><td class="yfnc_tabledata1" align="right">86</td><td class="yfnc_tabledata1" align="right">380</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00590000">AAPL140523C00590000</a></td><td class="yfnc_tabledata1" align="right"><b>5.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.24</b></span></td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">5.55</td><td class="yfnc_tabledata1" align="right">786</td><td class="yfnc_tabledata1" align="right">809</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523C00590000">AAPL7140523C00590000</a></td><td class="yfnc_tabledata1" align="right"><b>4.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.05</b></span></td><td class="yfnc_tabledata1" align="right">3.90</td><td class="yfnc_tabledata1" align="right">5.90</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">76</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00590000">AAPL140530C00590000</a></td><td class="yfnc_tabledata1" align="right"><b>7.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.40</b></span></td><td class="yfnc_tabledata1" align="right">7.15</td><td class="yfnc_tabledata1" align="right">7.30</td><td class="yfnc_tabledata1" align="right">196</td><td class="yfnc_tabledata1" align="right">649</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530C00590000">AAPL7140530C00590000</a></td><td class="yfnc_tabledata1" align="right"><b>7.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.80</b></span></td><td class="yfnc_tabledata1" align="right">5.30</td><td class="yfnc_tabledata1" align="right">8.10</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">73</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00592500">AAPL140517C00592500</a></td><td class="yfnc_tabledata1" align="right"><b>2.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.10</b></span></td><td class="yfnc_tabledata1" align="right">2.30</td><td class="yfnc_tabledata1" align="right">2.40</td><td class="yfnc_tabledata1" align="right">2,326</td><td class="yfnc_tabledata1" align="right">493</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00592500">AAPL7140517C00592500</a></td><td class="yfnc_tabledata1" align="right"><b>2.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.14</b></span></td><td class="yfnc_tabledata1" align="right">1.70</td><td class="yfnc_tabledata1" align="right">2.57</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">26</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00595000">AAPL140517C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>1.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.93</b></span></td><td class="yfnc_tabledata1" align="right">1.68</td><td class="yfnc_tabledata1" align="right">1.75</td><td class="yfnc_tabledata1" align="right">4,579</td><td class="yfnc_tabledata1" align="right">4,449</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00595000">AAPL7140517C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>1.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.85</b></span></td><td class="yfnc_tabledata1" align="right">1.54</td><td class="yfnc_tabledata1" align="right">1.86</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">160</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00595000">AAPL140523C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>3.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.95</b></span></td><td class="yfnc_tabledata1" align="right">3.70</td><td class="yfnc_tabledata1" align="right">3.75</td><td class="yfnc_tabledata1" align="right">652</td><td class="yfnc_tabledata1" align="right">747</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523C00595000">AAPL7140523C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>3.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.60</b></span></td><td class="yfnc_tabledata1" align="right">3.55</td><td class="yfnc_tabledata1" align="right">3.90</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">104</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00595000">AAPL140530C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>5.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.02</b></span></td><td class="yfnc_tabledata1" align="right">5.20</td><td class="yfnc_tabledata1" align="right">5.45</td><td class="yfnc_tabledata1" align="right">377</td><td class="yfnc_tabledata1" align="right">867</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530C00595000">AAPL7140530C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>4.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">4.10</b></span></td><td class="yfnc_tabledata1" align="right">5.10</td><td class="yfnc_tabledata1" align="right">5.45</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">24</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00597500">AAPL140517C00597500</a></td><td class="yfnc_tabledata1" align="right"><b>1.23</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00597500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.86</b></span></td><td class="yfnc_tabledata1" align="right">1.16</td><td class="yfnc_tabledata1" align="right">1.25</td><td class="yfnc_tabledata1" align="right">1,237</td><td class="yfnc_tabledata1" align="right">392</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00597500">AAPL7140517C00597500</a></td><td class="yfnc_tabledata1" align="right"><b>0.94</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00597500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.41</b></span></td><td class="yfnc_tabledata1" align="right">0.59</td><td class="yfnc_tabledata1" align="right">1.39</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00600000">AAPL140517C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>0.89</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.66</b></span></td><td class="yfnc_tabledata1" align="right">0.88</td><td class="yfnc_tabledata1" align="right">0.89</td><td class="yfnc_tabledata1" align="right">8,024</td><td class="yfnc_tabledata1" align="right">13,791</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00600000">AAPL7140517C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>0.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.55</b></span></td><td class="yfnc_tabledata1" align="right">0.78</td><td class="yfnc_tabledata1" align="right">0.95</td><td class="yfnc_tabledata1" align="right">33</td><td class="yfnc_tabledata1" align="right">1,692</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00600000">AAPL140523C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>2.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.83</b></span></td><td class="yfnc_tabledata1" align="right">2.35</td><td class="yfnc_tabledata1" align="right">2.54</td><td class="yfnc_tabledata1" align="right">1,997</td><td class="yfnc_tabledata1" align="right">1,364</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523C00600000">AAPL7140523C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>2.43</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.60</b></span></td><td class="yfnc_tabledata1" align="right">2.32</td><td class="yfnc_tabledata1" align="right">2.61</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">225</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00600000">AAPL140530C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>3.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.95</b></span></td><td class="yfnc_tabledata1" align="right">3.65</td><td class="yfnc_tabledata1" align="right">3.90</td><td class="yfnc_tabledata1" align="right">1,026</td><td class="yfnc_tabledata1" align="right">5,990</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530C00600000">AAPL7140530C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>3.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.30</b></span></td><td class="yfnc_tabledata1" align="right">3.75</td><td class="yfnc_tabledata1" align="right">3.95</td><td class="yfnc_tabledata1" align="right">253</td><td class="yfnc_tabledata1" align="right">882</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00602500">AAPL140517C00602500</a></td><td class="yfnc_tabledata1" align="right"><b>0.61</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00602500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.54</b></span></td><td class="yfnc_tabledata1" align="right">0.61</td><td class="yfnc_tabledata1" align="right">0.66</td><td class="yfnc_tabledata1" align="right">972</td><td class="yfnc_tabledata1" align="right">286</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00602500">AAPL7140517C00602500</a></td><td class="yfnc_tabledata1" align="right"><b>2.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00602500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">0.79</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00605000">AAPL140517C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>0.44</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.41</b></span></td><td class="yfnc_tabledata1" align="right">0.43</td><td class="yfnc_tabledata1" align="right">0.44</td><td class="yfnc_tabledata1" align="right">2,476</td><td class="yfnc_tabledata1" align="right">6,776</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00605000">AAPL7140517C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>0.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.33</b></span></td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">0.51</td><td class="yfnc_tabledata1" align="right">31</td><td class="yfnc_tabledata1" align="right">351</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00605000">AAPL140523C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>1.53</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.68</b></span></td><td class="yfnc_tabledata1" align="right">1.50</td><td class="yfnc_tabledata1" align="right">1.64</td><td class="yfnc_tabledata1" align="right">626</td><td class="yfnc_tabledata1" align="right">582</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00605000">AAPL140530C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>2.69</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.71</b></span></td><td class="yfnc_tabledata1" align="right">2.58</td><td class="yfnc_tabledata1" align="right">2.69</td><td class="yfnc_tabledata1" align="right">155</td><td class="yfnc_tabledata1" align="right">872</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00607500">AAPL140517C00607500</a></td><td class="yfnc_tabledata1" align="right"><b>0.33</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00607500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.30</b></span></td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">0.34</td><td class="yfnc_tabledata1" align="right">432</td><td class="yfnc_tabledata1" align="right">261</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00610000">AAPL140517C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>0.28</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.18</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.28</td><td class="yfnc_tabledata1" align="right">1,796</td><td class="yfnc_tabledata1" align="right">4,968</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00610000">AAPL7140517C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>0.23</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.79</b></span></td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">46</td><td class="yfnc_tabledata1" align="right">272</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00610000">AAPL140523C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>0.97</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.46</b></span></td><td class="yfnc_tabledata1" align="right">0.97</td><td class="yfnc_tabledata1" align="right">1.06</td><td class="yfnc_tabledata1" align="right">335</td><td class="yfnc_tabledata1" align="right">897</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00610000">AAPL140530C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>1.85</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.55</b></span></td><td class="yfnc_tabledata1" align="right">1.80</td><td class="yfnc_tabledata1" align="right">1.85</td><td class="yfnc_tabledata1" align="right">208</td><td class="yfnc_tabledata1" align="right">728</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00612500">AAPL140517C00612500</a></td><td class="yfnc_tabledata1" align="right"><b>0.19</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00612500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.19</b></span></td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">0.24</td><td class="yfnc_tabledata1" align="right">128</td><td class="yfnc_tabledata1" align="right">60</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00615000">AAPL140517C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>0.19</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.08</b></span></td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">1,125</td><td class="yfnc_tabledata1" align="right">3,790</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00615000">AAPL7140517C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>0.73</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">34</td><td class="yfnc_tabledata1" align="right">328</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00615000">AAPL140523C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>0.69</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.26</b></span></td><td class="yfnc_tabledata1" align="right">0.63</td><td class="yfnc_tabledata1" align="right">0.70</td><td class="yfnc_tabledata1" align="right">123</td><td class="yfnc_tabledata1" align="right">576</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00615000">AAPL140530C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>1.28</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.43</b></span></td><td class="yfnc_tabledata1" align="right">1.23</td><td class="yfnc_tabledata1" align="right">1.34</td><td class="yfnc_tabledata1" align="right">127</td><td class="yfnc_tabledata1" align="right">264</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00617500">AAPL140517C00617500</a></td><td class="yfnc_tabledata1" align="right"><b>0.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00617500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">44</td><td class="yfnc_tabledata1" align="right">148</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00620000">AAPL140517C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>0.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">696</td><td class="yfnc_tabledata1" align="right">3,306</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00620000">AAPL7140517C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">29</td><td class="yfnc_tabledata1" align="right">70</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00620000">AAPL140523C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.22</b></span></td><td class="yfnc_tabledata1" align="right">0.42</td><td class="yfnc_tabledata1" align="right">0.47</td><td class="yfnc_tabledata1" align="right">133</td><td class="yfnc_tabledata1" align="right">476</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00620000">AAPL140530C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>0.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.29</b></span></td><td class="yfnc_tabledata1" align="right">0.85</td><td class="yfnc_tabledata1" align="right">0.95</td><td class="yfnc_tabledata1" align="right">213</td><td class="yfnc_tabledata1" align="right">910</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=622.500000"><strong>622.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00622500">AAPL140517C00622500</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00622500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">83</td><td class="yfnc_tabledata1" align="right">174</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00625000">AAPL140517C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">465</td><td class="yfnc_tabledata1" align="right">3,311</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00625000">AAPL7140517C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>0.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">112</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00625000">AAPL140523C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>0.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.17</b></span></td><td class="yfnc_tabledata1" align="right">0.30</td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">139</td><td class="yfnc_tabledata1" align="right">284</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00625000">AAPL140530C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>0.57</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.43</b></span></td><td class="yfnc_tabledata1" align="right">0.59</td><td class="yfnc_tabledata1" align="right">0.69</td><td class="yfnc_tabledata1" align="right">15</td><td class="yfnc_tabledata1" align="right">443</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=627.500000"><strong>627.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00627500">AAPL140517C00627500</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00627500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">47</td><td class="yfnc_tabledata1" align="right">9</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00630000">AAPL140517C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">581</td><td class="yfnc_tabledata1" align="right">3,159</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00630000">AAPL7140517C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>1.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">31</td><td class="yfnc_tabledata1" align="right">77</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00630000">AAPL140523C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>0.23</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.17</b></span></td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">0.28</td><td class="yfnc_tabledata1" align="right">74</td><td class="yfnc_tabledata1" align="right">281</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00630000">AAPL140530C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>0.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.25</b></span></td><td class="yfnc_tabledata1" align="right">0.42</td><td class="yfnc_tabledata1" align="right">0.51</td><td class="yfnc_tabledata1" align="right">36</td><td class="yfnc_tabledata1" align="right">206</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00635000">AAPL140517C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">69</td><td class="yfnc_tabledata1" align="right">1,251</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00635000">AAPL7140517C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>0.37</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">84</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00635000">AAPL140523C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.10</b></span></td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">210</td><td class="yfnc_tabledata1" align="right">201</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00635000">AAPL140530C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>0.34</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.12</b></span></td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">17</td><td class="yfnc_tabledata1" align="right">377</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00640000">AAPL140517C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">454</td><td class="yfnc_tabledata1" align="right">2,284</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00640000">AAPL7140517C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>0.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00640000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">55</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00640000">AAPL140523C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">62</td><td class="yfnc_tabledata1" align="right">197</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00640000">AAPL140530C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>0.29</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.18</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">19</td><td class="yfnc_tabledata1" align="right">214</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00645000">AAPL140517C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00645000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">155</td><td class="yfnc_tabledata1" align="right">633</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00645000">AAPL7140517C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>0.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">44</td><td class="yfnc_tabledata1" align="right">90</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00645000">AAPL140523C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00645000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">193</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00645000">AAPL140530C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>0.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00645000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.20</b></span></td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">0.30</td><td class="yfnc_tabledata1" align="right">25</td><td class="yfnc_tabledata1" align="right">178</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00650000">AAPL140517C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">631</td><td class="yfnc_tabledata1" align="right">5,904</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00650000">AAPL7140517C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.46</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00650000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.28</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">72</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00650000">AAPL140523C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">44</td><td class="yfnc_tabledata1" align="right">243</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530C00650000">AAPL140530C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">225</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00655000">AAPL140517C00655000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00655000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">27</td><td class="yfnc_tabledata1" align="right">491</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517C00655000">AAPL7140517C00655000</a></td><td class="yfnc_tabledata1" align="right"><b>0.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517c00655000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">65</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00655000">AAPL140523C00655000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00655000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">71</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=660.000000"><strong>660.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00660000">AAPL140517C00660000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00660000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">582</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=660.000000"><strong>660.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00660000">AAPL140523C00660000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00660000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">62</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=665.000000"><strong>665.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00665000">AAPL140517C00665000</a></td><td class="yfnc_tabledata1" align="right"><b>0.06</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00665000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">316</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=665.000000"><strong>665.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00665000">AAPL140523C00665000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00665000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">55</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=670.000000"><strong>670.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00670000">AAPL140517C00670000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00670000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">841</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=670.000000"><strong>670.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00670000">AAPL140523C00670000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00670000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">123</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=675.000000"><strong>675.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00675000">AAPL140517C00675000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00675000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">113</td><td class="yfnc_tabledata1" align="right">483</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=675.000000"><strong>675.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00675000">AAPL140523C00675000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00675000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">44</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=680.000000"><strong>680.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00680000">AAPL140517C00680000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00680000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">95</td><td class="yfnc_tabledata1" align="right">2,580</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=680.000000"><strong>680.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00680000">AAPL140523C00680000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00680000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">50</td><td class="yfnc_tabledata1" align="right">50</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=685.000000"><strong>685.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00685000">AAPL140517C00685000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00685000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">38</td><td class="yfnc_tabledata1" align="right">236</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=685.000000"><strong>685.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00685000">AAPL140523C00685000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00685000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">18</td><td class="yfnc_tabledata1" align="right">17</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=690.000000"><strong>690.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00690000">AAPL140517C00690000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00690000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">109</td><td class="yfnc_tabledata1" align="right">430</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=695.000000"><strong>695.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00695000">AAPL140517C00695000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00695000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">53</td><td class="yfnc_tabledata1" align="right">188</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=695.000000"><strong>695.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00695000">AAPL140523C00695000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00695000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">32</td><td class="yfnc_tabledata1" align="right">66</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=700.000000"><strong>700.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00700000">AAPL140517C00700000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00700000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">63</td><td class="yfnc_tabledata1" align="right">1,329</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=700.000000"><strong>700.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00700000">AAPL140523C00700000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00700000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=705.000000"><strong>705.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00705000">AAPL140517C00705000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00705000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">57</td><td class="yfnc_tabledata1" align="right">457</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=710.000000"><strong>710.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00710000">AAPL140517C00710000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00710000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">494</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=710.000000"><strong>710.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523C00710000">AAPL140523C00710000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523c00710000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">282</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=715.000000"><strong>715.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00715000">AAPL140517C00715000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00715000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">36</td><td class="yfnc_tabledata1" align="right">293</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=720.000000"><strong>720.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00720000">AAPL140517C00720000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00720000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">331</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=725.000000"><strong>725.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00725000">AAPL140517C00725000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00725000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">599</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=730.000000"><strong>730.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00730000">AAPL140517C00730000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00730000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">146</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=735.000000"><strong>735.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00735000">AAPL140517C00735000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00735000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">116</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=740.000000"><strong>740.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00740000">AAPL140517C00740000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00740000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">51</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=745.000000"><strong>745.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00745000">AAPL140517C00745000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00745000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">13</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=750.000000"><strong>750.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00750000">AAPL140517C00750000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00750000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">213</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=755.000000"><strong>755.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00755000">AAPL140517C00755000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00755000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">58</td><td class="yfnc_tabledata1" align="right">79</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=760.000000"><strong>760.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00760000">AAPL140517C00760000</a></td><td class="yfnc_tabledata1" align="right"><b>0.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00760000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">39</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=770.000000"><strong>770.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00770000">AAPL140517C00770000</a></td><td class="yfnc_tabledata1" align="right"><b>0.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00770000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">99</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=775.000000"><strong>775.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00775000">AAPL140517C00775000</a></td><td class="yfnc_tabledata1" align="right"><b>0.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00775000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=795.000000"><strong>795.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00795000">AAPL140517C00795000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00795000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">55</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=800.000000"><strong>800.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00800000">AAPL140517C00800000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00800000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">47</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=805.000000"><strong>805.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517C00805000">AAPL140517C00805000</a></td><td class="yfnc_tabledata1" align="right"><b>0.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517c00805000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">10</td></tr></table></td></tr></table><table cellpadding="0" cellspacing="0" border="0"><tr><td height="10"></td></tr></table><table class="yfnc_mod_table_title1" width="100%" cellpadding="2" cellspacing="0" border="0"><tr valign="top"><td><small><b><strong class="yfi-module-title">Put Options</strong></b></small></td><td align="right">Expire at close Friday, May 30, 2014</td></tr></table><table class="yfnc_datamodoutline1" width="100%" cellpadding="0" cellspacing="0" border="0"><tr valign="top"><td><table border="0" cellpadding="3" cellspacing="1" width="100%"><tr><th scope="col" class="yfnc_tablehead1" width="12%" align="left">Strike</th><th scope="col" class="yfnc_tablehead1" width="12%">Symbol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Last</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Chg</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Bid</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Ask</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Vol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Open Int</th></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=280.000000"><strong>280.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00280000">AAPL140517P00280000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00280000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=290.000000"><strong>290.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00290000">AAPL140517P00290000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00290000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=295.000000"><strong>295.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00295000">AAPL140517P00295000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00295000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=300.000000"><strong>300.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00300000">AAPL140517P00300000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00300000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">23</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=305.000000"><strong>305.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00305000">AAPL140517P00305000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00305000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">20</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=310.000000"><strong>310.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00310000">AAPL140517P00310000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00310000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=315.000000"><strong>315.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00315000">AAPL140517P00315000</a></td><td class="yfnc_tabledata1" align="right"><b>0.12</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00315000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=320.000000"><strong>320.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00320000">AAPL140517P00320000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00320000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.08</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">7</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=325.000000"><strong>325.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00325000">AAPL140517P00325000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00325000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">185</td><td class="yfnc_tabledata1" align="right">342</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=330.000000"><strong>330.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00330000">AAPL140517P00330000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00330000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=335.000000"><strong>335.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00335000">AAPL140517P00335000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00335000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=340.000000"><strong>340.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00340000">AAPL140517P00340000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00340000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=345.000000"><strong>345.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00345000">AAPL140517P00345000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00345000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=350.000000"><strong>350.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00350000">AAPL140517P00350000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00350000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">60</td><td class="yfnc_tabledata1" align="right">636</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=355.000000"><strong>355.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00355000">AAPL140517P00355000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00355000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">63</td><td class="yfnc_tabledata1" align="right">92</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=360.000000"><strong>360.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00360000">AAPL140517P00360000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00360000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">167</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=365.000000"><strong>365.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00365000">AAPL140517P00365000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00365000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">28</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=370.000000"><strong>370.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00370000">AAPL140517P00370000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00370000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">27</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=375.000000"><strong>375.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00375000">AAPL140517P00375000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00375000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">15</td><td class="yfnc_tabledata1" align="right">36</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=380.000000"><strong>380.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00380000">AAPL140517P00380000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00380000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">303</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=385.000000"><strong>385.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00385000">AAPL140517P00385000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00385000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">331</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=390.000000"><strong>390.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00390000">AAPL140517P00390000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00390000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">33</td><td class="yfnc_tabledata1" align="right">239</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=395.000000"><strong>395.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00395000">AAPL140517P00395000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00395000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">270</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=400.000000"><strong>400.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00400000">AAPL140517P00400000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00400000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">431</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=405.000000"><strong>405.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00405000">AAPL140517P00405000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00405000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">284</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=410.000000"><strong>410.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00410000">AAPL140517P00410000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00410000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">400</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=415.000000"><strong>415.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00415000">AAPL140517P00415000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00415000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">401</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=420.000000"><strong>420.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00420000">AAPL140517P00420000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00420000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">489</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=425.000000"><strong>425.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00425000">AAPL140517P00425000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00425000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">863</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=430.000000"><strong>430.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00430000">AAPL140517P00430000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00430000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">367</td><td class="yfnc_tabledata1" align="right">3,892</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=435.000000"><strong>435.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00435000">AAPL140517P00435000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00435000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">956</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=435.000000"><strong>435.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00435000">AAPL7140517P00435000</a></td><td class="yfnc_tabledata1" align="right"><b>0.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00435000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">1.71</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=440.000000"><strong>440.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00440000">AAPL140517P00440000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00440000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">803</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=440.000000"><strong>440.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00440000">AAPL7140517P00440000</a></td><td class="yfnc_tabledata1" align="right"><b>0.69</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00440000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">1.71</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">13</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=445.000000"><strong>445.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00445000">AAPL140517P00445000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00445000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">1,616</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00450000">AAPL140517P00450000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00450000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">3,981</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00450000">AAPL7140517P00450000</a></td><td class="yfnc_tabledata1" align="right"><b>0.64</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00450000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">1.71</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">16</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=455.000000"><strong>455.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00455000">AAPL140517P00455000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00455000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">487</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=455.000000"><strong>455.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00455000">AAPL7140517P00455000</a></td><td class="yfnc_tabledata1" align="right"><b>1.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00455000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">1.71</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">56</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00460000">AAPL140517P00460000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00460000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">60</td><td class="yfnc_tabledata1" align="right">2,133</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00460000">AAPL7140517P00460000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00460000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.54</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">38</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=465.000000"><strong>465.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00465000">AAPL140517P00465000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00465000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1,617</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=465.000000"><strong>465.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00465000">AAPL7140517P00465000</a></td><td class="yfnc_tabledata1" align="right"><b>0.52</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00465000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.50</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">72</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00470000">AAPL140517P00470000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">8,005</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00470000">AAPL7140517P00470000</a></td><td class="yfnc_tabledata1" align="right"><b>0.73</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.43</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">61</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00470000">AAPL140523P00470000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00475000">AAPL140517P00475000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00475000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">3,076</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00475000">AAPL7140517P00475000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00475000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.34</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">142</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=480.000000"><strong>480.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00480000">AAPL140517P00480000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00480000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">79</td><td class="yfnc_tabledata1" align="right">3,648</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=480.000000"><strong>480.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00480000">AAPL7140517P00480000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00480000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.28</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">147</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00485000">AAPL140517P00485000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00485000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">180</td><td class="yfnc_tabledata1" align="right">2,581</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00485000">AAPL7140517P00485000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00485000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">178</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00485000">AAPL140523P00485000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00485000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00490000">AAPL140517P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00490000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">34</td><td class="yfnc_tabledata1" align="right">4,959</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00490000">AAPL7140517P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">26</td><td class="yfnc_tabledata1" align="right">511</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00490000">AAPL140523P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.12</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">75</td><td class="yfnc_tabledata1" align="right">94</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00490000">AAPL140530P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">20</td><td class="yfnc_tabledata1" align="right">140</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00490000">AAPL7140530P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">63</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=492.500000"><strong>492.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00492500">AAPL140530P00492500</a></td><td class="yfnc_tabledata1" align="right"><b>0.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00492500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">45</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=492.500000"><strong>492.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00492500">AAPL7140530P00492500</a></td><td class="yfnc_tabledata1" align="right"><b>3.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00492500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.42</td><td class="yfnc_tabledata1" align="right">172</td><td class="yfnc_tabledata1" align="right">175</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00495000">AAPL140517P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">23</td><td class="yfnc_tabledata1" align="right">4,303</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00495000">AAPL7140517P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">267</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00495000">AAPL140523P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00495000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">220</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00495000">AAPL7140523P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>4.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">427</td><td class="yfnc_tabledata1" align="right">433</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00495000">AAPL140530P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">119</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00495000">AAPL7140530P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>4.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.43</td><td class="yfnc_tabledata1" align="right">154</td><td class="yfnc_tabledata1" align="right">154</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=497.500000"><strong>497.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00497500">AAPL140530P00497500</a></td><td class="yfnc_tabledata1" align="right"><b>0.43</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00497500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=497.500000"><strong>497.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00497500">AAPL7140530P00497500</a></td><td class="yfnc_tabledata1" align="right"><b>4.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00497500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.44</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">145</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00500000">AAPL140517P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">187</td><td class="yfnc_tabledata1" align="right">10,044</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00500000">AAPL7140517P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">400</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00500000">AAPL140523P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">356</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00500000">AAPL7140523P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.31</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">106</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00500000">AAPL140530P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">89</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00500000">AAPL7140530P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.45</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">279</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=502.500000"><strong>502.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00502500">AAPL140523P00502500</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00502500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">217</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=502.500000"><strong>502.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00502500">AAPL140530P00502500</a></td><td class="yfnc_tabledata1" align="right"><b>0.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00502500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">25</td><td class="yfnc_tabledata1" align="right">49</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=502.500000"><strong>502.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00502500">AAPL7140530P00502500</a></td><td class="yfnc_tabledata1" align="right"><b>5.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00502500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.46</td><td class="yfnc_tabledata1" align="right">114</td><td class="yfnc_tabledata1" align="right">270</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00505000">AAPL140517P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00505000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">38</td><td class="yfnc_tabledata1" align="right">3,196</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00505000">AAPL7140517P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">217</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00505000">AAPL140523P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00505000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">46</td><td class="yfnc_tabledata1" align="right">659</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00505000">AAPL7140523P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">549</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00505000">AAPL140530P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00505000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">31</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00505000">AAPL7140530P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.54</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.46</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">305</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=507.500000"><strong>507.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00507500">AAPL140523P00507500</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00507500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">19</td><td class="yfnc_tabledata1" align="right">152</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=507.500000"><strong>507.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00507500">AAPL7140523P00507500</a></td><td class="yfnc_tabledata1" align="right"><b>6.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00507500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">109</td><td class="yfnc_tabledata1" align="right">527</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=507.500000"><strong>507.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00507500">AAPL140530P00507500</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00507500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">165</td><td class="yfnc_tabledata1" align="right">194</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=507.500000"><strong>507.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00507500">AAPL7140530P00507500</a></td><td class="yfnc_tabledata1" align="right"><b>8.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00507500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.48</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">500</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00510000">AAPL140517P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">92</td><td class="yfnc_tabledata1" align="right">10,771</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00510000">AAPL7140517P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">1,109</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00510000">AAPL140523P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">96</td><td class="yfnc_tabledata1" align="right">512</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00510000">AAPL7140523P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00510000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.38</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">76</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00510000">AAPL140530P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00510000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">123</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00510000">AAPL7140530P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.62</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00510000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">253</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=512.500000"><strong>512.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00512500">AAPL140523P00512500</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00512500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">28</td><td class="yfnc_tabledata1" align="right">116</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=512.500000"><strong>512.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00512500">AAPL140530P00512500</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00512500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">107</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=512.500000"><strong>512.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00512500">AAPL7140530P00512500</a></td><td class="yfnc_tabledata1" align="right"><b>9.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00512500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">54</td><td class="yfnc_tabledata1" align="right">54</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00515000">AAPL140517P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00515000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">338</td><td class="yfnc_tabledata1" align="right">3,916</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00515000">AAPL7140517P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00515000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">470</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00515000">AAPL140523P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00515000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">80</td><td class="yfnc_tabledata1" align="right">441</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00515000">AAPL7140523P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.86</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">19</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00515000">AAPL140530P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">207</td><td class="yfnc_tabledata1" align="right">298</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00515000">AAPL7140530P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>12.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=517.500000"><strong>517.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00517500">AAPL140523P00517500</a></td><td class="yfnc_tabledata1" align="right"><b>0.13</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00517500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">106</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=517.500000"><strong>517.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00517500">AAPL7140523P00517500</a></td><td class="yfnc_tabledata1" align="right"><b>0.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00517500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.40</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">30</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=517.500000"><strong>517.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00517500">AAPL140530P00517500</a></td><td class="yfnc_tabledata1" align="right"><b>0.19</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00517500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">232</td><td class="yfnc_tabledata1" align="right">244</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=517.500000"><strong>517.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00517500">AAPL7140530P00517500</a></td><td class="yfnc_tabledata1" align="right"><b>1.18</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00517500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00520000">AAPL140517P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.06</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">202</td><td class="yfnc_tabledata1" align="right">9,047</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00520000">AAPL7140517P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">357</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00520000">AAPL140523P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">62</td><td class="yfnc_tabledata1" align="right">387</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00520000">AAPL7140523P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">114</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00520000">AAPL140530P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">330</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00520000">AAPL7140530P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>13.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=522.500000"><strong>522.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00522500">AAPL140523P00522500</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00522500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">86</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=522.500000"><strong>522.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00522500">AAPL7140523P00522500</a></td><td class="yfnc_tabledata1" align="right"><b>0.83</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00522500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">12</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=522.500000"><strong>522.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00522500">AAPL140530P00522500</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00522500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">279</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=522.500000"><strong>522.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00522500">AAPL7140530P00522500</a></td><td class="yfnc_tabledata1" align="right"><b>19.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00522500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00525000">AAPL140517P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">450</td><td class="yfnc_tabledata1" align="right">3,526</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00525000">AAPL7140517P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.13</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">471</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00525000">AAPL140523P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">52</td><td class="yfnc_tabledata1" align="right">670</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00525000">AAPL7140523P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>16.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00525000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">73</td><td class="yfnc_tabledata1" align="right">86</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00525000">AAPL140530P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">69</td><td class="yfnc_tabledata1" align="right">348</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00525000">AAPL7140530P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>1.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00525000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.30</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=527.500000"><strong>527.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00527500">AAPL140523P00527500</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00527500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">318</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=527.500000"><strong>527.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00527500">AAPL7140523P00527500</a></td><td class="yfnc_tabledata1" align="right"><b>1.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00527500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=527.500000"><strong>527.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00527500">AAPL140530P00527500</a></td><td class="yfnc_tabledata1" align="right"><b>0.28</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00527500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">15</td><td class="yfnc_tabledata1" align="right">18</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=527.500000"><strong>527.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00527500">AAPL7140530P00527500</a></td><td class="yfnc_tabledata1" align="right"><b>1.46</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00527500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00530000">AAPL140517P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">530</td><td class="yfnc_tabledata1" align="right">13,138</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00530000">AAPL7140517P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">436</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00530000">AAPL140523P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.18</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">121</td><td class="yfnc_tabledata1" align="right">589</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00530000">AAPL7140523P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.19</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.26</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.24</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">38</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00530000">AAPL140530P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.26</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">28</td><td class="yfnc_tabledata1" align="right">4,407</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00530000">AAPL7140530P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>2.43</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=532.500000"><strong>532.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00532500">AAPL140523P00532500</a></td><td class="yfnc_tabledata1" align="right"><b>0.22</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00532500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">656</td><td class="yfnc_tabledata1" align="right">252</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=532.500000"><strong>532.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00532500">AAPL7140523P00532500</a></td><td class="yfnc_tabledata1" align="right"><b>17.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00532500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=532.500000"><strong>532.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00532500">AAPL140530P00532500</a></td><td class="yfnc_tabledata1" align="right"><b>0.33</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00532500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">32</td><td class="yfnc_tabledata1" align="right">33</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00535000">AAPL140517P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">425</td><td class="yfnc_tabledata1" align="right">3,948</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00535000">AAPL7140517P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.12</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">127</td><td class="yfnc_tabledata1" align="right">656</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00535000">AAPL140523P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.23</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">0.26</td><td class="yfnc_tabledata1" align="right">86</td><td class="yfnc_tabledata1" align="right">358</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00535000">AAPL7140523P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.31</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">40</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00535000">AAPL140530P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.26</td><td class="yfnc_tabledata1" align="right">0.37</td><td class="yfnc_tabledata1" align="right">26</td><td class="yfnc_tabledata1" align="right">221</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00535000">AAPL7140530P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>1.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">0.52</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=537.500000"><strong>537.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00537500">AAPL140523P00537500</a></td><td class="yfnc_tabledata1" align="right"><b>0.28</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00537500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.11</b></span></td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">56</td><td class="yfnc_tabledata1" align="right">148</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=537.500000"><strong>537.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00537500">AAPL7140523P00537500</a></td><td class="yfnc_tabledata1" align="right"><b>1.96</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00537500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">16</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=537.500000"><strong>537.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00537500">AAPL140530P00537500</a></td><td class="yfnc_tabledata1" align="right"><b>0.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00537500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">0.44</td><td class="yfnc_tabledata1" align="right">27</td><td class="yfnc_tabledata1" align="right">74</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00540000">AAPL140517P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">391</td><td class="yfnc_tabledata1" align="right">6,476</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00540000">AAPL7140517P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">415</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00540000">AAPL140523P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.32</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">19</td><td class="yfnc_tabledata1" align="right">321</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00540000">AAPL7140523P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00540000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.33</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">18</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00540000">AAPL140530P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.57</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.22</b></span></td><td class="yfnc_tabledata1" align="right">0.41</td><td class="yfnc_tabledata1" align="right">0.51</td><td class="yfnc_tabledata1" align="right">41</td><td class="yfnc_tabledata1" align="right">426</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00540000">AAPL7140530P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>1.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00540000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">0.54</td><td class="yfnc_tabledata1" align="right">132</td><td class="yfnc_tabledata1" align="right">134</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00542500">AAPL140523P00542500</a></td><td class="yfnc_tabledata1" align="right"><b>0.36</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00542500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">64</td><td class="yfnc_tabledata1" align="right">78</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00542500">AAPL7140523P00542500</a></td><td class="yfnc_tabledata1" align="right"><b>3.67</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00542500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">174</td><td class="yfnc_tabledata1" align="right">176</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00542500">AAPL140530P00542500</a></td><td class="yfnc_tabledata1" align="right"><b>0.66</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00542500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.48</td><td class="yfnc_tabledata1" align="right">0.57</td><td class="yfnc_tabledata1" align="right">102</td><td class="yfnc_tabledata1" align="right">124</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00542500">AAPL7140530P00542500</a></td><td class="yfnc_tabledata1" align="right"><b>5.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00542500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.47</td><td class="yfnc_tabledata1" align="right">0.59</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00545000">AAPL140517P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>0.13</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">259</td><td class="yfnc_tabledata1" align="right">4,469</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00545000">AAPL7140517P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>0.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.26</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">275</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00545000">AAPL140523P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>0.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">73</td><td class="yfnc_tabledata1" align="right">105</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00545000">AAPL7140523P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>1.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0.41</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">81</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00545000">AAPL140530P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>0.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.09</b></span></td><td class="yfnc_tabledata1" align="right">0.57</td><td class="yfnc_tabledata1" align="right">0.65</td><td class="yfnc_tabledata1" align="right">63</td><td class="yfnc_tabledata1" align="right">324</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00545000">AAPL7140530P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>1.48</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.55</td><td class="yfnc_tabledata1" align="right">0.67</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">68</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=547.500000"><strong>547.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00547500">AAPL140523P00547500</a></td><td class="yfnc_tabledata1" align="right"><b>0.53</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00547500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.27</b></span></td><td class="yfnc_tabledata1" align="right">0.33</td><td class="yfnc_tabledata1" align="right">0.43</td><td class="yfnc_tabledata1" align="right">51</td><td class="yfnc_tabledata1" align="right">78</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=547.500000"><strong>547.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00547500">AAPL7140523P00547500</a></td><td class="yfnc_tabledata1" align="right"><b>1.82</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00547500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">0.45</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">161</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=547.500000"><strong>547.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00547500">AAPL140530P00547500</a></td><td class="yfnc_tabledata1" align="right"><b>0.53</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00547500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.67</td><td class="yfnc_tabledata1" align="right">0.74</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">71</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00550000">AAPL140517P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">1,132</td><td class="yfnc_tabledata1" align="right">5,742</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00550000">AAPL7140517P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.39</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">619</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00550000">AAPL140523P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.40</td><td class="yfnc_tabledata1" align="right">0.45</td><td class="yfnc_tabledata1" align="right">91</td><td class="yfnc_tabledata1" align="right">241</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00550000">AAPL7140523P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>0.41</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00550000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.40</td><td class="yfnc_tabledata1" align="right">0.51</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">22</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00550000">AAPL140530P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>0.84</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.21</b></span></td><td class="yfnc_tabledata1" align="right">0.78</td><td class="yfnc_tabledata1" align="right">0.87</td><td class="yfnc_tabledata1" align="right">42</td><td class="yfnc_tabledata1" align="right">311</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00550000">AAPL7140530P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>1.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.49</b></span></td><td class="yfnc_tabledata1" align="right">0.74</td><td class="yfnc_tabledata1" align="right">0.90</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=552.500000"><strong>552.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00552500">AAPL140523P00552500</a></td><td class="yfnc_tabledata1" align="right"><b>0.72</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00552500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.36</b></span></td><td class="yfnc_tabledata1" align="right">0.48</td><td class="yfnc_tabledata1" align="right">0.58</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">68</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=552.500000"><strong>552.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00552500">AAPL140530P00552500</a></td><td class="yfnc_tabledata1" align="right"><b>1.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00552500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.29</b></span></td><td class="yfnc_tabledata1" align="right">0.93</td><td class="yfnc_tabledata1" align="right">1.04</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">58</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00555000">AAPL140517P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>0.19</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">618</td><td class="yfnc_tabledata1" align="right">3,546</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00555000">AAPL7140517P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.29</b></span></td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">413</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00555000">AAPL140523P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>0.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.59</td><td class="yfnc_tabledata1" align="right">0.66</td><td class="yfnc_tabledata1" align="right">125</td><td class="yfnc_tabledata1" align="right">232</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00555000">AAPL140530P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>1.27</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.44</b></span></td><td class="yfnc_tabledata1" align="right">1.11</td><td class="yfnc_tabledata1" align="right">1.22</td><td class="yfnc_tabledata1" align="right">52</td><td class="yfnc_tabledata1" align="right">204</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00555000">AAPL7140530P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>4.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.00</td><td class="yfnc_tabledata1" align="right">1.23</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00557500">AAPL140517P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>0.22</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">365</td><td class="yfnc_tabledata1" align="right">52</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00557500">AAPL140523P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>1.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.29</b></span></td><td class="yfnc_tabledata1" align="right">0.72</td><td class="yfnc_tabledata1" align="right">0.81</td><td class="yfnc_tabledata1" align="right">61</td><td class="yfnc_tabledata1" align="right">225</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00557500">AAPL7140523P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>4.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00557500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.66</td><td class="yfnc_tabledata1" align="right">0.86</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00557500">AAPL140530P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>1.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.26</b></span></td><td class="yfnc_tabledata1" align="right">1.32</td><td class="yfnc_tabledata1" align="right">1.45</td><td class="yfnc_tabledata1" align="right">20</td><td class="yfnc_tabledata1" align="right">71</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00557500">AAPL7140530P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>1.93</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.92</b></span></td><td class="yfnc_tabledata1" align="right">1.29</td><td class="yfnc_tabledata1" align="right">1.45</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00560000">AAPL140517P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>0.28</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">2,306</td><td class="yfnc_tabledata1" align="right">4,494</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00560000">AAPL7140517P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.18</b></span></td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">424</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00560000">AAPL140523P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>1.06</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.12</b></span></td><td class="yfnc_tabledata1" align="right">0.88</td><td class="yfnc_tabledata1" align="right">0.97</td><td class="yfnc_tabledata1" align="right">324</td><td class="yfnc_tabledata1" align="right">580</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00560000">AAPL7140523P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>1.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.45</b></span></td><td class="yfnc_tabledata1" align="right">0.84</td><td class="yfnc_tabledata1" align="right">1.10</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00560000">AAPL140530P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>1.84</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.24</b></span></td><td class="yfnc_tabledata1" align="right">1.58</td><td class="yfnc_tabledata1" align="right">1.73</td><td class="yfnc_tabledata1" align="right">230</td><td class="yfnc_tabledata1" align="right">599</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00560000">AAPL7140530P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>2.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.10</b></span></td><td class="yfnc_tabledata1" align="right">1.56</td><td class="yfnc_tabledata1" align="right">1.73</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">33</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00562500">AAPL140517P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>0.32</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">1,450</td><td class="yfnc_tabledata1" align="right">160</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00562500">AAPL140523P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>1.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">1.11</td><td class="yfnc_tabledata1" align="right">1.19</td><td class="yfnc_tabledata1" align="right">200</td><td class="yfnc_tabledata1" align="right">201</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00562500">AAPL7140523P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>3.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.02</td><td class="yfnc_tabledata1" align="right">1.26</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">115</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00562500">AAPL140530P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>2.67</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.71</b></span></td><td class="yfnc_tabledata1" align="right">1.91</td><td class="yfnc_tabledata1" align="right">2.05</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">112</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00562500">AAPL7140530P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>2.77</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.86</td><td class="yfnc_tabledata1" align="right">2.17</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">69</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00565000">AAPL140517P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">0.46</td><td class="yfnc_tabledata1" align="right">2,738</td><td class="yfnc_tabledata1" align="right">4,705</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00565000">AAPL7140517P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>0.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">0.52</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">493</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00565000">AAPL140523P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>1.42</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.38</td><td class="yfnc_tabledata1" align="right">1.48</td><td class="yfnc_tabledata1" align="right">449</td><td class="yfnc_tabledata1" align="right">660</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00565000">AAPL7140523P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>1.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.29</td><td class="yfnc_tabledata1" align="right">1.60</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">13</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00565000">AAPL140530P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>2.41</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.29</td><td class="yfnc_tabledata1" align="right">2.44</td><td class="yfnc_tabledata1" align="right">308</td><td class="yfnc_tabledata1" align="right">1,159</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00565000">AAPL7140530P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>1.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.18</td><td class="yfnc_tabledata1" align="right">2.48</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">63</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00567500">AAPL140517P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>0.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.53</td><td class="yfnc_tabledata1" align="right">0.55</td><td class="yfnc_tabledata1" align="right">1,213</td><td class="yfnc_tabledata1" align="right">260</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00567500">AAPL140523P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>1.99</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.22</b></span></td><td class="yfnc_tabledata1" align="right">1.71</td><td class="yfnc_tabledata1" align="right">1.83</td><td class="yfnc_tabledata1" align="right">111</td><td class="yfnc_tabledata1" align="right">194</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00567500">AAPL7140523P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>2.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00567500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.65</td><td class="yfnc_tabledata1" align="right">1.95</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">14</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00567500">AAPL140530P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>3.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.30</b></span></td><td class="yfnc_tabledata1" align="right">2.77</td><td class="yfnc_tabledata1" align="right">2.92</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">241</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00567500">AAPL7140530P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>3.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.55</b></span></td><td class="yfnc_tabledata1" align="right">2.67</td><td class="yfnc_tabledata1" align="right">3.05</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00570000">AAPL140517P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>0.74</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.73</td><td class="yfnc_tabledata1" align="right">0.74</td><td class="yfnc_tabledata1" align="right">4,847</td><td class="yfnc_tabledata1" align="right">4,582</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00570000">AAPL7140517P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>1.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.75</b></span></td><td class="yfnc_tabledata1" align="right">0.69</td><td class="yfnc_tabledata1" align="right">0.87</td><td class="yfnc_tabledata1" align="right">26</td><td class="yfnc_tabledata1" align="right">228</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00570000">AAPL140523P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>2.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">2.14</td><td class="yfnc_tabledata1" align="right">2.27</td><td class="yfnc_tabledata1" align="right">619</td><td class="yfnc_tabledata1" align="right">788</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00570000">AAPL7140523P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>3.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.15</b></span></td><td class="yfnc_tabledata1" align="right">2.07</td><td class="yfnc_tabledata1" align="right">2.32</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">15</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00570000">AAPL140530P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>3.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.14</b></span></td><td class="yfnc_tabledata1" align="right">3.25</td><td class="yfnc_tabledata1" align="right">3.50</td><td class="yfnc_tabledata1" align="right">260</td><td class="yfnc_tabledata1" align="right">999</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00570000">AAPL7140530P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>4.79</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.17</b></span></td><td class="yfnc_tabledata1" align="right">3.20</td><td class="yfnc_tabledata1" align="right">3.50</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">57</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00572500">AAPL140517P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>1.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">1.00</td><td class="yfnc_tabledata1" align="right">1.05</td><td class="yfnc_tabledata1" align="right">1,653</td><td class="yfnc_tabledata1" align="right">434</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00572500">AAPL140523P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>2.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.30</b></span></td><td class="yfnc_tabledata1" align="right">2.62</td><td class="yfnc_tabledata1" align="right">2.79</td><td class="yfnc_tabledata1" align="right">405</td><td class="yfnc_tabledata1" align="right">224</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00572500">AAPL7140523P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>4.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">2.55</td><td class="yfnc_tabledata1" align="right">2.92</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">78</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00572500">AAPL140530P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>5.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.45</b></span></td><td class="yfnc_tabledata1" align="right">3.90</td><td class="yfnc_tabledata1" align="right">4.15</td><td class="yfnc_tabledata1" align="right">275</td><td class="yfnc_tabledata1" align="right">376</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00572500">AAPL7140530P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>2.81</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00572500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.80</td><td class="yfnc_tabledata1" align="right">4.15</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">103</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00575000">AAPL140517P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>1.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">1.45</td><td class="yfnc_tabledata1" align="right">1.51</td><td class="yfnc_tabledata1" align="right">5,455</td><td class="yfnc_tabledata1" align="right">5,975</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00575000">AAPL7140517P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>2.78</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.63</b></span></td><td class="yfnc_tabledata1" align="right">1.32</td><td class="yfnc_tabledata1" align="right">1.64</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">284</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00575000">AAPL140523P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>3.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.21</b></span></td><td class="yfnc_tabledata1" align="right">3.25</td><td class="yfnc_tabledata1" align="right">3.45</td><td class="yfnc_tabledata1" align="right">417</td><td class="yfnc_tabledata1" align="right">604</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00575000">AAPL7140523P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>2.52</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00575000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.15</td><td class="yfnc_tabledata1" align="right">3.60</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00575000">AAPL140530P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>4.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.25</b></span></td><td class="yfnc_tabledata1" align="right">4.65</td><td class="yfnc_tabledata1" align="right">4.90</td><td class="yfnc_tabledata1" align="right">582</td><td class="yfnc_tabledata1" align="right">1,420</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00575000">AAPL7140530P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>6.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.95</b></span></td><td class="yfnc_tabledata1" align="right">4.55</td><td class="yfnc_tabledata1" align="right">4.90</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">111</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00577500">AAPL140517P00577500</a></td><td class="yfnc_tabledata1" align="right"><b>1.98</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00577500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">1.95</td><td class="yfnc_tabledata1" align="right">2.05</td><td class="yfnc_tabledata1" align="right">2,748</td><td class="yfnc_tabledata1" align="right">232</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00577500">AAPL140523P00577500</a></td><td class="yfnc_tabledata1" align="right"><b>4.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00577500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.10</b></span></td><td class="yfnc_tabledata1" align="right">4.00</td><td class="yfnc_tabledata1" align="right">4.20</td><td class="yfnc_tabledata1" align="right">197</td><td class="yfnc_tabledata1" align="right">200</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00577500">AAPL7140523P00577500</a></td><td class="yfnc_tabledata1" align="right"><b>6.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00577500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">3.05</b></span></td><td class="yfnc_tabledata1" align="right">3.90</td><td class="yfnc_tabledata1" align="right">4.35</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">276</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00580000">AAPL140517P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>2.72</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.14</b></span></td><td class="yfnc_tabledata1" align="right">2.70</td><td class="yfnc_tabledata1" align="right">2.75</td><td class="yfnc_tabledata1" align="right">7,127</td><td class="yfnc_tabledata1" align="right">4,696</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00580000">AAPL7140517P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>2.78</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.18</b></span></td><td class="yfnc_tabledata1" align="right">2.54</td><td class="yfnc_tabledata1" align="right">2.99</td><td class="yfnc_tabledata1" align="right">211</td><td class="yfnc_tabledata1" align="right">302</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00580000">AAPL140523P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>4.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.20</b></span></td><td class="yfnc_tabledata1" align="right">4.90</td><td class="yfnc_tabledata1" align="right">5.10</td><td class="yfnc_tabledata1" align="right">466</td><td class="yfnc_tabledata1" align="right">525</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00580000">AAPL7140523P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>5.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.40</b></span></td><td class="yfnc_tabledata1" align="right">4.75</td><td class="yfnc_tabledata1" align="right">5.20</td><td class="yfnc_tabledata1" align="right">45</td><td class="yfnc_tabledata1" align="right">28</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00580000">AAPL140530P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>6.72</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.46</b></span></td><td class="yfnc_tabledata1" align="right">6.45</td><td class="yfnc_tabledata1" align="right">6.70</td><td class="yfnc_tabledata1" align="right">191</td><td class="yfnc_tabledata1" align="right">560</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00580000">AAPL7140530P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>6.68</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.88</b></span></td><td class="yfnc_tabledata1" align="right">6.10</td><td class="yfnc_tabledata1" align="right">7.50</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">100</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00582500">AAPL140517P00582500</a></td><td class="yfnc_tabledata1" align="right"><b>3.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00582500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">3.55</td><td class="yfnc_tabledata1" align="right">3.75</td><td class="yfnc_tabledata1" align="right">3,184</td><td class="yfnc_tabledata1" align="right">607</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140517P00585000">AAPL140517P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>4.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140517p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.42</b></span></td><td class="yfnc_tabledata1" align="right">4.65</td><td class="yfnc_tabledata1" align="right">4.85</td><td class="yfnc_tabledata1" align="right">5,403</td><td class="yfnc_tabledata1" align="right">3,487</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140517P00585000">AAPL7140517P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>4.86</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140517p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.56</b></span></td><td class="yfnc_tabledata1" align="right">4.45</td><td class="yfnc_tabledata1" align="right">5.00</td><td class="yfnc_tabledata1" align="right">38</td><td class="yfnc_tabledata1" align="right">493</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140523P00585000">AAPL140523P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>7.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140523p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.44</b></span></td><td class="yfnc_tabledata1" align="right">7.05</td><td class="yfnc_tabledata1" align="right">7.20</td><td class="yfnc_tabledata1" align="right">598</td><td class="yfnc_tabledata1" align="right">1,335</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140523P00585000">AAPL7140523P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>7.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140523p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.75</b></span></td><td class="yfnc_tabledata1" align="right">6.85</td><td class="yfnc_tabledata1" align="right">9.50</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">30</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140530P00585000">AAPL140530P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>8.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140530p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.41</b></span></td><td class="yfnc_tabledata1" align="right">8.65</td><td class="yfnc_tabledata1" align="right">8.95</td><td class="yfnc_tabledata1" align="right">76</td><td class="yfnc_tabledata1" align="right">294</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140530P00585000">AAPL7140530P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>9.89</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140530p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.39</b></span></td><td class="yfnc_tabledata1" align="right">8.45</td><td class="yfnc_tabledata1" align="right">10.50</td><td class="yfnc_tabledata1" align="right">39</td><td class="yfnc_tabledata1" align="right">46</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00587500">AAPL140517P00587500</a></td><td class="yfnc_h" align="right"><b>6.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.50</b></span></td><td class="yfnc_h" align="right">5.90</td><td class="yfnc_h" align="right">6.10</td><td class="yfnc_h" align="right">1,375</td><td class="yfnc_h" align="right">367</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00587500">AAPL7140517P00587500</a></td><td class="yfnc_h" align="right"><b>8.67</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.67</b></span></td><td class="yfnc_h" align="right">5.75</td><td class="yfnc_h" align="right">6.30</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">6</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00587500">AAPL140523P00587500</a></td><td class="yfnc_h" align="right"><b>0.15</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.15</b></span></td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">0</td><td class="yfnc_h" align="right">119</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523P00587500">AAPL7140523P00587500</a></td><td class="yfnc_h" align="right"><b>0.01</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">0</td><td class="yfnc_h" align="right">265</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00587500">AAPL140530P00587500</a></td><td class="yfnc_h" align="right"><b>1.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.00</b></span></td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">0</td><td class="yfnc_h" align="right">20</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530P00587500">AAPL7140530P00587500</a></td><td class="yfnc_h" align="right"><b>0.03</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.03</b></span></td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">N/A</td><td class="yfnc_h" align="right">86</td><td class="yfnc_h" align="right">513</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00590000">AAPL140517P00590000</a></td><td class="yfnc_h" align="right"><b>7.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.84</b></span></td><td class="yfnc_h" align="right">7.40</td><td class="yfnc_h" align="right">7.65</td><td class="yfnc_h" align="right">2,914</td><td class="yfnc_h" align="right">4,498</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00590000">AAPL7140517P00590000</a></td><td class="yfnc_h" align="right"><b>7.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.30</b></span></td><td class="yfnc_h" align="right">7.05</td><td class="yfnc_h" align="right">7.80</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">274</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00590000">AAPL140523P00590000</a></td><td class="yfnc_h" align="right"><b>9.74</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.84</b></span></td><td class="yfnc_h" align="right">9.75</td><td class="yfnc_h" align="right">10.00</td><td class="yfnc_h" align="right">310</td><td class="yfnc_h" align="right">601</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523P00590000">AAPL7140523P00590000</a></td><td class="yfnc_h" align="right"><b>12.52</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">5.67</b></span></td><td class="yfnc_h" align="right">9.55</td><td class="yfnc_h" align="right">11.60</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">19</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00590000">AAPL140530P00590000</a></td><td class="yfnc_h" align="right"><b>11.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.45</b></span></td><td class="yfnc_h" align="right">11.30</td><td class="yfnc_h" align="right">11.70</td><td class="yfnc_h" align="right">37</td><td class="yfnc_h" align="right">285</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530P00590000">AAPL7140530P00590000</a></td><td class="yfnc_h" align="right"><b>13.92</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">3.92</b></span></td><td class="yfnc_h" align="right">11.05</td><td class="yfnc_h" align="right">13.85</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00592500">AAPL140517P00592500</a></td><td class="yfnc_h" align="right"><b>9.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.10</b></span></td><td class="yfnc_h" align="right">9.10</td><td class="yfnc_h" align="right">9.35</td><td class="yfnc_h" align="right">368</td><td class="yfnc_h" align="right">633</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00595000">AAPL140517P00595000</a></td><td class="yfnc_h" align="right"><b>11.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.30</b></span></td><td class="yfnc_h" align="right">10.95</td><td class="yfnc_h" align="right">11.30</td><td class="yfnc_h" align="right">400</td><td class="yfnc_h" align="right">1,569</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00595000">AAPL7140517P00595000</a></td><td class="yfnc_h" align="right"><b>11.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00595000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">10.70</td><td class="yfnc_h" align="right">13.00</td><td class="yfnc_h" align="right">24</td><td class="yfnc_h" align="right">140</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00595000">AAPL140523P00595000</a></td><td class="yfnc_h" align="right"><b>13.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.18</b></span></td><td class="yfnc_h" align="right">12.95</td><td class="yfnc_h" align="right">13.25</td><td class="yfnc_h" align="right">353</td><td class="yfnc_h" align="right">477</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523P00595000">AAPL7140523P00595000</a></td><td class="yfnc_h" align="right"><b>16.37</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">4.72</b></span></td><td class="yfnc_h" align="right">12.75</td><td class="yfnc_h" align="right">15.40</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00595000">AAPL140530P00595000</a></td><td class="yfnc_h" align="right"><b>15.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.65</b></span></td><td class="yfnc_h" align="right">14.45</td><td class="yfnc_h" align="right">14.75</td><td class="yfnc_h" align="right">19</td><td class="yfnc_h" align="right">228</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530P00595000">AAPL7140530P00595000</a></td><td class="yfnc_h" align="right"><b>17.57</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">6.07</b></span></td><td class="yfnc_h" align="right">14.10</td><td class="yfnc_h" align="right">16.95</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">19</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00597500">AAPL140517P00597500</a></td><td class="yfnc_h" align="right"><b>13.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00597500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.45</b></span></td><td class="yfnc_h" align="right">12.85</td><td class="yfnc_h" align="right">13.35</td><td class="yfnc_h" align="right">85</td><td class="yfnc_h" align="right">149</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00597500">AAPL7140517P00597500</a></td><td class="yfnc_h" align="right"><b>9.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00597500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">12.00</td><td class="yfnc_h" align="right">14.90</td><td class="yfnc_h" align="right">18</td><td class="yfnc_h" align="right">18</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00600000">AAPL140517P00600000</a></td><td class="yfnc_h" align="right"><b>15.15</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.55</b></span></td><td class="yfnc_h" align="right">15.00</td><td class="yfnc_h" align="right">15.50</td><td class="yfnc_h" align="right">282</td><td class="yfnc_h" align="right">2,184</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00600000">AAPL7140517P00600000</a></td><td class="yfnc_h" align="right"><b>18.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">8.30</b></span></td><td class="yfnc_h" align="right">14.80</td><td class="yfnc_h" align="right">16.80</td><td class="yfnc_h" align="right">7</td><td class="yfnc_h" align="right">142</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00600000">AAPL140523P00600000</a></td><td class="yfnc_h" align="right"><b>17.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.75</b></span></td><td class="yfnc_h" align="right">16.55</td><td class="yfnc_h" align="right">17.00</td><td class="yfnc_h" align="right">92</td><td class="yfnc_h" align="right">262</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140523P00600000">AAPL7140523P00600000</a></td><td class="yfnc_h" align="right"><b>15.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140523p00600000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.15</td><td class="yfnc_h" align="right">19.10</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00600000">AAPL140530P00600000</a></td><td class="yfnc_h" align="right"><b>18.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">3.56</b></span></td><td class="yfnc_h" align="right">17.80</td><td class="yfnc_h" align="right">18.30</td><td class="yfnc_h" align="right">36</td><td class="yfnc_h" align="right">133</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140530P00600000">AAPL7140530P00600000</a></td><td class="yfnc_h" align="right"><b>20.03</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140530p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">5.75</b></span></td><td class="yfnc_h" align="right">17.60</td><td class="yfnc_h" align="right">20.40</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">29</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00602500">AAPL140517P00602500</a></td><td class="yfnc_h" align="right"><b>17.67</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00602500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">16.17</b></span></td><td class="yfnc_h" align="right">17.25</td><td class="yfnc_h" align="right">17.75</td><td class="yfnc_h" align="right">12</td><td class="yfnc_h" align="right">63</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00605000">AAPL140517P00605000</a></td><td class="yfnc_h" align="right"><b>19.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.70</b></span></td><td class="yfnc_h" align="right">19.60</td><td class="yfnc_h" align="right">20.10</td><td class="yfnc_h" align="right">251</td><td class="yfnc_h" align="right">970</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00605000">AAPL7140517P00605000</a></td><td class="yfnc_h" align="right"><b>15.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">18.50</td><td class="yfnc_h" align="right">22.15</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">66</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00605000">AAPL140523P00605000</a></td><td class="yfnc_h" align="right"><b>21.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.90</b></span></td><td class="yfnc_h" align="right">20.65</td><td class="yfnc_h" align="right">21.15</td><td class="yfnc_h" align="right">305</td><td class="yfnc_h" align="right">198</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00605000">AAPL140530P00605000</a></td><td class="yfnc_h" align="right"><b>23.31</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.31</b></span></td><td class="yfnc_h" align="right">21.85</td><td class="yfnc_h" align="right">22.75</td><td class="yfnc_h" align="right">31</td><td class="yfnc_h" align="right">64</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00607500">AAPL140517P00607500</a></td><td class="yfnc_h" align="right"><b>21.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00607500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2,240.10</b></span></td><td class="yfnc_h" align="right">21.90</td><td class="yfnc_h" align="right">22.55</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00610000">AAPL140517P00610000</a></td><td class="yfnc_h" align="right"><b>24.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.00</b></span></td><td class="yfnc_h" align="right">24.40</td><td class="yfnc_h" align="right">24.95</td><td class="yfnc_h" align="right">28</td><td class="yfnc_h" align="right">417</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00610000">AAPL7140517P00610000</a></td><td class="yfnc_h" align="right"><b>16.04</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00610000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">22.90</td><td class="yfnc_h" align="right">26.95</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00610000">AAPL140523P00610000</a></td><td class="yfnc_h" align="right"><b>26.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.80</b></span></td><td class="yfnc_h" align="right">25.10</td><td class="yfnc_h" align="right">26.40</td><td class="yfnc_h" align="right">23</td><td class="yfnc_h" align="right">134</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00610000">AAPL140530P00610000</a></td><td class="yfnc_h" align="right"><b>30.14</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">5.21</b></span></td><td class="yfnc_h" align="right">25.90</td><td class="yfnc_h" align="right">27.10</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">132</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00612500">AAPL140517P00612500</a></td><td class="yfnc_h" align="right"><b>22.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00612500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2,357.80</b></span></td><td class="yfnc_h" align="right">26.75</td><td class="yfnc_h" align="right">28.25</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00615000">AAPL140517P00615000</a></td><td class="yfnc_h" align="right"><b>29.73</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.17</b></span></td><td class="yfnc_h" align="right">29.15</td><td class="yfnc_h" align="right">30.05</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">156</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00615000">AAPL140523P00615000</a></td><td class="yfnc_h" align="right"><b>25.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">29.75</td><td class="yfnc_h" align="right">31.05</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">46</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00615000">AAPL140530P00615000</a></td><td class="yfnc_h" align="right"><b>22.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">30.30</td><td class="yfnc_h" align="right">31.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00620000">AAPL140517P00620000</a></td><td class="yfnc_h" align="right"><b>34.97</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.22</b></span></td><td class="yfnc_h" align="right">33.95</td><td class="yfnc_h" align="right">35.65</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">275</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00620000">AAPL140523P00620000</a></td><td class="yfnc_h" align="right"><b>29.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">34.20</td><td class="yfnc_h" align="right">35.95</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">72</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00620000">AAPL140530P00620000</a></td><td class="yfnc_h" align="right"><b>28.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">34.50</td><td class="yfnc_h" align="right">36.25</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00625000">AAPL140517P00625000</a></td><td class="yfnc_h" align="right"><b>37.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">38.60</td><td class="yfnc_h" align="right">40.75</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">130</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00625000">AAPL7140517P00625000</a></td><td class="yfnc_h" align="right"><b>33.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">37.40</td><td class="yfnc_h" align="right">41.40</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00625000">AAPL140523P00625000</a></td><td class="yfnc_h" align="right"><b>32.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">39.00</td><td class="yfnc_h" align="right">40.80</td><td class="yfnc_h" align="right">28</td><td class="yfnc_h" align="right">32</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00625000">AAPL140530P00625000</a></td><td class="yfnc_h" align="right"><b>38.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">39.45</td><td class="yfnc_h" align="right">41.10</td><td class="yfnc_h" align="right">510</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00630000">AAPL140517P00630000</a></td><td class="yfnc_h" align="right"><b>43.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">43.20</td><td class="yfnc_h" align="right">45.70</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">246</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00630000">AAPL140523P00630000</a></td><td class="yfnc_h" align="right"><b>38.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">44.05</td><td class="yfnc_h" align="right">45.75</td><td class="yfnc_h" align="right">8</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00630000">AAPL140530P00630000</a></td><td class="yfnc_h" align="right"><b>41.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">43.75</td><td class="yfnc_h" align="right">45.90</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00635000">AAPL140517P00635000</a></td><td class="yfnc_h" align="right"><b>35.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">48.20</td><td class="yfnc_h" align="right">50.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">240</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00635000">AAPL7140517P00635000</a></td><td class="yfnc_h" align="right"><b>55.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">47.30</td><td class="yfnc_h" align="right">51.75</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00635000">AAPL140523P00635000</a></td><td class="yfnc_h" align="right"><b>44.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">48.45</td><td class="yfnc_h" align="right">50.75</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">6</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00635000">AAPL140530P00635000</a></td><td class="yfnc_h" align="right"><b>46.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">48.80</td><td class="yfnc_h" align="right">50.80</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00640000">AAPL140517P00640000</a></td><td class="yfnc_h" align="right"><b>50.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00640000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">53.15</td><td class="yfnc_h" align="right">55.65</td><td class="yfnc_h" align="right">40</td><td class="yfnc_h" align="right">35</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00640000">AAPL7140517P00640000</a></td><td class="yfnc_h" align="right"><b>102.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00640000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">52.60</td><td class="yfnc_h" align="right">56.70</td><td class="yfnc_h" align="right">32</td><td class="yfnc_h" align="right">42</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00640000">AAPL140523P00640000</a></td><td class="yfnc_h" align="right"><b>43.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00640000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">53.65</td><td class="yfnc_h" align="right">55.80</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00640000">AAPL140530P00640000</a></td><td class="yfnc_h" align="right"><b>47.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00640000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">53.70</td><td class="yfnc_h" align="right">55.75</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00645000">AAPL140517P00645000</a></td><td class="yfnc_h" align="right"><b>65.37</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">58.50</td><td class="yfnc_h" align="right">60.65</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140523P00645000">AAPL140523P00645000</a></td><td class="yfnc_h" align="right"><b>45.78</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140523p00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">58.65</td><td class="yfnc_h" align="right">60.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00645000">AAPL140530P00645000</a></td><td class="yfnc_h" align="right"><b>51.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">59.00</td><td class="yfnc_h" align="right">60.70</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00650000">AAPL140517P00650000</a></td><td class="yfnc_h" align="right"><b>64.52</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.52</b></span></td><td class="yfnc_h" align="right">64.00</td><td class="yfnc_h" align="right">65.65</td><td class="yfnc_h" align="right">500</td><td class="yfnc_h" align="right">4,292</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00650000">AAPL7140517P00650000</a></td><td class="yfnc_h" align="right"><b>62.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00650000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">62.35</td><td class="yfnc_h" align="right">66.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140530P00650000">AAPL140530P00650000</a></td><td class="yfnc_h" align="right"><b>65.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140530p00650000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">63.70</td><td class="yfnc_h" align="right">65.75</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00655000">AAPL140517P00655000</a></td><td class="yfnc_h" align="right"><b>68.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00655000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">68.15</td><td class="yfnc_h" align="right">70.65</td><td class="yfnc_h" align="right">24</td><td class="yfnc_h" align="right">45</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140517P00655000">AAPL7140517P00655000</a></td><td class="yfnc_h" align="right"><b>65.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140517p00655000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">67.40</td><td class="yfnc_h" align="right">71.80</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=660.000000"><strong>660.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00660000">AAPL140517P00660000</a></td><td class="yfnc_h" align="right"><b>71.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00660000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">73.55</td><td class="yfnc_h" align="right">75.65</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=675.000000"><strong>675.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00675000">AAPL140517P00675000</a></td><td class="yfnc_h" align="right"><b>88.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00675000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">88.55</td><td class="yfnc_h" align="right">90.85</td><td class="yfnc_h" align="right">65</td><td class="yfnc_h" align="right">66</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=680.000000"><strong>680.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00680000">AAPL140517P00680000</a></td><td class="yfnc_h" align="right"><b>92.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00680000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">93.90</td><td class="yfnc_h" align="right">95.80</td><td class="yfnc_h" align="right">1,850</td><td class="yfnc_h" align="right">600</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=695.000000"><strong>695.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00695000">AAPL140517P00695000</a></td><td class="yfnc_h" align="right"><b>107.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00695000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">108.60</td><td class="yfnc_h" align="right">110.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=700.000000"><strong>700.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00700000">AAPL140517P00700000</a></td><td class="yfnc_h" align="right"><b>118.53</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00700000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">8.38</b></span></td><td class="yfnc_h" align="right">113.15</td><td class="yfnc_h" align="right">115.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">160</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=710.000000"><strong>710.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00710000">AAPL140517P00710000</a></td><td class="yfnc_h" align="right"><b>190.57</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00710000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">123.65</td><td class="yfnc_h" align="right">125.65</td><td class="yfnc_h" align="right">0</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=715.000000"><strong>715.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00715000">AAPL140517P00715000</a></td><td class="yfnc_h" align="right"><b>133.46</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00715000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">49.24</b></span></td><td class="yfnc_h" align="right">128.60</td><td class="yfnc_h" align="right">130.65</td><td class="yfnc_h" align="right">7</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=720.000000"><strong>720.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00720000">AAPL140517P00720000</a></td><td class="yfnc_h" align="right"><b>157.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00720000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">133.60</td><td class="yfnc_h" align="right">135.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">15</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=725.000000"><strong>725.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00725000">AAPL140517P00725000</a></td><td class="yfnc_h" align="right"><b>204.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00725000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">138.65</td><td class="yfnc_h" align="right">140.65</td><td class="yfnc_h" align="right">8</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=740.000000"><strong>740.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00740000">AAPL140517P00740000</a></td><td class="yfnc_h" align="right"><b>152.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00740000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">153.60</td><td class="yfnc_h" align="right">155.65</td><td class="yfnc_h" align="right">133</td><td class="yfnc_h" align="right">133</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=750.000000"><strong>750.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00750000">AAPL140517P00750000</a></td><td class="yfnc_h" align="right"><b>164.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00750000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">21.15</b></span></td><td class="yfnc_h" align="right">163.65</td><td class="yfnc_h" align="right">165.65</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=780.000000"><strong>780.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00780000">AAPL140517P00780000</a></td><td class="yfnc_h" align="right"><b>189.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00780000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">193.65</td><td class="yfnc_h" align="right">195.75</td><td class="yfnc_h" align="right">22</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=790.000000"><strong>790.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00790000">AAPL140517P00790000</a></td><td class="yfnc_h" align="right"><b>199.37</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00790000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">203.80</td><td class="yfnc_h" align="right">205.65</td><td class="yfnc_h" align="right">33</td><td class="yfnc_h" align="right">33</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=800.000000"><strong>800.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00800000">AAPL140517P00800000</a></td><td class="yfnc_h" align="right"><b>208.26</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00800000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">213.60</td><td class="yfnc_h" align="right">215.70</td><td class="yfnc_h" align="right">121</td><td class="yfnc_h" align="right">121</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=805.000000"><strong>805.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140517P00805000">AAPL140517P00805000</a></td><td class="yfnc_h" align="right"><b>217.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140517p00805000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">218.55</td><td class="yfnc_h" align="right">220.75</td><td class="yfnc_h" align="right">34</td><td class="yfnc_h" align="right">34</td></tr></table></td></tr></table><table border="0" cellpadding="2" cellspacing="0"><tr><td width="1%"><table border="0" cellpadding="1" cellspacing="0" width="10" class="yfnc_d"><tr><td><table border="0" cellpadding="1" cellspacing="0" width="100%"><tr><td class="yfnc_h">&nbsp;&nbsp;&nbsp;</td></tr></table></td></tr></table></td><td><small>Highlighted options are in-the-money.</small></td></tr></table><p style="text-align:center"><a 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+window.xzq_d['0Mpyn2KLc24-']='(as$12rs1142r,aid$0Mpyn2KLc24-,bi$2023845051,cr$3880780551,ct$25,at$H,eob$gd1_match_id=-1:ypos=FB2)'; +</script><noscript><img width=1 height=1 alt="" src="http://csc.beap.bc.yahoo.com/yi?bv=1.0.0&bs=(134t1g972(gid$rTP7AzIwNi5Ye_pbUo7O_gAlMTA4LlNyw4___xMA,st$1400030096722431,si$4451051,sp$28951412,pv$1,v$2.0))&t=J_3-D_3&al=(as$12rs1142r,aid$0Mpyn2KLc24-,bi$2023845051,cr$3880780551,ct$25,at$H,eob$gd1_match_id=-1:ypos=FB2)"></noscript></span></td></tr></table><br><div class="rtq_leaf"><div class="rtq_div"><div class="yui-g"><div class="yfi_rt_quote_summary" id="yfi_rt_quote_summary"><div class="hd"><div class="title"><h2>Apple Inc. (AAPL)</h2> <span class="rtq_exch"><span class="rtq_dash">-</span>NasdaqGS </span><span class="wl_sign"></span></div></div><div class="yfi_rt_quote_summary_rt_top sigfig_promo_1"><div> <span class="time_rtq_ticker"><span id="yfs_l84_aapl">593.76</span></span> <span class="up_g time_rtq_content"><span id="yfs_c63_aapl"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> 0.93</span><span id="yfs_p43_aapl">(0.16%)</span> </span><span class="time_rtq"> <span id="yfs_t53_aapl"><span id="yfs_t53_aapl">4:00PM EDT</span></span></span></div><div><span class="rtq_separator">|</span>After Hours + : + <span class="yfs_rtq_quote"><span id="yfs_l86_aapl">593.12</span></span> <span class="down_r"><span id="yfs_c85_aapl"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> 0.64</span> <span id="yfs_c86_aapl"> (0.11%)</span></span><span class="time_rtq"> <span id="yfs_t54_aapl">7:59PM EDT</span></span></div></div><style> + #yfi_toolbox_mini_rtq.sigfig_promo { + bottom:45px !important; + } + </style><div class="sigfig_promo" id="yfi_toolbox_mini_rtq"><div class="promo_text">Get the big picture on all your investments.</div><div class="promo_link"><a href="https://finance.yahoo.com/portfolio/ytosv2">Sync your Yahoo portfolio now</a></div></div></div></div></div></div><table width="580" id="yfncsumtab" cellpadding="0" cellspacing="0" border="0"><tr><td colspan="3"><table border="0" cellspacing="0" class="yfnc_modtitle1" style="border-bottom:1px solid #dcdcdc; margin-bottom:10px; width:100%;"><form action="/q/op" accept-charset="utf-8"><tr><td><big><b>Options</b></big></td><td align="right"><small><b>Get Options for:</b> <input name="s" id="pageTicker" size="10" /></small><input id="get_quote_logic_opt" name="ql" value="1" type="hidden" autocomplete="off"><input value="GO" type="submit" style="margin-left:3px;" class="rapid-nf"></td></tr></form></table></td></tr><tr valign="top"><td>View By Expiration: <a href="/q/op?s=AAPL&amp;m=2014-05">May 14</a> | <strong>Jun 14</strong> | <a href="/q/op?s=AAPL&amp;m=2014-07">Jul 14</a> | <a href="/q/op?s=AAPL&amp;m=2014-08">Aug 14</a> | <a href="/q/op?s=AAPL&amp;m=2014-10">Oct 14</a> | <a href="/q/op?s=AAPL&amp;m=2015-01">Jan 15</a> | <a href="/q/op?s=AAPL&amp;m=2016-01">Jan 16</a><table cellpadding="0" cellspacing="0" border="0"><tr><td height="2"></td></tr></table><table class="yfnc_mod_table_title1" width="100%" cellpadding="2" cellspacing="0" border="0"><tr valign="top"><td><small><b><strong class="yfi-module-title">Call Options</strong></b></small></td><td align="right">Expire at close Saturday, June 21, 2014</td></tr></table><table class="yfnc_datamodoutline1" width="100%" cellpadding="0" cellspacing="0" border="0"><tr valign="top"><td><table border="0" cellpadding="3" cellspacing="1" width="100%"><tr><th scope="col" class="yfnc_tablehead1" width="12%" align="left">Strike</th><th scope="col" class="yfnc_tablehead1" width="12%">Symbol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Last</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Chg</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Bid</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Ask</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Vol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Open Int</th></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=300.000000"><strong>300.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00300000">AAPL140621C00300000</a></td><td class="yfnc_h" align="right"><b>229.24</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00300000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">293.05</td><td class="yfnc_h" align="right">294.50</td><td class="yfnc_h" align="right">15</td><td class="yfnc_h" align="right">15</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=330.000000"><strong>330.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00330000">AAPL140621C00330000</a></td><td class="yfnc_h" align="right"><b>184.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00330000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">263.05</td><td class="yfnc_h" align="right">264.55</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=400.000000"><strong>400.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00400000">AAPL140621C00400000</a></td><td class="yfnc_h" align="right"><b>192.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00400000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">193.10</td><td class="yfnc_h" align="right">194.60</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=420.000000"><strong>420.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00420000">AAPL140621C00420000</a></td><td class="yfnc_h" align="right"><b>171.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00420000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">173.05</td><td class="yfnc_h" align="right">174.60</td><td class="yfnc_h" align="right">157</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=430.000000"><strong>430.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00430000">AAPL140621C00430000</a></td><td class="yfnc_h" align="right"><b>163.48</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00430000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.48</b></span></td><td class="yfnc_h" align="right">163.10</td><td class="yfnc_h" align="right">164.45</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00450000">AAPL140621C00450000</a></td><td class="yfnc_h" align="right"><b>131.63</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00450000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">143.05</td><td class="yfnc_h" align="right">144.60</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">19</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00450000">AAPL7140621C00450000</a></td><td class="yfnc_h" align="right"><b>112.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00450000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">142.00</td><td class="yfnc_h" align="right">145.80</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00460000">AAPL140621C00460000</a></td><td class="yfnc_h" align="right"><b>131.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00460000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">133.10</td><td class="yfnc_h" align="right">134.55</td><td class="yfnc_h" align="right">136</td><td class="yfnc_h" align="right">21</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=465.000000"><strong>465.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00465000">AAPL140621C00465000</a></td><td class="yfnc_h" align="right"><b>124.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00465000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">128.15</td><td class="yfnc_h" align="right">129.55</td><td class="yfnc_h" align="right">146</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00470000">AAPL140621C00470000</a></td><td class="yfnc_h" align="right"><b>122.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">123.15</td><td class="yfnc_h" align="right">124.45</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">56</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00475000">AAPL140621C00475000</a></td><td class="yfnc_h" align="right"><b>115.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00475000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">118.10</td><td class="yfnc_h" align="right">119.55</td><td class="yfnc_h" align="right">195</td><td class="yfnc_h" align="right">11</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00475000">AAPL7140621C00475000</a></td><td class="yfnc_h" align="right"><b>117.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00475000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">117.10</td><td class="yfnc_h" align="right">120.75</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=480.000000"><strong>480.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00480000">AAPL140621C00480000</a></td><td class="yfnc_h" align="right"><b>112.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00480000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">113.20</td><td class="yfnc_h" align="right">114.50</td><td class="yfnc_h" align="right">126</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00485000">AAPL140621C00485000</a></td><td class="yfnc_h" align="right"><b>106.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00485000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">108.10</td><td class="yfnc_h" align="right">109.55</td><td class="yfnc_h" align="right">124</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00490000">AAPL140621C00490000</a></td><td class="yfnc_h" align="right"><b>103.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00490000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.20</b></span></td><td class="yfnc_h" align="right">103.20</td><td class="yfnc_h" align="right">104.60</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">21</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00495000">AAPL140621C00495000</a></td><td class="yfnc_h" align="right"><b>92.86</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">98.35</td><td class="yfnc_h" align="right">99.50</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00500000">AAPL140606C00500000</a></td><td class="yfnc_h" align="right"><b>85.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">92.95</td><td class="yfnc_h" align="right">94.60</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00500000">AAPL140613C00500000</a></td><td class="yfnc_h" align="right"><b>93.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.35</b></span></td><td class="yfnc_h" align="right">93.00</td><td class="yfnc_h" align="right">94.65</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613C00500000">AAPL7140613C00500000</a></td><td class="yfnc_h" align="right"><b>82.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613c00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">91.95</td><td class="yfnc_h" align="right">95.80</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00500000">AAPL140621C00500000</a></td><td class="yfnc_h" align="right"><b>94.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.70</b></span></td><td class="yfnc_h" align="right">93.40</td><td class="yfnc_h" align="right">94.55</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">615</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00500000">AAPL7140621C00500000</a></td><td class="yfnc_h" align="right"><b>89.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">92.25</td><td class="yfnc_h" align="right">95.80</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00505000">AAPL140621C00505000</a></td><td class="yfnc_h" align="right"><b>78.38</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">88.40</td><td class="yfnc_h" align="right">89.65</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">76</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00505000">AAPL7140621C00505000</a></td><td class="yfnc_h" align="right"><b>97.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">87.25</td><td class="yfnc_h" align="right">90.65</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00510000">AAPL140621C00510000</a></td><td class="yfnc_h" align="right"><b>83.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.30</b></span></td><td class="yfnc_h" align="right">83.35</td><td class="yfnc_h" align="right">84.70</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">152</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00510000">AAPL7140621C00510000</a></td><td class="yfnc_h" align="right"><b>84.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00510000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">82.20</td><td class="yfnc_h" align="right">85.90</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00515000">AAPL140621C00515000</a></td><td class="yfnc_h" align="right"><b>69.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">78.40</td><td class="yfnc_h" align="right">79.70</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">25</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00520000">AAPL140606C00520000</a></td><td class="yfnc_h" align="right"><b>69.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">72.95</td><td class="yfnc_h" align="right">74.55</td><td class="yfnc_h" align="right">63</td><td class="yfnc_h" align="right">15</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00520000">AAPL140621C00520000</a></td><td class="yfnc_h" align="right"><b>75.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.75</b></span></td><td class="yfnc_h" align="right">73.50</td><td class="yfnc_h" align="right">74.75</td><td class="yfnc_h" align="right">42</td><td class="yfnc_h" align="right">278</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00520000">AAPL7140621C00520000</a></td><td class="yfnc_h" align="right"><b>71.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">72.25</td><td class="yfnc_h" align="right">75.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">21</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00525000">AAPL140621C00525000</a></td><td class="yfnc_h" align="right"><b>66.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.45</b></span></td><td class="yfnc_h" align="right">68.60</td><td class="yfnc_h" align="right">69.85</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">199</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00525000">AAPL7140621C00525000</a></td><td class="yfnc_h" align="right"><b>67.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00525000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">67.40</td><td class="yfnc_h" align="right">70.80</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">28</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00530000">AAPL140613C00530000</a></td><td class="yfnc_h" align="right"><b>63.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">10.95</b></span></td><td class="yfnc_h" align="right">63.30</td><td class="yfnc_h" align="right">65.00</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00530000">AAPL140621C00530000</a></td><td class="yfnc_h" align="right"><b>63.78</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.57</b></span></td><td class="yfnc_h" align="right">63.60</td><td class="yfnc_h" align="right">64.95</td><td class="yfnc_h" align="right">28</td><td class="yfnc_h" align="right">359</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00530000">AAPL7140621C00530000</a></td><td class="yfnc_h" align="right"><b>64.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.75</b></span></td><td class="yfnc_h" align="right">62.45</td><td class="yfnc_h" align="right">65.80</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">26</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00535000">AAPL140621C00535000</a></td><td class="yfnc_h" align="right"><b>59.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.90</b></span></td><td class="yfnc_h" align="right">58.80</td><td class="yfnc_h" align="right">60.10</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">155</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00535000">AAPL7140621C00535000</a></td><td class="yfnc_h" align="right"><b>48.18</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">57.60</td><td class="yfnc_h" align="right">61.10</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">38</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00540000">AAPL140606C00540000</a></td><td class="yfnc_h" align="right"><b>52.42</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">10.12</b></span></td><td class="yfnc_h" align="right">53.45</td><td class="yfnc_h" align="right">54.70</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00540000">AAPL140613C00540000</a></td><td class="yfnc_h" align="right"><b>52.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00540000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">53.55</td><td class="yfnc_h" align="right">55.10</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00540000">AAPL140621C00540000</a></td><td class="yfnc_h" align="right"><b>55.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.05</b></span></td><td class="yfnc_h" align="right">54.10</td><td class="yfnc_h" align="right">55.00</td><td class="yfnc_h" align="right">14</td><td class="yfnc_h" align="right">440</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00540000">AAPL7140621C00540000</a></td><td class="yfnc_h" align="right"><b>54.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.20</b></span></td><td class="yfnc_h" align="right">53.25</td><td class="yfnc_h" align="right">56.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00545000">AAPL140606C00545000</a></td><td class="yfnc_h" align="right"><b>43.42</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">48.40</td><td class="yfnc_h" align="right">49.70</td><td class="yfnc_h" align="right">30</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00545000">AAPL140621C00545000</a></td><td class="yfnc_h" align="right"><b>50.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.00</b></span></td><td class="yfnc_h" align="right">49.45</td><td class="yfnc_h" align="right">50.55</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">622</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00545000">AAPL7140621C00545000</a></td><td class="yfnc_h" align="right"><b>53.88</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">48.50</td><td class="yfnc_h" align="right">51.30</td><td class="yfnc_h" align="right">15</td><td class="yfnc_h" align="right">33</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00545000">AAPL140627C00545000</a></td><td class="yfnc_h" align="right"><b>41.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">49.75</td><td class="yfnc_h" align="right">51.10</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00550000">AAPL140606C00550000</a></td><td class="yfnc_h" align="right"><b>44.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.00</b></span></td><td class="yfnc_h" align="right">43.70</td><td class="yfnc_h" align="right">44.85</td><td class="yfnc_h" align="right">72</td><td class="yfnc_h" align="right">97</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00550000">AAPL140613C00550000</a></td><td class="yfnc_h" align="right"><b>44.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.50</b></span></td><td class="yfnc_h" align="right">44.25</td><td class="yfnc_h" align="right">45.65</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00550000">AAPL140621C00550000</a></td><td class="yfnc_h" align="right"><b>45.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.50</b></span></td><td class="yfnc_h" align="right">44.75</td><td class="yfnc_h" align="right">45.70</td><td class="yfnc_h" align="right">39</td><td class="yfnc_h" align="right">2,403</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00550000">AAPL7140621C00550000</a></td><td class="yfnc_h" align="right"><b>45.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">8.50</b></span></td><td class="yfnc_h" align="right">44.10</td><td class="yfnc_h" align="right">46.60</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">223</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00550000">AAPL140627C00550000</a></td><td class="yfnc_h" align="right"><b>43.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00550000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">45.25</td><td class="yfnc_h" align="right">46.60</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00555000">AAPL140606C00555000</a></td><td class="yfnc_h" align="right"><b>34.53</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">38.70</td><td class="yfnc_h" align="right">40.00</td><td class="yfnc_h" align="right">99</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00555000">AAPL140613C00555000</a></td><td class="yfnc_h" align="right"><b>38.72</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">4.87</b></span></td><td class="yfnc_h" align="right">39.70</td><td class="yfnc_h" align="right">41.20</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00555000">AAPL140621C00555000</a></td><td class="yfnc_h" align="right"><b>40.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.95</b></span></td><td class="yfnc_h" align="right">40.40</td><td class="yfnc_h" align="right">41.35</td><td class="yfnc_h" align="right">28</td><td class="yfnc_h" align="right">885</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00555000">AAPL7140621C00555000</a></td><td class="yfnc_h" align="right"><b>39.01</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">39.85</td><td class="yfnc_h" align="right">42.15</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">126</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00560000">AAPL140606C00560000</a></td><td class="yfnc_h" align="right"><b>33.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.50</b></span></td><td class="yfnc_h" align="right">34.10</td><td class="yfnc_h" align="right">35.30</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">84</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00560000">AAPL140613C00560000</a></td><td class="yfnc_h" align="right"><b>35.31</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_h" align="right">35.35</td><td class="yfnc_h" align="right">36.45</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">117</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613C00560000">AAPL7140613C00560000</a></td><td class="yfnc_h" align="right"><b>33.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613c00560000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">34.60</td><td class="yfnc_h" align="right">36.90</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00560000">AAPL140621C00560000</a></td><td class="yfnc_h" align="right"><b>35.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.25</b></span></td><td class="yfnc_h" align="right">36.05</td><td class="yfnc_h" align="right">37.00</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">2,934</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00560000">AAPL7140621C00560000</a></td><td class="yfnc_h" align="right"><b>35.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.25</b></span></td><td class="yfnc_h" align="right">35.25</td><td class="yfnc_h" align="right">37.50</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">556</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00560000">AAPL140627C00560000</a></td><td class="yfnc_h" align="right"><b>34.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00560000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">36.55</td><td class="yfnc_h" align="right">38.00</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00562500">AAPL140606C00562500</a></td><td class="yfnc_h" align="right"><b>30.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">31.85</td><td class="yfnc_h" align="right">33.00</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">124</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00562500">AAPL7140606C00562500</a></td><td class="yfnc_h" align="right"><b>23.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">30.80</td><td class="yfnc_h" align="right">34.05</td><td class="yfnc_h" align="right">9</td><td class="yfnc_h" align="right">9</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00565000">AAPL140606C00565000</a></td><td class="yfnc_h" align="right"><b>29.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.50</b></span></td><td class="yfnc_h" align="right">29.65</td><td class="yfnc_h" align="right">30.70</td><td class="yfnc_h" align="right">8</td><td class="yfnc_h" align="right">455</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00565000">AAPL7140606C00565000</a></td><td class="yfnc_h" align="right"><b>29.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.77</b></span></td><td class="yfnc_h" align="right">29.15</td><td class="yfnc_h" align="right">31.80</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00565000">AAPL140613C00565000</a></td><td class="yfnc_h" align="right"><b>29.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">31.05</td><td class="yfnc_h" align="right">32.15</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">27</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613C00565000">AAPL7140613C00565000</a></td><td class="yfnc_h" align="right"><b>35.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613c00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">30.40</td><td class="yfnc_h" align="right">32.65</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00565000">AAPL140621C00565000</a></td><td class="yfnc_h" align="right"><b>31.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.50</b></span></td><td class="yfnc_h" align="right">31.70</td><td class="yfnc_h" align="right">32.80</td><td class="yfnc_h" align="right">41</td><td class="yfnc_h" align="right">1,573</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00565000">AAPL7140621C00565000</a></td><td class="yfnc_h" align="right"><b>32.41</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">31.60</td><td class="yfnc_h" align="right">33.50</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">387</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00567500">AAPL140613C00567500</a></td><td class="yfnc_h" align="right"><b>27.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00567500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">28.90</td><td class="yfnc_h" align="right">30.10</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">9</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00570000">AAPL140606C00570000</a></td><td class="yfnc_h" align="right"><b>26.36</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.46</b></span></td><td class="yfnc_h" align="right">25.55</td><td class="yfnc_h" align="right">26.05</td><td class="yfnc_h" align="right">12</td><td class="yfnc_h" align="right">319</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00570000">AAPL7140606C00570000</a></td><td class="yfnc_h" align="right"><b>18.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00570000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">24.20</td><td class="yfnc_h" align="right">26.90</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">19</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00570000">AAPL140613C00570000</a></td><td class="yfnc_h" align="right"><b>27.49</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00570000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">26.80</td><td class="yfnc_h" align="right">28.05</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00570000">AAPL140621C00570000</a></td><td class="yfnc_h" align="right"><b>28.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.77</b></span></td><td class="yfnc_h" align="right">28.30</td><td class="yfnc_h" align="right">28.50</td><td class="yfnc_h" align="right">2,432</td><td class="yfnc_h" align="right">4,953</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00570000">AAPL7140621C00570000</a></td><td class="yfnc_h" align="right"><b>29.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">1.60</b></span></td><td class="yfnc_h" align="right">27.40</td><td class="yfnc_h" align="right">28.85</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">655</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00570000">AAPL140627C00570000</a></td><td class="yfnc_h" align="right"><b>22.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00570000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">28.90</td><td class="yfnc_h" align="right">29.90</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00572500">AAPL140613C00572500</a></td><td class="yfnc_h" align="right"><b>25.94</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">3.19</b></span></td><td class="yfnc_h" align="right">24.95</td><td class="yfnc_h" align="right">26.05</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00575000">AAPL140606C00575000</a></td><td class="yfnc_h" align="right"><b>21.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.30</b></span></td><td class="yfnc_h" align="right">21.15</td><td class="yfnc_h" align="right">21.95</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">439</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00575000">AAPL7140606C00575000</a></td><td class="yfnc_h" align="right"><b>25.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00575000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">20.50</td><td class="yfnc_h" align="right">22.95</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">6</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00575000">AAPL140613C00575000</a></td><td class="yfnc_h" align="right"><b>23.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.50</b></span></td><td class="yfnc_h" align="right">23.40</td><td class="yfnc_h" align="right">24.20</td><td class="yfnc_h" align="right">13</td><td class="yfnc_h" align="right">30</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00575000">AAPL140621C00575000</a></td><td class="yfnc_h" align="right"><b>24.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.65</b></span></td><td class="yfnc_h" align="right">24.60</td><td class="yfnc_h" align="right">24.85</td><td class="yfnc_h" align="right">201</td><td class="yfnc_h" align="right">2,135</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00575000">AAPL7140621C00575000</a></td><td class="yfnc_h" align="right"><b>24.93</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.93</b></span></td><td class="yfnc_h" align="right">24.30</td><td class="yfnc_h" align="right">25.25</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">384</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00577500">AAPL140613C00577500</a></td><td class="yfnc_h" align="right"><b>20.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00577500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">21.30</td><td class="yfnc_h" align="right">22.05</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00580000">AAPL140606C00580000</a></td><td class="yfnc_h" align="right"><b>18.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.20</b></span></td><td class="yfnc_h" align="right">17.70</td><td class="yfnc_h" align="right">18.15</td><td class="yfnc_h" align="right">12</td><td class="yfnc_h" align="right">433</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00580000">AAPL7140606C00580000</a></td><td class="yfnc_h" align="right"><b>16.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00580000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.70</td><td class="yfnc_h" align="right">19.15</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">14</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00580000">AAPL140613C00580000</a></td><td class="yfnc_h" align="right"><b>20.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00580000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">19.65</td><td class="yfnc_h" align="right">20.45</td><td class="yfnc_h" align="right">51</td><td class="yfnc_h" align="right">82</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00580000">AAPL140621C00580000</a></td><td class="yfnc_h" align="right"><b>20.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.60</b></span></td><td class="yfnc_h" align="right">21.15</td><td class="yfnc_h" align="right">21.40</td><td class="yfnc_h" align="right">523</td><td class="yfnc_h" align="right">3,065</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00580000">AAPL7140621C00580000</a></td><td class="yfnc_h" align="right"><b>21.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.15</b></span></td><td class="yfnc_h" align="right">20.95</td><td class="yfnc_h" align="right">21.80</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">246</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00580000">AAPL140627C00580000</a></td><td class="yfnc_h" align="right"><b>22.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.20</b></span></td><td class="yfnc_h" align="right">21.80</td><td class="yfnc_h" align="right">22.55</td><td class="yfnc_h" align="right">30</td><td class="yfnc_h" align="right">47</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00582500">AAPL140613C00582500</a></td><td class="yfnc_h" align="right"><b>13.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00582500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">18.05</td><td class="yfnc_h" align="right">18.75</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613C00582500">AAPL7140613C00582500</a></td><td class="yfnc_h" align="right"><b>15.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613c00582500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.80</td><td class="yfnc_h" align="right">19.70</td><td class="yfnc_h" align="right">30</td><td class="yfnc_h" align="right">30</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00582500">AAPL140627C00582500</a></td><td class="yfnc_h" align="right"><b>20.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00582500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">20.35</td><td class="yfnc_h" align="right">21.00</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">9</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00585000">AAPL140606C00585000</a></td><td class="yfnc_h" align="right"><b>14.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.85</b></span></td><td class="yfnc_h" align="right">14.40</td><td class="yfnc_h" align="right">14.70</td><td class="yfnc_h" align="right">19</td><td class="yfnc_h" align="right">372</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00585000">AAPL7140606C00585000</a></td><td class="yfnc_h" align="right"><b>14.14</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.08</b></span></td><td class="yfnc_h" align="right">13.45</td><td class="yfnc_h" align="right">15.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">28</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00585000">AAPL140613C00585000</a></td><td class="yfnc_h" align="right"><b>17.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.85</b></span></td><td class="yfnc_h" align="right">16.45</td><td class="yfnc_h" align="right">17.15</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">177</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00585000">AAPL140621C00585000</a></td><td class="yfnc_h" align="right"><b>17.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.03</b></span></td><td class="yfnc_h" align="right">18.10</td><td class="yfnc_h" align="right">18.25</td><td class="yfnc_h" align="right">161</td><td class="yfnc_h" align="right">2,407</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00585000">AAPL7140621C00585000</a></td><td class="yfnc_h" align="right"><b>17.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.65</b></span></td><td class="yfnc_h" align="right">17.85</td><td class="yfnc_h" align="right">18.60</td><td class="yfnc_h" align="right">99</td><td class="yfnc_h" align="right">304</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00585000">AAPL140627C00585000</a></td><td class="yfnc_h" align="right"><b>19.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.43</b></span></td><td class="yfnc_h" align="right">18.85</td><td class="yfnc_h" align="right">19.45</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">11</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00587500">AAPL140613C00587500</a></td><td class="yfnc_h" align="right"><b>15.65</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00587500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">15.05</td><td class="yfnc_h" align="right">15.65</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">34</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613C00587500">AAPL7140613C00587500</a></td><td class="yfnc_h" align="right"><b>14.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613c00587500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">14.70</td><td class="yfnc_h" align="right">16.75</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">22</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00587500">AAPL140627C00587500</a></td><td class="yfnc_h" align="right"><b>17.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00587500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">17.50</td><td class="yfnc_h" align="right">18.00</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">27</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606C00590000">AAPL140606C00590000</a></td><td class="yfnc_h" align="right"><b>11.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.90</b></span></td><td class="yfnc_h" align="right">11.35</td><td class="yfnc_h" align="right">11.70</td><td class="yfnc_h" align="right">55</td><td class="yfnc_h" align="right">1,297</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606C00590000">AAPL7140606C00590000</a></td><td class="yfnc_h" align="right"><b>11.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.87</b></span></td><td class="yfnc_h" align="right">10.20</td><td class="yfnc_h" align="right">12.40</td><td class="yfnc_h" align="right">6</td><td class="yfnc_h" align="right">45</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00590000">AAPL140613C00590000</a></td><td class="yfnc_h" align="right"><b>14.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.37</b></span></td><td class="yfnc_h" align="right">13.65</td><td class="yfnc_h" align="right">14.25</td><td class="yfnc_h" align="right">38</td><td class="yfnc_h" align="right">448</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621C00590000">AAPL140621C00590000</a></td><td class="yfnc_h" align="right"><b>15.15</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.59</b></span></td><td class="yfnc_h" align="right">15.25</td><td class="yfnc_h" align="right">15.40</td><td class="yfnc_h" align="right">618</td><td class="yfnc_h" align="right">14,296</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621C00590000">AAPL7140621C00590000</a></td><td class="yfnc_h" align="right"><b>15.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.80</b></span></td><td class="yfnc_h" align="right">15.05</td><td class="yfnc_h" align="right">15.85</td><td class="yfnc_h" align="right">19</td><td class="yfnc_h" align="right">230</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00590000">AAPL140627C00590000</a></td><td class="yfnc_h" align="right"><b>16.35</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.95</b></span></td><td class="yfnc_h" align="right">16.15</td><td class="yfnc_h" align="right">16.65</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">57</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140627C00590000">AAPL7140627C00590000</a></td><td class="yfnc_h" align="right"><b>14.13</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140627c00590000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">15.00</td><td class="yfnc_h" align="right">17.55</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613C00592500">AAPL140613C00592500</a></td><td class="yfnc_h" align="right"><b>12.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613c00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.35</b></span></td><td class="yfnc_h" align="right">12.50</td><td class="yfnc_h" align="right">12.85</td><td class="yfnc_h" align="right">29</td><td class="yfnc_h" align="right">60</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613C00592500">AAPL7140613C00592500</a></td><td class="yfnc_h" align="right"><b>12.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613c00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">2.50</b></span></td><td class="yfnc_h" align="right">12.15</td><td class="yfnc_h" align="right">14.35</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627C00592500">AAPL140627C00592500</a></td><td class="yfnc_h" align="right"><b>15.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627c00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.65</b></span></td><td class="yfnc_h" align="right">14.80</td><td class="yfnc_h" align="right">15.30</td><td class="yfnc_h" align="right">40</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140627C00592500">AAPL7140627C00592500</a></td><td class="yfnc_h" align="right"><b>14.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140627c00592500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">14.55</td><td class="yfnc_h" align="right">16.55</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00595000">AAPL140606C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>8.78</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.52</b></span></td><td class="yfnc_tabledata1" align="right">8.80</td><td class="yfnc_tabledata1" align="right">9.05</td><td class="yfnc_tabledata1" align="right">315</td><td class="yfnc_tabledata1" align="right">1,315</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00595000">AAPL7140606C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>9.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.67</b></span></td><td class="yfnc_tabledata1" align="right">8.20</td><td class="yfnc_tabledata1" align="right">10.15</td><td class="yfnc_tabledata1" align="right">42</td><td class="yfnc_tabledata1" align="right">83</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00595000">AAPL140613C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>11.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">11.45</td><td class="yfnc_tabledata1" align="right">11.60</td><td class="yfnc_tabledata1" align="right">292</td><td class="yfnc_tabledata1" align="right">318</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00595000">AAPL7140613C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>9.38</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00595000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">11.00</td><td class="yfnc_tabledata1" align="right">12.45</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00595000">AAPL140621C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>12.54</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.66</b></span></td><td class="yfnc_tabledata1" align="right">12.65</td><td class="yfnc_tabledata1" align="right">12.85</td><td class="yfnc_tabledata1" align="right">857</td><td class="yfnc_tabledata1" align="right">2,741</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00595000">AAPL7140621C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>13.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.25</b></span></td><td class="yfnc_tabledata1" align="right">12.45</td><td class="yfnc_tabledata1" align="right">13.35</td><td class="yfnc_tabledata1" align="right">17</td><td class="yfnc_tabledata1" align="right">317</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00595000">AAPL140627C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>13.94</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.86</b></span></td><td class="yfnc_tabledata1" align="right">13.60</td><td class="yfnc_tabledata1" align="right">14.05</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">60</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00595000">AAPL7140627C00595000</a></td><td class="yfnc_tabledata1" align="right"><b>14.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.76</b></span></td><td class="yfnc_tabledata1" align="right">13.20</td><td class="yfnc_tabledata1" align="right">15.10</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">7</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00597500">AAPL140613C00597500</a></td><td class="yfnc_tabledata1" align="right"><b>9.96</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00597500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">10.00</td><td class="yfnc_tabledata1" align="right">10.55</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">74</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00597500">AAPL140627C00597500</a></td><td class="yfnc_tabledata1" align="right"><b>12.85</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00597500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">12.65</td><td class="yfnc_tabledata1" align="right">12.90</td><td class="yfnc_tabledata1" align="right">52</td><td class="yfnc_tabledata1" align="right">36</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00600000">AAPL140606C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>6.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.40</b></span></td><td class="yfnc_tabledata1" align="right">6.65</td><td class="yfnc_tabledata1" align="right">6.90</td><td class="yfnc_tabledata1" align="right">323</td><td class="yfnc_tabledata1" align="right">1,841</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00600000">AAPL7140606C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>6.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.80</b></span></td><td class="yfnc_tabledata1" align="right">6.55</td><td class="yfnc_tabledata1" align="right">7.10</td><td class="yfnc_tabledata1" align="right">15</td><td class="yfnc_tabledata1" align="right">179</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00600000">AAPL140613C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>9.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.40</b></span></td><td class="yfnc_tabledata1" align="right">9.20</td><td class="yfnc_tabledata1" align="right">9.40</td><td class="yfnc_tabledata1" align="right">123</td><td class="yfnc_tabledata1" align="right">690</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00600000">AAPL7140613C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>9.43</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.23</b></span></td><td class="yfnc_tabledata1" align="right">8.85</td><td class="yfnc_tabledata1" align="right">10.00</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">18</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00600000">AAPL140621C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>10.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.50</b></span></td><td class="yfnc_tabledata1" align="right">10.50</td><td class="yfnc_tabledata1" align="right">10.60</td><td class="yfnc_tabledata1" align="right">827</td><td class="yfnc_tabledata1" align="right">8,816</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00600000">AAPL7140621C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>10.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.85</b></span></td><td class="yfnc_tabledata1" align="right">10.20</td><td class="yfnc_tabledata1" align="right">11.00</td><td class="yfnc_tabledata1" align="right">68</td><td class="yfnc_tabledata1" align="right">416</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00600000">AAPL140627C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>11.63</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.87</b></span></td><td class="yfnc_tabledata1" align="right">11.35</td><td class="yfnc_tabledata1" align="right">11.80</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">34</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00600000">AAPL7140627C00600000</a></td><td class="yfnc_tabledata1" align="right"><b>9.22</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00600000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">11.20</td><td class="yfnc_tabledata1" align="right">12.65</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00602500">AAPL140613C00602500</a></td><td class="yfnc_tabledata1" align="right"><b>8.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00602500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">8.05</td><td class="yfnc_tabledata1" align="right">8.50</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">51</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00602500">AAPL7140613C00602500</a></td><td class="yfnc_tabledata1" align="right"><b>6.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00602500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">8.05</td><td class="yfnc_tabledata1" align="right">8.50</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00605000">AAPL140606C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>5.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.27</b></span></td><td class="yfnc_tabledata1" align="right">4.90</td><td class="yfnc_tabledata1" align="right">5.10</td><td class="yfnc_tabledata1" align="right">476</td><td class="yfnc_tabledata1" align="right">849</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00605000">AAPL7140606C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>5.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.85</td><td class="yfnc_tabledata1" align="right">5.30</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">132</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00605000">AAPL140613C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>7.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.70</b></span></td><td class="yfnc_tabledata1" align="right">7.15</td><td class="yfnc_tabledata1" align="right">7.60</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">112</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00605000">AAPL7140613C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>7.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.15</td><td class="yfnc_tabledata1" align="right">9.00</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">9</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00605000">AAPL140621C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>8.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.60</b></span></td><td class="yfnc_tabledata1" align="right">8.50</td><td class="yfnc_tabledata1" align="right">8.60</td><td class="yfnc_tabledata1" align="right">419</td><td class="yfnc_tabledata1" align="right">2,326</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00605000">AAPL7140621C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>8.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">8.35</td><td class="yfnc_tabledata1" align="right">8.65</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">738</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00605000">AAPL140627C00605000</a></td><td class="yfnc_tabledata1" align="right"><b>9.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.10</b></span></td><td class="yfnc_tabledata1" align="right">9.40</td><td class="yfnc_tabledata1" align="right">9.80</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">17</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00607500">AAPL140613C00607500</a></td><td class="yfnc_tabledata1" align="right"><b>5.72</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00607500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">6.35</td><td class="yfnc_tabledata1" align="right">6.75</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">9</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00607500">AAPL7140613C00607500</a></td><td class="yfnc_tabledata1" align="right"><b>10.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00607500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">6.35</td><td class="yfnc_tabledata1" align="right">6.75</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00607500">AAPL140627C00607500</a></td><td class="yfnc_tabledata1" align="right"><b>9.64</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00607500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">8.55</td><td class="yfnc_tabledata1" align="right">8.90</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">19</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00610000">AAPL140606C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>3.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.55</b></span></td><td class="yfnc_tabledata1" align="right">3.55</td><td class="yfnc_tabledata1" align="right">3.70</td><td class="yfnc_tabledata1" align="right">648</td><td class="yfnc_tabledata1" align="right">924</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00610000">AAPL7140606C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>3.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.25</b></span></td><td class="yfnc_tabledata1" align="right">3.50</td><td class="yfnc_tabledata1" align="right">3.95</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">135</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00610000">AAPL140613C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>6.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.25</b></span></td><td class="yfnc_tabledata1" align="right">5.60</td><td class="yfnc_tabledata1" align="right">6.00</td><td class="yfnc_tabledata1" align="right">26</td><td class="yfnc_tabledata1" align="right">131</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00610000">AAPL7140613C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>9.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00610000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.60</td><td class="yfnc_tabledata1" align="right">6.00</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00610000">AAPL140621C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>6.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.64</b></span></td><td class="yfnc_tabledata1" align="right">6.85</td><td class="yfnc_tabledata1" align="right">7.00</td><td class="yfnc_tabledata1" align="right">497</td><td class="yfnc_tabledata1" align="right">2,521</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00610000">AAPL7140621C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>7.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.20</b></span></td><td class="yfnc_tabledata1" align="right">6.75</td><td class="yfnc_tabledata1" align="right">7.00</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">423</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00610000">AAPL140627C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>8.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.44</b></span></td><td class="yfnc_tabledata1" align="right">7.75</td><td class="yfnc_tabledata1" align="right">8.10</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">40</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00610000">AAPL7140627C00610000</a></td><td class="yfnc_tabledata1" align="right"><b>8.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00610000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.55</td><td class="yfnc_tabledata1" align="right">9.10</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00612500">AAPL140613C00612500</a></td><td class="yfnc_tabledata1" align="right"><b>5.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00612500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.90</td><td class="yfnc_tabledata1" align="right">5.35</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">45</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00612500">AAPL7140613C00612500</a></td><td class="yfnc_tabledata1" align="right"><b>6.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00612500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.90</td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00612500">AAPL140627C00612500</a></td><td class="yfnc_tabledata1" align="right"><b>5.57</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00612500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.00</td><td class="yfnc_tabledata1" align="right">7.40</td><td class="yfnc_tabledata1" align="right">20</td><td class="yfnc_tabledata1" align="right">21</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00615000">AAPL140606C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>2.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">2.54</td><td class="yfnc_tabledata1" align="right">2.73</td><td class="yfnc_tabledata1" align="right">105</td><td class="yfnc_tabledata1" align="right">619</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00615000">AAPL7140606C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>2.82</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.17</b></span></td><td class="yfnc_tabledata1" align="right">2.42</td><td class="yfnc_tabledata1" align="right">2.83</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">135</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00615000">AAPL140613C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>4.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.20</b></span></td><td class="yfnc_tabledata1" align="right">4.35</td><td class="yfnc_tabledata1" align="right">4.75</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">51</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00615000">AAPL140621C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>5.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.60</b></span></td><td class="yfnc_tabledata1" align="right">5.45</td><td class="yfnc_tabledata1" align="right">5.55</td><td class="yfnc_tabledata1" align="right">247</td><td class="yfnc_tabledata1" align="right">1,992</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00615000">AAPL7140621C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>5.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">5.35</td><td class="yfnc_tabledata1" align="right">5.70</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">74</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00615000">AAPL140627C00615000</a></td><td class="yfnc_tabledata1" align="right"><b>7.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">6.30</td><td class="yfnc_tabledata1" align="right">6.60</td><td class="yfnc_tabledata1" align="right">39</td><td class="yfnc_tabledata1" align="right">38</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00617500">AAPL140613C00617500</a></td><td class="yfnc_tabledata1" align="right"><b>4.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00617500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.65</b></span></td><td class="yfnc_tabledata1" align="right">3.85</td><td class="yfnc_tabledata1" align="right">4.20</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00617500">AAPL7140613C00617500</a></td><td class="yfnc_tabledata1" align="right"><b>6.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00617500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.80</td><td class="yfnc_tabledata1" align="right">4.15</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00617500">AAPL140627C00617500</a></td><td class="yfnc_tabledata1" align="right"><b>4.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00617500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.60</td><td class="yfnc_tabledata1" align="right">5.95</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">21</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00617500">AAPL7140627C00617500</a></td><td class="yfnc_tabledata1" align="right"><b>4.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00617500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">6.60</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00620000">AAPL140606C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>1.88</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.41</b></span></td><td class="yfnc_tabledata1" align="right">1.84</td><td class="yfnc_tabledata1" align="right">1.97</td><td class="yfnc_tabledata1" align="right">217</td><td class="yfnc_tabledata1" align="right">738</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00620000">AAPL7140606C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>2.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.60</b></span></td><td class="yfnc_tabledata1" align="right">1.78</td><td class="yfnc_tabledata1" align="right">2.15</td><td class="yfnc_tabledata1" align="right">18</td><td class="yfnc_tabledata1" align="right">46</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00620000">AAPL140613C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>3.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.55</b></span></td><td class="yfnc_tabledata1" align="right">3.35</td><td class="yfnc_tabledata1" align="right">3.70</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">140</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00620000">AAPL7140613C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>3.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.07</b></span></td><td class="yfnc_tabledata1" align="right">3.30</td><td class="yfnc_tabledata1" align="right">3.65</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00620000">AAPL140621C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>4.26</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.49</b></span></td><td class="yfnc_tabledata1" align="right">4.30</td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">249</td><td class="yfnc_tabledata1" align="right">7,992</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00620000">AAPL7140621C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>4.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.35</b></span></td><td class="yfnc_tabledata1" align="right">4.20</td><td class="yfnc_tabledata1" align="right">4.45</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">226</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00620000">AAPL140627C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>5.48</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.27</b></span></td><td class="yfnc_tabledata1" align="right">5.05</td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">76</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00620000">AAPL7140627C00620000</a></td><td class="yfnc_tabledata1" align="right"><b>4.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.65</td><td class="yfnc_tabledata1" align="right">5.95</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">16</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=622.500000"><strong>622.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00622500">AAPL140613C00622500</a></td><td class="yfnc_tabledata1" align="right"><b>3.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00622500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.45</b></span></td><td class="yfnc_tabledata1" align="right">2.93</td><td class="yfnc_tabledata1" align="right">3.25</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=622.500000"><strong>622.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00622500">AAPL7140613C00622500</a></td><td class="yfnc_tabledata1" align="right"><b>3.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00622500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.90</td><td class="yfnc_tabledata1" align="right">3.30</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=622.500000"><strong>622.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00622500">AAPL7140627C00622500</a></td><td class="yfnc_tabledata1" align="right"><b>4.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00622500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00625000">AAPL140606C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>1.53</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.04</b></span></td><td class="yfnc_tabledata1" align="right">1.36</td><td class="yfnc_tabledata1" align="right">1.44</td><td class="yfnc_tabledata1" align="right">2,164</td><td class="yfnc_tabledata1" align="right">2,256</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00625000">AAPL7140606C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>1.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.50</b></span></td><td class="yfnc_tabledata1" align="right">1.30</td><td class="yfnc_tabledata1" align="right">1.48</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">29</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00625000">AAPL140613C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>2.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.45</b></span></td><td class="yfnc_tabledata1" align="right">2.58</td><td class="yfnc_tabledata1" align="right">2.84</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">54</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00625000">AAPL7140613C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>2.18</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.53</td><td class="yfnc_tabledata1" align="right">2.83</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00625000">AAPL140621C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>3.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.40</b></span></td><td class="yfnc_tabledata1" align="right">3.40</td><td class="yfnc_tabledata1" align="right">3.45</td><td class="yfnc_tabledata1" align="right">411</td><td class="yfnc_tabledata1" align="right">2,307</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00625000">AAPL7140621C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>3.64</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.11</b></span></td><td class="yfnc_tabledata1" align="right">3.20</td><td class="yfnc_tabledata1" align="right">3.60</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">230</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00625000">AAPL140627C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>4.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.20</b></span></td><td class="yfnc_tabledata1" align="right">4.10</td><td class="yfnc_tabledata1" align="right">4.35</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">28</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00625000">AAPL7140627C00625000</a></td><td class="yfnc_tabledata1" align="right"><b>4.42</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00625000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.22</b></span></td><td class="yfnc_tabledata1" align="right">3.90</td><td class="yfnc_tabledata1" align="right">4.95</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=627.500000"><strong>627.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00627500">AAPL140613C00627500</a></td><td class="yfnc_tabledata1" align="right"><b>2.76</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00627500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.26</td><td class="yfnc_tabledata1" align="right">2.52</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=627.500000"><strong>627.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00627500">AAPL140627C00627500</a></td><td class="yfnc_tabledata1" align="right"><b>3.94</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00627500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.26</b></span></td><td class="yfnc_tabledata1" align="right">3.65</td><td class="yfnc_tabledata1" align="right">3.95</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">17</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=627.500000"><strong>627.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00627500">AAPL7140627C00627500</a></td><td class="yfnc_tabledata1" align="right"><b>3.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00627500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.45</td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00630000">AAPL140606C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>1.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.13</b></span></td><td class="yfnc_tabledata1" align="right">0.95</td><td class="yfnc_tabledata1" align="right">1.07</td><td class="yfnc_tabledata1" align="right">139</td><td class="yfnc_tabledata1" align="right">409</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00630000">AAPL7140606C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>1.77</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.89</td><td class="yfnc_tabledata1" align="right">1.10</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00630000">AAPL140613C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>2.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.22</b></span></td><td class="yfnc_tabledata1" align="right">1.97</td><td class="yfnc_tabledata1" align="right">2.14</td><td class="yfnc_tabledata1" align="right">41</td><td class="yfnc_tabledata1" align="right">568</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00630000">AAPL7140613C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>5.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.97</td><td class="yfnc_tabledata1" align="right">2.18</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00630000">AAPL140621C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>2.69</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.38</b></span></td><td class="yfnc_tabledata1" align="right">2.68</td><td class="yfnc_tabledata1" align="right">2.73</td><td class="yfnc_tabledata1" align="right">732</td><td class="yfnc_tabledata1" align="right">5,019</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00630000">AAPL7140621C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>3.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.52</td><td class="yfnc_tabledata1" align="right">2.85</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">148</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00630000">AAPL140627C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>3.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.45</b></span></td><td class="yfnc_tabledata1" align="right">3.30</td><td class="yfnc_tabledata1" align="right">3.55</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">39</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00630000">AAPL7140627C00630000</a></td><td class="yfnc_tabledata1" align="right"><b>3.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.04</td><td class="yfnc_tabledata1" align="right">3.95</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=632.500000"><strong>632.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00632500">AAPL140613C00632500</a></td><td class="yfnc_tabledata1" align="right"><b>1.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00632500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">1.72</td><td class="yfnc_tabledata1" align="right">1.88</td><td class="yfnc_tabledata1" align="right">109</td><td class="yfnc_tabledata1" align="right">25</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=632.500000"><strong>632.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00632500">AAPL7140613C00632500</a></td><td class="yfnc_tabledata1" align="right"><b>3.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00632500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.65</td><td class="yfnc_tabledata1" align="right">1.96</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00635000">AAPL140606C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>0.74</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.69</td><td class="yfnc_tabledata1" align="right">0.79</td><td class="yfnc_tabledata1" align="right">47</td><td class="yfnc_tabledata1" align="right">252</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00635000">AAPL7140606C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>2.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.51</td><td class="yfnc_tabledata1" align="right">0.84</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">22</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00635000">AAPL140613C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>1.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.30</b></span></td><td class="yfnc_tabledata1" align="right">1.53</td><td class="yfnc_tabledata1" align="right">1.66</td><td class="yfnc_tabledata1" align="right">45</td><td class="yfnc_tabledata1" align="right">60</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00635000">AAPL7140613C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>3.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.43</td><td class="yfnc_tabledata1" align="right">1.82</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00635000">AAPL140621C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>2.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.36</b></span></td><td class="yfnc_tabledata1" align="right">2.11</td><td class="yfnc_tabledata1" align="right">2.16</td><td class="yfnc_tabledata1" align="right">217</td><td class="yfnc_tabledata1" align="right">1,188</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621C00635000">AAPL7140621C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>2.44</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621c00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.95</td><td class="yfnc_tabledata1" align="right">2.18</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">57</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00635000">AAPL140627C00635000</a></td><td class="yfnc_tabledata1" align="right"><b>2.94</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00635000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.16</b></span></td><td class="yfnc_tabledata1" align="right">2.65</td><td class="yfnc_tabledata1" align="right">2.85</td><td class="yfnc_tabledata1" align="right">27</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00640000">AAPL140606C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>0.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.15</b></span></td><td class="yfnc_tabledata1" align="right">0.50</td><td class="yfnc_tabledata1" align="right">0.64</td><td class="yfnc_tabledata1" align="right">39</td><td class="yfnc_tabledata1" align="right">260</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00640000">AAPL7140606C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>0.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00640000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.47</td><td class="yfnc_tabledata1" align="right">0.66</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">24</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00640000">AAPL140613C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>1.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.23</b></span></td><td class="yfnc_tabledata1" align="right">1.15</td><td class="yfnc_tabledata1" align="right">1.31</td><td class="yfnc_tabledata1" align="right">141</td><td class="yfnc_tabledata1" align="right">187</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00640000">AAPL7140613C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>1.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.21</b></span></td><td class="yfnc_tabledata1" align="right">1.15</td><td class="yfnc_tabledata1" align="right">1.33</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">13</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00640000">AAPL140621C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>1.66</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.27</b></span></td><td class="yfnc_tabledata1" align="right">1.66</td><td class="yfnc_tabledata1" align="right">1.70</td><td class="yfnc_tabledata1" align="right">112</td><td class="yfnc_tabledata1" align="right">1,451</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00640000">AAPL140627C00640000</a></td><td class="yfnc_tabledata1" align="right"><b>2.29</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.18</b></span></td><td class="yfnc_tabledata1" align="right">2.11</td><td class="yfnc_tabledata1" align="right">2.31</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">7</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00645000">AAPL140606C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>0.44</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00645000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">0.38</td><td class="yfnc_tabledata1" align="right">0.49</td><td class="yfnc_tabledata1" align="right">33</td><td class="yfnc_tabledata1" align="right">147</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00645000">AAPL7140606C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>1.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">0.54</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00645000">AAPL140613C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>1.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.90</td><td class="yfnc_tabledata1" align="right">1.03</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">89</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00645000">AAPL7140613C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>2.39</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.88</td><td class="yfnc_tabledata1" align="right">1.07</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00645000">AAPL140621C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>1.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00645000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.26</b></span></td><td class="yfnc_tabledata1" align="right">1.32</td><td class="yfnc_tabledata1" align="right">1.36</td><td class="yfnc_tabledata1" align="right">147</td><td class="yfnc_tabledata1" align="right">848</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00645000">AAPL140627C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>1.78</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00645000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.13</b></span></td><td class="yfnc_tabledata1" align="right">1.68</td><td class="yfnc_tabledata1" align="right">1.88</td><td class="yfnc_tabledata1" align="right">23</td><td class="yfnc_tabledata1" align="right">14</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00645000">AAPL7140627C00645000</a></td><td class="yfnc_tabledata1" align="right"><b>1.91</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.17</td><td class="yfnc_tabledata1" align="right">2.12</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606C00650000">AAPL140606C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.29</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.15</b></span></td><td class="yfnc_tabledata1" align="right">0.28</td><td class="yfnc_tabledata1" align="right">0.40</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">742</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606C00650000">AAPL7140606C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606c00650000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.44</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">27</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613C00650000">AAPL140613C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613c00650000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.69</td><td class="yfnc_tabledata1" align="right">0.84</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">124</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613C00650000">AAPL7140613C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>0.78</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.46</b></span></td><td class="yfnc_tabledata1" align="right">0.67</td><td class="yfnc_tabledata1" align="right">0.87</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00650000">AAPL140621C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>1.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.22</b></span></td><td class="yfnc_tabledata1" align="right">1.06</td><td class="yfnc_tabledata1" align="right">1.10</td><td class="yfnc_tabledata1" align="right">694</td><td class="yfnc_tabledata1" align="right">10,025</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627C00650000">AAPL140627C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>1.46</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627c00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.13</b></span></td><td class="yfnc_tabledata1" align="right">1.36</td><td class="yfnc_tabledata1" align="right">1.57</td><td class="yfnc_tabledata1" align="right">28</td><td class="yfnc_tabledata1" align="right">32</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627C00650000">AAPL7140627C00650000</a></td><td class="yfnc_tabledata1" align="right"><b>3.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627c00650000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.52</td><td class="yfnc_tabledata1" align="right">1.86</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00655000">AAPL140621C00655000</a></td><td class="yfnc_tabledata1" align="right"><b>0.87</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00655000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.23</b></span></td><td class="yfnc_tabledata1" align="right">0.86</td><td class="yfnc_tabledata1" align="right">0.88</td><td class="yfnc_tabledata1" align="right">166</td><td class="yfnc_tabledata1" align="right">1,192</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=660.000000"><strong>660.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00660000">AAPL140621C00660000</a></td><td class="yfnc_tabledata1" align="right"><b>0.74</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00660000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.12</b></span></td><td class="yfnc_tabledata1" align="right">0.69</td><td class="yfnc_tabledata1" align="right">0.72</td><td class="yfnc_tabledata1" align="right">153</td><td class="yfnc_tabledata1" align="right">1,066</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=665.000000"><strong>665.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00665000">AAPL140621C00665000</a></td><td class="yfnc_tabledata1" align="right"><b>0.63</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00665000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.10</b></span></td><td class="yfnc_tabledata1" align="right">0.57</td><td class="yfnc_tabledata1" align="right">0.60</td><td class="yfnc_tabledata1" align="right">62</td><td class="yfnc_tabledata1" align="right">462</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=670.000000"><strong>670.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00670000">AAPL140621C00670000</a></td><td class="yfnc_tabledata1" align="right"><b>0.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00670000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.13</b></span></td><td class="yfnc_tabledata1" align="right">0.48</td><td class="yfnc_tabledata1" align="right">0.50</td><td class="yfnc_tabledata1" align="right">49</td><td class="yfnc_tabledata1" align="right">475</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=675.000000"><strong>675.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00675000">AAPL140621C00675000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00675000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.08</b></span></td><td class="yfnc_tabledata1" align="right">0.41</td><td class="yfnc_tabledata1" align="right">0.42</td><td class="yfnc_tabledata1" align="right">31</td><td class="yfnc_tabledata1" align="right">377</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=680.000000"><strong>680.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00680000">AAPL140621C00680000</a></td><td class="yfnc_tabledata1" align="right"><b>0.36</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00680000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.09</b></span></td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">25</td><td class="yfnc_tabledata1" align="right">456</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=685.000000"><strong>685.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00685000">AAPL140621C00685000</a></td><td class="yfnc_tabledata1" align="right"><b>0.31</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00685000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.09</b></span></td><td class="yfnc_tabledata1" align="right">0.30</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">393</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=690.000000"><strong>690.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00690000">AAPL140621C00690000</a></td><td class="yfnc_tabledata1" align="right"><b>0.26</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00690000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.26</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">18</td><td class="yfnc_tabledata1" align="right">485</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=695.000000"><strong>695.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00695000">AAPL140621C00695000</a></td><td class="yfnc_tabledata1" align="right"><b>0.26</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00695000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">0.24</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">333</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=700.000000"><strong>700.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00700000">AAPL140621C00700000</a></td><td class="yfnc_tabledata1" align="right"><b>0.21</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00700000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.06</b></span></td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">111</td><td class="yfnc_tabledata1" align="right">3,476</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=705.000000"><strong>705.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00705000">AAPL140621C00705000</a></td><td class="yfnc_tabledata1" align="right"><b>0.22</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00705000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">25</td><td class="yfnc_tabledata1" align="right">790</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=710.000000"><strong>710.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00710000">AAPL140621C00710000</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00710000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">520</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=715.000000"><strong>715.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00715000">AAPL140621C00715000</a></td><td class="yfnc_tabledata1" align="right"><b>0.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00715000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">441</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=720.000000"><strong>720.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00720000">AAPL140621C00720000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00720000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">265</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=725.000000"><strong>725.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00725000">AAPL140621C00725000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00725000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">178</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=730.000000"><strong>730.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00730000">AAPL140621C00730000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00730000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">38</td><td class="yfnc_tabledata1" align="right">260</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=735.000000"><strong>735.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00735000">AAPL140621C00735000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00735000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">116</td><td class="yfnc_tabledata1" align="right">687</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=740.000000"><strong>740.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00740000">AAPL140621C00740000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00740000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">98</td><td class="yfnc_tabledata1" align="right">870</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=745.000000"><strong>745.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00745000">AAPL140621C00745000</a></td><td class="yfnc_tabledata1" align="right"><b>0.06</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00745000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">171</td><td class="yfnc_tabledata1" align="right">659</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=750.000000"><strong>750.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00750000">AAPL140621C00750000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00750000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">1,954</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=755.000000"><strong>755.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00755000">AAPL140621C00755000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00755000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">210</td><td class="yfnc_tabledata1" align="right">558</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=760.000000"><strong>760.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00760000">AAPL140621C00760000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00760000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">65</td><td class="yfnc_tabledata1" align="right">3,809</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=765.000000"><strong>765.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00765000">AAPL140621C00765000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00765000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">2,394</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=770.000000"><strong>770.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00770000">AAPL140621C00770000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00770000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.02</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">274</td><td class="yfnc_tabledata1" align="right">4,282</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=775.000000"><strong>775.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00775000">AAPL140621C00775000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00775000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">50</td><td class="yfnc_tabledata1" align="right">2,627</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=780.000000"><strong>780.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00780000">AAPL140621C00780000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00780000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">500</td><td class="yfnc_tabledata1" align="right">1,941</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=785.000000"><strong>785.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621C00785000">AAPL140621C00785000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621c00785000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">36</td><td class="yfnc_tabledata1" align="right">2,514</td></tr></table></td></tr></table><table cellpadding="0" cellspacing="0" border="0"><tr><td height="10"></td></tr></table><table class="yfnc_mod_table_title1" width="100%" cellpadding="2" cellspacing="0" border="0"><tr valign="top"><td><small><b><strong class="yfi-module-title">Put Options</strong></b></small></td><td align="right">Expire at close Saturday, June 21, 2014</td></tr></table><table class="yfnc_datamodoutline1" width="100%" cellpadding="0" cellspacing="0" border="0"><tr valign="top"><td><table border="0" cellpadding="3" cellspacing="1" width="100%"><tr><th scope="col" class="yfnc_tablehead1" width="12%" align="left">Strike</th><th scope="col" class="yfnc_tablehead1" width="12%">Symbol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Last</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Chg</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Bid</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Ask</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Vol</th><th scope="col" class="yfnc_tablehead1" width="12%" align="right">Open Int</th></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=280.000000"><strong>280.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00280000">AAPL140621P00280000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00280000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=300.000000"><strong>300.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00300000">AAPL140621P00300000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00300000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">44</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=325.000000"><strong>325.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00325000">AAPL140621P00325000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00325000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=330.000000"><strong>330.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00330000">AAPL140621P00330000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00330000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">20</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=335.000000"><strong>335.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00335000">AAPL140621P00335000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00335000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">42</td><td class="yfnc_tabledata1" align="right">42</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=345.000000"><strong>345.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00345000">AAPL140621P00345000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00345000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">66</td><td class="yfnc_tabledata1" align="right">66</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=350.000000"><strong>350.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00350000">AAPL140621P00350000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00350000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">102</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=360.000000"><strong>360.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00360000">AAPL140621P00360000</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00360000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">20</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=365.000000"><strong>365.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00365000">AAPL140621P00365000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00365000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">180</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=370.000000"><strong>370.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00370000">AAPL140621P00370000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00370000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=375.000000"><strong>375.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00375000">AAPL140621P00375000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00375000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=380.000000"><strong>380.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00380000">AAPL140621P00380000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00380000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">26</td><td class="yfnc_tabledata1" align="right">28</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=385.000000"><strong>385.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00385000">AAPL140621P00385000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00385000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">176</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=390.000000"><strong>390.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00390000">AAPL140621P00390000</a></td><td class="yfnc_tabledata1" align="right"><b>0.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00390000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">104</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=395.000000"><strong>395.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00395000">AAPL140621P00395000</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00395000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">15</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=400.000000"><strong>400.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00400000">AAPL140621P00400000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00400000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">981</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=405.000000"><strong>405.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00405000">AAPL140621P00405000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00405000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">17</td><td class="yfnc_tabledata1" align="right">49</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=410.000000"><strong>410.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00410000">AAPL140621P00410000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00410000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">109</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=415.000000"><strong>415.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00415000">AAPL140621P00415000</a></td><td class="yfnc_tabledata1" align="right"><b>0.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00415000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">97</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=420.000000"><strong>420.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00420000">AAPL140621P00420000</a></td><td class="yfnc_tabledata1" align="right"><b>0.01</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00420000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">40</td><td class="yfnc_tabledata1" align="right">256</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=425.000000"><strong>425.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00425000">AAPL140621P00425000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00425000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">685</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=425.000000"><strong>425.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00425000">AAPL7140621P00425000</a></td><td class="yfnc_tabledata1" align="right"><b>0.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00425000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=430.000000"><strong>430.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00430000">AAPL140621P00430000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00430000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">412</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=435.000000"><strong>435.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00435000">AAPL140621P00435000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00435000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">199</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=435.000000"><strong>435.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00435000">AAPL7140621P00435000</a></td><td class="yfnc_tabledata1" align="right"><b>0.31</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00435000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=440.000000"><strong>440.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00440000">AAPL140621P00440000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00440000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1,593</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=440.000000"><strong>440.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00440000">AAPL7140621P00440000</a></td><td class="yfnc_tabledata1" align="right"><b>1.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00440000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">16</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=445.000000"><strong>445.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00445000">AAPL140621P00445000</a></td><td class="yfnc_tabledata1" align="right"><b>0.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00445000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.03</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">233</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00450000">AAPL140621P00450000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00450000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">273</td><td class="yfnc_tabledata1" align="right">2,123</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=450.000000"><strong>450.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00450000">AAPL7140621P00450000</a></td><td class="yfnc_tabledata1" align="right"><b>1.27</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00450000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=455.000000"><strong>455.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00455000">AAPL140621P00455000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00455000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">29</td><td class="yfnc_tabledata1" align="right">245</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00460000">AAPL140621P00460000</a></td><td class="yfnc_tabledata1" align="right"><b>0.06</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00460000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up"> <b style="color:#008800;">0.01</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">506</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=460.000000"><strong>460.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00460000">AAPL7140621P00460000</a></td><td class="yfnc_tabledata1" align="right"><b>0.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00460000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=465.000000"><strong>465.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00465000">AAPL140621P00465000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00465000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">27</td><td class="yfnc_tabledata1" align="right">1,123</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=465.000000"><strong>465.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00465000">AAPL7140621P00465000</a></td><td class="yfnc_tabledata1" align="right"><b>2.99</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00465000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">64</td><td class="yfnc_tabledata1" align="right">98</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00470000">AAPL140621P00470000</a></td><td class="yfnc_tabledata1" align="right"><b>0.04</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00470000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.02</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">1,279</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=470.000000"><strong>470.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00470000">AAPL7140621P00470000</a></td><td class="yfnc_tabledata1" align="right"><b>4.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00470000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">13</td><td class="yfnc_tabledata1" align="right">54</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00475000">AAPL140621P00475000</a></td><td class="yfnc_tabledata1" align="right"><b>0.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00475000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">41</td><td class="yfnc_tabledata1" align="right">2,028</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=475.000000"><strong>475.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00475000">AAPL7140621P00475000</a></td><td class="yfnc_tabledata1" align="right"><b>0.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00475000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.24</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">79</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=480.000000"><strong>480.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00480000">AAPL140621P00480000</a></td><td class="yfnc_tabledata1" align="right"><b>0.07</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00480000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">23</td><td class="yfnc_tabledata1" align="right">2,070</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=480.000000"><strong>480.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00480000">AAPL7140621P00480000</a></td><td class="yfnc_tabledata1" align="right"><b>0.22</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00480000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">32</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00485000">AAPL140621P00485000</a></td><td class="yfnc_tabledata1" align="right"><b>0.08</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00485000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.05</b></span></td><td class="yfnc_tabledata1" align="right">0.07</td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">871</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=485.000000"><strong>485.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00485000">AAPL7140621P00485000</a></td><td class="yfnc_tabledata1" align="right"><b>4.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00485000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">39</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00490000">AAPL140606P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.34</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">66</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00490000">AAPL140621P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>0.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00490000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.07</b></span></td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">131</td><td class="yfnc_tabledata1" align="right">2,770</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=490.000000"><strong>490.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00490000">AAPL7140621P00490000</a></td><td class="yfnc_tabledata1" align="right"><b>5.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00490000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.30</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">87</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=492.500000"><strong>492.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00492500">AAPL140606P00492500</a></td><td class="yfnc_tabledata1" align="right"><b>0.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00492500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">12</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00495000">AAPL140606P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>0.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">8</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00495000">AAPL140621P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>0.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00495000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.08</b></span></td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">64</td><td class="yfnc_tabledata1" align="right">2,606</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=495.000000"><strong>495.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00495000">AAPL7140621P00495000</a></td><td class="yfnc_tabledata1" align="right"><b>5.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00495000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">22</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00500000">AAPL140606P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.21</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00500000">AAPL7140606P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>1.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">30</td><td class="yfnc_tabledata1" align="right">30</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00500000">AAPL140613P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.33</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00500000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.05</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">12</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00500000">AAPL140621P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.13</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.11</b></span></td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">267</td><td class="yfnc_tabledata1" align="right">2,383</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=500.000000"><strong>500.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00500000">AAPL7140621P00500000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00500000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.15</b></span></td><td class="yfnc_tabledata1" align="right">0.09</td><td class="yfnc_tabledata1" align="right">0.24</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">96</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00505000">AAPL140606P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.86</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">N/A</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">16</td><td class="yfnc_tabledata1" align="right">17</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00505000">AAPL140621P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00505000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.11</b></span></td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">0.17</td><td class="yfnc_tabledata1" align="right">487</td><td class="yfnc_tabledata1" align="right">861</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=505.000000"><strong>505.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00505000">AAPL7140621P00505000</a></td><td class="yfnc_tabledata1" align="right"><b>0.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00505000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">34</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=507.500000"><strong>507.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00507500">AAPL140606P00507500</a></td><td class="yfnc_tabledata1" align="right"><b>0.27</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00507500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.01</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">18</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00510000">AAPL140606P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00510000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">20</td><td class="yfnc_tabledata1" align="right">35</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00510000">AAPL140621P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.13</b></span></td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">227</td><td class="yfnc_tabledata1" align="right">2,308</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=510.000000"><strong>510.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00510000">AAPL7140621P00510000</a></td><td class="yfnc_tabledata1" align="right"><b>0.23</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00510000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.15</b></span></td><td class="yfnc_tabledata1" align="right">0.12</td><td class="yfnc_tabledata1" align="right">0.30</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">112</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00515000">AAPL140606P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.29</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">50</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00515000">AAPL140613P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00515000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.13</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00515000">AAPL140621P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00515000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.17</b></span></td><td class="yfnc_tabledata1" align="right">0.23</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">159</td><td class="yfnc_tabledata1" align="right">1,634</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=515.000000"><strong>515.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00515000">AAPL7140621P00515000</a></td><td class="yfnc_tabledata1" align="right"><b>0.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00515000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.75</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.35</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">24</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=517.500000"><strong>517.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00517500">AAPL140606P00517500</a></td><td class="yfnc_tabledata1" align="right"><b>0.41</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00517500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.03</td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">23</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00520000">AAPL140606P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.13</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.16</b></span></td><td class="yfnc_tabledata1" align="right">0.04</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">128</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00520000">AAPL140613P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.34</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">20</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00520000">AAPL140621P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.31</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00520000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.21</b></span></td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">295</td><td class="yfnc_tabledata1" align="right">2,777</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=520.000000"><strong>520.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00520000">AAPL7140621P00520000</a></td><td class="yfnc_tabledata1" align="right"><b>0.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00520000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.16</td><td class="yfnc_tabledata1" align="right">0.40</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">125</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=522.500000"><strong>522.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00522500">AAPL140606P00522500</a></td><td class="yfnc_tabledata1" align="right"><b>0.54</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00522500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00525000">AAPL140606P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.17</b></span></td><td class="yfnc_tabledata1" align="right">0.08</td><td class="yfnc_tabledata1" align="right">0.19</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">204</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00525000">AAPL140613P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00525000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.37</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">41</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00525000">AAPL140621P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>0.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00525000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.21</b></span></td><td class="yfnc_tabledata1" align="right">0.38</td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">212</td><td class="yfnc_tabledata1" align="right">2,729</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=525.000000"><strong>525.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00525000">AAPL7140621P00525000</a></td><td class="yfnc_tabledata1" align="right"><b>1.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00525000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.34</td><td class="yfnc_tabledata1" align="right">0.49</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">94</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=527.500000"><strong>527.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00527500">AAPL140606P00527500</a></td><td class="yfnc_tabledata1" align="right"><b>0.17</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00527500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.29</b></span></td><td class="yfnc_tabledata1" align="right">0.10</td><td class="yfnc_tabledata1" align="right">0.20</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">22</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00530000">AAPL140606P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.56</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">72</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00530000">AAPL7140606P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>3.24</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.06</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00530000">AAPL140613P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.76</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.34</td><td class="yfnc_tabledata1" align="right">0.45</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">27</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00530000">AAPL140621P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.52</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.26</b></span></td><td class="yfnc_tabledata1" align="right">0.48</td><td class="yfnc_tabledata1" align="right">0.50</td><td class="yfnc_tabledata1" align="right">289</td><td class="yfnc_tabledata1" align="right">2,675</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00530000">AAPL7140621P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>1.44</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00530000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.42</td><td class="yfnc_tabledata1" align="right">0.60</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">75</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=530.000000"><strong>530.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00530000">AAPL140627P00530000</a></td><td class="yfnc_tabledata1" align="right"><b>0.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00530000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.23</b></span></td><td class="yfnc_tabledata1" align="right">0.68</td><td class="yfnc_tabledata1" align="right">0.85</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">122</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=532.500000"><strong>532.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00532500">AAPL140606P00532500</a></td><td class="yfnc_tabledata1" align="right"><b>0.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00532500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.11</td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00535000">AAPL140606P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.41</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.14</td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">40</td><td class="yfnc_tabledata1" align="right">100</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00535000">AAPL7140606P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>1.12</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.15</td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00535000">AAPL140613P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.56</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.35</b></span></td><td class="yfnc_tabledata1" align="right">0.44</td><td class="yfnc_tabledata1" align="right">0.55</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">108</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00535000">AAPL7140613P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.51</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.64</b></span></td><td class="yfnc_tabledata1" align="right">0.29</td><td class="yfnc_tabledata1" align="right">0.58</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00535000">AAPL140621P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>0.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00535000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.30</b></span></td><td class="yfnc_tabledata1" align="right">0.63</td><td class="yfnc_tabledata1" align="right">0.65</td><td class="yfnc_tabledata1" align="right">117</td><td class="yfnc_tabledata1" align="right">1,322</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=535.000000"><strong>535.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00535000">AAPL7140621P00535000</a></td><td class="yfnc_tabledata1" align="right"><b>2.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00535000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.58</td><td class="yfnc_tabledata1" align="right">0.75</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">42</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=537.500000"><strong>537.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00537500">AAPL140606P00537500</a></td><td class="yfnc_tabledata1" align="right"><b>0.33</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00537500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.18</td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">36</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00540000">AAPL140606P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.29</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.12</b></span></td><td class="yfnc_tabledata1" align="right">0.22</td><td class="yfnc_tabledata1" align="right">0.32</td><td class="yfnc_tabledata1" align="right">29</td><td class="yfnc_tabledata1" align="right">133</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00540000">AAPL7140606P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>4.85</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00540000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.21</td><td class="yfnc_tabledata1" align="right">0.37</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00540000">AAPL140613P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>1.09</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00540000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.60</td><td class="yfnc_tabledata1" align="right">0.70</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">17</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00540000">AAPL140621P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.85</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.38</b></span></td><td class="yfnc_tabledata1" align="right">0.82</td><td class="yfnc_tabledata1" align="right">0.85</td><td class="yfnc_tabledata1" align="right">144</td><td class="yfnc_tabledata1" align="right">3,077</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00540000">AAPL7140621P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>0.88</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.42</b></span></td><td class="yfnc_tabledata1" align="right">0.69</td><td class="yfnc_tabledata1" align="right">0.95</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">369</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=540.000000"><strong>540.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00540000">AAPL140627P00540000</a></td><td class="yfnc_tabledata1" align="right"><b>1.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00540000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.93</b></span></td><td class="yfnc_tabledata1" align="right">1.15</td><td class="yfnc_tabledata1" align="right">1.33</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">64</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=542.500000"><strong>542.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00542500">AAPL140606P00542500</a></td><td class="yfnc_tabledata1" align="right"><b>1.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00542500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.39</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">118</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00545000">AAPL140606P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>0.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.69</b></span></td><td class="yfnc_tabledata1" align="right">0.27</td><td class="yfnc_tabledata1" align="right">0.44</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">107</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00545000">AAPL7140606P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>1.91</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.25</td><td class="yfnc_tabledata1" align="right">0.46</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00545000">AAPL140613P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>0.87</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.27</b></span></td><td class="yfnc_tabledata1" align="right">0.81</td><td class="yfnc_tabledata1" align="right">0.90</td><td class="yfnc_tabledata1" align="right">64</td><td class="yfnc_tabledata1" align="right">135</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00545000">AAPL7140613P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>3.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.56</td><td class="yfnc_tabledata1" align="right">0.98</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00545000">AAPL140621P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>1.14</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.41</b></span></td><td class="yfnc_tabledata1" align="right">1.09</td><td class="yfnc_tabledata1" align="right">1.13</td><td class="yfnc_tabledata1" align="right">385</td><td class="yfnc_tabledata1" align="right">4,203</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00545000">AAPL7140621P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>3.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00545000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.95</td><td class="yfnc_tabledata1" align="right">1.21</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">426</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=545.000000"><strong>545.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00545000">AAPL140627P00545000</a></td><td class="yfnc_tabledata1" align="right"><b>1.58</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00545000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.98</b></span></td><td class="yfnc_tabledata1" align="right">1.50</td><td class="yfnc_tabledata1" align="right">1.65</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=547.500000"><strong>547.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00547500">AAPL140606P00547500</a></td><td class="yfnc_tabledata1" align="right"><b>0.49</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00547500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.28</b></span></td><td class="yfnc_tabledata1" align="right">0.36</td><td class="yfnc_tabledata1" align="right">0.49</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">53</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=547.500000"><strong>547.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00547500">AAPL7140606P00547500</a></td><td class="yfnc_tabledata1" align="right"><b>2.43</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00547500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.31</td><td class="yfnc_tabledata1" align="right">0.52</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00550000">AAPL140606P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>0.49</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.23</b></span></td><td class="yfnc_tabledata1" align="right">0.42</td><td class="yfnc_tabledata1" align="right">0.49</td><td class="yfnc_tabledata1" align="right">26</td><td class="yfnc_tabledata1" align="right">431</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00550000">AAPL7140606P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>1.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00550000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.24</td><td class="yfnc_tabledata1" align="right">0.60</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00550000">AAPL140613P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>1.16</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.46</b></span></td><td class="yfnc_tabledata1" align="right">1.08</td><td class="yfnc_tabledata1" align="right">1.20</td><td class="yfnc_tabledata1" align="right">69</td><td class="yfnc_tabledata1" align="right">100</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00550000">AAPL7140613P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>1.11</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.69</b></span></td><td class="yfnc_tabledata1" align="right">0.98</td><td class="yfnc_tabledata1" align="right">1.27</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00550000">AAPL140621P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>1.47</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.55</b></span></td><td class="yfnc_tabledata1" align="right">1.45</td><td class="yfnc_tabledata1" align="right">1.49</td><td class="yfnc_tabledata1" align="right">344</td><td class="yfnc_tabledata1" align="right">4,118</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00550000">AAPL7140621P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>1.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.07</b></span></td><td class="yfnc_tabledata1" align="right">1.33</td><td class="yfnc_tabledata1" align="right">1.66</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">348</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=550.000000"><strong>550.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00550000">AAPL140627P00550000</a></td><td class="yfnc_tabledata1" align="right"><b>2.54</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00550000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.71</b></span></td><td class="yfnc_tabledata1" align="right">1.95</td><td class="yfnc_tabledata1" align="right">2.12</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">83</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=552.500000"><strong>552.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00552500">AAPL140606P00552500</a></td><td class="yfnc_tabledata1" align="right"><b>0.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00552500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.29</b></span></td><td class="yfnc_tabledata1" align="right">0.52</td><td class="yfnc_tabledata1" align="right">0.63</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">33</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=552.500000"><strong>552.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00552500">AAPL7140606P00552500</a></td><td class="yfnc_tabledata1" align="right"><b>4.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00552500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.33</td><td class="yfnc_tabledata1" align="right">0.68</td><td class="yfnc_tabledata1" align="right">30</td><td class="yfnc_tabledata1" align="right">30</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00555000">AAPL140606P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>0.89</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.12</b></span></td><td class="yfnc_tabledata1" align="right">0.62</td><td class="yfnc_tabledata1" align="right">0.74</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">372</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00555000">AAPL7140606P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>3.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.43</td><td class="yfnc_tabledata1" align="right">0.78</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00555000">AAPL140613P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>1.52</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.60</b></span></td><td class="yfnc_tabledata1" align="right">1.47</td><td class="yfnc_tabledata1" align="right">1.60</td><td class="yfnc_tabledata1" align="right">102</td><td class="yfnc_tabledata1" align="right">113</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00555000">AAPL7140613P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>4.70</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.26</td><td class="yfnc_tabledata1" align="right">1.64</td><td class="yfnc_tabledata1" align="right">20</td><td class="yfnc_tabledata1" align="right">20</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00555000">AAPL140621P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>2.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.56</b></span></td><td class="yfnc_tabledata1" align="right">1.94</td><td class="yfnc_tabledata1" align="right">1.98</td><td class="yfnc_tabledata1" align="right">388</td><td class="yfnc_tabledata1" align="right">1,057</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00555000">AAPL7140621P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>2.65</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00555000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.11</b></span></td><td class="yfnc_tabledata1" align="right">1.80</td><td class="yfnc_tabledata1" align="right">2.11</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">27</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=555.000000"><strong>555.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00555000">AAPL140627P00555000</a></td><td class="yfnc_tabledata1" align="right"><b>3.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00555000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.53</td><td class="yfnc_tabledata1" align="right">2.72</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">16</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00557500">AAPL140606P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>0.84</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.38</b></span></td><td class="yfnc_tabledata1" align="right">0.75</td><td class="yfnc_tabledata1" align="right">0.89</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">67</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00557500">AAPL7140606P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>3.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00557500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.65</td><td class="yfnc_tabledata1" align="right">0.92</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=557.500000"><strong>557.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00557500">AAPL140627P00557500</a></td><td class="yfnc_tabledata1" align="right"><b>2.96</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00557500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.59</b></span></td><td class="yfnc_tabledata1" align="right">2.87</td><td class="yfnc_tabledata1" align="right">3.10</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">19</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00560000">AAPL140606P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>0.98</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.31</b></span></td><td class="yfnc_tabledata1" align="right">0.90</td><td class="yfnc_tabledata1" align="right">1.00</td><td class="yfnc_tabledata1" align="right">41</td><td class="yfnc_tabledata1" align="right">396</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00560000">AAPL7140606P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>1.73</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00560000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.72</td><td class="yfnc_tabledata1" align="right">1.06</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">12</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00560000">AAPL140613P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>2.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.49</b></span></td><td class="yfnc_tabledata1" align="right">2.00</td><td class="yfnc_tabledata1" align="right">2.23</td><td class="yfnc_tabledata1" align="right">15</td><td class="yfnc_tabledata1" align="right">148</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00560000">AAPL140621P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>2.64</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.69</b></span></td><td class="yfnc_tabledata1" align="right">2.56</td><td class="yfnc_tabledata1" align="right">2.63</td><td class="yfnc_tabledata1" align="right">387</td><td class="yfnc_tabledata1" align="right">5,780</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00560000">AAPL7140621P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>3.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.89</b></span></td><td class="yfnc_tabledata1" align="right">2.45</td><td class="yfnc_tabledata1" align="right">2.74</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">130</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=560.000000"><strong>560.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00560000">AAPL140627P00560000</a></td><td class="yfnc_tabledata1" align="right"><b>3.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00560000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.70</b></span></td><td class="yfnc_tabledata1" align="right">3.20</td><td class="yfnc_tabledata1" align="right">3.50</td><td class="yfnc_tabledata1" align="right">21</td><td class="yfnc_tabledata1" align="right">75</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00562500">AAPL140606P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>1.21</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.34</b></span></td><td class="yfnc_tabledata1" align="right">1.10</td><td class="yfnc_tabledata1" align="right">1.23</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">154</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00562500">AAPL7140606P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>4.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">0.91</td><td class="yfnc_tabledata1" align="right">1.25</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00562500">AAPL140613P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>2.51</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00562500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.54</b></span></td><td class="yfnc_tabledata1" align="right">2.29</td><td class="yfnc_tabledata1" align="right">2.57</td><td class="yfnc_tabledata1" align="right">37</td><td class="yfnc_tabledata1" align="right">79</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=562.500000"><strong>562.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00562500">AAPL7140613P00562500</a></td><td class="yfnc_tabledata1" align="right"><b>6.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00562500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.09</td><td class="yfnc_tabledata1" align="right">2.49</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">10</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00565000">AAPL140606P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>1.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.29</b></span></td><td class="yfnc_tabledata1" align="right">1.35</td><td class="yfnc_tabledata1" align="right">1.42</td><td class="yfnc_tabledata1" align="right">46</td><td class="yfnc_tabledata1" align="right">1,051</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00565000">AAPL7140606P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>1.82</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.33</b></span></td><td class="yfnc_tabledata1" align="right">1.28</td><td class="yfnc_tabledata1" align="right">1.48</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">7</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00565000">AAPL140613P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>2.87</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.59</b></span></td><td class="yfnc_tabledata1" align="right">2.66</td><td class="yfnc_tabledata1" align="right">2.95</td><td class="yfnc_tabledata1" align="right">30</td><td class="yfnc_tabledata1" align="right">191</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00565000">AAPL7140613P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>6.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.42</td><td class="yfnc_tabledata1" align="right">2.87</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">13</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00565000">AAPL140621P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>3.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.70</b></span></td><td class="yfnc_tabledata1" align="right">3.40</td><td class="yfnc_tabledata1" align="right">3.45</td><td class="yfnc_tabledata1" align="right">253</td><td class="yfnc_tabledata1" align="right">2,292</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00565000">AAPL7140621P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>8.32</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00565000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.25</td><td class="yfnc_tabledata1" align="right">3.60</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">222</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=565.000000"><strong>565.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00565000">AAPL140627P00565000</a></td><td class="yfnc_tabledata1" align="right"><b>4.96</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00565000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.82</b></span></td><td class="yfnc_tabledata1" align="right">4.20</td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00567500">AAPL140613P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>3.22</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00567500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.78</b></span></td><td class="yfnc_tabledata1" align="right">3.10</td><td class="yfnc_tabledata1" align="right">3.40</td><td class="yfnc_tabledata1" align="right">15</td><td class="yfnc_tabledata1" align="right">47</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00567500">AAPL140627P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>5.52</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00567500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.70</td><td class="yfnc_tabledata1" align="right">5.00</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">33</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=567.500000"><strong>567.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627P00567500">AAPL7140627P00567500</a></td><td class="yfnc_tabledata1" align="right"><b>8.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627p00567500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.35</td><td class="yfnc_tabledata1" align="right">6.10</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00570000">AAPL140606P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>2.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.53</b></span></td><td class="yfnc_tabledata1" align="right">1.94</td><td class="yfnc_tabledata1" align="right">2.12</td><td class="yfnc_tabledata1" align="right">117</td><td class="yfnc_tabledata1" align="right">469</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00570000">AAPL7140606P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>7.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00570000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">1.83</td><td class="yfnc_tabledata1" align="right">2.17</td><td class="yfnc_tabledata1" align="right">7</td><td class="yfnc_tabledata1" align="right">15</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00570000">AAPL140613P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>4.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.44</b></span></td><td class="yfnc_tabledata1" align="right">3.55</td><td class="yfnc_tabledata1" align="right">3.95</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">78</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00570000">AAPL7140613P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>8.03</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00570000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.55</td><td class="yfnc_tabledata1" align="right">3.80</td><td class="yfnc_tabledata1" align="right">6</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00570000">AAPL140621P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>4.55</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.80</b></span></td><td class="yfnc_tabledata1" align="right">4.45</td><td class="yfnc_tabledata1" align="right">4.55</td><td class="yfnc_tabledata1" align="right">759</td><td class="yfnc_tabledata1" align="right">3,360</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00570000">AAPL7140621P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>4.68</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">5.32</b></span></td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">4.65</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">80</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00570000">AAPL140627P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>6.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00570000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.12</b></span></td><td class="yfnc_tabledata1" align="right">5.30</td><td class="yfnc_tabledata1" align="right">5.60</td><td class="yfnc_tabledata1" align="right">8</td><td class="yfnc_tabledata1" align="right">40</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=570.000000"><strong>570.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627P00570000">AAPL7140627P00570000</a></td><td class="yfnc_tabledata1" align="right"><b>8.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627p00570000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.00</td><td class="yfnc_tabledata1" align="right">6.75</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00572500">AAPL140613P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>4.38</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00572500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.69</b></span></td><td class="yfnc_tabledata1" align="right">4.10</td><td class="yfnc_tabledata1" align="right">4.50</td><td class="yfnc_tabledata1" align="right">18</td><td class="yfnc_tabledata1" align="right">93</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00572500">AAPL7140613P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>8.35</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00572500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.10</td><td class="yfnc_tabledata1" align="right">4.40</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">11</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00572500">AAPL140627P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>7.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00572500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.95</td><td class="yfnc_tabledata1" align="right">6.30</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">3</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=572.500000"><strong>572.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627P00572500">AAPL7140627P00572500</a></td><td class="yfnc_tabledata1" align="right"><b>10.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627p00572500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.60</td><td class="yfnc_tabledata1" align="right">7.50</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00575000">AAPL140606P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>3.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.85</b></span></td><td class="yfnc_tabledata1" align="right">2.83</td><td class="yfnc_tabledata1" align="right">2.97</td><td class="yfnc_tabledata1" align="right">95</td><td class="yfnc_tabledata1" align="right">265</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00575000">AAPL7140606P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>6.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00575000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">2.76</td><td class="yfnc_tabledata1" align="right">3.10</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">63</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00575000">AAPL140613P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>5.10</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.90</b></span></td><td class="yfnc_tabledata1" align="right">4.70</td><td class="yfnc_tabledata1" align="right">5.15</td><td class="yfnc_tabledata1" align="right">22</td><td class="yfnc_tabledata1" align="right">144</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00575000">AAPL7140613P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>6.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00575000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">4.60</td><td class="yfnc_tabledata1" align="right">5.10</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">22</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00575000">AAPL140621P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>5.86</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.97</b></span></td><td class="yfnc_tabledata1" align="right">5.70</td><td class="yfnc_tabledata1" align="right">5.85</td><td class="yfnc_tabledata1" align="right">506</td><td class="yfnc_tabledata1" align="right">1,326</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00575000">AAPL7140621P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>7.79</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00575000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.70</td><td class="yfnc_tabledata1" align="right">5.95</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">306</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=575.000000"><strong>575.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00575000">AAPL140627P00575000</a></td><td class="yfnc_tabledata1" align="right"><b>7.48</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00575000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.77</b></span></td><td class="yfnc_tabledata1" align="right">6.65</td><td class="yfnc_tabledata1" align="right">7.00</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">23</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00577500">AAPL140613P00577500</a></td><td class="yfnc_tabledata1" align="right"><b>5.73</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00577500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.42</b></span></td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">5.85</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">63</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00577500">AAPL7140613P00577500</a></td><td class="yfnc_tabledata1" align="right"><b>10.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00577500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.40</td><td class="yfnc_tabledata1" align="right">5.80</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">5</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=577.500000"><strong>577.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00577500">AAPL140627P00577500</a></td><td class="yfnc_tabledata1" align="right"><b>10.34</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00577500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.45</td><td class="yfnc_tabledata1" align="right">7.80</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00580000">AAPL140606P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>4.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.92</b></span></td><td class="yfnc_tabledata1" align="right">4.00</td><td class="yfnc_tabledata1" align="right">4.20</td><td class="yfnc_tabledata1" align="right">171</td><td class="yfnc_tabledata1" align="right">301</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00580000">AAPL7140606P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>5.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00580000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">3.95</td><td class="yfnc_tabledata1" align="right">4.35</td><td class="yfnc_tabledata1" align="right">33</td><td class="yfnc_tabledata1" align="right">46</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00580000">AAPL140613P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>6.58</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.42</b></span></td><td class="yfnc_tabledata1" align="right">6.20</td><td class="yfnc_tabledata1" align="right">6.60</td><td class="yfnc_tabledata1" align="right">14</td><td class="yfnc_tabledata1" align="right">86</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00580000">AAPL7140613P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>7.40</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00580000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">6.05</td><td class="yfnc_tabledata1" align="right">6.60</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">22</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00580000">AAPL140621P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>7.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.06</b></span></td><td class="yfnc_tabledata1" align="right">7.30</td><td class="yfnc_tabledata1" align="right">7.45</td><td class="yfnc_tabledata1" align="right">513</td><td class="yfnc_tabledata1" align="right">3,276</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00580000">AAPL7140621P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>8.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.60</b></span></td><td class="yfnc_tabledata1" align="right">7.20</td><td class="yfnc_tabledata1" align="right">7.60</td><td class="yfnc_tabledata1" align="right">24</td><td class="yfnc_tabledata1" align="right">162</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=580.000000"><strong>580.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00580000">AAPL140627P00580000</a></td><td class="yfnc_tabledata1" align="right"><b>9.45</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00580000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.60</b></span></td><td class="yfnc_tabledata1" align="right">8.35</td><td class="yfnc_tabledata1" align="right">8.70</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">27</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00582500">AAPL140613P00582500</a></td><td class="yfnc_tabledata1" align="right"><b>7.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00582500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.20</b></span></td><td class="yfnc_tabledata1" align="right">7.00</td><td class="yfnc_tabledata1" align="right">7.45</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">31</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00582500">AAPL7140613P00582500</a></td><td class="yfnc_tabledata1" align="right"><b>12.85</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00582500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.00</td><td class="yfnc_tabledata1" align="right">8.85</td><td class="yfnc_tabledata1" align="right">72</td><td class="yfnc_tabledata1" align="right">72</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=582.500000"><strong>582.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00582500">AAPL140627P00582500</a></td><td class="yfnc_tabledata1" align="right"><b>11.95</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00582500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">9.25</td><td class="yfnc_tabledata1" align="right">9.65</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">384</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00585000">AAPL140606P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>5.60</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.85</b></span></td><td class="yfnc_tabledata1" align="right">5.55</td><td class="yfnc_tabledata1" align="right">5.95</td><td class="yfnc_tabledata1" align="right">561</td><td class="yfnc_tabledata1" align="right">271</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00585000">AAPL7140606P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>7.48</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00585000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">5.45</td><td class="yfnc_tabledata1" align="right">6.05</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">12</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00585000">AAPL140613P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>9.15</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.75</b></span></td><td class="yfnc_tabledata1" align="right">7.95</td><td class="yfnc_tabledata1" align="right">8.40</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">159</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00585000">AAPL7140613P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>10.05</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00585000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.75</td><td class="yfnc_tabledata1" align="right">10.35</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">1</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00585000">AAPL140621P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>9.30</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.15</b></span></td><td class="yfnc_tabledata1" align="right">9.15</td><td class="yfnc_tabledata1" align="right">9.30</td><td class="yfnc_tabledata1" align="right">293</td><td class="yfnc_tabledata1" align="right">1,145</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00585000">AAPL7140621P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>9.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.90</b></span></td><td class="yfnc_tabledata1" align="right">9.10</td><td class="yfnc_tabledata1" align="right">9.80</td><td class="yfnc_tabledata1" align="right">20</td><td class="yfnc_tabledata1" align="right">171</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00585000">AAPL140627P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>10.25</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00585000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.65</b></span></td><td class="yfnc_tabledata1" align="right">10.25</td><td class="yfnc_tabledata1" align="right">10.60</td><td class="yfnc_tabledata1" align="right">11</td><td class="yfnc_tabledata1" align="right">6</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=585.000000"><strong>585.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627P00585000">AAPL7140627P00585000</a></td><td class="yfnc_tabledata1" align="right"><b>13.82</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627p00585000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">9.95</td><td class="yfnc_tabledata1" align="right">12.05</td><td class="yfnc_tabledata1" align="right">3</td><td class="yfnc_tabledata1" align="right">7</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00587500">AAPL140613P00587500</a></td><td class="yfnc_tabledata1" align="right"><b>9.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00587500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.20</b></span></td><td class="yfnc_tabledata1" align="right">9.00</td><td class="yfnc_tabledata1" align="right">9.25</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">99</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00587500">AAPL7140613P00587500</a></td><td class="yfnc_tabledata1" align="right"><b>13.50</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00587500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">8.65</td><td class="yfnc_tabledata1" align="right">11.35</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=587.500000"><strong>587.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00587500">AAPL140627P00587500</a></td><td class="yfnc_tabledata1" align="right"><b>14.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00587500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">11.30</td><td class="yfnc_tabledata1" align="right">11.70</td><td class="yfnc_tabledata1" align="right">4</td><td class="yfnc_tabledata1" align="right">54</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140606P00590000">AAPL140606P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>7.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140606p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.20</b></span></td><td class="yfnc_tabledata1" align="right">7.60</td><td class="yfnc_tabledata1" align="right">7.85</td><td class="yfnc_tabledata1" align="right">264</td><td class="yfnc_tabledata1" align="right">546</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140606P00590000">AAPL7140606P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>17.67</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140606p00590000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">7.35</td><td class="yfnc_tabledata1" align="right">9.40</td><td class="yfnc_tabledata1" align="right">9</td><td class="yfnc_tabledata1" align="right">38</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00590000">AAPL140613P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>10.80</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.55</b></span></td><td class="yfnc_tabledata1" align="right">10.05</td><td class="yfnc_tabledata1" align="right">10.50</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">211</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140621P00590000">AAPL140621P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>11.39</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140621p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.97</b></span></td><td class="yfnc_tabledata1" align="right">11.30</td><td class="yfnc_tabledata1" align="right">11.45</td><td class="yfnc_tabledata1" align="right">946</td><td class="yfnc_tabledata1" align="right">2,582</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140621P00590000">AAPL7140621P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>12.39</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140621p00590000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.79</b></span></td><td class="yfnc_tabledata1" align="right">11.25</td><td class="yfnc_tabledata1" align="right">11.60</td><td class="yfnc_tabledata1" align="right">5</td><td class="yfnc_tabledata1" align="right">98</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00590000">AAPL140627P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>15.90</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00590000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">12.40</td><td class="yfnc_tabledata1" align="right">12.80</td><td class="yfnc_tabledata1" align="right">12</td><td class="yfnc_tabledata1" align="right">43</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=590.000000"><strong>590.00</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140627P00590000">AAPL7140627P00590000</a></td><td class="yfnc_tabledata1" align="right"><b>14.75</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140627p00590000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">12.10</td><td class="yfnc_tabledata1" align="right">14.70</td><td class="yfnc_tabledata1" align="right">2</td><td class="yfnc_tabledata1" align="right">4</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140613P00592500">AAPL140613P00592500</a></td><td class="yfnc_tabledata1" align="right"><b>12.20</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140613p00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.00</b></span></td><td class="yfnc_tabledata1" align="right">11.20</td><td class="yfnc_tabledata1" align="right">11.80</td><td class="yfnc_tabledata1" align="right">10</td><td class="yfnc_tabledata1" align="right">17</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL7140613P00592500">AAPL7140613P00592500</a></td><td class="yfnc_tabledata1" align="right"><b>13.02</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl7140613p00592500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.43</b></span></td><td class="yfnc_tabledata1" align="right">11.00</td><td class="yfnc_tabledata1" align="right">13.60</td><td class="yfnc_tabledata1" align="right">1</td><td class="yfnc_tabledata1" align="right">2</td></tr><tr><td class="yfnc_tabledata1" nowrap><a href="/q/op?s=AAPL&amp;k=592.500000"><strong>592.50</strong></a></td><td class="yfnc_tabledata1"><a href="/q?s=AAPL140627P00592500">AAPL140627P00592500</a></td><td class="yfnc_tabledata1" align="right"><b>17.00</b></td><td class="yfnc_tabledata1" align="right"><span id="yfs_c10_aapl140627p00592500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_tabledata1" align="right">13.60</td><td class="yfnc_tabledata1" align="right">13.90</td><td class="yfnc_tabledata1" align="right">48</td><td class="yfnc_tabledata1" align="right">40</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00595000">AAPL140606P00595000</a></td><td class="yfnc_h" align="right"><b>10.15</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.95</b></span></td><td class="yfnc_h" align="right">10.00</td><td class="yfnc_h" align="right">10.30</td><td class="yfnc_h" align="right">57</td><td class="yfnc_h" align="right">406</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606P00595000">AAPL7140606P00595000</a></td><td class="yfnc_h" align="right"><b>18.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606p00595000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">9.70</td><td class="yfnc_h" align="right">12.00</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">25</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00595000">AAPL140613P00595000</a></td><td class="yfnc_h" align="right"><b>13.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">2.50</b></span></td><td class="yfnc_h" align="right">12.50</td><td class="yfnc_h" align="right">12.95</td><td class="yfnc_h" align="right">18</td><td class="yfnc_h" align="right">168</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613P00595000">AAPL7140613P00595000</a></td><td class="yfnc_h" align="right"><b>16.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613p00595000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">12.20</td><td class="yfnc_h" align="right">14.85</td><td class="yfnc_h" align="right">13</td><td class="yfnc_h" align="right">13</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00595000">AAPL140621P00595000</a></td><td class="yfnc_h" align="right"><b>14.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.89</b></span></td><td class="yfnc_h" align="right">13.80</td><td class="yfnc_h" align="right">13.90</td><td class="yfnc_h" align="right">185</td><td class="yfnc_h" align="right">1,228</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00595000">AAPL7140621P00595000</a></td><td class="yfnc_h" align="right"><b>23.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00595000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">13.70</td><td class="yfnc_h" align="right">14.50</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">77</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=595.000000"><strong>595.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00595000">AAPL140627P00595000</a></td><td class="yfnc_h" align="right"><b>15.07</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00595000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.68</b></span></td><td class="yfnc_h" align="right">14.90</td><td class="yfnc_h" align="right">15.15</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">27</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00597500">AAPL140613P00597500</a></td><td class="yfnc_h" align="right"><b>17.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00597500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">13.90</td><td class="yfnc_h" align="right">14.65</td><td class="yfnc_h" align="right">9</td><td class="yfnc_h" align="right">33</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=597.500000"><strong>597.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00597500">AAPL140627P00597500</a></td><td class="yfnc_h" align="right"><b>25.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00597500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.20</td><td class="yfnc_h" align="right">16.50</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">34</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00600000">AAPL140606P00600000</a></td><td class="yfnc_h" align="right"><b>13.02</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.98</b></span></td><td class="yfnc_h" align="right">12.80</td><td class="yfnc_h" align="right">13.40</td><td class="yfnc_h" align="right">29</td><td class="yfnc_h" align="right">153</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606P00600000">AAPL7140606P00600000</a></td><td class="yfnc_h" align="right"><b>13.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.90</b></span></td><td class="yfnc_h" align="right">12.00</td><td class="yfnc_h" align="right">15.10</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">24</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00600000">AAPL140613P00600000</a></td><td class="yfnc_h" align="right"><b>15.53</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.19</b></span></td><td class="yfnc_h" align="right">15.30</td><td class="yfnc_h" align="right">15.75</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">214</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613P00600000">AAPL7140613P00600000</a></td><td class="yfnc_h" align="right"><b>24.82</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613p00600000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">14.90</td><td class="yfnc_h" align="right">17.50</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00600000">AAPL140621P00600000</a></td><td class="yfnc_h" align="right"><b>16.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00600000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.95</b></span></td><td class="yfnc_h" align="right">16.45</td><td class="yfnc_h" align="right">16.65</td><td class="yfnc_h" align="right">218</td><td class="yfnc_h" align="right">2,099</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00600000">AAPL7140621P00600000</a></td><td class="yfnc_h" align="right"><b>22.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00600000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.45</td><td class="yfnc_h" align="right">17.30</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">71</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00600000">AAPL140627P00600000</a></td><td class="yfnc_h" align="right"><b>20.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00600000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">17.60</td><td class="yfnc_h" align="right">18.10</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=600.000000"><strong>600.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140627P00600000">AAPL7140627P00600000</a></td><td class="yfnc_h" align="right"><b>20.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140627p00600000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.95</td><td class="yfnc_h" align="right">19.50</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00602500">AAPL140613P00602500</a></td><td class="yfnc_h" align="right"><b>19.37</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00602500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">16.80</td><td class="yfnc_h" align="right">17.50</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">23</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=602.500000"><strong>602.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00602500">AAPL140627P00602500</a></td><td class="yfnc_h" align="right"><b>28.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00602500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">19.10</td><td class="yfnc_h" align="right">19.60</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00605000">AAPL140606P00605000</a></td><td class="yfnc_h" align="right"><b>16.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">0.70</b></span></td><td class="yfnc_h" align="right">16.10</td><td class="yfnc_h" align="right">16.35</td><td class="yfnc_h" align="right">17</td><td class="yfnc_h" align="right">695</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606P00605000">AAPL7140606P00605000</a></td><td class="yfnc_h" align="right"><b>20.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606p00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">15.00</td><td class="yfnc_h" align="right">18.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00605000">AAPL140613P00605000</a></td><td class="yfnc_h" align="right"><b>18.65</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.85</b></span></td><td class="yfnc_h" align="right">18.35</td><td class="yfnc_h" align="right">19.05</td><td class="yfnc_h" align="right">7</td><td class="yfnc_h" align="right">41</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613P00605000">AAPL7140613P00605000</a></td><td class="yfnc_h" align="right"><b>23.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613p00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">17.10</td><td class="yfnc_h" align="right">20.00</td><td class="yfnc_h" align="right">11</td><td class="yfnc_h" align="right">11</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00605000">AAPL140621P00605000</a></td><td class="yfnc_h" align="right"><b>19.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00605000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.10</b></span></td><td class="yfnc_h" align="right">19.55</td><td class="yfnc_h" align="right">19.70</td><td class="yfnc_h" align="right">118</td><td class="yfnc_h" align="right">468</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=605.000000"><strong>605.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00605000">AAPL7140621P00605000</a></td><td class="yfnc_h" align="right"><b>30.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00605000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">19.50</td><td class="yfnc_h" align="right">20.35</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613P00607500">AAPL7140613P00607500</a></td><td class="yfnc_h" align="right"><b>25.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613p00607500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">19.15</td><td class="yfnc_h" align="right">21.75</td><td class="yfnc_h" align="right">12</td><td class="yfnc_h" align="right">12</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=607.500000"><strong>607.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00607500">AAPL140627P00607500</a></td><td class="yfnc_h" align="right"><b>26.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00607500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">22.20</td><td class="yfnc_h" align="right">22.85</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00610000">AAPL140606P00610000</a></td><td class="yfnc_h" align="right"><b>20.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.10</b></span></td><td class="yfnc_h" align="right">19.70</td><td class="yfnc_h" align="right">20.00</td><td class="yfnc_h" align="right">34</td><td class="yfnc_h" align="right">73</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606P00610000">AAPL7140606P00610000</a></td><td class="yfnc_h" align="right"><b>24.10</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606p00610000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">19.00</td><td class="yfnc_h" align="right">21.85</td><td class="yfnc_h" align="right">25</td><td class="yfnc_h" align="right">26</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00610000">AAPL140613P00610000</a></td><td class="yfnc_h" align="right"><b>24.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00610000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">21.65</td><td class="yfnc_h" align="right">22.60</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00610000">AAPL140621P00610000</a></td><td class="yfnc_h" align="right"><b>23.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00610000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.00</b></span></td><td class="yfnc_h" align="right">22.80</td><td class="yfnc_h" align="right">23.10</td><td class="yfnc_h" align="right">61</td><td class="yfnc_h" align="right">493</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=610.000000"><strong>610.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00610000">AAPL7140621P00610000</a></td><td class="yfnc_h" align="right"><b>27.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00610000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">22.75</td><td class="yfnc_h" align="right">23.65</td><td class="yfnc_h" align="right">24</td><td class="yfnc_h" align="right">28</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00612500">AAPL140613P00612500</a></td><td class="yfnc_h" align="right"><b>34.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00612500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">23.40</td><td class="yfnc_h" align="right">24.35</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613P00612500">AAPL7140613P00612500</a></td><td class="yfnc_h" align="right"><b>26.65</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613p00612500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">22.35</td><td class="yfnc_h" align="right">25.75</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=612.500000"><strong>612.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00612500">AAPL140627P00612500</a></td><td class="yfnc_h" align="right"><b>26.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00612500"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">8.45</b></span></td><td class="yfnc_h" align="right">25.65</td><td class="yfnc_h" align="right">26.30</td><td class="yfnc_h" align="right">25</td><td class="yfnc_h" align="right">25</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00615000">AAPL140606P00615000</a></td><td class="yfnc_h" align="right"><b>23.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00615000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">3.20</b></span></td><td class="yfnc_h" align="right">23.70</td><td class="yfnc_h" align="right">24.05</td><td class="yfnc_h" align="right">34</td><td class="yfnc_h" align="right">35</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00615000">AAPL140613P00615000</a></td><td class="yfnc_h" align="right"><b>34.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">25.30</td><td class="yfnc_h" align="right">26.40</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">6</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00615000">AAPL140621P00615000</a></td><td class="yfnc_h" align="right"><b>37.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">26.50</td><td class="yfnc_h" align="right">26.75</td><td class="yfnc_h" align="right">13</td><td class="yfnc_h" align="right">184</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=615.000000"><strong>615.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00615000">AAPL7140621P00615000</a></td><td class="yfnc_h" align="right"><b>82.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00615000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">26.40</td><td class="yfnc_h" align="right">27.15</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00617500">AAPL140613P00617500</a></td><td class="yfnc_h" align="right"><b>37.45</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00617500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">27.25</td><td class="yfnc_h" align="right">28.25</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">17</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=617.500000"><strong>617.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140627P00617500">AAPL7140627P00617500</a></td><td class="yfnc_h" align="right"><b>39.68</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140627p00617500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">27.90</td><td class="yfnc_h" align="right">31.25</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00620000">AAPL140606P00620000</a></td><td class="yfnc_h" align="right"><b>28.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00620000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.05</b></span></td><td class="yfnc_h" align="right">27.65</td><td class="yfnc_h" align="right">28.75</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140606P00620000">AAPL7140606P00620000</a></td><td class="yfnc_h" align="right"><b>32.56</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140606p00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">26.60</td><td class="yfnc_h" align="right">29.95</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00620000">AAPL140613P00620000</a></td><td class="yfnc_h" align="right"><b>32.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">29.20</td><td class="yfnc_h" align="right">30.65</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">7</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00620000">AAPL140621P00620000</a></td><td class="yfnc_h" align="right"><b>33.18</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">30.30</td><td class="yfnc_h" align="right">31.00</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">123</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=620.000000"><strong>620.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140627P00620000">AAPL7140627P00620000</a></td><td class="yfnc_h" align="right"><b>42.87</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140627p00620000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">29.90</td><td class="yfnc_h" align="right">33.10</td><td class="yfnc_h" align="right">3</td><td class="yfnc_h" align="right">3</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00625000">AAPL140606P00625000</a></td><td class="yfnc_h" align="right"><b>43.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">32.10</td><td class="yfnc_h" align="right">33.15</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">33</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00625000">AAPL140613P00625000</a></td><td class="yfnc_h" align="right"><b>37.90</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">33.40</td><td class="yfnc_h" align="right">34.50</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">20</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00625000">AAPL140621P00625000</a></td><td class="yfnc_h" align="right"><b>37.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">34.10</td><td class="yfnc_h" align="right">35.10</td><td class="yfnc_h" align="right">21</td><td class="yfnc_h" align="right">142</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=625.000000"><strong>625.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00625000">AAPL7140621P00625000</a></td><td class="yfnc_h" align="right"><b>91.40</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00625000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">33.75</td><td class="yfnc_h" align="right">36.40</td><td class="yfnc_h" align="right">20</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=627.500000"><strong>627.50</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00627500">AAPL140627P00627500</a></td><td class="yfnc_h" align="right"><b>43.55</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00627500"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">36.80</td><td class="yfnc_h" align="right">38.00</td><td class="yfnc_h" align="right">10</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00630000">AAPL140606P00630000</a></td><td class="yfnc_h" align="right"><b>41.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">36.35</td><td class="yfnc_h" align="right">38.10</td><td class="yfnc_h" align="right">14</td><td class="yfnc_h" align="right">14</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00630000">AAPL140613P00630000</a></td><td class="yfnc_h" align="right"><b>38.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">7.00</b></span></td><td class="yfnc_h" align="right">37.75</td><td class="yfnc_h" align="right">38.95</td><td class="yfnc_h" align="right">25</td><td class="yfnc_h" align="right">10</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140613P00630000">AAPL7140613P00630000</a></td><td class="yfnc_h" align="right"><b>41.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140613p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">36.95</td><td class="yfnc_h" align="right">39.75</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00630000">AAPL140621P00630000</a></td><td class="yfnc_h" align="right"><b>39.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00630000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">10.85</b></span></td><td class="yfnc_h" align="right">38.35</td><td class="yfnc_h" align="right">39.35</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">236</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00630000">AAPL7140621P00630000</a></td><td class="yfnc_h" align="right"><b>38.95</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">38.00</td><td class="yfnc_h" align="right">40.65</td><td class="yfnc_h" align="right">5</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=630.000000"><strong>630.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140627P00630000">AAPL140627P00630000</a></td><td class="yfnc_h" align="right"><b>41.85</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140627p00630000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">39.05</td><td class="yfnc_h" align="right">40.15</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140606P00635000">AAPL140606P00635000</a></td><td class="yfnc_h" align="right"><b>46.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140606p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">41.40</td><td class="yfnc_h" align="right">42.75</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140613P00635000">AAPL140613P00635000</a></td><td class="yfnc_h" align="right"><b>45.30</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140613p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">42.25</td><td class="yfnc_h" align="right">43.40</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">8</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00635000">AAPL140621P00635000</a></td><td class="yfnc_h" align="right"><b>45.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">42.75</td><td class="yfnc_h" align="right">43.90</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">124</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=635.000000"><strong>635.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL7140621P00635000">AAPL7140621P00635000</a></td><td class="yfnc_h" align="right"><b>47.20</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl7140621p00635000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">42.10</td><td class="yfnc_h" align="right">45.00</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=640.000000"><strong>640.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00640000">AAPL140621P00640000</a></td><td class="yfnc_h" align="right"><b>48.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00640000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">1.50</b></span></td><td class="yfnc_h" align="right">47.30</td><td class="yfnc_h" align="right">48.60</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">76</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=645.000000"><strong>645.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00645000">AAPL140621P00645000</a></td><td class="yfnc_h" align="right"><b>54.50</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00645000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">51.95</td><td class="yfnc_h" align="right">53.10</td><td class="yfnc_h" align="right">23</td><td class="yfnc_h" align="right">78</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=650.000000"><strong>650.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00650000">AAPL140621P00650000</a></td><td class="yfnc_h" align="right"><b>58.00</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00650000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">8.90</b></span></td><td class="yfnc_h" align="right">56.70</td><td class="yfnc_h" align="right">57.85</td><td class="yfnc_h" align="right">2</td><td class="yfnc_h" align="right">144</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=655.000000"><strong>655.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00655000">AAPL140621P00655000</a></td><td class="yfnc_h" align="right"><b>60.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00655000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">61.50</td><td class="yfnc_h" align="right">62.70</td><td class="yfnc_h" align="right">23</td><td class="yfnc_h" align="right">51</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=660.000000"><strong>660.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00660000">AAPL140621P00660000</a></td><td class="yfnc_h" align="right"><b>69.80</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00660000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">66.35</td><td class="yfnc_h" align="right">67.55</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=665.000000"><strong>665.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00665000">AAPL140621P00665000</a></td><td class="yfnc_h" align="right"><b>69.25</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00665000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">71.05</td><td class="yfnc_h" align="right">72.40</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=670.000000"><strong>670.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00670000">AAPL140621P00670000</a></td><td class="yfnc_h" align="right"><b>78.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00670000"><img width="10" height="14" style="margin-right:-2px;" border="0" src="http://l.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="neg_arrow" alt="Down"> <b style="color:#cc0000;">9.77</b></span></td><td class="yfnc_h" align="right">76.10</td><td class="yfnc_h" align="right">77.40</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">5</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=680.000000"><strong>680.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00680000">AAPL140621P00680000</a></td><td class="yfnc_h" align="right"><b>81.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00680000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">86.00</td><td class="yfnc_h" align="right">87.25</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=685.000000"><strong>685.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00685000">AAPL140621P00685000</a></td><td class="yfnc_h" align="right"><b>99.75</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00685000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">90.80</td><td class="yfnc_h" align="right">92.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=700.000000"><strong>700.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00700000">AAPL140621P00700000</a></td><td class="yfnc_h" align="right"><b>138.05</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00700000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">105.55</td><td class="yfnc_h" align="right">107.30</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">2</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=740.000000"><strong>740.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00740000">AAPL140621P00740000</a></td><td class="yfnc_h" align="right"><b>155.70</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00740000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">145.80</td><td class="yfnc_h" align="right">147.25</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=750.000000"><strong>750.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00750000">AAPL140621P00750000</a></td><td class="yfnc_h" align="right"><b>159.65</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00750000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">155.75</td><td class="yfnc_h" align="right">157.20</td><td class="yfnc_h" align="right">4</td><td class="yfnc_h" align="right">4</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=755.000000"><strong>755.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00755000">AAPL140621P00755000</a></td><td class="yfnc_h" align="right"><b>168.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00755000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">160.75</td><td class="yfnc_h" align="right">162.25</td><td class="yfnc_h" align="right">1</td><td class="yfnc_h" align="right">1</td></tr><tr><td class="yfnc_h" nowrap><a href="/q/op?s=AAPL&amp;k=760.000000"><strong>760.00</strong></a></td><td class="yfnc_h"><a href="/q?s=AAPL140621P00760000">AAPL140621P00760000</a></td><td class="yfnc_h" align="right"><b>173.60</b></td><td class="yfnc_h" align="right"><span id="yfs_c10_aapl140621p00760000"> <b style="color:#000000;">0.00</b></span></td><td class="yfnc_h" align="right">165.75</td><td class="yfnc_h" align="right">167.20</td><td class="yfnc_h" align="right">8</td><td class="yfnc_h" align="right">8</td></tr></table></td></tr></table><table border="0" cellpadding="2" cellspacing="0"><tr><td width="1%"><table border="0" cellpadding="1" cellspacing="0" width="10" class="yfnc_d"><tr><td><table border="0" cellpadding="1" cellspacing="0" width="100%"><tr><td class="yfnc_h">&nbsp;&nbsp;&nbsp;</td></tr></table></td></tr></table></td><td><small>Highlighted options are in-the-money.</small></td></tr></table><p style="text-align:center"><a href="/q/os?s=AAPL&amp;m=2014-06-21"><strong>Expand to Straddle View...</strong></a></p><p class="yfi_disclaimer">Currency in USD.</p></td><td width="15"></td><td width="1%" class="skycell"><!--ADS:LOCATION=SKY--><div style="min-height:620px; _height:620px; width:160px;margin:0pt auto;"><iframe src="https://ca.adserver.yahoo.com/a?f=1184585072&p=cafinance&l=SKY&c=h&at=content%3d'no_expandable'&site-country=us&rs=guid:rTP7AzIwNi5Ye_pbUo7O_gAlMTA4LlNyw4___xMA;spid:28951412;ypos:SKY;ypos:1400030096.781443" width=160 height=600 marginwidth=0 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src="http://csc.beap.bc.yahoo.com/yi?bv=1.0.0&bs=(134t1g972(gid$rTP7AzIwNi5Ye_pbUo7O_gAlMTA4LlNyw4___xMA,st$1400030096722431,si$4451051,sp$28951412,pv$1,v$2.0))&t=J_3-D_3"></noscript> +<!-- c14.finance.gq1.yahoo.com compressed/chunked Wed May 14 01:14:55 UTC 2014 --> diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index cf7c906a273b1..c13553d14b861 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -3,11 +3,12 @@ import warnings import nose from nose.tools import assert_equal -from datetime import datetime +from datetime import datetime, date +import os import numpy as np import pandas as pd -from pandas import DataFrame +from pandas import DataFrame, Timestamp from pandas.io import data as web from pandas.io.data import DataReader, SymbolWarning, RemoteDataError from pandas.util.testing import (assert_series_equal, assert_produces_warning, @@ -242,6 +243,12 @@ def setUpClass(cls): year = year + 1 month = 1 cls.expiry = datetime(year, month, 1) + cls.dirpath = tm.get_data_path() + cls.html1 = os.path.join(cls.dirpath, 'yahoo_options1.html') + cls.html2 = os.path.join(cls.dirpath, 'yahoo_options2.html') + cls.root1 = cls.aapl._parse_url(cls.html1) + cls.root2 = cls.aapl._parse_url(cls.html2) + @classmethod def tearDownClass(cls): @@ -251,29 +258,31 @@ def tearDownClass(cls): @network def test_get_options_data(self): try: - calls, puts = self.aapl.get_options_data(expiry=self.expiry) + options = self.aapl.get_options_data(expiry=self.expiry) except RemoteDataError as e: nose.SkipTest(e) else: - assert len(calls)>1 - assert len(puts)>1 + assert len(options)>1 + def test_get_options_data(self): # regression test GH6105 - self.assertRaises(ValueError,self.aapl.get_options_data,month=3) - self.assertRaises(ValueError,self.aapl.get_options_data,year=1992) + self.assertRaises(ValueError, self.aapl.get_options_data, month=3) + self.assertRaises(ValueError, self.aapl.get_options_data, year=1992) @network def test_get_near_stock_price(self): try: - calls, puts = self.aapl.get_near_stock_price(call=True, put=True, + options = self.aapl.get_near_stock_price(call=True, put=True, expiry=self.expiry) except RemoteDataError as e: nose.SkipTest(e) else: - self.assertEqual(len(calls), 5) - self.assertEqual(len(puts), 5) + assert len(options)> 1 + + self.assertTrue(len(options) > 1) + @network def test_get_call_data(self): @@ -293,6 +302,52 @@ def test_get_put_data(self): else: assert len(puts)>1 + @network + def test_get_expiry_months(self): + try: + dates = self.aapl._get_expiry_months() + except RemoteDataError: + raise nose.SkipTest("RemoteDataError thrown no dates found") + self.assertTrue(len(dates) > 1) + + @network + def test_get_all_data(self): + try: + data = self.aapl.get_all_data(put=True) + except RemoteDataError: + raise nose.SkipTest("RemoteDataError thrown") + + self.assertTrue(len(data) > 1) + + @network + def test_get_all_data_calls_only(self): + try: + data = self.aapl.get_all_data(call=True, put=False) + except RemoteDataError: + raise nose.SkipTest("RemoteDataError thrown") + + self.assertTrue(len(data) > 1) + + def test_sample_page_price_quote_time1(self): + #Tests the weekend quote time format + price, quote_time = self.aapl._get_underlying_price(self.root1) + self.assertIsInstance(price, (int, float, complex)) + self.assertIsInstance(quote_time, (datetime, Timestamp)) + + def test_sample_page_price_quote_time2(self): + #Tests the weekday quote time format + price, quote_time = self.aapl._get_underlying_price(self.root2) + self.assertIsInstance(price, (int, float, complex)) + self.assertIsInstance(quote_time, (datetime, Timestamp)) + + def test_sample_page_chg_float(self): + #Tests that numeric columns with comma's are appropriately dealt with + tables = self.root1.xpath('.//table') + data = web._parse_options_data(tables[self.aapl._TABLE_LOC['puts']]) + option_data = self.aapl._process_data(data) + self.assertEqual(option_data['Chg'].dtype, 'float64') + + class TestOptionsWarnings(tm.TestCase): @classmethod @@ -327,7 +382,7 @@ def test_get_options_data_warning(self): def test_get_near_stock_price_warning(self): with assert_produces_warning(): try: - calls_near, puts_near = self.aapl.get_near_stock_price(call=True, + options_near = self.aapl.get_near_stock_price(call=True, put=True, month=self.month, year=self.year)
... a ticker. Also added a few helper functions. These functions could be applied to refactor some of the other methods.
https://api.github.com/repos/pandas-dev/pandas/pulls/5602
2013-11-27T20:55:58Z
2014-06-17T14:44:30Z
2014-06-17T14:44:30Z
2014-06-17T15:56:36Z
TST/API: test the list of NA values in the csv parser. add N/A, #NA as independent default values (GH5521)
diff --git a/doc/source/io.rst b/doc/source/io.rst index 0f34e94084878..a6f022d85272e 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -564,7 +564,7 @@ the corresponding equivalent values will also imply a missing value (in this cas ``[5.0,5]`` are recognized as ``NaN``. To completely override the default values that are recognized as missing, specify ``keep_default_na=False``. -The default ``NaN`` recognized values are ``['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', 'NA', +The default ``NaN`` recognized values are ``['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A','N/A', 'NA', '#NA', 'NULL', 'NaN', 'nan']``. .. code-block:: python diff --git a/doc/source/release.rst b/doc/source/release.rst index cb17e7b3d4b23..35e00d9ed9850 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -126,7 +126,7 @@ Improvements to existing features (:issue:`4039`) with improved validation for all (:issue:`4039`, :issue:`4794`) - A Series of dtype ``timedelta64[ns]`` can now be divided/multiplied - by an integer series (:issue`4521`) + by an integer series (:issue:`4521`) - A Series of dtype ``timedelta64[ns]`` can now be divided by another ``timedelta64[ns]`` object to yield a ``float64`` dtyped Series. This is frequency conversion; astyping is also supported. @@ -409,6 +409,8 @@ API Changes - raise/warn ``SettingWithCopyError/Warning`` exception/warning when setting of a copy thru chained assignment is detected, settable via option ``mode.chained_assignment`` + - test the list of ``NA`` values in the csv parser. add ``N/A``, ``#NA`` as independent default + na values (:issue:`5521`) Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index c10cb84de34fd..e62ecd5a541df 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -438,7 +438,7 @@ def read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds): # no longer excluding inf representations # '1.#INF','-1.#INF', '1.#INF000000', _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', - '#N/A N/A', 'NA', '#NA', 'NULL', 'NaN', + '#N/A','N/A', 'NA', '#NA', 'NULL', 'NaN', 'nan', '']) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 84736f16e7cba..37d3c6c55ba65 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -683,6 +683,31 @@ def test_non_string_na_values(self): tm.assert_frame_equal(result6,good_compare) tm.assert_frame_equal(result7,good_compare) + def test_default_na_values(self): + _NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', + '#N/A','N/A', 'NA', '#NA', 'NULL', 'NaN', + 'nan', '']) + + nv = len(_NA_VALUES) + def f(i, v): + if i == 0: + buf = '' + elif i > 0: + buf = ''.join([','] * i) + + buf = "{0}{1}".format(buf,v) + + if i < nv-1: + buf = "{0}{1}".format(buf,''.join([','] * (nv-i-1))) + + return buf + + data = StringIO('\n'.join([ f(i, v) for i, v in enumerate(_NA_VALUES) ])) + + expected = DataFrame(np.nan,columns=range(nv),index=range(nv)) + df = self.read_csv(data, header=None) + tm.assert_frame_equal(df, expected) + def test_custom_na_values(self): data = """A,B,C ignore,this,row
closes #5521
https://api.github.com/repos/pandas-dev/pandas/pulls/5601
2013-11-27T19:18:20Z
2013-11-27T19:56:34Z
2013-11-27T19:56:34Z
2014-06-24T17:42:26Z
BUG: replace with a scalar works like a list of to_replace for compat with 0.12 (GH5319)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 5ab5e9063a2c5..09d7c1ae9a415 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -41,6 +41,10 @@ def is_dictlike(x): def _single_replace(self, to_replace, method, inplace, limit): + if self.ndim != 1: + raise TypeError('cannot replace {0} with method {1} on a {2}'.format(to_replace, + method,type(self).__name__)) + orig_dtype = self.dtype result = self if inplace else self.copy() fill_f = com._get_fill_func(method) @@ -2033,6 +2037,11 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, self._consolidate_inplace() if value is None: + # passing a single value that is scalar like + # when value is None (GH5319), for compat + if not is_dictlike(to_replace) and not is_dictlike(regex): + to_replace = [ to_replace ] + if isinstance(to_replace, (tuple, list)): return _single_replace(self, to_replace, method, inplace, limit) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 280b0476efe98..00e907aeb9986 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -6975,6 +6975,7 @@ def test_replace_inplace(self): assert_frame_equal(tsframe, self.tsframe.fillna(0)) self.assertRaises(TypeError, self.tsframe.replace, nan, inplace=True) + self.assertRaises(TypeError, self.tsframe.replace, nan) # mixed type self.mixed_frame['foo'][5:20] = nan diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 5a6f790d5851e..72bb4e66b6fec 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -5140,6 +5140,18 @@ def test_replace(self): result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) assert_series_equal(result, Series([4, 3, 2, 1, 0])) + # API change from 0.12? + # GH 5319 + ser = Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + result = ser.replace([np.nan]) + assert_series_equal(result, expected) + + ser = Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + result = ser.replace(np.nan) + assert_series_equal(result, expected) + def test_replace_with_single_list(self): ser = Series([0, 1, 2, 3, 4]) result = ser.replace([1,2,3])
closes #5319 Essentitally this is `ffill` ``` In [1]: Series([1,np.nan,2]).replace([np.nan]) Out[1]: 0 1 1 1 2 2 dtype: float64 In [2]: Series([1,np.nan,2]).replace(np.nan) Out[2]: 0 1 1 1 2 2 dtype: float64 ``` A bit confusing on a Frame, so raise an error ``` In [3]: DataFrame(randn(5,2)).replace(np.nan) TypeError: cannot replace [nan] with method pad on a DataFrame ``` This was the exception before this PR on a Frame ``` TypeError: If "to_replace" and "value" are both None and "to_replace" is not a list, then regex must be a mapping ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5600
2013-11-27T17:50:11Z
2013-12-01T20:00:10Z
2013-12-01T20:00:10Z
2014-06-14T11:01:36Z
DOC: limit code snippet output to max_rows=15
diff --git a/doc/source/10min.rst b/doc/source/10min.rst index 85aafd6787f16..90198fa48bcb4 100644 --- a/doc/source/10min.rst +++ b/doc/source/10min.rst @@ -13,6 +13,7 @@ import pandas as pd np.set_printoptions(precision=4, suppress=True) options.display.mpl_style='default' + options.display.max_rows=15 #### portions of this were borrowed from the #### Pandas cheatsheet diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 99cb0e8f67b09..a1724710e585e 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -9,6 +9,7 @@ randn = np.random.randn np.set_printoptions(precision=4, suppress=True) from pandas.compat import lrange + options.display.max_rows=15 ============================== Essential Basic Functionality diff --git a/doc/source/comparison_with_r.rst b/doc/source/comparison_with_r.rst index ef609aaa7d70c..c05ec01df6bcc 100644 --- a/doc/source/comparison_with_r.rst +++ b/doc/source/comparison_with_r.rst @@ -1,6 +1,12 @@ .. currentmodule:: pandas .. _compare_with_r: +.. ipython:: python + :suppress: + + from pandas import * + options.display.max_rows=15 + Comparison with R / R libraries ******************************* diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 85c6b88d740da..66e0d457e33b6 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -13,6 +13,7 @@ import matplotlib.pyplot as plt plt.close('all') options.display.mpl_style='default' + options.display.max_rows=15 Computational tools =================== diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 12891b030deb9..2bc76283bbedd 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -10,6 +10,7 @@ import os np.random.seed(123456) from pandas import * + options.display.max_rows=15 import pandas as pd randn = np.random.randn randint = np.random.randint diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index 828797deff5cf..3cc93d2f3e122 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -1,14 +1,6 @@ .. currentmodule:: pandas .. _dsintro: -************************ -Intro to Data Structures -************************ - -We'll start with a quick, non-comprehensive overview of the fundamental data -structures in pandas to get you started. The fundamental behavior about data -types, indexing, and axis labeling / alignment apply across all of the -objects. To get started, import numpy and load pandas into your namespace: .. ipython:: python :suppress: @@ -18,6 +10,17 @@ objects. To get started, import numpy and load pandas into your namespace: randn = np.random.randn np.set_printoptions(precision=4, suppress=True) set_option('display.precision', 4, 'display.max_columns', 8) + options.display.max_rows=15 + + +************************ +Intro to Data Structures +************************ + +We'll start with a quick, non-comprehensive overview of the fundamental data +structures in pandas to get you started. The fundamental behavior about data +types, indexing, and axis labeling / alignment apply across all of the +objects. To get started, import numpy and load pandas into your namespace: .. ipython:: python diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index 4e9e62a2f0e3e..2f31e1920ae0a 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -9,6 +9,7 @@ import csv from pandas import DataFrame import pandas as pd + pd.options.display.max_rows=15 import numpy as np np.random.seed(123456) diff --git a/doc/source/faq.rst b/doc/source/faq.rst index 21d581f12c53f..d64b799a865d1 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -12,6 +12,7 @@ Frequently Asked Questions (FAQ) import numpy as np np.random.seed(123456) from pandas import * + options.display.max_rows=15 randn = np.random.randn randint = np.random.randint np.set_printoptions(precision=4, suppress=True) diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst index 1edd6fa1705ff..97699aa32890d 100644 --- a/doc/source/gotchas.rst +++ b/doc/source/gotchas.rst @@ -7,6 +7,7 @@ import os import numpy as np from pandas import * + options.display.max_rows=15 randn = np.random.randn np.set_printoptions(precision=4, suppress=True) from pandas.compat import lrange diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 705db807ddf8f..41539b5ce283e 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -7,6 +7,7 @@ import numpy as np np.random.seed(123456) from pandas import * + options.display.max_rows=15 randn = np.random.randn np.set_printoptions(precision=4, suppress=True) import matplotlib.pyplot as plt diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index b95c515831f55..8813e9d838d22 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -9,6 +9,7 @@ import random np.random.seed(123456) from pandas import * + options.display.max_rows=15 import pandas as pd randn = np.random.randn randint = np.random.randint diff --git a/doc/source/io.rst b/doc/source/io.rst index 0ed4c45bc3a86..0f34e94084878 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -20,6 +20,7 @@ plt.close('all') from pandas import * + options.display.max_rows=15 import pandas.util.testing as tm clipdf = DataFrame({'A':[1,2,3],'B':[4,5,6],'C':['p','q','r']}, index=['x','y','z']) diff --git a/doc/source/merging.rst b/doc/source/merging.rst index bc3bec4de654d..a68fc6e0739d5 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -8,6 +8,7 @@ np.random.seed(123456) from numpy import nan from pandas import * + options.display.max_rows=15 randn = np.random.randn np.set_printoptions(precision=4, suppress=True) diff --git a/doc/source/missing_data.rst b/doc/source/missing_data.rst index f953aeaa2a8a9..10053f61d8574 100644 --- a/doc/source/missing_data.rst +++ b/doc/source/missing_data.rst @@ -1,6 +1,12 @@ .. currentmodule:: pandas .. _missing_data: +.. ipython:: python + :suppress: + + from pandas import * + options.display.max_rows=15 + ************************* Working with missing data ************************* diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index 4f5c5a03a1be5..5af5685ed1f56 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -2,6 +2,13 @@ .. _rpy: +.. ipython:: python + :suppress: + + from pandas import * + options.display.max_rows=15 + + ****************** rpy2 / R interface ****************** diff --git a/doc/source/release.rst b/doc/source/release.rst index ccc34a4051508..793d1144531ec 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -20,6 +20,7 @@ plt.close('all') from pandas import * + options.display.max_rows=15 import pandas.util.testing as tm ************* diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index b950876738852..f0eb687174ff8 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -19,6 +19,7 @@ plt.close('all') from pandas import * + options.display.max_rows=15 import pandas.util.testing as tm ****************** diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst index 7415963296f7e..37a87e7c3d8f6 100644 --- a/doc/source/reshaping.rst +++ b/doc/source/reshaping.rst @@ -7,6 +7,7 @@ import numpy as np np.random.seed(123456) from pandas import * + options.display.max_rows=15 from pandas.core.reshape import * import pandas.util.testing as tm randn = np.random.randn diff --git a/doc/source/rplot.rst b/doc/source/rplot.rst index 8ede1a41f8dd8..12ade83261fb7 100644 --- a/doc/source/rplot.rst +++ b/doc/source/rplot.rst @@ -7,6 +7,7 @@ import numpy as np np.random.seed(123456) from pandas import * + options.display.max_rows=15 import pandas.util.testing as tm randn = np.random.randn np.set_printoptions(precision=4, suppress=True) diff --git a/doc/source/sparse.rst b/doc/source/sparse.rst index 67f5c4f3403c1..391aae1cd9105 100644 --- a/doc/source/sparse.rst +++ b/doc/source/sparse.rst @@ -13,6 +13,7 @@ import matplotlib.pyplot as plt plt.close('all') options.display.mpl_style='default' + options.display.max_rows = 15 ********************** Sparse data structures diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index bfae9638f5377..b43a8fff496b6 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -11,6 +11,7 @@ randn = np.random.randn randint = np.random.randint np.set_printoptions(precision=4, suppress=True) + options.display.max_rows=15 from dateutil.relativedelta import relativedelta from pandas.tseries.api import * from pandas.tseries.offsets import * diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 6e357d6d38e49..29f4cf2588c2f 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -13,6 +13,7 @@ import matplotlib.pyplot as plt plt.close('all') options.display.mpl_style = 'default' + options.display.max_rows = 15 from pandas.compat import lrange ************************ diff --git a/doc/source/whatsnew.rst b/doc/source/whatsnew.rst index 563847955877e..ad2af07d3d723 100644 --- a/doc/source/whatsnew.rst +++ b/doc/source/whatsnew.rst @@ -9,6 +9,7 @@ from pandas import * randn = np.random.randn np.set_printoptions(precision=4, suppress=True) + options.display.max_rows = 15 ********** What's New
Most noticable in timeseries chapter.
https://api.github.com/repos/pandas-dev/pandas/pulls/5599
2013-11-27T16:03:07Z
2013-11-27T16:03:16Z
2013-11-27T16:03:16Z
2014-07-16T08:41:43Z
TST: dtype issue in test_groupby.py on windows (GH5595)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 51f608e20c738..d9bdc3adcd041 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -323,7 +323,7 @@ def func(dataf): # GH5592 # inconcistent return type df = DataFrame(dict(A = [ 'Tiger', 'Tiger', 'Tiger', 'Lamb', 'Lamb', 'Pony', 'Pony' ], - B = np.arange(7))) + B = Series(np.arange(7),dtype='int64'))) def f(grp): return grp.iloc[0] expected = df.groupby('A').first()
closes #5595
https://api.github.com/repos/pandas-dev/pandas/pulls/5598
2013-11-27T12:55:23Z
2013-11-27T13:28:17Z
2013-11-27T13:28:17Z
2014-07-09T19:32:06Z
Add image for whatsnew docs
diff --git a/doc/source/_static/df_repr_truncated.png b/doc/source/_static/df_repr_truncated.png new file mode 100644 index 0000000000000..8f60270358761 Binary files /dev/null and b/doc/source/_static/df_repr_truncated.png differ
This should have been in PR #5550, but the directory is gitignored, and I missed it.
https://api.github.com/repos/pandas-dev/pandas/pulls/5594
2013-11-26T23:16:56Z
2013-11-27T18:31:42Z
2013-11-27T18:31:42Z
2014-06-27T12:06:24Z
BUG: Bug in groupby returning non-consistent types when user function returns a None, (GH5992)
diff --git a/doc/source/release.rst b/doc/source/release.rst index ccc34a4051508..9c448aa7083c8 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -814,6 +814,7 @@ Bug Fixes - Bug in delitem on a Series (:issue:`5542`) - Bug fix in apply when using custom function and objects are not mutated (:issue:`5545`) - Bug in selecting from a non-unique index with ``loc`` (:issue:`5553`) + - Bug in groupby returning non-consistent types when user function returns a ``None``, (:issue:`5592`) pandas 0.12.0 ------------- diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 1d5691edb6313..98c17e4f424f5 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2122,11 +2122,23 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): else: key_index = Index(keys, name=key_names[0]) - if isinstance(values[0], (np.ndarray, Series)): - if isinstance(values[0], Series): + + # make Nones an empty object + if com._count_not_none(*values) != len(values): + v = None + for v in values: + if v is not None: + break + if v is None: + return DataFrame() + values = [ x if x is not None else v._constructor(**v._construct_axes_dict()) for x in values ] + + v = values[0] + + if isinstance(v, (np.ndarray, Series)): + if isinstance(v, Series): applied_index = self.obj._get_axis(self.axis) - all_indexed_same = _all_indexes_same([x.index - for x in values]) + all_indexed_same = _all_indexes_same([x.index for x in values ]) singular_series = (len(values) == 1 and applied_index.nlevels == 1) @@ -2165,13 +2177,13 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): stacked_values = np.vstack([np.asarray(x) for x in values]) - columns = values[0].index + columns = v.index index = key_index else: stacked_values = np.vstack([np.asarray(x) for x in values]).T - index = values[0].index + index = v.index columns = key_index except (ValueError, AttributeError): diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 1ee7268c0ca82..51f608e20c738 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -7,7 +7,7 @@ from datetime import datetime from numpy import nan -from pandas import bdate_range, Timestamp +from pandas import date_range,bdate_range, Timestamp from pandas.core.index import Index, MultiIndex, Int64Index from pandas.core.common import rands from pandas.core.api import Categorical, DataFrame @@ -259,7 +259,7 @@ def test_groupby_bounds_check(self): def test_groupby_grouper_f_sanity_checked(self): import pandas as pd - dates = pd.date_range('01-Jan-2013', periods=12, freq='MS') + dates = date_range('01-Jan-2013', periods=12, freq='MS') ts = pd.TimeSeries(np.random.randn(12), index=dates) # GH3035 @@ -320,6 +320,34 @@ def func(dataf): result = df.groupby('X',squeeze=False).count() tm.assert_isinstance(result,DataFrame) + # GH5592 + # inconcistent return type + df = DataFrame(dict(A = [ 'Tiger', 'Tiger', 'Tiger', 'Lamb', 'Lamb', 'Pony', 'Pony' ], + B = np.arange(7))) + def f(grp): + return grp.iloc[0] + expected = df.groupby('A').first() + result = df.groupby('A').apply(f)[['B']] + assert_frame_equal(result,expected) + + def f(grp): + if grp.name == 'Tiger': + return None + return grp.iloc[0] + result = df.groupby('A').apply(f)[['B']] + e = expected.copy() + e.loc['Tiger'] = np.nan + assert_frame_equal(result,e) + + def f(grp): + if grp.name == 'Pony': + return None + return grp.iloc[0] + result = df.groupby('A').apply(f)[['B']] + e = expected.copy() + e.loc['Pony'] = np.nan + assert_frame_equal(result,e) + def test_agg_regression1(self): grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month]) result = grouped.agg(np.mean)
closes #5592
https://api.github.com/repos/pandas-dev/pandas/pulls/5593
2013-11-26T19:22:51Z
2013-11-26T19:49:55Z
2013-11-26T19:49:55Z
2014-06-21T12:52:16Z
PERF: perf enhancements in indexing/query/eval
diff --git a/pandas/computation/align.py b/pandas/computation/align.py index 233f2b61dc463..b61169e1f55e0 100644 --- a/pandas/computation/align.py +++ b/pandas/computation/align.py @@ -151,7 +151,8 @@ def _align_core(terms): f = partial(ti.reindex_axis, reindexer, axis=axis, copy=False) - if pd.lib.is_bool_array(ti.values): + # need to fill if we have a bool dtype/array + if isinstance(ti, (np.ndarray, pd.Series)) and ti.dtype == object and pd.lib.is_bool_array(ti.values): r = f(fill_value=True) else: r = f() diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index bcb5570eae1fc..0baa596778996 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -183,6 +183,8 @@ def update(self, level=None): while sl >= 0: frame = frame.f_back sl -= 1 + if frame is None: + break frames.append(frame) for f in frames[::-1]: self.locals.update(f.f_locals) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 88cf898d354e9..5d658410a0e32 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3061,9 +3061,9 @@ def combine_first(self, other): Examples -------- a's values prioritized, use values from b to fill holes: - + >>> a.combine_first(b) - + Returns ------- @@ -3623,7 +3623,7 @@ def join(self, other, on=None, how='left', lsuffix='', rsuffix='', how : {'left', 'right', 'outer', 'inner'} How to handle indexes of the two objects. Default: 'left' for joining on index, None otherwise - + * left: use calling frame's index * right: use input frame's index * outer: form union of indexes diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 5be71afc18663..5ab5e9063a2c5 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1092,7 +1092,7 @@ def take(self, indices, axis=0, convert=True): new_data = self._data.reindex_axis(new_items, indexer=indices, axis=0) else: - new_data = self._data.take(indices, axis=baxis) + new_data = self._data.take(indices, axis=baxis, verify=convert) return self._constructor(new_data)\ ._setitem_copy(True)\ .__finalize__(self) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index fcb9a82b96211..a258135597078 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -978,7 +978,7 @@ def _get_slice_axis(self, slice_obj, axis=0): if isinstance(indexer, slice): return self._slice(indexer, axis=axis, typ='iloc') else: - return self.obj.take(indexer, axis=axis) + return self.obj.take(indexer, axis=axis, convert=False) class _IXIndexer(_NDFrameIndexer): @@ -1038,7 +1038,7 @@ def _get_slice_axis(self, slice_obj, axis=0): if isinstance(indexer, slice): return self._slice(indexer, axis=axis, typ='iloc') else: - return self.obj.take(indexer, axis=axis) + return self.obj.take(indexer, axis=axis, convert=False) class _LocIndexer(_LocationIndexer): @@ -1195,7 +1195,7 @@ def _get_slice_axis(self, slice_obj, axis=0): return self._slice(slice_obj, axis=axis, raise_on_error=True, typ='iloc') else: - return self.obj.take(slice_obj, axis=axis) + return self.obj.take(slice_obj, axis=axis, convert=False) def _getitem_axis(self, key, axis=0): diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 79667ecddc8a6..959d0186030cd 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3247,10 +3247,9 @@ def take(self, indexer, new_index=None, axis=1, verify=True): if verify: indexer = _maybe_convert_indices(indexer, n) - - if ((indexer == -1) | (indexer >= n)).any(): - raise Exception('Indices must be nonzero and less than ' - 'the axis length') + if ((indexer == -1) | (indexer >= n)).any(): + raise Exception('Indices must be nonzero and less than ' + 'the axis length') new_axes = list(self.axes) if new_index is None: diff --git a/vb_suite/eval.py b/vb_suite/eval.py index 506d00b8bf9f9..3b0efa9e88f48 100644 --- a/vb_suite/eval.py +++ b/vb_suite/eval.py @@ -139,3 +139,14 @@ query_datetime_index = Benchmark("df.query('index < ts')", index_setup, start_date=datetime(2013, 9, 27)) + +setup = setup + """ +N = 1000000 +df = DataFrame({'a': np.random.randn(N)}) +min_val = df['a'].min() +max_val = df['a'].max() +""" + +query_with_boolean_selection = Benchmark("df.query('(a >= min_val) & (a <= max_val)')", + index_setup, start_date=datetime(2013, 9, 27)) +
perf improvement in eval/query (and general take-like indexing) ``` In [1]: N = 1000000 In [2]: df = DataFrame({'a': np.random.randn(N)}) In [3]: min_val = df['a'].min() In [4]: max_val = df['a'].max() ``` current master: 66980c68d188842b1c4d80d3508f539baab3cbe8 ``` In [5]: %timeit df.query('(a >= min_val) & (a <= max_val)') 10 loops, best of 3: 31.6 ms per loop ``` This PR ``` In [5]: %timeit df.query('(a >= min_val) & (a <= max_val)') 10 loops, best of 3: 20.2 ms per loop ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5590
2013-11-26T14:09:14Z
2013-11-26T14:28:18Z
2013-11-26T14:28:18Z
2014-07-16T08:41:34Z
BUG: to_html doesn't truncate index to max_rows before formatting
diff --git a/pandas/core/format.py b/pandas/core/format.py index 1ca68b8d47e09..cc6f5bd516a19 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -819,14 +819,14 @@ def _write_body(self, indent): def _write_regular_rows(self, fmt_values, indent, truncated): ncols = min(len(self.columns), self.max_cols) - + nrows = min(len(self.frame), self.max_rows) fmt = self.fmt._get_formatter('__index__') if fmt is not None: - index_values = self.frame.index.map(fmt) + index_values = self.frame.index[:nrows].map(fmt) else: - index_values = self.frame.index.format() + index_values = self.frame.index[:nrows].format() - for i in range(min(len(self.frame), self.max_rows)): + for i in range(nrows): row = [] row.append(index_values[i]) row.extend(fmt_values[j][i] for j in range(ncols)) diff --git a/vb_suite/frame_methods.py b/vb_suite/frame_methods.py index b7754e28629d0..a7c863345b9c5 100644 --- a/vb_suite/frame_methods.py +++ b/vb_suite/frame_methods.py @@ -145,6 +145,20 @@ def f(x): frame_to_string_floats = Benchmark('df.to_string()', setup, start_date=datetime(2010, 6, 1)) +#---------------------------------------------------------------------- +# to_html + +setup = common_setup + """ +nrows=500 +df = DataFrame(randn(nrows, 10)) +df[0]=period_range("2000","2010",nrows) +df[1]=range(nrows) + +""" + +frame_to_html_mixed = Benchmark('df.to_html()', setup, + start_date=datetime(2010, 6, 1)) + # insert many columns setup = common_setup + """
@takluyver, missed an O(N) there. #5588
https://api.github.com/repos/pandas-dev/pandas/pulls/5589
2013-11-26T12:39:53Z
2013-11-26T12:55:50Z
2013-11-26T12:55:50Z
2014-06-24T12:51:13Z
Fix typo
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 83e26d83a9363..bfae9638f5377 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -1115,7 +1115,7 @@ Localization of Timestamps functions just like DatetimeIndex and TimeSeries: rng[5].tz_localize('Asia/Shanghai') -Operations between TimeSeries in difficult time zones will yield UTC +Operations between TimeSeries in different time zones will yield UTC TimeSeries, aligning the data on the UTC timestamps: .. ipython:: python
While I know timezone conversion might be difficult, I think you mean different here :smile:
https://api.github.com/repos/pandas-dev/pandas/pulls/5587
2013-11-25T19:12:21Z
2013-11-25T19:18:40Z
2013-11-25T19:18:40Z
2014-07-12T13:20:39Z
BUG/TST: reset setitem_copy on object enlargement
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index f2d75d3fd21c5..cfbd9335ef9a0 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1,6 +1,5 @@ #!/usr/bin/env python -import unittest import functools from itertools import product @@ -104,10 +103,11 @@ def _is_py3_complex_incompat(result, expected): _good_arith_ops = com.difference(_arith_ops_syms, _special_case_arith_ops_syms) -class TestEvalNumexprPandas(unittest.TestCase): +class TestEvalNumexprPandas(tm.TestCase): @classmethod def setUpClass(cls): + super(TestEvalNumexprPandas, cls).setUpClass() skip_if_no_ne() import numexpr as ne cls.ne = ne @@ -116,6 +116,7 @@ def setUpClass(cls): @classmethod def tearDownClass(cls): + super(TestEvalNumexprPandas, cls).tearDownClass() del cls.engine, cls.parser if hasattr(cls, 'ne'): del cls.ne @@ -707,6 +708,7 @@ class TestEvalNumexprPython(TestEvalNumexprPandas): @classmethod def setUpClass(cls): + super(TestEvalNumexprPython, cls).setUpClass() skip_if_no_ne() import numexpr as ne cls.ne = ne @@ -733,6 +735,7 @@ class TestEvalPythonPython(TestEvalNumexprPython): @classmethod def setUpClass(cls): + super(TestEvalPythonPython, cls).setUpClass() cls.engine = 'python' cls.parser = 'python' @@ -761,6 +764,7 @@ class TestEvalPythonPandas(TestEvalPythonPython): @classmethod def setUpClass(cls): + super(TestEvalPythonPandas, cls).setUpClass() cls.engine = 'python' cls.parser = 'pandas' @@ -1024,10 +1028,11 @@ def test_performance_warning_for_poor_alignment(self): #------------------------------------ # slightly more complex ops -class TestOperationsNumExprPandas(unittest.TestCase): +class TestOperationsNumExprPandas(tm.TestCase): @classmethod def setUpClass(cls): + super(TestOperationsNumExprPandas, cls).setUpClass() skip_if_no_ne() cls.engine = 'numexpr' cls.parser = 'pandas' @@ -1035,6 +1040,7 @@ def setUpClass(cls): @classmethod def tearDownClass(cls): + super(TestOperationsNumExprPandas, cls).tearDownClass() del cls.engine, cls.parser def eval(self, *args, **kwargs): @@ -1337,6 +1343,7 @@ class TestOperationsNumExprPython(TestOperationsNumExprPandas): @classmethod def setUpClass(cls): + super(TestOperationsNumExprPython, cls).setUpClass() if not _USE_NUMEXPR: raise nose.SkipTest("numexpr engine not installed") cls.engine = 'numexpr' @@ -1404,6 +1411,7 @@ class TestOperationsPythonPython(TestOperationsNumExprPython): @classmethod def setUpClass(cls): + super(TestOperationsPythonPython, cls).setUpClass() cls.engine = cls.parser = 'python' cls.arith_ops = expr._arith_ops_syms + expr._cmp_ops_syms cls.arith_ops = filter(lambda x: x not in ('in', 'not in'), @@ -1414,6 +1422,7 @@ class TestOperationsPythonPandas(TestOperationsNumExprPandas): @classmethod def setUpClass(cls): + super(TestOperationsPythonPandas, cls).setUpClass() cls.engine = 'python' cls.parser = 'pandas' cls.arith_ops = expr._arith_ops_syms + expr._cmp_ops_syms diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 5d658410a0e32..6ef6d8c75216f 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1563,7 +1563,7 @@ def _ixs(self, i, axis=0, copy=False): # a location index by definition i = _maybe_convert_indices(i, len(self._get_axis(axis))) - return self.reindex(i, takeable=True) + return self.reindex(i, takeable=True)._setitem_copy(True) else: new_values, copy = self._data.fast_2d_xs(i, copy=copy) return Series(new_values, index=self.columns, @@ -2714,7 +2714,7 @@ def trans(v): self._clear_item_cache() else: - return self.take(indexer, axis=axis, convert=False) + return self.take(indexer, axis=axis, convert=False, is_copy=False) def sortlevel(self, level=0, axis=0, ascending=True, inplace=False): """ @@ -2760,7 +2760,7 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False): self._clear_item_cache() else: - return self.take(indexer, axis=axis, convert=False) + return self.take(indexer, axis=axis, convert=False, is_copy=False) def swaplevel(self, i, j, axis=0): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 5ab5e9063a2c5..f3097a616ff95 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1064,7 +1064,7 @@ def __delitem__(self, key): except KeyError: pass - def take(self, indices, axis=0, convert=True): + def take(self, indices, axis=0, convert=True, is_copy=True): """ Analogous to ndarray.take @@ -1073,6 +1073,7 @@ def take(self, indices, axis=0, convert=True): indices : list / array of ints axis : int, default 0 convert : translate neg to pos indices (default) + is_copy : mark the returned frame as a copy Returns ------- @@ -1090,12 +1091,17 @@ def take(self, indices, axis=0, convert=True): labels = self._get_axis(axis) new_items = labels.take(indices) new_data = self._data.reindex_axis(new_items, indexer=indices, - axis=0) + axis=baxis) else: - new_data = self._data.take(indices, axis=baxis, verify=convert) - return self._constructor(new_data)\ - ._setitem_copy(True)\ - .__finalize__(self) + new_data = self._data.take(indices, axis=baxis) + + result = self._constructor(new_data).__finalize__(self) + + # maybe set copy if we didn't actually change the index + if is_copy and not result._get_axis(axis).equals(self._get_axis(axis)): + result = result._setitem_copy(is_copy) + + return result # TODO: Check if this was clearer in 0.12 def select(self, crit, axis=0): diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index a258135597078..08f935539ecfc 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -209,6 +209,7 @@ def _setitem_with_indexer(self, indexer, value): labels = _safe_append_to_index(index, key) self.obj._data = self.obj.reindex_axis(labels, i)._data self.obj._maybe_update_cacher(clear=True) + self.obj._setitem_copy(False) if isinstance(labels, MultiIndex): self.obj.sortlevel(inplace=True) diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py index 6ee0afa1c8c07..3556dfd999d40 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/io/tests/test_clipboard.py @@ -1,5 +1,3 @@ -import unittest - import numpy as np from numpy.random import randint @@ -18,9 +16,10 @@ raise nose.SkipTest("no clipboard found") -class TestClipboard(unittest.TestCase): +class TestClipboard(tm.TestCase): @classmethod def setUpClass(cls): + super(TestClipboard, cls).setUpClass() cls.data = {} cls.data['string'] = mkdf(5, 3, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) @@ -43,6 +42,7 @@ def setUpClass(cls): @classmethod def tearDownClass(cls): + super(TestClipboard, cls).tearDownClass() del cls.data_types, cls.data def check_round_trip_frame(self, data_type, excel=None, sep=None): diff --git a/pandas/io/tests/test_cparser.py b/pandas/io/tests/test_cparser.py index 8db9c7de6cbcd..0b104ffba4242 100644 --- a/pandas/io/tests/test_cparser.py +++ b/pandas/io/tests/test_cparser.py @@ -9,7 +9,6 @@ import os import sys import re -import unittest import nose @@ -32,7 +31,7 @@ import pandas.parser as parser -class TestCParser(unittest.TestCase): +class TestCParser(tm.TestCase): def setUp(self): self.dirpath = tm.get_data_path() @@ -132,7 +131,7 @@ def test_integer_thousands(self): expected = [123456, 12500] tm.assert_almost_equal(result[0], expected) - + def test_integer_thousands_alt(self): data = '123.456\n12.500' diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index 4e2331f05001d..831be69b9db5f 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -1,6 +1,5 @@ from __future__ import print_function from pandas import compat -import unittest import warnings import nose from nose.tools import assert_equal @@ -35,15 +34,17 @@ def assert_n_failed_equals_n_null_columns(wngs, obj, cls=SymbolWarning): assert msgs.str.contains('|'.join(failed_symbols)).all() -class TestGoogle(unittest.TestCase): +class TestGoogle(tm.TestCase): @classmethod def setUpClass(cls): + super(TestGoogle, cls).setUpClass() cls.locales = tm.get_locales(prefix='en_US') if not cls.locales: raise nose.SkipTest("US English locale not available for testing") @classmethod def tearDownClass(cls): + super(TestGoogle, cls).tearDownClass() del cls.locales @network @@ -105,9 +106,10 @@ def test_get_multi2(self): assert_n_failed_equals_n_null_columns(w, result) -class TestYahoo(unittest.TestCase): +class TestYahoo(tm.TestCase): @classmethod def setUpClass(cls): + super(TestYahoo, cls).setUpClass() _skip_if_no_lxml() @network @@ -224,9 +226,10 @@ def test_get_date_ret_index(self): assert np.issubdtype(pan.values.dtype, np.floating) -class TestYahooOptions(unittest.TestCase): +class TestYahooOptions(tm.TestCase): @classmethod def setUpClass(cls): + super(TestYahooOptions, cls).setUpClass() _skip_if_no_lxml() # aapl has monthlies @@ -241,6 +244,7 @@ def setUpClass(cls): @classmethod def tearDownClass(cls): + super(TestYahooOptions, cls).tearDownClass() del cls.aapl, cls.expiry @network @@ -283,9 +287,10 @@ def test_get_put_data(self): assert len(puts)>1 -class TestOptionsWarnings(unittest.TestCase): +class TestOptionsWarnings(tm.TestCase): @classmethod def setUpClass(cls): + super(TestOptionsWarnings, cls).setUpClass() _skip_if_no_lxml() with assert_produces_warning(FutureWarning): @@ -300,6 +305,7 @@ def setUpClass(cls): @classmethod def tearDownClass(cls): + super(TestOptionsWarnings, cls).tearDownClass() del cls.aapl, cls.year, cls.month @network @@ -342,7 +348,7 @@ def test_get_put_data_warning(self): warnings.warn("IndexError thrown no tables found") -class TestDataReader(unittest.TestCase): +class TestDataReader(tm.TestCase): def test_is_s3_url(self): from pandas.io.common import _is_s3_url self.assert_(_is_s3_url("s3://pandas/somethingelse.com")) @@ -372,7 +378,7 @@ def test_read_famafrench(self): assert isinstance(ff, dict) -class TestFred(unittest.TestCase): +class TestFred(tm.TestCase): @network def test_fred(self): """ diff --git a/pandas/io/tests/test_date_converters.py b/pandas/io/tests/test_date_converters.py index 8c1009b904857..74dad8537bb88 100644 --- a/pandas/io/tests/test_date_converters.py +++ b/pandas/io/tests/test_date_converters.py @@ -4,7 +4,6 @@ import os import sys import re -import unittest import nose @@ -22,9 +21,9 @@ from pandas import compat from pandas.lib import Timestamp import pandas.io.date_converters as conv +import pandas.util.testing as tm - -class TestConverters(unittest.TestCase): +class TestConverters(tm.TestCase): def setUp(self): self.years = np.array([2007, 2008]) diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 861f3a785ce22..3446eb07a111e 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -3,7 +3,6 @@ from pandas.compat import u, range, map from datetime import datetime import os -import unittest import nose @@ -86,7 +85,7 @@ def read_csv(self, *args, **kwds): return read_csv(*args, **kwds) -class ExcelReaderTests(SharedItems, unittest.TestCase): +class ExcelReaderTests(SharedItems, tm.TestCase): def test_parse_cols_int(self): _skip_if_no_openpyxl() _skip_if_no_xlrd() @@ -942,7 +941,7 @@ def test_swapped_columns(self): tm.assert_series_equal(write_frame['B'], read_frame['B']) -class OpenpyxlTests(ExcelWriterBase, unittest.TestCase): +class OpenpyxlTests(ExcelWriterBase, tm.TestCase): ext = '.xlsx' engine_name = 'openpyxl' check_skip = staticmethod(_skip_if_no_openpyxl) @@ -975,7 +974,7 @@ def test_to_excel_styleconverter(self): xlsx_style.alignment.vertical) -class XlwtTests(ExcelWriterBase, unittest.TestCase): +class XlwtTests(ExcelWriterBase, tm.TestCase): ext = '.xls' engine_name = 'xlwt' check_skip = staticmethod(_skip_if_no_xlwt) @@ -1002,13 +1001,13 @@ def test_to_excel_styleconverter(self): self.assertEquals(xlwt.Alignment.VERT_TOP, xls_style.alignment.vert) -class XlsxWriterTests(ExcelWriterBase, unittest.TestCase): +class XlsxWriterTests(ExcelWriterBase, tm.TestCase): ext = '.xlsx' engine_name = 'xlsxwriter' check_skip = staticmethod(_skip_if_no_xlsxwriter) -class OpenpyxlTests_NoMerge(ExcelWriterBase, unittest.TestCase): +class OpenpyxlTests_NoMerge(ExcelWriterBase, tm.TestCase): ext = '.xlsx' engine_name = 'openpyxl' check_skip = staticmethod(_skip_if_no_openpyxl) @@ -1017,7 +1016,7 @@ class OpenpyxlTests_NoMerge(ExcelWriterBase, unittest.TestCase): merge_cells = False -class XlwtTests_NoMerge(ExcelWriterBase, unittest.TestCase): +class XlwtTests_NoMerge(ExcelWriterBase, tm.TestCase): ext = '.xls' engine_name = 'xlwt' check_skip = staticmethod(_skip_if_no_xlwt) @@ -1026,7 +1025,7 @@ class XlwtTests_NoMerge(ExcelWriterBase, unittest.TestCase): merge_cells = False -class XlsxWriterTests_NoMerge(ExcelWriterBase, unittest.TestCase): +class XlsxWriterTests_NoMerge(ExcelWriterBase, tm.TestCase): ext = '.xlsx' engine_name = 'xlsxwriter' check_skip = staticmethod(_skip_if_no_xlsxwriter) @@ -1035,7 +1034,7 @@ class XlsxWriterTests_NoMerge(ExcelWriterBase, unittest.TestCase): merge_cells = False -class ExcelWriterEngineTests(unittest.TestCase): +class ExcelWriterEngineTests(tm.TestCase): def test_ExcelWriter_dispatch(self): with tm.assertRaisesRegexp(ValueError, 'No engine'): ExcelWriter('nothing') diff --git a/pandas/io/tests/test_ga.py b/pandas/io/tests/test_ga.py index 166917799ca82..33ead20b6815f 100644 --- a/pandas/io/tests/test_ga.py +++ b/pandas/io/tests/test_ga.py @@ -1,5 +1,4 @@ import os -import unittest from datetime import datetime import nose @@ -7,6 +6,7 @@ from pandas import DataFrame from pandas.util.testing import network, assert_frame_equal, with_connectivity_check from numpy.testing.decorators import slow +import pandas.util.testing as tm try: import httplib2 @@ -17,7 +17,7 @@ except ImportError: raise nose.SkipTest("need httplib2 and auth libs") -class TestGoogle(unittest.TestCase): +class TestGoogle(tm.TestCase): _multiprocess_can_split_ = True @@ -103,17 +103,17 @@ def test_v2_advanced_segment_format(self): advanced_segment_id = 1234567 query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id) assert query['segment'] == 'gaid::' + str(advanced_segment_id), "An integer value should be formatted as an advanced segment." - + def test_v2_dynamic_segment_format(self): dynamic_segment_id = 'medium==referral' query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=dynamic_segment_id) assert query['segment'] == 'dynamic::ga:' + str(dynamic_segment_id), "A string value with more than just letters and numbers should be formatted as a dynamic segment." - + def test_v3_advanced_segment_common_format(self): advanced_segment_id = 'aZwqR234' query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id) assert query['segment'] == 'gaid::' + str(advanced_segment_id), "A string value with just letters and numbers should be formatted as an advanced segment." - + def test_v3_advanced_segment_weird_format(self): advanced_segment_id = 'aZwqR234-s1' query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id) diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index f56c1aa042421..ec051d008b3f3 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -3,7 +3,6 @@ import os import shutil import subprocess -import unittest import numpy as np @@ -41,18 +40,18 @@ def GetTableSchema(self,table_dict): class FakeApiClient: def __init__(self): self._fakejobs = FakeJobs() - + def jobs(self): return self._fakejobs class FakeJobs: - def __init__(self): + def __init__(self): self._fakequeryresults = FakeResults() def getQueryResults(self, job_id=None, project_id=None, max_results=None, timeout_ms=None, **kwargs): - return self._fakequeryresults + return self._fakequeryresults class FakeResults: def execute(self): @@ -74,7 +73,7 @@ def execute(self): #################################################################################### -class test_gbq(unittest.TestCase): +class TestGbq(tm.TestCase): def setUp(self): with open(self.fake_job_path, 'r') as fin: self.fake_job = ast.literal_eval(fin.read()) @@ -102,7 +101,7 @@ def setUp(self): ('othello', 1603, 'brawl', 2), ('othello', 1603, "'", 17), ('othello', 1603, 'troubled', 1) - ], + ], dtype=[('corpus', 'S16'), ('corpus_date', '<i8'), ('word', 'S16'), @@ -137,29 +136,32 @@ def setUp(self): 'TRUE_BOOLEAN', 'FALSE_BOOLEAN', 'NULL_BOOLEAN']] - + @classmethod - def setUpClass(self): + def setUpClass(cls): # Integration tests require a valid bigquery token # be present in the user's home directory. This # can be generated with 'bq init' in the command line - self.dirpath = tm.get_data_path() + super(TestGbq, cls).setUpClass() + cls.dirpath = tm.get_data_path() home = os.path.expanduser("~") - self.bq_token = os.path.join(home, '.bigquery.v2.token') - self.fake_job_path = os.path.join(self.dirpath, 'gbq_fake_job.txt') - + cls.bq_token = os.path.join(home, '.bigquery.v2.token') + cls.fake_job_path = os.path.join(cls.dirpath, 'gbq_fake_job.txt') + # If we're using a valid token, make a test dataset # Note, dataset functionality is beyond the scope # of the module under test, so we rely on the command # line utility for this. - if os.path.exists(self.bq_token): + if os.path.exists(cls.bq_token): subprocess.call(['bq','mk', '-d', 'pandas_testing_dataset']) @classmethod - def tearDownClass(self): + def tearDownClass(cls): + super(TestGbq, cls).tearDownClass() + # If we're using a valid token, remove the test dataset # created. - if os.path.exists(self.bq_token): + if os.path.exists(cls.bq_token): subprocess.call(['bq', 'rm', '-r', '-f', '-d', 'pandas_testing_dataset']) @with_connectivity_check @@ -167,7 +169,7 @@ def test_valid_authentication(self): # If the user has a token file, they should recieve a client from gbq._authenticate if not os.path.exists(self.bq_token): raise nose.SkipTest('Skipped because authentication information is not available.') - + self.assertTrue(gbq._authenticate is not None, 'Authentication To GBQ Failed') @with_connectivity_check @@ -205,14 +207,14 @@ def test_data_small(self): 'An element in the result DataFrame didn\'t match the sample set') def test_index_column(self): - # A user should be able to specify an index column for return + # A user should be able to specify an index column for return result_frame = gbq._parse_data(FakeClient(), self.fake_job, index_col='word') correct_frame = DataFrame(self.correct_data_small) correct_frame.set_index('word', inplace=True) self.assertTrue(result_frame.index.name == correct_frame.index.name) def test_column_order(self): - # A User should be able to specify the order in which columns are returned in the dataframe + # A User should be able to specify the order in which columns are returned in the dataframe col_order = ['corpus_date', 'word_count', 'corpus', 'word'] result_frame = gbq._parse_data(FakeClient(), self.fake_job, col_order=col_order) tm.assert_index_equal(result_frame.columns, DataFrame(self.correct_data_small)[col_order].columns) @@ -279,8 +281,8 @@ def test_download_all_data_types(self): @with_connectivity_check def test_table_exists(self): # Given a table name in the format {dataset}.{tablename}, if a table exists, - # the GetTableReference should accurately indicate this. - # This could possibly change in future implementations of bq, + # the GetTableReference should accurately indicate this. + # This could possibly change in future implementations of bq, # but it is the simplest way to provide users with appropriate # error messages regarding schemas. if not os.path.exists(self.bq_token): @@ -309,7 +311,7 @@ def test_upload_new_table_schema_error(self): df = DataFrame(self.correct_data_small) with self.assertRaises(gbq.SchemaMissing): gbq.to_gbq(df, 'pandas_testing_dataset.test_database', schema=None, col_order=None, if_exists='fail') - + @with_connectivity_check def test_upload_replace_schema_error(self): # Attempting to replace an existing table without specifying a schema should fail @@ -319,7 +321,7 @@ def test_upload_replace_schema_error(self): df = DataFrame(self.correct_data_small) with self.assertRaises(gbq.SchemaMissing): gbq.to_gbq(df, 'pandas_testing_dataset.test_database', schema=None, col_order=None, if_exists='replace') - + @with_connectivity_check def test_upload_public_data_error(self): # Attempting to upload to a public, read-only, dataset should fail @@ -432,7 +434,7 @@ def test_upload_replace(self): 'contributor_ip','contributor_id','contributor_username','timestamp', 'is_minor','is_bot','reversion_id','comment','num_characters']) gbq.to_gbq(df1, 'pandas_testing_dataset.test_data5', schema=schema, col_order=None, if_exists='fail') - + array2 = [['TESTING_GBQ', 999999999, 'hi', 0, True, 9999999999, '00.000.00.000', 1, 'hola', 99999999, False, False, 1, 'Jedi', 11210]] @@ -441,7 +443,7 @@ def test_upload_replace(self): 'contributor_ip','contributor_id','contributor_username','timestamp', 'is_minor','is_bot','reversion_id','comment','num_characters']) gbq.to_gbq(df2, 'pandas_testing_dataset.test_data5', schema=schema, col_order=None, if_exists='replace') - + # Read the table and confirm the new data is all that is there a = gbq.read_gbq("SELECT * FROM pandas_testing_dataset.test_data5") self.assertTrue((a == df2).all().all()) diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py index c26048d4cf20b..893b1768b00c3 100644 --- a/pandas/io/tests/test_html.py +++ b/pandas/io/tests/test_html.py @@ -3,7 +3,6 @@ import os import re import warnings -import unittest try: from importlib import import_module @@ -85,9 +84,10 @@ def test_bs4_version_fails(): flavor='bs4') -class TestReadHtml(unittest.TestCase): +class TestReadHtml(tm.TestCase): @classmethod def setUpClass(cls): + super(TestReadHtml, cls).setUpClass() _skip_if_none_of(('bs4', 'html5lib')) def read_html(self, *args, **kwargs): @@ -582,9 +582,10 @@ def test_parse_dates_combine(self): tm.assert_frame_equal(newdf, res[0]) -class TestReadHtmlLxml(unittest.TestCase): +class TestReadHtmlLxml(tm.TestCase): @classmethod def setUpClass(cls): + super(TestReadHtmlLxml, cls).setUpClass() _skip_if_no('lxml') def read_html(self, *args, **kwargs): diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py index 6d392eb265752..084bc63188e2b 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/test_json/test_pandas.py @@ -2,7 +2,6 @@ from pandas.compat import range, lrange, StringIO from pandas import compat import os -import unittest import numpy as np @@ -27,7 +26,7 @@ _mixed_frame = _frame.copy() -class TestPandasContainer(unittest.TestCase): +class TestPandasContainer(tm.TestCase): def setUp(self): self.dirpath = tm.get_data_path() diff --git a/pandas/io/tests/test_json_norm.py b/pandas/io/tests/test_json_norm.py index e96a89e71f12d..8084446d2d246 100644 --- a/pandas/io/tests/test_json_norm.py +++ b/pandas/io/tests/test_json_norm.py @@ -1,5 +1,4 @@ import nose -import unittest from pandas import DataFrame import numpy as np @@ -15,7 +14,7 @@ def _assert_equal_data(left, right): tm.assert_frame_equal(left, right) -class TestJSONNormalize(unittest.TestCase): +class TestJSONNormalize(tm.TestCase): def setUp(self): self.state_data = [ @@ -165,7 +164,7 @@ def test_record_prefix(self): tm.assert_frame_equal(result, expected) -class TestNestedToRecord(unittest.TestCase): +class TestNestedToRecord(tm.TestCase): def test_flat_stays_flat(self): recs = [dict(flat1=1,flat2=2), diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 28c541e3735c9..1563406b1f8af 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -1,5 +1,4 @@ import nose -import unittest import datetime import numpy as np @@ -44,7 +43,7 @@ def check_arbitrary(a, b): assert(a == b) -class Test(unittest.TestCase): +class TestPackers(tm.TestCase): def setUp(self): self.path = '__%s__.msg' % tm.rands(10) @@ -57,7 +56,7 @@ def encode_decode(self, x, **kwargs): to_msgpack(p, x, **kwargs) return read_msgpack(p, **kwargs) -class TestAPI(Test): +class TestAPI(TestPackers): def test_string_io(self): @@ -94,7 +93,7 @@ def test_iterator_with_string_io(self): for i, result in enumerate(read_msgpack(s,iterator=True)): tm.assert_frame_equal(result,dfs[i]) -class TestNumpy(Test): +class TestNumpy(TestPackers): def test_numpy_scalar_float(self): x = np.float32(np.random.rand()) @@ -187,7 +186,7 @@ def test_list_mixed(self): x_rec = self.encode_decode(x) tm.assert_almost_equal(x,x_rec) -class TestBasic(Test): +class TestBasic(TestPackers): def test_timestamp(self): @@ -219,7 +218,7 @@ def test_timedeltas(self): self.assert_(i == i_rec) -class TestIndex(Test): +class TestIndex(TestPackers): def setUp(self): super(TestIndex, self).setUp() @@ -273,7 +272,7 @@ def test_unicode(self): #self.assert_(i.equals(i_rec)) -class TestSeries(Test): +class TestSeries(TestPackers): def setUp(self): super(TestSeries, self).setUp() @@ -312,7 +311,7 @@ def test_basic(self): assert_series_equal(i, i_rec) -class TestNDFrame(Test): +class TestNDFrame(TestPackers): def setUp(self): super(TestNDFrame, self).setUp() @@ -374,7 +373,7 @@ def test_iterator(self): check_arbitrary(packed, l[i]) -class TestSparse(Test): +class TestSparse(TestPackers): def _check_roundtrip(self, obj, comparator, **kwargs): diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 84736f16e7cba..563e9c136cad2 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -6,7 +6,6 @@ import os import sys import re -import unittest import nose import platform @@ -2049,7 +2048,7 @@ def test_catch_too_many_names(self): tm.assertRaises(Exception, read_csv, StringIO(data), header=0, names=['a', 'b', 'c', 'd']) -class TestPythonParser(ParserTests, unittest.TestCase): +class TestPythonParser(ParserTests, tm.TestCase): def test_negative_skipfooter_raises(self): text = """#foo,a,b,c #foo,a,b,c @@ -2364,7 +2363,7 @@ def test_iteration_open_handle(self): tm.assert_series_equal(result, expected) -class TestFwfColspaceSniffing(unittest.TestCase): +class TestFwfColspaceSniffing(tm.TestCase): def test_full_file(self): # File with all values test = '''index A B C @@ -2464,7 +2463,7 @@ def test_variable_width_unicode(self): header=None, encoding='utf8')) -class TestCParserHighMemory(ParserTests, unittest.TestCase): +class TestCParserHighMemory(ParserTests, tm.TestCase): def read_csv(self, *args, **kwds): kwds = kwds.copy() @@ -2504,7 +2503,7 @@ def test_usecols(self): raise nose.SkipTest("Usecols is not supported in C High Memory engine.") -class TestCParserLowMemory(ParserTests, unittest.TestCase): +class TestCParserLowMemory(ParserTests, tm.TestCase): def read_csv(self, *args, **kwds): kwds = kwds.copy() @@ -2831,7 +2830,7 @@ def test_invalid_c_parser_opts_with_not_c_parser(self): engine)): read_csv(StringIO(data), engine=engine, **kwargs) -class TestParseSQL(unittest.TestCase): +class TestParseSQL(tm.TestCase): def test_convert_sql_column_floats(self): arr = np.array([1.5, None, 3, 4.2], dtype=object) diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index ea769a0515a78..b70248d1ef3f4 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -5,7 +5,6 @@ from datetime import datetime, timedelta import operator import pickle as pkl -import unittest import nose import os @@ -24,7 +23,7 @@ def _read_pickle(vf, encoding=None, compat=False): with open(vf,'rb') as fh: pc.load(fh, encoding=encoding, compat=compat) -class TestPickle(unittest.TestCase): +class TestPickle(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index ba69f7a834dad..78d9dcb1fb888 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -1,5 +1,4 @@ import nose -import unittest import sys import os import warnings @@ -118,7 +117,7 @@ def compat_assert_produces_warning(w,f): f() -class TestHDFStore(unittest.TestCase): +class TestHDFStore(tm.TestCase): def setUp(self): warnings.filterwarnings(action='ignore', category=FutureWarning) diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index f135a3619e03c..38770def8eb7c 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -1,5 +1,4 @@ from __future__ import print_function -import unittest import sqlite3 import sys @@ -52,7 +51,7 @@ def _skip_if_no_MySQLdb(): except ImportError: raise nose.SkipTest('MySQLdb not installed, skipping') -class TestSQLite(unittest.TestCase): +class TestSQLite(tm.TestCase): def setUp(self): self.db = sqlite3.connect(':memory:') @@ -243,7 +242,7 @@ def test_onecolumn_of_integer(self): tm.assert_frame_equal(result,mono_df) -class TestMySQL(unittest.TestCase): +class TestMySQL(tm.TestCase): def setUp(self): _skip_if_no_MySQLdb() @@ -487,8 +486,5 @@ def test_keyword_as_column_names(self): if __name__ == '__main__': - # unittest.main() - # nose.runmodule(argv=[__file__,'-vvs','-x', '--pdb-failure'], - # exit=False) nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 0a87ada7097cb..76dae396c04ed 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -2,7 +2,6 @@ from datetime import datetime import os -import unittest import warnings import nose @@ -15,7 +14,7 @@ from pandas.util.misc import is_little_endian from pandas import compat -class StataTests(unittest.TestCase): +class TestStata(tm.TestCase): def setUp(self): # Unit test datasets for dta7 - dta9 (old stata formats 104, 105 and 107) can be downloaded from: diff --git a/pandas/io/tests/test_wb.py b/pandas/io/tests/test_wb.py index 60b4d8d462723..9549372823af8 100644 --- a/pandas/io/tests/test_wb.py +++ b/pandas/io/tests/test_wb.py @@ -1,42 +1,47 @@ import nose import pandas +from pandas.compat import u from pandas.util.testing import network from pandas.util.testing import assert_frame_equal from numpy.testing.decorators import slow from pandas.io.wb import search, download +import pandas.util.testing as tm +class TestWB(tm.TestCase): -@slow -@network -def test_wdi_search(): - raise nose.SkipTest("skipping for now") - expected = {u('id'): {2634: u('GDPPCKD'), - 4649: u('NY.GDP.PCAP.KD'), - 4651: u('NY.GDP.PCAP.KN'), - 4653: u('NY.GDP.PCAP.PP.KD')}, - u('name'): {2634: u('GDP per Capita, constant US$, ' - 'millions'), - 4649: u('GDP per capita (constant 2000 US$)'), - 4651: u('GDP per capita (constant LCU)'), - 4653: u('GDP per capita, PPP (constant 2005 ' - 'international $)')}} - result = search('gdp.*capita.*constant').ix[:, :2] - expected = pandas.DataFrame(expected) - expected.index = result.index - assert_frame_equal(result, expected) - - -@slow -@network -def test_wdi_download(): - raise nose.SkipTest("skipping for now") - expected = {'GDPPCKN': {(u('United States'), u('2003')): u('40800.0735367688'), (u('Canada'), u('2004')): u('37857.1261134552'), (u('United States'), u('2005')): u('42714.8594790102'), (u('Canada'), u('2003')): u('37081.4575704003'), (u('United States'), u('2004')): u('41826.1728310667'), (u('Mexico'), u('2003')): u('72720.0691255285'), (u('Mexico'), u('2004')): u('74751.6003347038'), (u('Mexico'), u('2005')): u('76200.2154469437'), (u('Canada'), u('2005')): u('38617.4563629611')}, 'GDPPCKD': {(u('United States'), u('2003')): u('40800.0735367688'), (u('Canada'), u('2004')): u('34397.055116118'), (u('United States'), u('2005')): u('42714.8594790102'), (u('Canada'), u('2003')): u('33692.2812368928'), (u('United States'), u('2004')): u('41826.1728310667'), (u('Mexico'), u('2003')): u('7608.43848670658'), (u('Mexico'), u('2004')): u('7820.99026814334'), (u('Mexico'), u('2005')): u('7972.55364129367'), (u('Canada'), u('2005')): u('35087.8925933298')}} - expected = pandas.DataFrame(expected) - result = download(country=['CA', 'MX', 'US', 'junk'], indicator=['GDPPCKD', - 'GDPPCKN', 'junk'], start=2003, end=2005) - expected.index = result.index - assert_frame_equal(result, pandas.DataFrame(expected)) + @slow + @network + def test_wdi_search(self): + raise nose.SkipTest + + expected = {u('id'): {2634: u('GDPPCKD'), + 4649: u('NY.GDP.PCAP.KD'), + 4651: u('NY.GDP.PCAP.KN'), + 4653: u('NY.GDP.PCAP.PP.KD')}, + u('name'): {2634: u('GDP per Capita, constant US$, ' + 'millions'), + 4649: u('GDP per capita (constant 2000 US$)'), + 4651: u('GDP per capita (constant LCU)'), + 4653: u('GDP per capita, PPP (constant 2005 ' + 'international $)')}} + result = search('gdp.*capita.*constant').ix[:, :2] + expected = pandas.DataFrame(expected) + expected.index = result.index + assert_frame_equal(result, expected) + + + @slow + @network + def test_wdi_download(self): + raise nose.SkipTest + + expected = {'GDPPCKN': {(u('United States'), u('2003')): u('40800.0735367688'), (u('Canada'), u('2004')): u('37857.1261134552'), (u('United States'), u('2005')): u('42714.8594790102'), (u('Canada'), u('2003')): u('37081.4575704003'), (u('United States'), u('2004')): u('41826.1728310667'), (u('Mexico'), u('2003')): u('72720.0691255285'), (u('Mexico'), u('2004')): u('74751.6003347038'), (u('Mexico'), u('2005')): u('76200.2154469437'), (u('Canada'), u('2005')): u('38617.4563629611')}, 'GDPPCKD': {(u('United States'), u('2003')): u('40800.0735367688'), (u('Canada'), u('2004')): u('34397.055116118'), (u('United States'), u('2005')): u('42714.8594790102'), (u('Canada'), u('2003')): u('33692.2812368928'), (u('United States'), u('2004')): u('41826.1728310667'), (u('Mexico'), u('2003')): u('7608.43848670658'), (u('Mexico'), u('2004')): u('7820.99026814334'), (u('Mexico'), u('2005')): u('7972.55364129367'), (u('Canada'), u('2005')): u('35087.8925933298')}} + expected = pandas.DataFrame(expected) + result = download(country=['CA', 'MX', 'US', 'junk'], indicator=['GDPPCKD', + 'GDPPCKN', 'junk'], start=2003, end=2005) + expected.index = result.index + assert_frame_equal(result, pandas.DataFrame(expected)) if __name__ == '__main__': diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 21ab1c4354316..86fc4598fc1c8 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -5,7 +5,6 @@ import operator import pickle -import unittest from pandas.core.series import Series from pandas.core.common import notnull @@ -23,7 +22,7 @@ def assert_sp_array_equal(left, right): assert(left.fill_value == right.fill_value) -class TestSparseArray(unittest.TestCase): +class TestSparseArray(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py index f820142a6e71d..8cbebad61c068 100644 --- a/pandas/sparse/tests/test_libsparse.py +++ b/pandas/sparse/tests/test_libsparse.py @@ -1,5 +1,3 @@ -from unittest import TestCase - from pandas import Series import nose @@ -235,7 +233,7 @@ def _check_case(xloc, xlen, yloc, ylen, eloc, elen): check_cases(_check_case) -class TestBlockIndex(TestCase): +class TestBlockIndex(tm.TestCase): def test_equals(self): index = BlockIndex(10, [0, 4], [2, 5]) @@ -274,7 +272,7 @@ def test_to_block_index(self): self.assert_(index.to_block_index() is index) -class TestIntIndex(TestCase): +class TestIntIndex(tm.TestCase): def test_equals(self): index = IntIndex(10, [0, 1, 2, 3, 4]) @@ -299,7 +297,7 @@ def test_to_int_index(self): self.assert_(index.to_int_index() is index) -class TestSparseOperators(TestCase): +class TestSparseOperators(tm.TestCase): def _nan_op_tests(self, sparse_op, python_op): def _check_case(xloc, xlen, yloc, ylen, eloc, elen): diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index b3f2a8b3b8136..bd05a7093fd7c 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -1,6 +1,5 @@ # pylint: disable-msg=E1101,W0612 -from unittest import TestCase import operator from datetime import datetime @@ -119,7 +118,7 @@ def assert_sp_panel_equal(left, right, exact_indices=True): assert(item in left) -class TestSparseSeries(TestCase, +class TestSparseSeries(tm.TestCase, test_series.CheckNameIntegration): _multiprocess_can_split_ = True @@ -742,11 +741,11 @@ def test_combine_first(self): assert_sp_series_equal(result, expected) -class TestSparseTimeSeries(TestCase): +class TestSparseTimeSeries(tm.TestCase): pass -class TestSparseDataFrame(TestCase, test_frame.SafeForSparse): +class TestSparseDataFrame(tm.TestCase, test_frame.SafeForSparse): klass = SparseDataFrame _multiprocess_can_split_ = True @@ -1562,7 +1561,7 @@ def panel_data3(): }, index=index) -class TestSparsePanel(TestCase, +class TestSparsePanel(tm.TestCase, test_panel.SafeForLongAndSparse, test_panel.SafeForSparse): _multiprocess_can_split_ = True diff --git a/pandas/stats/tests/common.py b/pandas/stats/tests/common.py index 2866a36bc435a..717eb51292796 100644 --- a/pandas/stats/tests/common.py +++ b/pandas/stats/tests/common.py @@ -2,13 +2,14 @@ from datetime import datetime import string -import unittest import nose import numpy as np from pandas import DataFrame, bdate_range from pandas.util.testing import assert_almost_equal # imported in other tests +import pandas.util.testing as tm + N = 100 K = 4 @@ -52,7 +53,7 @@ def check_for_statsmodels(): raise nose.SkipTest('no statsmodels') -class BaseTest(unittest.TestCase): +class BaseTest(tm.TestCase): def setUp(self): check_for_scipy() check_for_statsmodels() diff --git a/pandas/stats/tests/test_math.py b/pandas/stats/tests/test_math.py index 008fffdc1db06..32ec2ff2c0853 100644 --- a/pandas/stats/tests/test_math.py +++ b/pandas/stats/tests/test_math.py @@ -1,4 +1,3 @@ -import unittest import nose from datetime import datetime @@ -26,7 +25,7 @@ _have_statsmodels = False -class TestMath(unittest.TestCase): +class TestMath(tm.TestCase): _nan_locs = np.arange(20, 40) _inf_locs = np.array([]) diff --git a/pandas/stats/tests/test_moments.py b/pandas/stats/tests/test_moments.py index 5c7112a2b0981..7381d4c1ae0b4 100644 --- a/pandas/stats/tests/test_moments.py +++ b/pandas/stats/tests/test_moments.py @@ -1,4 +1,3 @@ -import unittest import nose import sys import functools @@ -24,7 +23,7 @@ def _skip_if_no_scipy(): except ImportError: raise nose.SkipTest("no scipy.stats") -class TestMoments(unittest.TestCase): +class TestMoments(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/stats/tests/test_ols.py b/pandas/stats/tests/test_ols.py index 69a101021f27d..476dec8c19435 100644 --- a/pandas/stats/tests/test_ols.py +++ b/pandas/stats/tests/test_ols.py @@ -9,7 +9,6 @@ from datetime import datetime from pandas import compat from distutils.version import LooseVersion -import unittest import nose import numpy as np from numpy.testing.decorators import slow @@ -70,6 +69,7 @@ class TestOLS(BaseTest): @classmethod def setUpClass(cls): + super(TestOLS, cls).setUpClass() try: import matplotlib as mpl mpl.use('Agg', warn=False) @@ -252,7 +252,7 @@ def test_ols_object_dtype(self): summary = repr(model) -class TestOLSMisc(unittest.TestCase): +class TestOLSMisc(tm.TestCase): _multiprocess_can_split_ = True @@ -260,7 +260,8 @@ class TestOLSMisc(unittest.TestCase): For test coverage with faux data ''' @classmethod - def setupClass(cls): + def setUpClass(cls): + super(TestOLSMisc, cls).setUpClass() if not _have_statsmodels: raise nose.SkipTest("no statsmodels") @@ -804,7 +805,7 @@ def _period_slice(panelModel, i): return slice(L, R) -class TestOLSFilter(unittest.TestCase): +class TestOLSFilter(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 6458d7c31d689..2cbccbaf5c66b 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -1,5 +1,4 @@ from pandas.compat import range -import unittest import numpy as np @@ -10,7 +9,7 @@ import pandas.util.testing as tm -class TestMatch(unittest.TestCase): +class TestMatch(tm.TestCase): _multiprocess_can_split_ = True def test_ints(self): @@ -30,7 +29,7 @@ def test_strings(self): self.assert_(np.array_equal(result, expected)) -class TestUnique(unittest.TestCase): +class TestUnique(tm.TestCase): _multiprocess_can_split_ = True def test_ints(self): @@ -63,7 +62,7 @@ def test_on_index_object(self): tm.assert_almost_equal(result, expected) -class TestValueCounts(unittest.TestCase): +class TestValueCounts(tm.TestCase): _multiprocess_can_split_ = True def test_value_counts(self): @@ -86,7 +85,7 @@ def test_value_counts_bins(self): result = algos.value_counts(s, bins=2, sort=False) self.assertEqual(result.tolist(), [2, 2]) - self.assertEqual(result.index[0], 0.997) + self.assertEqual(result.index[0], 0.997) self.assertEqual(result.index[1], 2.5) def test_value_counts_dtypes(self): diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 5d5a269b90428..3cb3528b6fff4 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -1,11 +1,10 @@ import re -import unittest import numpy as np import pandas.compat as compat from pandas.compat import u from pandas.core.base import FrozenList, FrozenNDArray from pandas.util.testing import assertRaisesRegexp, assert_isinstance - +import pandas.util.testing as tm class CheckStringMixin(object): def test_string_methods_dont_fail(self): @@ -63,7 +62,7 @@ def check_result(self, result, expected, klass=None): self.assertEqual(result, expected) -class TestFrozenList(CheckImmutable, CheckStringMixin, unittest.TestCase): +class TestFrozenList(CheckImmutable, CheckStringMixin, tm.TestCase): mutable_methods = ('extend', 'pop', 'remove', 'insert') unicode_container = FrozenList([u("\u05d0"), u("\u05d1"), "c"]) @@ -89,7 +88,7 @@ def test_inplace(self): self.check_result(r, self.lst) -class TestFrozenNDArray(CheckImmutable, CheckStringMixin, unittest.TestCase): +class TestFrozenNDArray(CheckImmutable, CheckStringMixin, tm.TestCase): mutable_methods = ('put', 'itemset', 'fill') unicode_container = FrozenNDArray([u("\u05d0"), u("\u05d1"), "c"]) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index f41f6a9858b47..7f7af41b635b6 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -2,7 +2,6 @@ from datetime import datetime from pandas.compat import range, lrange, u -import unittest import nose import re @@ -17,7 +16,7 @@ import pandas.util.testing as tm -class TestCategorical(unittest.TestCase): +class TestCategorical(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 7b4ea855f2f9d..2dd82870e697c 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -1,6 +1,5 @@ from datetime import datetime import re -import unittest import nose from nose.tools import assert_equal @@ -350,7 +349,7 @@ def test_ensure_int32(): assert(result.dtype == np.int32) -class TestEnsureNumeric(unittest.TestCase): +class TestEnsureNumeric(tm.TestCase): def test_numeric_values(self): # Test integer self.assertEqual(nanops._ensure_numeric(1), 1, 'Failed for int') @@ -457,7 +456,7 @@ def test_is_recompilable(): assert not com.is_re_compilable(f) -class TestTake(unittest.TestCase): +class TestTake(tm.TestCase): # standard incompatible fill error fill_error = re.compile("Incompatible type for fill_value") diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 6284e4551e167..7d392586c159b 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -1,7 +1,6 @@ from __future__ import print_function # pylint: disable-msg=W0612,E1101 -import unittest import nose from numpy.random import randn @@ -48,7 +47,7 @@ _mixed2_panel = Panel(dict(ItemA=_mixed2, ItemB=(_mixed2 + 3))) -class TestExpressions(unittest.TestCase): +class TestExpressions(tm.TestCase): _multiprocess_can_split_ = False @@ -341,7 +340,6 @@ def testit(): testit() if __name__ == '__main__': - # unittest.main() import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 8e23176e9d005..7abe9b8be552e 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -6,7 +6,6 @@ import itertools import os import sys -import unittest from textwrap import dedent import warnings @@ -57,7 +56,7 @@ def has_expanded_repr(df): return False -class TestDataFrameFormatting(unittest.TestCase): +class TestDataFrameFormatting(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -1622,7 +1621,7 @@ def test_to_latex(self): """ self.assertEqual(withoutindex_result, withoutindex_expected) -class TestSeriesFormatting(unittest.TestCase): +class TestSeriesFormatting(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -1809,7 +1808,7 @@ def test_mixed_datetime64(self): self.assertTrue('2012-01-01' in result) -class TestEngFormatter(unittest.TestCase): +class TestEngFormatter(tm.TestCase): _multiprocess_can_split_ = True def test_eng_float_formatter(self): @@ -2014,7 +2013,7 @@ def _three_digit_exp(): return '%.4g' % 1.7e8 == '1.7e+008' -class TestFloatArrayFormatter(unittest.TestCase): +class TestFloatArrayFormatter(tm.TestCase): def test_misc(self): obj = fmt.FloatArrayFormatter(np.array([], dtype=np.float64)) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 280b0476efe98..456c41849d06d 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -7,7 +7,6 @@ import operator import re import csv -import unittest import nose import functools import itertools @@ -1872,7 +1871,7 @@ def test_add_prefix_suffix(self): self.assert_(np.array_equal(with_suffix.columns, expected)) -class TestDataFrame(unittest.TestCase, CheckIndexing, +class TestDataFrame(tm.TestCase, CheckIndexing, SafeForSparse): klass = DataFrame @@ -12026,15 +12025,18 @@ def check_raise_on_panel4d_with_multiindex(self, parser, engine): pd.eval('p4d + 1', parser=parser, engine=engine) -class TestDataFrameQueryNumExprPandas(unittest.TestCase): +class TestDataFrameQueryNumExprPandas(tm.TestCase): + @classmethod def setUpClass(cls): + super(TestDataFrameQueryNumExprPandas, cls).setUpClass() cls.engine = 'numexpr' cls.parser = 'pandas' skip_if_no_ne() @classmethod def tearDownClass(cls): + super(TestDataFrameQueryNumExprPandas, cls).tearDownClass() del cls.engine, cls.parser def test_date_query_method(self): @@ -12253,17 +12255,15 @@ def test_chained_cmp_and_in(self): class TestDataFrameQueryNumExprPython(TestDataFrameQueryNumExprPandas): + @classmethod def setUpClass(cls): + super(TestDataFrameQueryNumExprPython, cls).setUpClass() cls.engine = 'numexpr' cls.parser = 'python' skip_if_no_ne(cls.engine) cls.frame = _frame.copy() - @classmethod - def tearDownClass(cls): - del cls.frame, cls.engine, cls.parser - def test_date_query_method(self): engine, parser = self.engine, self.parser df = DataFrame(randn(5, 3)) @@ -12356,28 +12356,22 @@ def test_nested_scope(self): class TestDataFrameQueryPythonPandas(TestDataFrameQueryNumExprPandas): + @classmethod def setUpClass(cls): + super(TestDataFrameQueryPythonPandas, cls).setUpClass() cls.engine = 'python' cls.parser = 'pandas' cls.frame = _frame.copy() - @classmethod - def tearDownClass(cls): - del cls.frame, cls.engine, cls.parser - - class TestDataFrameQueryPythonPython(TestDataFrameQueryNumExprPython): + @classmethod def setUpClass(cls): + super(TestDataFrameQueryPythonPython, cls).setUpClass() cls.engine = cls.parser = 'python' cls.frame = _frame.copy() - @classmethod - def tearDownClass(cls): - del cls.frame, cls.engine, cls.parser - - PARSERS = 'python', 'pandas' ENGINES = 'python', 'numexpr' @@ -12510,17 +12504,15 @@ def test_object_array_eq_ne(self): yield self.check_object_array_eq_ne, parser, engine -class TestDataFrameEvalNumExprPandas(unittest.TestCase): +class TestDataFrameEvalNumExprPandas(tm.TestCase): + @classmethod def setUpClass(cls): + super(TestDataFrameEvalNumExprPandas, cls).setUpClass() cls.engine = 'numexpr' cls.parser = 'pandas' skip_if_no_ne() - @classmethod - def tearDownClass(cls): - del cls.engine, cls.parser - def setUp(self): self.frame = DataFrame(randn(10, 3), columns=list('abc')) @@ -12540,38 +12532,29 @@ def test_bool_arith_expr(self): class TestDataFrameEvalNumExprPython(TestDataFrameEvalNumExprPandas): + @classmethod def setUpClass(cls): + super(TestDataFrameEvalNumExprPython, cls).setUpClass() cls.engine = 'numexpr' cls.parser = 'python' skip_if_no_ne() - @classmethod - def tearDownClass(cls): - del cls.engine, cls.parser - - class TestDataFrameEvalPythonPandas(TestDataFrameEvalNumExprPandas): + @classmethod def setUpClass(cls): + super(TestDataFrameEvalPythonPandas, cls).setUpClass() cls.engine = 'python' cls.parser = 'pandas' - @classmethod - def tearDownClass(cls): - del cls.engine, cls.parser - - class TestDataFrameEvalPythonPython(TestDataFrameEvalNumExprPython): + @classmethod def setUpClass(cls): + super(TestDataFrameEvalPythonPython, cls).tearDownClass() cls.engine = cls.parser = 'python' - @classmethod - def tearDownClass(cls): - del cls.engine, cls.parser - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index cf9b2d174faea..97e25f105db70 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -2,7 +2,6 @@ from datetime import datetime, timedelta import operator -import unittest import nose import numpy as np @@ -350,7 +349,7 @@ def test_head_tail(self): self._compare(o.head(-3), o.head(7)) self._compare(o.tail(-3), o.tail(7)) -class TestSeries(unittest.TestCase, Generic): +class TestSeries(tm.TestCase, Generic): _typ = Series _comparator = lambda self, x, y: assert_series_equal(x,y) @@ -576,7 +575,7 @@ def test_interp_nonmono_raise(self): with tm.assertRaises(ValueError): s.interpolate(method='krogh') -class TestDataFrame(unittest.TestCase, Generic): +class TestDataFrame(tm.TestCase, Generic): _typ = DataFrame _comparator = lambda self, x, y: assert_frame_equal(x,y) @@ -769,11 +768,41 @@ def test_spline(self): expected = Series([1, 2, 3, 4, 5, 6, 7]) assert_series_equal(result, expected) - -class TestPanel(unittest.TestCase, Generic): +class TestPanel(tm.TestCase, Generic): _typ = Panel _comparator = lambda self, x, y: assert_panel_equal(x, y) + +class TestNDFrame(tm.TestCase): + # tests that don't fit elsewhere + + def test_squeeze(self): + # noop + for s in [ tm.makeFloatSeries(), tm.makeStringSeries(), tm.makeObjectSeries() ]: + tm.assert_series_equal(s.squeeze(),s) + for df in [ tm.makeTimeDataFrame() ]: + tm.assert_frame_equal(df.squeeze(),df) + for p in [ tm.makePanel() ]: + tm.assert_panel_equal(p.squeeze(),p) + for p4d in [ tm.makePanel4D() ]: + tm.assert_panel4d_equal(p4d.squeeze(),p4d) + + # squeezing + df = tm.makeTimeDataFrame().reindex(columns=['A']) + tm.assert_series_equal(df.squeeze(),df['A']) + + p = tm.makePanel().reindex(items=['ItemA']) + tm.assert_frame_equal(p.squeeze(),p['ItemA']) + + p = tm.makePanel().reindex(items=['ItemA'],minor_axis=['A']) + tm.assert_series_equal(p.squeeze(),p.ix['ItemA',:,'A']) + + p4d = tm.makePanel4D().reindex(labels=['label1']) + tm.assert_panel_equal(p4d.squeeze(),p4d['label1']) + + p4d = tm.makePanel4D().reindex(labels=['label1'],items=['ItemA']) + tm.assert_frame_equal(p4d.squeeze(),p4d.ix['label1','ItemA']) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 8f48c2d5951f2..7c8b17fb14abe 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1,7 +1,6 @@ import nose import os import string -import unittest from distutils.version import LooseVersion from datetime import datetime, date, timedelta @@ -30,7 +29,7 @@ def _skip_if_no_scipy(): @tm.mplskip -class TestSeriesPlots(unittest.TestCase): +class TestSeriesPlots(tm.TestCase): def setUp(self): import matplotlib as mpl self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1') @@ -351,7 +350,7 @@ def test_dup_datetime_index_plot(self): @tm.mplskip -class TestDataFramePlots(unittest.TestCase): +class TestDataFramePlots(tm.TestCase): def setUp(self): import matplotlib as mpl self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1') @@ -449,7 +448,7 @@ def test_plot_xy(self): # columns.inferred_type == 'mixed' # TODO add MultiIndex test - + @slow def test_xcompat(self): import pandas as pd @@ -540,10 +539,10 @@ def test_plot_scatter(self): df = DataFrame(randn(6, 4), index=list(string.ascii_letters[:6]), columns=['x', 'y', 'z', 'four']) - + _check_plot_works(df.plot, x='x', y='y', kind='scatter') _check_plot_works(df.plot, x=1, y=2, kind='scatter') - + with tm.assertRaises(ValueError): df.plot(x='x', kind='scatter') with tm.assertRaises(ValueError): @@ -946,7 +945,7 @@ def test_invalid_kind(self): @tm.mplskip -class TestDataFrameGroupByPlots(unittest.TestCase): +class TestDataFrameGroupByPlots(tm.TestCase): def tearDown(self): tm.close() diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index d9bdc3adcd041..76fee1702d64a 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1,6 +1,5 @@ from __future__ import print_function import nose -import unittest from numpy.testing.decorators import slow @@ -49,7 +48,7 @@ def commonSetUp(self): index=self.dateRange) -class TestGroupBy(unittest.TestCase): +class TestGroupBy(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 9a18e3c8562f6..d102ac999cab0 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -5,7 +5,6 @@ import operator import pickle import re -import unittest import nose import warnings import os @@ -34,7 +33,7 @@ from pandas import _np_version_under1p7 -class TestIndex(unittest.TestCase): +class TestIndex(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -691,7 +690,7 @@ def test_join_self(self): self.assert_(res is joined) -class TestFloat64Index(unittest.TestCase): +class TestFloat64Index(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -784,7 +783,7 @@ def test_astype(self): self.check_is_index(result) -class TestInt64Index(unittest.TestCase): +class TestInt64Index(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -1203,7 +1202,7 @@ def test_slice_keep_name(self): self.assertEqual(idx.name, idx[1:].name) -class TestMultiIndex(unittest.TestCase): +class TestMultiIndex(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -1381,8 +1380,10 @@ def test_set_value_keeps_names(self): columns=['one', 'two', 'three', 'four'], index=idx) df = df.sortlevel() + self.assert_(df._is_copy is False) self.assertEqual(df.index.names, ('Name', 'Number')) df = df.set_value(('grethe', '4'), 'one', 99.34) + self.assert_(df._is_copy is False) self.assertEqual(df.index.names, ('Name', 'Number')) def test_names(self): diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 02b5812c3e653..44160609235df 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1,5 +1,4 @@ # pylint: disable-msg=W0612,E1101 -import unittest import nose import itertools import warnings @@ -84,7 +83,7 @@ def _axify(obj, key, axis): return k -class TestIndexing(unittest.TestCase): +class TestIndexing(tm.TestCase): _multiprocess_can_split_ = True @@ -1049,6 +1048,8 @@ def f(name,df2): return Series(np.arange(df2.shape[0]),name=df2.index.values[0]).reindex(f_index) new_df = pd.concat([ f(name,df2) for name, df2 in grp ],axis=1).T + # we are actually operating on a copy here + # but in this case, that's ok for name, df2 in grp: new_vals = np.arange(df2.shape[0]) df.ix[name, 'new_col'] = new_vals @@ -1769,7 +1770,8 @@ def f(): 'c' : [42,42,2,3,4,42,6]}) def f(): - df[df.a.str.startswith('o')]['c'] = 42 + indexer = df.a.str.startswith('o') + df[indexer]['c'] = 42 self.assertRaises(com.SettingWithCopyError, f) df['c'][df.a.str.startswith('o')] = 42 assert_frame_equal(df,expected) @@ -1785,7 +1787,8 @@ def f(): # warnings pd.set_option('chained_assignment','warn') df = DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]}) - df.loc[0]['A'] = 111 + with tm.assert_produces_warning(expected_warning=com.SettingWithCopyWarning): + df.loc[0]['A'] = 111 # make sure that _is_copy is picked up reconstruction # GH5475 @@ -1797,6 +1800,55 @@ def f(): df2["B"] = df2["A"] df2["B"] = df2["A"] + # a suprious raise as we are setting the entire column here + # GH5597 + pd.set_option('chained_assignment','raise') + from string import ascii_letters as letters + + def random_text(nobs=100): + df = [] + for i in range(nobs): + idx= np.random.randint(len(letters), size=2) + idx.sort() + df.append([letters[idx[0]:idx[1]]]) + + return DataFrame(df, columns=['letters']) + + df = random_text(100000) + + # always a copy + x = df.iloc[[0,1,2]] + self.assert_(x._is_copy is True) + x = df.iloc[[0,1,2,4]] + self.assert_(x._is_copy is True) + + # explicity copy + indexer = df.letters.apply(lambda x : len(x) > 10) + df = df.ix[indexer].copy() + self.assert_(df._is_copy is False) + df['letters'] = df['letters'].apply(str.lower) + + # implicity take + df = random_text(100000) + indexer = df.letters.apply(lambda x : len(x) > 10) + df = df.ix[indexer] + self.assert_(df._is_copy is True) + df.loc[:,'letters'] = df['letters'].apply(str.lower) + + # this will raise + #df['letters'] = df['letters'].apply(str.lower) + + df = random_text(100000) + indexer = df.letters.apply(lambda x : len(x) > 10) + df.ix[indexer,'letters'] = df.ix[indexer,'letters'].apply(str.lower) + + # an identical take, so no copy + df = DataFrame({'a' : [1]}).dropna() + self.assert_(df._is_copy is False) + df['a'] += 1 + + pd.set_option('chained_assignment','warn') + def test_float64index_slicing_bug(self): # GH 5557, related to slicing a float index ser = {256: 2321.0, 1: 78.0, 2: 2716.0, 3: 0.0, 4: 369.0, 5: 0.0, 6: 269.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 3536.0, 11: 0.0, 12: 24.0, 13: 0.0, 14: 931.0, 15: 0.0, 16: 101.0, 17: 78.0, 18: 9643.0, 19: 0.0, 20: 0.0, 21: 0.0, 22: 63761.0, 23: 0.0, 24: 446.0, 25: 0.0, 26: 34773.0, 27: 0.0, 28: 729.0, 29: 78.0, 30: 0.0, 31: 0.0, 32: 3374.0, 33: 0.0, 34: 1391.0, 35: 0.0, 36: 361.0, 37: 0.0, 38: 61808.0, 39: 0.0, 40: 0.0, 41: 0.0, 42: 6677.0, 43: 0.0, 44: 802.0, 45: 0.0, 46: 2691.0, 47: 0.0, 48: 3582.0, 49: 0.0, 50: 734.0, 51: 0.0, 52: 627.0, 53: 70.0, 54: 2584.0, 55: 0.0, 56: 324.0, 57: 0.0, 58: 605.0, 59: 0.0, 60: 0.0, 61: 0.0, 62: 3989.0, 63: 10.0, 64: 42.0, 65: 0.0, 66: 904.0, 67: 0.0, 68: 88.0, 69: 70.0, 70: 8172.0, 71: 0.0, 72: 0.0, 73: 0.0, 74: 64902.0, 75: 0.0, 76: 347.0, 77: 0.0, 78: 36605.0, 79: 0.0, 80: 379.0, 81: 70.0, 82: 0.0, 83: 0.0, 84: 3001.0, 85: 0.0, 86: 1630.0, 87: 7.0, 88: 364.0, 89: 0.0, 90: 67404.0, 91: 9.0, 92: 0.0, 93: 0.0, 94: 7685.0, 95: 0.0, 96: 1017.0, 97: 0.0, 98: 2831.0, 99: 0.0, 100: 2963.0, 101: 0.0, 102: 854.0, 103: 0.0, 104: 0.0, 105: 0.0, 106: 0.0, 107: 0.0, 108: 0.0, 109: 0.0, 110: 0.0, 111: 0.0, 112: 0.0, 113: 0.0, 114: 0.0, 115: 0.0, 116: 0.0, 117: 0.0, 118: 0.0, 119: 0.0, 120: 0.0, 121: 0.0, 122: 0.0, 123: 0.0, 124: 0.0, 125: 0.0, 126: 67744.0, 127: 22.0, 128: 264.0, 129: 0.0, 260: 197.0, 268: 0.0, 265: 0.0, 269: 0.0, 261: 0.0, 266: 1198.0, 267: 0.0, 262: 2629.0, 258: 775.0, 257: 0.0, 263: 0.0, 259: 0.0, 264: 163.0, 250: 10326.0, 251: 0.0, 252: 1228.0, 253: 0.0, 254: 2769.0, 255: 0.0} diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index b0a64d282e814..701b240479a62 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -1,6 +1,5 @@ # pylint: disable=W0102 -import unittest import nose import numpy as np @@ -88,7 +87,7 @@ def create_singleblockmanager(blocks): return SingleBlockManager(blocks, [items]) -class TestBlock(unittest.TestCase): +class TestBlock(tm.TestCase): _multiprocess_can_split_ = True @@ -234,7 +233,7 @@ def test_repr(self): pass -class TestBlockManager(unittest.TestCase): +class TestBlockManager(tm.TestCase): _multiprocess_can_split_ = True @@ -586,9 +585,6 @@ def test_missing_unicode_key(self): pass # this is the expected exception if __name__ == '__main__': - # unittest.main() import nose - # nose.runmodule(argv=[__file__,'-vvs','-x', '--pdb-failure'], - # exit=False) nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index bd431843a6b20..151f222d7357a 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -1,6 +1,5 @@ # pylint: disable-msg=W0612,E1101,W0141 import nose -import unittest from numpy.random import randn import numpy as np @@ -21,7 +20,7 @@ import pandas.index as _index -class TestMultiLevel(unittest.TestCase): +class TestMultiLevel(tm.TestCase): _multiprocess_can_split_ = True @@ -1860,9 +1859,6 @@ def test_multiindex_set_index(self): if __name__ == '__main__': - # unittest.main() import nose - # nose.runmodule(argv=[__file__,'-vvs','-x', '--pdb-failure'], - # exit=False) nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_ndframe.py b/pandas/tests/test_ndframe.py deleted file mode 100644 index edafeb64af98e..0000000000000 --- a/pandas/tests/test_ndframe.py +++ /dev/null @@ -1,47 +0,0 @@ -import unittest - -import numpy as np - -from pandas.core.generic import NDFrame -import pandas.util.testing as t - - -class TestNDFrame(unittest.TestCase): - - _multiprocess_can_split_ = True - - def setUp(self): - tdf = t.makeTimeDataFrame() - self.ndf = NDFrame(tdf._data) - - def test_squeeze(self): - # noop - for s in [ t.makeFloatSeries(), t.makeStringSeries(), t.makeObjectSeries() ]: - t.assert_series_equal(s.squeeze(),s) - for df in [ t.makeTimeDataFrame() ]: - t.assert_frame_equal(df.squeeze(),df) - for p in [ t.makePanel() ]: - t.assert_panel_equal(p.squeeze(),p) - for p4d in [ t.makePanel4D() ]: - t.assert_panel4d_equal(p4d.squeeze(),p4d) - - # squeezing - df = t.makeTimeDataFrame().reindex(columns=['A']) - t.assert_series_equal(df.squeeze(),df['A']) - - p = t.makePanel().reindex(items=['ItemA']) - t.assert_frame_equal(p.squeeze(),p['ItemA']) - - p = t.makePanel().reindex(items=['ItemA'],minor_axis=['A']) - t.assert_series_equal(p.squeeze(),p.ix['ItemA',:,'A']) - - p4d = t.makePanel4D().reindex(labels=['label1']) - t.assert_panel_equal(p4d.squeeze(),p4d['label1']) - - p4d = t.makePanel4D().reindex(labels=['label1'],items=['ItemA']) - t.assert_frame_equal(p4d.squeeze(),p4d.ix['label1','ItemA']) - -if __name__ == '__main__': - import nose - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 96f14a09180ed..1122b4c7dcfb4 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -2,7 +2,6 @@ from datetime import datetime import operator -import unittest import nose import numpy as np @@ -811,7 +810,7 @@ def test_set_value(self): tm.add_nans(_panel) -class TestPanel(unittest.TestCase, PanelTests, CheckIndexing, +class TestPanel(tm.TestCase, PanelTests, CheckIndexing, SafeForLongAndSparse, SafeForSparse): _multiprocess_can_split_ = True @@ -1782,7 +1781,7 @@ def test_update_raise(self): **{'raise_conflict': True}) -class TestLongPanel(unittest.TestCase): +class TestLongPanel(tm.TestCase): """ LongPanel no longer exists, but... """ diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 4d5d29e08fa9f..ea6602dbb0be6 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -2,7 +2,6 @@ from pandas.compat import range, lrange import os import operator -import unittest import nose import numpy as np @@ -543,7 +542,7 @@ def test_set_value(self): self.assert_(com.is_float_dtype(res3['l4'].values)) -class TestPanel4d(unittest.TestCase, CheckIndexing, SafeForSparse, +class TestPanel4d(tm.TestCase, CheckIndexing, SafeForSparse, SafeForLongAndSparse): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_panelnd.py b/pandas/tests/test_panelnd.py index 3c86998c5630a..92083afb38f41 100644 --- a/pandas/tests/test_panelnd.py +++ b/pandas/tests/test_panelnd.py @@ -1,7 +1,6 @@ from datetime import datetime import os import operator -import unittest import nose import numpy as np @@ -19,7 +18,7 @@ import pandas.util.testing as tm -class TestPanelnd(unittest.TestCase): +class TestPanelnd(tm.TestCase): def setUp(self): pass diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py index c4e75fcb41d45..c6eb9739cf3d2 100644 --- a/pandas/tests/test_reshape.py +++ b/pandas/tests/test_reshape.py @@ -3,7 +3,6 @@ from datetime import datetime, timedelta import operator import os -import unittest import nose @@ -23,7 +22,7 @@ _multiprocess_can_split_ = True -class TestMelt(unittest.TestCase): +class TestMelt(tm.TestCase): def setUp(self): self.df = tm.makeTimeDataFrame()[:10] @@ -148,7 +147,7 @@ def test_multiindex(self): self.assertEqual(res.columns.tolist(), ['CAP', 'low', 'value']) -class TestGetDummies(unittest.TestCase): +class TestGetDummies(tm.TestCase): def test_basic(self): s_list = list('abc') s_series = Series(s_list) @@ -199,7 +198,7 @@ def test_include_na(self): exp_just_na = DataFrame(Series(1.0,index=[0]),columns=[nan]) assert_array_equal(res_just_na.values, exp_just_na.values) -class TestConvertDummies(unittest.TestCase): +class TestConvertDummies(tm.TestCase): def test_convert_dummies(self): df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'], @@ -225,7 +224,7 @@ def test_convert_dummies(self): tm.assert_frame_equal(result2, expected2) -class TestLreshape(unittest.TestCase): +class TestLreshape(tm.TestCase): def test_pairs(self): data = {'birthdt': ['08jan2009', '20dec2008', '30dec2008', diff --git a/pandas/tests/test_rplot.py b/pandas/tests/test_rplot.py index d59b182b77d4c..ddfce477a320d 100644 --- a/pandas/tests/test_rplot.py +++ b/pandas/tests/test_rplot.py @@ -1,5 +1,4 @@ from pandas.compat import range -import unittest import pandas.tools.rplot as rplot import pandas.util.testing as tm from pandas import read_csv @@ -33,7 +32,7 @@ def between(a, b, x): @tm.mplskip -class TestUtilityFunctions(unittest.TestCase): +class TestUtilityFunctions(tm.TestCase): """ Tests for RPlot utility functions. """ @@ -102,7 +101,7 @@ def test_sequence_layers(self): @tm.mplskip -class TestTrellis(unittest.TestCase): +class TestTrellis(tm.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/tips.csv') self.data = read_csv(path, sep=',') @@ -150,7 +149,7 @@ def test_trellis_cols_rows(self): @tm.mplskip -class TestScaleGradient(unittest.TestCase): +class TestScaleGradient(tm.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') self.data = read_csv(path, sep=',') @@ -170,7 +169,7 @@ def test_gradient(self): @tm.mplskip -class TestScaleGradient2(unittest.TestCase): +class TestScaleGradient2(tm.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') self.data = read_csv(path, sep=',') @@ -198,7 +197,7 @@ def test_gradient2(self): @tm.mplskip -class TestScaleRandomColour(unittest.TestCase): +class TestScaleRandomColour(tm.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') self.data = read_csv(path, sep=',') @@ -218,7 +217,7 @@ def test_random_colour(self): @tm.mplskip -class TestScaleConstant(unittest.TestCase): +class TestScaleConstant(tm.TestCase): def test_scale_constant(self): scale = rplot.ScaleConstant(1.0) self.assertEqual(scale(None, None), 1.0) @@ -226,7 +225,7 @@ def test_scale_constant(self): self.assertEqual(scale(None, None), "test") -class TestScaleSize(unittest.TestCase): +class TestScaleSize(tm.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') self.data = read_csv(path, sep=',') @@ -247,7 +246,7 @@ def f(): @tm.mplskip -class TestRPlot(unittest.TestCase): +class TestRPlot(tm.TestCase): def test_rplot1(self): import matplotlib.pyplot as plt path = os.path.join(curpath(), 'data/tips.csv') @@ -295,4 +294,5 @@ def test_rplot_iris(self): if __name__ == '__main__': + import unittest unittest.main() diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 5a6f790d5851e..0f67fce8b3314 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -2,7 +2,6 @@ from datetime import datetime, timedelta import operator -import unittest import string from itertools import product, starmap from distutils.version import LooseVersion @@ -246,7 +245,7 @@ def test_to_sparse_pass_name(self): self.assertEquals(result.name, self.ts.name) -class TestNanops(unittest.TestCase): +class TestNanops(tm.TestCase): _multiprocess_can_split_ = True @@ -298,7 +297,7 @@ class SafeForSparse(object): _ts = tm.makeTimeSeries() -class TestSeries(unittest.TestCase, CheckNameIntegration): +class TestSeries(tm.TestCase, CheckNameIntegration): _multiprocess_can_split_ = True @@ -5336,7 +5335,7 @@ def test_numpy_unique(self): result = np.unique(self.ts) -class TestSeriesNonUnique(unittest.TestCase): +class TestSeriesNonUnique(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_stats.py b/pandas/tests/test_stats.py index e3533afc71e95..7e2144e801122 100644 --- a/pandas/tests/test_stats.py +++ b/pandas/tests/test_stats.py @@ -1,6 +1,5 @@ from pandas import compat import nose -import unittest from numpy import nan import numpy as np @@ -11,9 +10,10 @@ from pandas.util.testing import (assert_frame_equal, assert_series_equal, assert_almost_equal) +import pandas.util.testing as tm -class TestRank(unittest.TestCase): +class TestRank(tm.TestCase): _multiprocess_can_split_ = True s = Series([1, 3, 4, 2, nan, 2, 1, 5, nan, 3]) df = DataFrame({'A': s, 'B': s}) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index c75fec44c9f24..15193e44bd5cf 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -4,7 +4,6 @@ import os import operator import re -import unittest import warnings import nose @@ -26,7 +25,7 @@ import pandas.core.strings as strings -class TestStringMethods(unittest.TestCase): +class TestStringMethods(tm.TestCase): _multiprocess_can_split_ = True @@ -480,7 +479,7 @@ def test_extract(self): tm.assert_frame_equal(result, exp) # no groups - s = Series(['A1', 'B2', 'C3']) + s = Series(['A1', 'B2', 'C3']) f = lambda: s.str.extract('[ABC][123]') self.assertRaises(ValueError, f) @@ -492,7 +491,7 @@ def test_extract(self): result = s.str.extract('(_)') exp = Series([NA, NA, NA]) tm.assert_series_equal(result, exp) - + # two groups, no matches result = s.str.extract('(_)(_)') exp = DataFrame([[NA, NA], [NA, NA], [NA, NA]]) diff --git a/pandas/tests/test_tseries.py b/pandas/tests/test_tseries.py index c1eda35417fd7..7215b9dbf934b 100644 --- a/pandas/tests/test_tseries.py +++ b/pandas/tests/test_tseries.py @@ -1,17 +1,15 @@ -import unittest from numpy import nan import numpy as np from pandas import Index, isnull, Timestamp from pandas.util.testing import assert_almost_equal -import pandas.util.testing as common +import pandas.util.testing as tm from pandas.compat import range, lrange, zip import pandas.lib as lib import pandas.algos as algos from datetime import datetime - -class TestTseriesUtil(unittest.TestCase): +class TestTseriesUtil(tm.TestCase): _multiprocess_can_split_ = True def test_combineFunc(self): @@ -421,7 +419,7 @@ def test_series_bin_grouper(): assert_almost_equal(counts, exp_counts) -class TestBinGroupers(unittest.TestCase): +class TestBinGroupers(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -560,7 +558,7 @@ def test_try_parse_dates(): assert(np.array_equal(result, expected)) -class TestTypeInference(unittest.TestCase): +class TestTypeInference(tm.TestCase): _multiprocess_can_split_ = True def test_length_zero(self): @@ -653,11 +651,11 @@ def test_to_object_array_tuples(self): pass -class TestMoments(unittest.TestCase): +class TestMoments(tm.TestCase): pass -class TestReducer(unittest.TestCase): +class TestReducer(tm.TestCase): def test_int_index(self): from pandas.core.series import Series @@ -685,7 +683,7 @@ def test_int_index(self): assert_almost_equal(result, expected) -class TestTsUtil(unittest.TestCase): +class TestTsUtil(tm.TestCase): def test_min_valid(self): # Ensure that Timestamp.min is a valid Timestamp Timestamp(Timestamp.min) @@ -700,7 +698,7 @@ def test_to_datetime_bijective(self): self.assertEqual(Timestamp(Timestamp.max.to_pydatetime()).value/1000, Timestamp.max.value/1000) self.assertEqual(Timestamp(Timestamp.min.to_pydatetime()).value/1000, Timestamp.min.value/1000) -class TestPeriodField(unittest.TestCase): +class TestPeriodField(tm.TestCase): def test_get_period_field_raises_on_out_of_range(self): from pandas import tslib diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index eec134ebeb990..e3b448b650767 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1,7 +1,6 @@ # pylint: disable=E1103 import nose -import unittest from datetime import datetime from numpy.random import randn @@ -39,7 +38,7 @@ def get_test_data(ngroups=NGROUPS, n=N): return arr -class TestMerge(unittest.TestCase): +class TestMerge(tm.TestCase): _multiprocess_can_split_ = True @@ -818,7 +817,7 @@ def _check_merge(x, y): assert_frame_equal(result, expected, check_names=False) # TODO check_names on merge? -class TestMergeMulti(unittest.TestCase): +class TestMergeMulti(tm.TestCase): def setUp(self): self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], @@ -1082,7 +1081,7 @@ def _join_by_hand(a, b, how='left'): return a_re.reindex(columns=result_columns) -class TestConcatenate(unittest.TestCase): +class TestConcatenate(tm.TestCase): _multiprocess_can_split_ = True @@ -1840,7 +1839,7 @@ def test_concat_mixed_types_fails(self): with tm.assertRaisesRegexp(TypeError, "Cannot concatenate.+"): concat([df, df[0]], axis=1) -class TestOrderedMerge(unittest.TestCase): +class TestOrderedMerge(tm.TestCase): def setUp(self): self.left = DataFrame({'key': ['a', 'c', 'e'], diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 6c18b6582c4cc..0ede6bd2bd46d 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -1,5 +1,4 @@ import datetime -import unittest import numpy as np from numpy.testing import assert_equal @@ -12,7 +11,7 @@ import pandas.util.testing as tm -class TestPivotTable(unittest.TestCase): +class TestPivotTable(tm.TestCase): _multiprocess_can_split_ = True @@ -320,7 +319,7 @@ def test_margins_no_values_two_row_two_cols(self): self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0]) -class TestCrosstab(unittest.TestCase): +class TestCrosstab(tm.TestCase): def setUp(self): df = DataFrame({'A': ['foo', 'foo', 'foo', 'foo', diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py index 3200f336376af..ba51946173d5f 100644 --- a/pandas/tools/tests/test_tile.py +++ b/pandas/tools/tests/test_tile.py @@ -1,6 +1,5 @@ import os import nose -import unittest import numpy as np from pandas.compat import zip @@ -17,7 +16,7 @@ from numpy.testing import assert_equal, assert_almost_equal -class TestCut(unittest.TestCase): +class TestCut(tm.TestCase): def test_simple(self): data = np.ones(5) @@ -120,9 +119,9 @@ def test_inf_handling(self): result = cut(data, [-np.inf, 2, 4, np.inf]) result_ser = cut(data_ser, [-np.inf, 2, 4, np.inf]) - + ex_levels = ['(-inf, 2]', '(2, 4]', '(4, inf]'] - + np.testing.assert_array_equal(result.levels, ex_levels) np.testing.assert_array_equal(result_ser.levels, ex_levels) self.assertEquals(result[5], '(4, inf]') diff --git a/pandas/tools/tests/test_tools.py b/pandas/tools/tests/test_tools.py index b57ff68c97e3d..2c70427f79559 100644 --- a/pandas/tools/tests/test_tools.py +++ b/pandas/tools/tests/test_tools.py @@ -1,22 +1,23 @@ -# import unittest - from pandas import DataFrame from pandas.tools.describe import value_range import numpy as np +import pandas.util.testing as tm + +class TestTools(tm.TestCase): -def test_value_range(): - df = DataFrame(np.random.randn(5, 5)) - df.ix[0, 2] = -5 - df.ix[2, 0] = 5 + def test_value_range(self): + df = DataFrame(np.random.randn(5, 5)) + df.ix[0, 2] = -5 + df.ix[2, 0] = 5 - res = value_range(df) + res = value_range(df) - assert(res['Minimum'] == -5) - assert(res['Maximum'] == 5) + self.assert_(res['Minimum'] == -5) + self.assert_(res['Maximum'] == 5) - df.ix[0, 1] = np.NaN + df.ix[0, 1] = np.NaN - assert(res['Minimum'] == -5) - assert(res['Maximum'] == 5) + self.assert_(res['Minimum'] == -5) + self.assert_(res['Maximum'] == 5) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index 66ae52983b692..36cfb4870a8fe 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -1,8 +1,6 @@ import os import locale import codecs -import unittest - import nose import numpy as np @@ -16,7 +14,7 @@ LOCALE_OVERRIDE = os.environ.get('LOCALE_OVERRIDE', None) -class TestCartesianProduct(unittest.TestCase): +class TestCartesianProduct(tm.TestCase): def test_simple(self): x, y = list('ABC'), [1, 22] @@ -26,9 +24,11 @@ def test_simple(self): assert_equal(result, expected) -class TestLocaleUtils(unittest.TestCase): +class TestLocaleUtils(tm.TestCase): + @classmethod def setUpClass(cls): + super(TestLocaleUtils, cls).setUpClass() cls.locales = tm.get_locales() if not cls.locales: @@ -39,6 +39,7 @@ def setUpClass(cls): @classmethod def tearDownClass(cls): + super(TestLocaleUtils, cls).tearDownClass() del cls.locales def test_get_locales(self): diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tseries/tests/test_converter.py index 7cb84b5134a9a..29137f9cb3e50 100644 --- a/pandas/tseries/tests/test_converter.py +++ b/pandas/tseries/tests/test_converter.py @@ -1,12 +1,12 @@ from datetime import datetime, time, timedelta, date import sys import os -import unittest import nose import numpy as np from pandas.compat import u +import pandas.util.testing as tm try: import pandas.tseries.converter as converter @@ -18,7 +18,7 @@ def test_timtetonum_accepts_unicode(): assert(converter.time2num("00:01") == converter.time2num(u("00:01"))) -class TestDateTimeConverter(unittest.TestCase): +class TestDateTimeConverter(tm.TestCase): def setUp(self): self.dtc = converter.DatetimeConverter() diff --git a/pandas/tseries/tests/test_cursor.py b/pandas/tseries/tests/test_cursor.py deleted file mode 100644 index fc02a83cbe639..0000000000000 --- a/pandas/tseries/tests/test_cursor.py +++ /dev/null @@ -1,196 +0,0 @@ - -""" - -class TestNewOffsets(unittest.TestCase): - - def test_yearoffset(self): - off = lib.YearOffset(dayoffset=0, biz=0, anchor=datetime(2002,1,1)) - - for i in range(500): - t = lib.Timestamp(off.ts) - self.assert_(t.day == 1) - self.assert_(t.month == 1) - self.assert_(t.year == 2002 + i) - next(off) - - for i in range(499, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t.day == 1) - self.assert_(t.month == 1) - self.assert_(t.year == 2002 + i) - - off = lib.YearOffset(dayoffset=-1, biz=0, anchor=datetime(2002,1,1)) - - for i in range(500): - t = lib.Timestamp(off.ts) - self.assert_(t.month == 12) - self.assert_(t.day == 31) - self.assert_(t.year == 2001 + i) - next(off) - - for i in range(499, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t.month == 12) - self.assert_(t.day == 31) - self.assert_(t.year == 2001 + i) - - off = lib.YearOffset(dayoffset=-1, biz=-1, anchor=datetime(2002,1,1)) - - stack = [] - - for i in range(500): - t = lib.Timestamp(off.ts) - stack.append(t) - self.assert_(t.month == 12) - self.assert_(t.day == 31 or t.day == 30 or t.day == 29) - self.assert_(t.year == 2001 + i) - self.assert_(t.weekday() < 5) - next(off) - - for i in range(499, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t == stack.pop()) - self.assert_(t.month == 12) - self.assert_(t.day == 31 or t.day == 30 or t.day == 29) - self.assert_(t.year == 2001 + i) - self.assert_(t.weekday() < 5) - - def test_monthoffset(self): - off = lib.MonthOffset(dayoffset=0, biz=0, anchor=datetime(2002,1,1)) - - for i in range(12): - t = lib.Timestamp(off.ts) - self.assert_(t.day == 1) - self.assert_(t.month == 1 + i) - self.assert_(t.year == 2002) - next(off) - - for i in range(11, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t.day == 1) - self.assert_(t.month == 1 + i) - self.assert_(t.year == 2002) - - off = lib.MonthOffset(dayoffset=-1, biz=0, anchor=datetime(2002,1,1)) - - for i in range(12): - t = lib.Timestamp(off.ts) - self.assert_(t.day >= 28) - self.assert_(t.month == (12 if i == 0 else i)) - self.assert_(t.year == 2001 + (i != 0)) - next(off) - - for i in range(11, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t.day >= 28) - self.assert_(t.month == (12 if i == 0 else i)) - self.assert_(t.year == 2001 + (i != 0)) - - off = lib.MonthOffset(dayoffset=-1, biz=-1, anchor=datetime(2002,1,1)) - - stack = [] - - for i in range(500): - t = lib.Timestamp(off.ts) - stack.append(t) - if t.month != 2: - self.assert_(t.day >= 28) - else: - self.assert_(t.day >= 26) - self.assert_(t.weekday() < 5) - next(off) - - for i in range(499, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t == stack.pop()) - if t.month != 2: - self.assert_(t.day >= 28) - else: - self.assert_(t.day >= 26) - self.assert_(t.weekday() < 5) - - for i in (-2, -1, 1, 2): - for j in (-1, 0, 1): - off1 = lib.MonthOffset(dayoffset=i, biz=j, stride=12, - anchor=datetime(2002,1,1)) - off2 = lib.YearOffset(dayoffset=i, biz=j, - anchor=datetime(2002,1,1)) - - for k in range(500): - self.assert_(off1.ts == off2.ts) - next(off1) - next(off2) - - for k in range(500): - self.assert_(off1.ts == off2.ts) - off1.prev() - off2.prev() - - def test_dayoffset(self): - off = lib.DayOffset(biz=0, anchor=datetime(2002,1,1)) - - us_in_day = 1e6 * 60 * 60 * 24 - - t0 = lib.Timestamp(off.ts) - for i in range(500): - next(off) - t1 = lib.Timestamp(off.ts) - self.assert_(t1.value - t0.value == us_in_day) - t0 = t1 - - t0 = lib.Timestamp(off.ts) - for i in range(499, -1, -1): - off.prev() - t1 = lib.Timestamp(off.ts) - self.assert_(t0.value - t1.value == us_in_day) - t0 = t1 - - off = lib.DayOffset(biz=1, anchor=datetime(2002,1,1)) - - t0 = lib.Timestamp(off.ts) - for i in range(500): - next(off) - t1 = lib.Timestamp(off.ts) - self.assert_(t1.weekday() < 5) - self.assert_(t1.value - t0.value == us_in_day or - t1.value - t0.value == 3 * us_in_day) - t0 = t1 - - t0 = lib.Timestamp(off.ts) - for i in range(499, -1, -1): - off.prev() - t1 = lib.Timestamp(off.ts) - self.assert_(t1.weekday() < 5) - self.assert_(t0.value - t1.value == us_in_day or - t0.value - t1.value == 3 * us_in_day) - t0 = t1 - - - def test_dayofmonthoffset(self): - for week in (-1, 0, 1): - for day in (0, 2, 4): - off = lib.DayOfMonthOffset(week=-1, day=day, - anchor=datetime(2002,1,1)) - - stack = [] - - for i in range(500): - t = lib.Timestamp(off.ts) - stack.append(t) - self.assert_(t.weekday() == day) - next(off) - - for i in range(499, -1, -1): - off.prev() - t = lib.Timestamp(off.ts) - self.assert_(t == stack.pop()) - self.assert_(t.weekday() == day) - - -""" diff --git a/pandas/tseries/tests/test_daterange.py b/pandas/tseries/tests/test_daterange.py index 3b40e75194d11..0af3b6281530b 100644 --- a/pandas/tseries/tests/test_daterange.py +++ b/pandas/tseries/tests/test_daterange.py @@ -1,7 +1,6 @@ from datetime import datetime from pandas.compat import range import pickle -import unittest import nose import numpy as np @@ -39,7 +38,7 @@ def eq_gen_range(kwargs, expected): START, END = datetime(2009, 1, 1), datetime(2010, 1, 1) -class TestGenRangeGeneration(unittest.TestCase): +class TestGenRangeGeneration(tm.TestCase): def test_generate(self): rng1 = list(generate_range(START, END, offset=datetools.bday)) rng2 = list(generate_range(START, END, time_rule='B')) @@ -68,7 +67,7 @@ def test_3(self): []) -class TestDateRange(unittest.TestCase): +class TestDateRange(tm.TestCase): def setUp(self): self.rng = bdate_range(START, END) @@ -410,7 +409,7 @@ def test_range_closed(self): self.assert_(expected_right.equals(right)) -class TestCustomDateRange(unittest.TestCase): +class TestCustomDateRange(tm.TestCase): def setUp(self): _skip_if_no_cday() diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index f1078f44efd13..ad9c93592a26c 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -2,7 +2,6 @@ from pandas.compat import range import sys import os -import unittest import nose @@ -18,7 +17,7 @@ import pandas.lib as lib from pandas import _np_version_under1p7 - +import pandas.util.testing as tm def test_to_offset_multiple(): freqstr = '2h30min' @@ -87,7 +86,7 @@ def test_anchored_shortcuts(): _dti = DatetimeIndex -class TestFrequencyInference(unittest.TestCase): +class TestFrequencyInference(tm.TestCase): def test_raise_if_too_few(self): index = _dti(['12/31/1998', '1/3/1999']) @@ -159,7 +158,7 @@ def test_week_of_month(self): for day in days: for i in range(1, 5): self._check_generated_range('1/1/2000', 'WOM-%d%s' % (i, day)) - + def test_week_of_month_fake(self): #All of these dates are on same day of week and are 4 or 5 weeks apart index = DatetimeIndex(["2013-08-27","2013-10-01","2013-10-29","2013-11-26"]) diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 008bda0a676bf..047bd244fef93 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -2,7 +2,6 @@ from dateutil.relativedelta import relativedelta from pandas.compat import range from pandas import compat -import unittest import nose from nose.tools import assert_raises @@ -95,7 +94,7 @@ def test_to_m8(): ### DateOffset Tests ##### -class TestBase(unittest.TestCase): +class TestBase(tm.TestCase): _offset = None def test_apply_out_of_range(self): @@ -1304,25 +1303,25 @@ def test_get_year_end(self): self.assertEqual(makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.SUN).get_year_end(datetime(2013,1,1)), datetime(2013,9,1)) self.assertEqual(makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.FRI).get_year_end(datetime(2013,1,1)), datetime(2013,8,30)) - offset_n = FY5253(weekday=WeekDay.TUE, startingMonth=12, + offset_n = FY5253(weekday=WeekDay.TUE, startingMonth=12, variation="nearest") self.assertEqual(offset_n.get_year_end(datetime(2012,1,1)), datetime(2013,1,1)) self.assertEqual(offset_n.get_year_end(datetime(2012,1,10)), datetime(2013,1,1)) - - self.assertEqual(offset_n.get_year_end(datetime(2013,1,1)), datetime(2013,12,31)) - self.assertEqual(offset_n.get_year_end(datetime(2013,1,2)), datetime(2013,12,31)) - self.assertEqual(offset_n.get_year_end(datetime(2013,1,3)), datetime(2013,12,31)) + + self.assertEqual(offset_n.get_year_end(datetime(2013,1,1)), datetime(2013,12,31)) + self.assertEqual(offset_n.get_year_end(datetime(2013,1,2)), datetime(2013,12,31)) + self.assertEqual(offset_n.get_year_end(datetime(2013,1,3)), datetime(2013,12,31)) self.assertEqual(offset_n.get_year_end(datetime(2013,1,10)), datetime(2013,12,31)) - + JNJ = FY5253(n=1, startingMonth=12, weekday=6, variation="nearest") self.assertEqual(JNJ.get_year_end(datetime(2006, 1, 1)), datetime(2006, 12, 31)) - + def test_onOffset(self): offset_lom_aug_sat = makeFY5253NearestEndMonth(1, startingMonth=8, weekday=WeekDay.SAT) offset_lom_aug_thu = makeFY5253NearestEndMonth(1, startingMonth=8, weekday=WeekDay.THU) - offset_n = FY5253(weekday=WeekDay.TUE, startingMonth=12, + offset_n = FY5253(weekday=WeekDay.TUE, startingMonth=12, variation="nearest") - + tests = [ # From Wikipedia (see: http://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar#Saturday_nearest_the_end_of_month) # 2006-09-02 2006 September 2 @@ -1369,7 +1368,7 @@ def test_onOffset(self): #From Micron, see: http://google.brand.edgar-online.com/?sym=MU&formtypeID=7 (offset_lom_aug_thu, datetime(2012, 8, 30), True), (offset_lom_aug_thu, datetime(2011, 9, 1), True), - + (offset_n, datetime(2012, 12, 31), False), (offset_n, datetime(2013, 1, 1), True), (offset_n, datetime(2013, 1, 2), False), @@ -1379,16 +1378,16 @@ def test_onOffset(self): assertOnOffset(offset, date, expected) def test_apply(self): - date_seq_nem_8_sat = [datetime(2006, 9, 2), datetime(2007, 9, 1), - datetime(2008, 8, 30), datetime(2009, 8, 29), + date_seq_nem_8_sat = [datetime(2006, 9, 2), datetime(2007, 9, 1), + datetime(2008, 8, 30), datetime(2009, 8, 29), datetime(2010, 8, 28), datetime(2011, 9, 3)] - - JNJ = [datetime(2005, 1, 2), datetime(2006, 1, 1), - datetime(2006, 12, 31), datetime(2007, 12, 30), - datetime(2008, 12, 28), datetime(2010, 1, 3), - datetime(2011, 1, 2), datetime(2012, 1, 1), + + JNJ = [datetime(2005, 1, 2), datetime(2006, 1, 1), + datetime(2006, 12, 31), datetime(2007, 12, 30), + datetime(2008, 12, 28), datetime(2010, 1, 3), + datetime(2011, 1, 2), datetime(2012, 1, 1), datetime(2012, 12, 30)] - + DEC_SAT = FY5253(n=-1, startingMonth=12, weekday=5, variation="nearest") tests = [ @@ -1547,7 +1546,7 @@ def test_year_has_extra_week(self): def test_get_weeks(self): sat_dec_1 = makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1) sat_dec_4 = makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=4) - + self.assertEqual(sat_dec_1.get_weeks(datetime(2011, 4, 2)), [14, 13, 13, 13]) self.assertEqual(sat_dec_4.get_weeks(datetime(2011, 4, 2)), [13, 13, 13, 14]) self.assertEqual(sat_dec_1.get_weeks(datetime(2010, 12, 25)), [13, 13, 13, 13]) @@ -1558,9 +1557,9 @@ def test_onOffset(self): offset_nem_sat_aug_4 = makeFY5253NearestEndMonthQuarter(1, startingMonth=8, weekday=WeekDay.SAT, qtr_with_extra_week=4) offset_nem_thu_aug_4 = makeFY5253NearestEndMonthQuarter(1, startingMonth=8, weekday=WeekDay.THU, qtr_with_extra_week=4) - offset_n = FY5253(weekday=WeekDay.TUE, startingMonth=12, + offset_n = FY5253(weekday=WeekDay.TUE, startingMonth=12, variation="nearest", qtr_with_extra_week=4) - + tests = [ #From Wikipedia (offset_nem_sat_aug_4, datetime(2006, 9, 2), True), @@ -1622,12 +1621,12 @@ def test_offset(self): assertEq(offset, datetime(2012, 5, 31), datetime(2012, 8, 30)) assertEq(offset, datetime(2012, 5, 30), datetime(2012, 5, 31)) - - offset2 = FY5253Quarter(weekday=5, startingMonth=12, + + offset2 = FY5253Quarter(weekday=5, startingMonth=12, variation="last", qtr_with_extra_week=4) - + assertEq(offset2, datetime(2013,1,15), datetime(2013, 3, 30)) - + class TestQuarterBegin(TestBase): def test_repr(self): @@ -2281,7 +2280,7 @@ def test_compare_ticks(): assert(kls(3) != kls(4)) -class TestOffsetNames(unittest.TestCase): +class TestOffsetNames(tm.TestCase): def test_get_offset_name(self): assertRaisesRegexp(ValueError, 'Bad rule.*BusinessDays', get_offset_name, BDay(2)) @@ -2350,7 +2349,7 @@ def test_quarterly_dont_normalize(): assert(result.time() == date.time()) -class TestOffsetAliases(unittest.TestCase): +class TestOffsetAliases(tm.TestCase): def setUp(self): _offset_map.clear() @@ -2423,7 +2422,7 @@ def get_all_subclasses(cls): ret | get_all_subclasses(this_subclass) return ret -class TestCaching(unittest.TestCase): +class TestCaching(tm.TestCase): no_simple_ctr = [WeekOfMonth, FY5253, FY5253Quarter, LastWeekOfMonth] @@ -2474,7 +2473,7 @@ def test_week_of_month_index_creation(self): self.assertTrue(inst2 in _daterange_cache) -class TestReprNames(unittest.TestCase): +class TestReprNames(tm.TestCase): def test_str_for_named_is_name(self): # look at all the amazing combinations! month_prefixes = ['A', 'AS', 'BA', 'BAS', 'Q', 'BQ', 'BQS', 'QS'] diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index de6918eb8a1d1..ca0eba59fe5fe 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -6,9 +6,7 @@ """ -from unittest import TestCase from datetime import datetime, date, timedelta -import unittest from numpy.ma.testutils import assert_equal @@ -33,11 +31,9 @@ from numpy.testing import assert_array_equal -class TestPeriodProperties(TestCase): +class TestPeriodProperties(tm.TestCase): "Test properties such as year, month, weekday, etc...." # - def __init__(self, *args, **kwds): - TestCase.__init__(self, *args, **kwds) def test_quarterly_negative_ordinals(self): p = Period(ordinal=-1, freq='Q-DEC') @@ -494,12 +490,9 @@ def noWrap(item): return item -class TestFreqConversion(TestCase): +class TestFreqConversion(tm.TestCase): "Test frequency conversion of date objects" - def __init__(self, *args, **kwds): - TestCase.__init__(self, *args, **kwds) - def test_asfreq_corner(self): val = Period(freq='A', year=2007) self.assertRaises(ValueError, val.asfreq, '5t') @@ -1074,7 +1067,8 @@ def test_conv_secondly(self): assert_equal(ival_S.asfreq('S'), ival_S) -class TestPeriodIndex(TestCase): +class TestPeriodIndex(tm.TestCase): + def setUp(self): pass @@ -2168,12 +2162,9 @@ def _permute(obj): return obj.take(np.random.permutation(len(obj))) -class TestMethods(TestCase): +class TestMethods(tm.TestCase): "Base test class for MaskedArrays." - def __init__(self, *args, **kwds): - TestCase.__init__(self, *args, **kwds) - def test_add(self): dt1 = Period(freq='D', year=2008, month=1, day=1) dt2 = Period(freq='D', year=2008, month=1, day=2) @@ -2183,7 +2174,7 @@ def test_add(self): self.assertRaises(TypeError, dt1.__add__, dt2) -class TestPeriodRepresentation(unittest.TestCase): +class TestPeriodRepresentation(tm.TestCase): """ Wish to match NumPy units """ @@ -2244,7 +2235,7 @@ def test_negone_ordinals(self): repr(period) -class TestComparisons(unittest.TestCase): +class TestComparisons(tm.TestCase): def setUp(self): self.january1 = Period('2000-01', 'M') self.january2 = Period('2000-01', 'M') diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index 233c9f249ab38..e55dd96d64ca0 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -1,6 +1,5 @@ from datetime import datetime, timedelta, date, time -import unittest import nose from pandas.compat import lrange, zip @@ -27,7 +26,7 @@ def _skip_if_no_scipy(): @tm.mplskip -class TestTSPlot(unittest.TestCase): +class TestTSPlot(tm.TestCase): def setUp(self): freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q', 'Y'] idx = [period_range('12/31/1999', freq=x, periods=100) for x in freq] diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index c60d4b3fd48d1..707b052031d60 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -16,7 +16,6 @@ import pandas.tseries.offsets as offsets import pandas as pd -import unittest import nose from pandas.util.testing import (assert_series_equal, assert_almost_equal, @@ -33,7 +32,7 @@ def _skip_if_no_pytz(): raise nose.SkipTest("pytz not installed") -class TestResample(unittest.TestCase): +class TestResample(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -662,7 +661,7 @@ def _simple_pts(start, end, freq='D'): return TimeSeries(np.random.randn(len(rng)), index=rng) -class TestResamplePeriodIndex(unittest.TestCase): +class TestResamplePeriodIndex(tm.TestCase): _multiprocess_can_split_ = True @@ -1055,7 +1054,7 @@ def test_resample_doesnt_truncate(self): self.assertEquals(result.index[0], dates[0]) -class TestTimeGrouper(unittest.TestCase): +class TestTimeGrouper(tm.TestCase): def setUp(self): self.ts = Series(np.random.randn(1000), diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index df03851ca4ddb..1d34c5b91d5ed 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -2,7 +2,6 @@ from datetime import datetime, timedelta import nose -import unittest import numpy as np import pandas as pd @@ -24,7 +23,7 @@ def _skip_if_numpy_not_friendly(): if _np_version_under1p7: raise nose.SkipTest("numpy < 1.7") -class TestTimedeltas(unittest.TestCase): +class TestTimedeltas(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index a7bd2250f95e3..f2f137e18a15c 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -2,7 +2,6 @@ from datetime import datetime, time, timedelta, date import sys import os -import unittest import operator from distutils.version import LooseVersion @@ -51,7 +50,7 @@ def _skip_if_no_pytz(): raise nose.SkipTest("pytz not installed") -class TestTimeSeriesDuplicates(unittest.TestCase): +class TestTimeSeriesDuplicates(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -271,7 +270,7 @@ def assert_range_equal(left, right): assert(left.tz == right.tz) -class TestTimeSeries(unittest.TestCase): +class TestTimeSeries(tm.TestCase): _multiprocess_can_split_ = True def test_is_(self): @@ -1420,7 +1419,7 @@ def test_normalize(self): result = rng.normalize() expected = date_range('1/1/2000', periods=10, freq='D') self.assert_(result.equals(expected)) - + rng_ns = pd.DatetimeIndex(np.array([1380585623454345752, 1380585612343234312]).astype("datetime64[ns]")) rng_ns_normalized = rng_ns.normalize() expected = pd.DatetimeIndex(np.array([1380585600000000000, 1380585600000000000]).astype("datetime64[ns]")) @@ -1878,7 +1877,7 @@ def _simple_ts(start, end, freq='D'): return Series(np.random.randn(len(rng)), index=rng) -class TestDatetimeIndex(unittest.TestCase): +class TestDatetimeIndex(tm.TestCase): _multiprocess_can_split_ = True def test_hash_error(self): @@ -2217,7 +2216,7 @@ def test_join_with_period_index(self): df.columns.join(s.index, how=join) -class TestDatetime64(unittest.TestCase): +class TestDatetime64(tm.TestCase): """ Also test supoprt for datetime64[ns] in Series / DataFrame """ @@ -2431,7 +2430,7 @@ def test_slice_locs_indexerror(self): s.ix[datetime(1900, 1, 1):datetime(2100, 1, 1)] -class TestSeriesDatetime64(unittest.TestCase): +class TestSeriesDatetime64(tm.TestCase): def setUp(self): self.series = Series(date_range('1/1/2000', periods=10)) @@ -2550,7 +2549,7 @@ def test_string_index_series_name_converted(self): self.assertEquals(result.name, df.index[2]) -class TestTimestamp(unittest.TestCase): +class TestTimestamp(tm.TestCase): def test_class_ops(self): _skip_if_no_pytz() @@ -2794,7 +2793,7 @@ def test_timestamp_compare_series(self): tm.assert_series_equal(result, expected) -class TestSlicing(unittest.TestCase): +class TestSlicing(tm.TestCase): def test_slice_year(self): dti = DatetimeIndex(freq='B', start=datetime(2005, 1, 1), periods=500) diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index 083de95895d18..d82f91767d413 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -2,7 +2,6 @@ from datetime import datetime, time, timedelta, tzinfo, date import sys import os -import unittest import nose import numpy as np @@ -65,7 +64,7 @@ def dst(self, dt): fixed_off_no_name = FixedOffset(-330, None) -class TestTimeZoneSupport(unittest.TestCase): +class TestTimeZoneSupport(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): @@ -366,18 +365,18 @@ def test_infer_dst(self): tz = pytz.timezone('US/Eastern') dr = date_range(datetime(2011, 11, 6, 0), periods=5, freq=datetools.Hour()) - self.assertRaises(pytz.AmbiguousTimeError, dr.tz_localize, + self.assertRaises(pytz.AmbiguousTimeError, dr.tz_localize, tz, infer_dst=True) - + # With repeated hours, we can infer the transition - dr = date_range(datetime(2011, 11, 6, 0), periods=5, + dr = date_range(datetime(2011, 11, 6, 0), periods=5, freq=datetools.Hour(), tz=tz) - di = DatetimeIndex(['11/06/2011 00:00', '11/06/2011 01:00', - '11/06/2011 01:00', '11/06/2011 02:00', + di = DatetimeIndex(['11/06/2011 00:00', '11/06/2011 01:00', + '11/06/2011 01:00', '11/06/2011 02:00', '11/06/2011 03:00']) localized = di.tz_localize(tz, infer_dst=True) self.assert_(np.array_equal(dr, localized)) - + # When there is no dst transition, nothing special happens dr = date_range(datetime(2011, 6, 1, 0), periods=10, freq=datetools.Hour()) @@ -673,7 +672,7 @@ def test_datetimeindex_tz(self): self.assert_(idx1.equals(other)) -class TestTimeZones(unittest.TestCase): +class TestTimeZones(tm.TestCase): _multiprocess_can_split_ = True def setUp(self): diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 40dbb2d3712af..9a8c19bdc00ab 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -1,4 +1,3 @@ -import unittest import nose import numpy as np @@ -7,15 +6,12 @@ import datetime from pandas.core.api import Timestamp - from pandas.tslib import period_asfreq, period_ordinal - from pandas.tseries.frequencies import get_freq - from pandas import _np_version_under1p7 +import pandas.util.testing as tm - -class TestTimestamp(unittest.TestCase): +class TestTimestamp(tm.TestCase): def test_bounds_with_different_units(self): out_of_bounds_dates = ( '1677-09-21', @@ -61,7 +57,7 @@ def test_barely_oob_dts(self): # One us more than the maximum is an error self.assertRaises(ValueError, tslib.Timestamp, max_ts_us + one_us) -class TestDatetimeParsingWrappers(unittest.TestCase): +class TestDatetimeParsingWrappers(tm.TestCase): def test_does_not_convert_mixed_integer(self): bad_date_strings = ( '-50000', @@ -91,7 +87,7 @@ def test_does_not_convert_mixed_integer(self): ) -class TestArrayToDatetime(unittest.TestCase): +class TestArrayToDatetime(tm.TestCase): def test_parsing_valid_dates(self): arr = np.array(['01-01-2013', '01-02-2013'], dtype=object) self.assert_( @@ -194,7 +190,7 @@ def test_coerce_of_invalid_datetimes(self): ) -class TestTimestampNsOperations(unittest.TestCase): +class TestTimestampNsOperations(tm.TestCase): def setUp(self): if _np_version_under1p7: raise nose.SkipTest('numpy >= 1.7 required') @@ -224,7 +220,7 @@ def test_nanosecond_string_parsing(self): self.assertEqual(self.timestamp.value, 1367392545123456000) -class TestTslib(unittest.TestCase): +class TestTslib(tm.TestCase): def test_intraday_conversion_factors(self): self.assertEqual(period_asfreq(1, get_freq('D'), get_freq('H'), False), 24) @@ -283,7 +279,7 @@ def test_period_ordinal_business_day(self): # Tuesday self.assertEqual(11418, period_ordinal(2013, 10, 8, 0, 0, 0, 0, 0, get_freq('B'))) -class TestTomeStampOps(unittest.TestCase): +class TestTomeStampOps(tm.TestCase): def test_timestamp_and_datetime(self): self.assertEqual((Timestamp(datetime.datetime(2013, 10,13)) - datetime.datetime(2013, 10,12)).days, 1) self.assertEqual((datetime.datetime(2013, 10, 12) - Timestamp(datetime.datetime(2013, 10,13))).days, -1) diff --git a/pandas/tseries/tests/test_util.py b/pandas/tseries/tests/test_util.py index 8bf448118561d..b10c4351c8725 100644 --- a/pandas/tseries/tests/test_util.py +++ b/pandas/tseries/tests/test_util.py @@ -1,6 +1,5 @@ from pandas.compat import range import nose -import unittest import numpy as np from numpy.testing.decorators import slow @@ -14,7 +13,7 @@ from pandas.tseries.util import pivot_annual, isleapyear -class TestPivotAnnual(unittest.TestCase): +class TestPivotAnnual(tm.TestCase): """ New pandas of scikits.timeseries pivot_annual """ diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 2ea570d6f8e94..0c4e083b54eda 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -11,6 +11,7 @@ import os import subprocess import locale +import unittest from datetime import datetime from functools import wraps, partial @@ -52,6 +53,18 @@ K = 4 _RAISE_NETWORK_ERROR_DEFAULT = False +class TestCase(unittest.TestCase): + + @classmethod + def setUpClass(cls): + pd.set_option('chained_assignment','raise') + #print("setting up: {0}".format(cls)) + + @classmethod + def tearDownClass(cls): + #print("tearing down up: {0}".format(cls)) + pass + # NOTE: don't pass an NDFrame or index to this function - may not handle it # well. assert_almost_equal = _testing.assert_almost_equal
related #5581 closes #5597
https://api.github.com/repos/pandas-dev/pandas/pulls/5584
2013-11-25T14:12:59Z
2013-11-29T19:13:20Z
2013-11-29T19:13:20Z
2014-06-19T16:10:09Z
CLN/BUG: Fix stacklevel on setting with copy warning.
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index b57c353fff566..5be71afc18663 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1015,8 +1015,11 @@ def _setitem_copy(self, copy): self._is_copy = copy return self - def _check_setitem_copy(self): - """ validate if we are doing a settitem on a chained copy """ + def _check_setitem_copy(self, stacklevel=4): + """ validate if we are doing a settitem on a chained copy. + + If you call this function, be sure to set the stacklevel such that the + user will see the error *at the level of setting*""" if self._is_copy: value = config.get_option('mode.chained_assignment') @@ -1026,7 +1029,7 @@ def _check_setitem_copy(self): if value == 'raise': raise SettingWithCopyError(t) elif value == 'warn': - warnings.warn(t, SettingWithCopyWarning) + warnings.warn(t, SettingWithCopyWarning, stacklevel=stacklevel) def __delitem__(self, key): """
May not be perfect, but gets us most of the way there. Resolves [@TomAugspurger's comment](https://github.com/pydata/pandas/pull/5390#issuecomment-29166038) on #5390. Put the following in a file called `test.py`. ``` python from pandas import DataFrame def myfunction(): df = DataFrame({"A": [1, 2, 3, 4, 5], "B": [3.125, 4.12, 3.1, 6.2, 7.]}) row = df.loc[0] row["A"] = 0 def anotherfunction(): myfunction() def third_function(): anotherfunction() third_function() ``` Running it will generate this warning: ``` test.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead row["A"] = 0 ``` I don't think it's worth testing (especially because there's not necessarily a simple way to do it).
https://api.github.com/repos/pandas-dev/pandas/pulls/5581
2013-11-24T21:22:48Z
2013-11-24T22:43:43Z
2013-11-24T22:43:42Z
2014-07-16T08:41:24Z
BUG: Add separate import for numpy.ma.mrecords (GH5577)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 6a1ecfed15896..d84c0dd9ea5f7 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -200,9 +200,9 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, elif isinstance(data, dict): mgr = self._init_dict(data, index, columns, dtype=dtype) elif isinstance(data, ma.MaskedArray): - + import numpy.ma.mrecords as mrecords # masked recarray - if isinstance(data, ma.mrecords.MaskedRecords): + if isinstance(data, mrecords.MaskedRecords): mgr = _masked_rec_array_to_mgr(data, index, columns, dtype, copy)
Fixes https://github.com/pydata/pandas/issues/5577
https://api.github.com/repos/pandas-dev/pandas/pulls/5579
2013-11-23T19:06:48Z
2013-11-24T01:08:16Z
2013-11-24T01:08:16Z
2014-06-26T19:33:48Z
Win32 fix2
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 465d51cab2599..861f3a785ce22 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -488,7 +488,7 @@ def test_int_types(self): frame.to_excel(path, 'test1') reader = ExcelFile(path) recons = reader.parse('test1') - int_frame = frame.astype(int) + int_frame = frame.astype(np.int64) tm.assert_frame_equal(int_frame, recons) recons2 = read_excel(path, 'test1') tm.assert_frame_equal(int_frame, recons2) @@ -616,7 +616,7 @@ def test_roundtrip_indexlabels(self): has_index_names=self.merge_cells ).astype(np.int64) frame.index.names = ['test'] - self.assertAlmostEqual(frame.index.names, recons.index.names) + tm.assert_frame_equal(frame,recons.astype(bool)) with ensure_clean(self.ext) as path: diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 6b986fa87ccce..28c541e3735c9 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -3,6 +3,8 @@ import datetime import numpy as np +import sys +from distutils.version import LooseVersion from pandas import compat from pandas.compat import u @@ -197,6 +199,11 @@ def test_timestamp(self): def test_datetimes(self): + # fails under 2.6/win32 (np.datetime64 seems broken) + + if LooseVersion(sys.version) < '2.7': + raise nose.SkipTest('2.6 with np.datetime64 is broken') + for i in [datetime.datetime( 2013, 1, 1), datetime.datetime(2013, 1, 1, 5, 1), datetime.date(2013, 1, 1), np.datetime64(datetime.datetime(2013, 1, 5, 2, 15))]: diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 141aef3051b23..7b23b306d2927 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -65,7 +65,7 @@ def _sparse_array_op(left, right, op, name): try: fill_value = op(left.fill_value, right.fill_value) - except ZeroDivisionError: + except: fill_value = nan return SparseArray(result, sparse_index=result_index, diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py index 3d2b67f33861d..21ab1c4354316 100644 --- a/pandas/sparse/tests/test_array.py +++ b/pandas/sparse/tests/test_array.py @@ -144,10 +144,15 @@ def _check_op(op, first, second): res4 = op(first, 4) tm.assert_isinstance(res4, SparseArray) - exp = op(first.values, 4) - exp_fv = op(first.fill_value, 4) - assert_almost_equal(res4.fill_value, exp_fv) - assert_almost_equal(res4.values, exp) + + # ignore this if the actual op raises (e.g. pow) + try: + exp = op(first.values, 4) + exp_fv = op(first.fill_value, 4) + assert_almost_equal(res4.fill_value, exp_fv) + assert_almost_equal(res4.values, exp) + except (ValueError) : + pass def _check_inplace_op(op): tmp = arr1.copy() diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py index 86a43f648526b..3200f336376af 100644 --- a/pandas/tools/tests/test_tile.py +++ b/pandas/tools/tests/test_tile.py @@ -116,7 +116,7 @@ def test_na_handling(self): def test_inf_handling(self): data = np.arange(6) - data_ser = Series(data) + data_ser = Series(data,dtype='int64') result = cut(data, [-np.inf, 2, 4, np.inf]) result_ser = cut(data_ser, [-np.inf, 2, 4, np.inf]) diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index 0c1d6d1c1cbab..99fa1eaba79cc 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -222,7 +222,9 @@ def _format_levels(bins, prec, right=True, def _format_label(x, precision=3): fmt_str = '%%.%dg' % precision - if com.is_float(x): + if np.isinf(x): + return str(x) + elif com.is_float(x): frac, whole = np.modf(x) sgn = '-' if x < 0 else '' whole = abs(whole)
https://api.github.com/repos/pandas-dev/pandas/pulls/5574
2013-11-22T22:42:13Z
2013-11-22T23:30:13Z
2013-11-22T23:30:13Z
2014-06-19T00:06:19Z
DOC/TST: Series.reorder_levels() can take names; added tests
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index d84c0dd9ea5f7..e1dafc60e64d8 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2794,9 +2794,9 @@ def reorder_levels(self, order, axis=0): Parameters ---------- - order : list of int + order : list of int or list of str List representing new level order. Reference level by number - not by key. + (position) or by key (label). axis : int Where to reorder levels. diff --git a/pandas/core/series.py b/pandas/core/series.py index 9a2eb9da3a6a4..14c932dbe93f2 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1869,7 +1869,7 @@ def reorder_levels(self, order): Parameters ---------- order: list of int representing new level order. - (reference level by number not by key) + (reference level by number or key) axis: where to reorder levels Returns diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 5762171b38918..280b0476efe98 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -9185,6 +9185,46 @@ def test_select(self): assert_frame_equal(result, expected, check_names=False) # TODO should reindex check_names? + def test_reorder_levels(self): + index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]], + labels=[[0, 0, 0, 0, 0, 0], + [0, 1, 2, 0, 1, 2], + [0, 1, 0, 1, 0, 1]], + names=['L0', 'L1', 'L2']) + df = DataFrame({'A': np.arange(6), 'B': np.arange(6)}, index=index) + + # no change, position + result = df.reorder_levels([0, 1, 2]) + assert_frame_equal(df, result) + + # no change, labels + result = df.reorder_levels(['L0', 'L1', 'L2']) + assert_frame_equal(df, result) + + # rotate, position + result = df.reorder_levels([1, 2, 0]) + e_idx = MultiIndex(levels=[['one', 'two', 'three'], [0, 1], ['bar']], + labels=[[0, 1, 2, 0, 1, 2], + [0, 1, 0, 1, 0, 1], + [0, 0, 0, 0, 0, 0]], + names=['L1', 'L2', 'L0']) + expected = DataFrame({'A': np.arange(6), 'B': np.arange(6)}, + index=e_idx) + assert_frame_equal(result, expected) + + result = df.reorder_levels([0, 0, 0]) + e_idx = MultiIndex(levels=[['bar'], ['bar'], ['bar']], + labels=[[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]], + names=['L0', 'L0', 'L0']) + expected = DataFrame({'A': np.arange(6), 'B': np.arange(6)}, + index=e_idx) + assert_frame_equal(result, expected) + + result = df.reorder_levels(['L0', 'L0', 'L0']) + assert_frame_equal(result, expected) + def test_sort_index(self): frame = DataFrame(np.random.randn(4, 4), index=[1, 2, 3, 4], columns=['A', 'B', 'C', 'D']) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index c44ede057adb2..5a6f790d5851e 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -1854,6 +1854,44 @@ def test_argsort_stable(self): self.assert_(np.array_equal(qindexer, qexpected)) self.assert_(not np.array_equal(qindexer, mindexer)) + def test_reorder_levels(self): + index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]], + labels=[[0, 0, 0, 0, 0, 0], + [0, 1, 2, 0, 1, 2], + [0, 1, 0, 1, 0, 1]], + names=['L0', 'L1', 'L2']) + s = Series(np.arange(6), index=index) + + # no change, position + result = s.reorder_levels([0, 1, 2]) + assert_series_equal(s, result) + + # no change, labels + result = s.reorder_levels(['L0', 'L1', 'L2']) + assert_series_equal(s, result) + + # rotate, position + result = s.reorder_levels([1, 2, 0]) + e_idx = MultiIndex(levels=[['one', 'two', 'three'], [0, 1], ['bar']], + labels=[[0, 1, 2, 0, 1, 2], + [0, 1, 0, 1, 0, 1], + [0, 0, 0, 0, 0, 0]], + names=['L1', 'L2', 'L0']) + expected = Series(np.arange(6), index=e_idx) + assert_series_equal(result, expected) + + result = s.reorder_levels([0, 0, 0]) + e_idx = MultiIndex(levels=[['bar'], ['bar'], ['bar']], + labels=[[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]], + names=['L0', 'L0', 'L0']) + expected = Series(range(6), index=e_idx) + assert_series_equal(result, expected) + + result = s.reorder_levels(['L0', 'L0', 'L0']) + assert_series_equal(result, expected) + def test_cumsum(self): self._check_accum_op('cumsum')
Minor change to the docstring. It said you must refer to the position (not keys). Keys work: ``` python In [18]: res.head() Out[18]: same_employer stamp -3 1996-02-01 2 -2 1996-01-01 1 1996-02-01 1 1996-03-01 4 1 1996-01-01 13877 dtype: int64 In [19]: res.reorder_levels(['stamp', 'same_employer']).head() Out[19]: stamp same_employer 1996-02-01 -3 2 1996-01-01 -2 1 1996-02-01 -2 1 1996-03-01 -2 4 1996-01-01 1 13877 dtype: int64 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5573
2013-11-22T21:54:25Z
2013-11-24T07:31:28Z
2013-11-24T07:31:28Z
2016-11-03T12:37:34Z
PERF: slow conversion of single series to data frame
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 5e617671f5c49..a031b0550c734 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4575,7 +4575,7 @@ def extract_index(data): def _prep_ndarray(values, copy=True): - if not isinstance(values, np.ndarray): + if not isinstance(values, (np.ndarray,Series)): if len(values) == 0: return np.empty((0, 0), dtype=object) diff --git a/vb_suite/frame_ctor.py b/vb_suite/frame_ctor.py index f8d6e5d548ae5..1d8df95de9fe3 100644 --- a/vb_suite/frame_ctor.py +++ b/vb_suite/frame_ctor.py @@ -25,19 +25,24 @@ frame_ctor_nested_dict = Benchmark("DataFrame(data)", setup) # From JSON-like stuff - frame_ctor_list_of_dict = Benchmark("DataFrame(dict_list)", setup, start_date=datetime(2011, 12, 20)) series_ctor_from_dict = Benchmark("Series(some_dict)", setup) # nested dict, integer indexes, regression described in #621 - setup = common_setup + """ data = dict((i,dict((j,float(j)) for j in xrange(100))) for i in xrange(2000)) """ frame_ctor_nested_dict_int64 = Benchmark("DataFrame(data)", setup) +# from a mi-series +setup = common_setup + """ +mi = MultiIndex.from_tuples([(x,y) for x in range(100) for y in range(100)]) +s = Series(randn(10000), index=mi) +""" +frame_from_series = Benchmark("DataFrame(s)", setup) + #---------------------------------------------------------------------- # get_numeric_data
``` ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- frame_from_series | 0.1043 | 17.8473 | 0.0058 | ``` an oversight in handling a case like: `DataFrame(a_series)`
https://api.github.com/repos/pandas-dev/pandas/pulls/5568
2013-11-21T21:42:56Z
2013-11-21T21:44:06Z
2013-11-21T21:44:06Z
2014-06-23T14:23:49Z
TST/API/BUG: resolve scoping issues in pytables query where rhs is a compound selection or scoped variable
diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index 4c6ea3ecdae7d..bcb5570eae1fc 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -125,6 +125,10 @@ def __init__(self, gbls=None, lcls=None, level=1, resolvers=None, self.globals['True'] = True self.globals['False'] = False + # function defs + self.globals['list'] = list + self.globals['tuple'] = tuple + res_keys = (list(o.keys()) for o in self.resolvers) self.resolver_keys = frozenset(reduce(operator.add, res_keys, [])) self._global_resolvers = self.resolvers + (self.locals, self.globals) @@ -505,21 +509,21 @@ def _possibly_evaluate_binop(self, op, op_class, lhs, rhs, maybe_eval_in_python=('==', '!=')): res = op(lhs, rhs) - if (res.op in _cmp_ops_syms and lhs.is_datetime or rhs.is_datetime and - self.engine != 'pytables'): - # all date ops must be done in python bc numexpr doesn't work well - # with NaT - return self._possibly_eval(res, self.binary_ops) + if self.engine != 'pytables': + if (res.op in _cmp_ops_syms and getattr(lhs,'is_datetime',False) or getattr(rhs,'is_datetime',False)): + # all date ops must be done in python bc numexpr doesn't work well + # with NaT + return self._possibly_eval(res, self.binary_ops) if res.op in eval_in_python: # "in"/"not in" ops are always evaluated in python return self._possibly_eval(res, eval_in_python) - elif (lhs.return_type == object or rhs.return_type == object and - self.engine != 'pytables'): - # evaluate "==" and "!=" in python if either of our operands has an - # object return type - return self._possibly_eval(res, eval_in_python + - maybe_eval_in_python) + elif self.engine != 'pytables': + if (getattr(lhs,'return_type',None) == object or getattr(rhs,'return_type',None) == object): + # evaluate "==" and "!=" in python if either of our operands has an + # object return type + return self._possibly_eval(res, eval_in_python + + maybe_eval_in_python) return res def visit_BinOp(self, node, **kwargs): @@ -635,7 +639,7 @@ def visit_Attribute(self, node, **kwargs): raise ValueError("Invalid Attribute context {0}".format(ctx.__name__)) - def visit_Call(self, node, **kwargs): + def visit_Call(self, node, side=None, **kwargs): # this can happen with: datetime.datetime if isinstance(node.func, ast.Attribute): diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index a521bfb3cfec9..7716bc0051159 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -401,8 +401,15 @@ def visit_Assign(self, node, **kwargs): return self.visit(cmpr) def visit_Subscript(self, node, **kwargs): + # only allow simple suscripts + value = self.visit(node.value) slobj = self.visit(node.slice) + try: + value = value.value + except: + pass + try: return self.const_type(value[slobj], self.env) except TypeError: @@ -416,9 +423,16 @@ def visit_Attribute(self, node, **kwargs): ctx = node.ctx.__class__ if ctx == ast.Load: # resolve the value - resolved = self.visit(value).value + resolved = self.visit(value) + + # try to get the value to see if we are another expression + try: + resolved = resolved.value + except (AttributeError): + pass + try: - return getattr(resolved, attr) + return self.term_type(getattr(resolved, attr), self.env) except AttributeError: # something like datetime.datetime where scope is overriden diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 49f60a7051ba9..d2fe1e0638192 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -294,6 +294,10 @@ def read_hdf(path_or_buf, key, **kwargs): """ + # grab the scope + if 'where' in kwargs: + kwargs['where'] = _ensure_term(kwargs['where']) + f = lambda store, auto_close: store.select( key, auto_close=auto_close, **kwargs) diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 759f63a962e57..ba69f7a834dad 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -81,14 +81,19 @@ def ensure_clean_store(path, mode='a', complevel=None, complib=None, def ensure_clean_path(path): """ return essentially a named temporary file that is not opened - and deleted on existing + and deleted on existing; if path is a list, then create and + return list of filenames """ - try: - filename = create_tempfile(path) - yield filename + if isinstance(path, list): + filenames = [ create_tempfile(p) for p in path ] + yield filenames + else: + filenames = [ create_tempfile(path) ] + yield filenames[0] finally: - safe_remove(filename) + for f in filenames: + safe_remove(f) # set these parameters so we don't have file sharing tables.parameters.MAX_NUMEXPR_THREADS = 1 @@ -3124,6 +3129,70 @@ def test_frame_select_complex(self): expected = df.loc[df.index>df.index[3]].reindex(columns=['A','B']) tm.assert_frame_equal(result, expected) + def test_frame_select_complex2(self): + + with ensure_clean_path(['parms.hdf','hist.hdf']) as paths: + + pp, hh = paths + + # use non-trivial selection criteria + parms = DataFrame({ 'A' : [1,1,2,2,3] }) + parms.to_hdf(pp,'df',mode='w',format='table',data_columns=['A']) + + selection = read_hdf(pp,'df',where='A=[2,3]') + hist = DataFrame(np.random.randn(25,1),columns=['data'], + index=MultiIndex.from_tuples([ (i,j) for i in range(5) for j in range(5) ], + names=['l1','l2'])) + + hist.to_hdf(hh,'df',mode='w',format='table') + + expected = read_hdf(hh,'df',where=Term('l1','=',[2,3,4])) + + # list like + result = read_hdf(hh,'df',where=Term('l1','=',selection.index.tolist())) + assert_frame_equal(result, expected) + l = selection.index.tolist() + + # sccope with list like + store = HDFStore(hh) + result = store.select('df',where='l1=l') + assert_frame_equal(result, expected) + store.close() + + result = read_hdf(hh,'df',where='l1=l') + assert_frame_equal(result, expected) + + # index + index = selection.index + result = read_hdf(hh,'df',where='l1=index') + assert_frame_equal(result, expected) + + result = read_hdf(hh,'df',where='l1=selection.index') + assert_frame_equal(result, expected) + + result = read_hdf(hh,'df',where='l1=selection.index.tolist()') + assert_frame_equal(result, expected) + + result = read_hdf(hh,'df',where='l1=list(selection.index)') + assert_frame_equal(result, expected) + + # sccope with index + store = HDFStore(hh) + + result = store.select('df',where='l1=index') + assert_frame_equal(result, expected) + + result = store.select('df',where='l1=selection.index') + assert_frame_equal(result, expected) + + result = store.select('df',where='l1=selection.index.tolist()') + assert_frame_equal(result, expected) + + result = store.select('df',where='l1=list(selection.index)') + assert_frame_equal(result, expected) + + store.close() + def test_invalid_filtering(self): # can't use more than one filter (atm)
e.g. 'l1=selection.index' and 'l1=l', where l = selection.index were failing example reproduced here: http://stackoverflow.com/questions/20111542/selecting-rows-from-an-hdfstore-given-list-of-indexes ``` In [5]: parms = DataFrame({ 'A' : [1,1,2,2,3] }) In [6]: parms Out[6]: A 0 1 1 1 2 2 3 2 4 3 In [7]: parms.to_hdf('parms.hdf','df',mode='w',format='table',data_columns=['A']) In [8]: selection = pd.read_hdf('parms.hdf','df',where='A=[2,3]') In [9]: selection Out[9]: A 2 2 3 2 4 3 In [10]: hist = DataFrame(np.random.randn(25,1),columns=['data'], ....: index=MultiIndex.from_tuples([ (i,j) for i in range(5) for j in range(5) ], ....: names=['l1','l2'])) In [11]: hist Out[11]: data l1 l2 0 0 1.232358 1 -2.677047 2 -0.168854 3 0.538848 4 -0.678224 1 0 0.092575 1 1.297578 2 -1.489906 3 -1.380054 4 0.701762 2 0 1.397368 1 0.198522 2 1.034036 3 0.650406 4 1.823683 3 0 0.045635 1 -0.213975 2 -1.221950 3 -0.145615 4 -1.187883 4 0 -0.782221 1 -0.626280 2 -0.331885 3 -0.975978 4 2.006322 ``` this works in 0.12 ``` In [15]: pd.read_hdf('hist.hdf','df',where=pd.Term('l1','=',selection.index.tolist())) Out[15]: data l1 l2 2 0 1.397368 1 0.198522 2 1.034036 3 0.650406 4 1.823683 3 0 0.045635 1 -0.213975 2 -1.221950 3 -0.145615 4 -1.187883 4 0 -0.782221 1 -0.626280 2 -0.331885 3 -0.975978 4 2.006322 ``` This works in 0.13 as well ``` In [16]: pd.read_hdf('hist.hdf','df',where='l1=selection.index') Out[16]: data l1 l2 2 0 1.397368 1 0.198522 2 1.034036 3 0.650406 4 1.823683 3 0 0.045635 1 -0.213975 2 -1.221950 3 -0.145615 4 -1.187883 4 0 -0.782221 1 -0.626280 2 -0.331885 3 -0.975978 4 2.006322 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5566
2013-11-21T14:24:45Z
2013-11-21T15:07:41Z
2013-11-21T15:07:41Z
2014-07-16T08:41:13Z
ENH: short-circuit config lookup when exact key given GH5147
diff --git a/pandas/core/config.py b/pandas/core/config.py index ac48232ec618f..bb591947d21de 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -512,6 +512,11 @@ def _select_options(pat): if pat=="all", returns all registered options """ + # short-circuit for exact key + if pat in _registered_options: + return [pat] + + # else look through all of them keys = sorted(_registered_options.keys()) if pat == 'all': # reserved key return keys
Related #5147, #5457 before ``` In [4]: %timeit -r 10 pd.get_option("display.max_rows") 10000 loops, best of 10: 53.3 us per loop ``` after ``` In [1]: %timeit -r 10 pd.get_option("display.max_rows") 100000 loops, best of 10: 5.5 us per loop ``` jeff's version with no error checking or rerouting deprecated keys ``` In [1]: %timeit -r 10 pd.core.config._get_option_fast("display.max_rows") 1000000 loops, best of 10: 792 ns per loop ``` Probably makes no practical difference.
https://api.github.com/repos/pandas-dev/pandas/pulls/5565
2013-11-21T13:21:48Z
2013-11-21T13:22:16Z
2013-11-21T13:22:16Z
2014-06-26T05:41:17Z
ENH: Add wide_to_long convenience function
diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index a39e415abe519..ea6e300906da9 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -615,6 +615,23 @@ Enhancements ser = Series([1, 3, np.nan, np.nan, np.nan, 11]) ser.interpolate(limit=2) +- Added ``wide_to_long`` panel data convenience function. + + .. ipython:: python + + import pandas as pd + import numpy as np + np.random.seed(123) + df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"}, + "A1980" : {0 : "d", 1 : "e", 2 : "f"}, + "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7}, + "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1}, + "X" : dict(zip(range(3), np.random.randn(3))) + }) + df["id"] = df.index + df + wide_to_long(df, ["A", "B"], i="id", j="year") + .. _scipy: http://www.scipy.org .. _documentation: http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation .. _guide: http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html diff --git a/pandas/core/api.py b/pandas/core/api.py index 28118c60776ce..d75c075d22d7c 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -15,7 +15,7 @@ from pandas.core.panel4d import Panel4D from pandas.core.groupby import groupby from pandas.core.reshape import (pivot_simple as pivot, get_dummies, - lreshape) + lreshape, wide_to_long) WidePanel = Panel diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 1178a853c6619..d421fa36326aa 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -786,6 +786,89 @@ def lreshape(data, groups, dropna=True, label=None): return DataFrame(mdata, columns=id_cols + pivot_cols) +def wide_to_long(df, stubnames, i, j): + """ + Wide panel to long format. Less flexible but more user-friendly than melt. + + Parameters + ---------- + df : DataFrame + The wide-format DataFrame + stubnames : list + A list of stub names. The wide format variables are assumed to + start with the stub names. + i : str + The name of the id variable. + j : str + The name of the subobservation variable. + stubend : str + Regex to match for the end of the stubs. + + Returns + ------- + DataFrame + A DataFrame that contains each stub name as a variable as well as + variables for i and j. + + Examples + -------- + >>> import pandas as pd + >>> import numpy as np + >>> np.random.seed(123) + >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"}, + ... "A1980" : {0 : "d", 1 : "e", 2 : "f"}, + ... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7}, + ... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1}, + ... "X" : dict(zip(range(3), np.random.randn(3))) + ... }) + >>> df["id"] = df.index + >>> df + A1970 A1980 B1970 B1980 X id + 0 a d 2.5 3.2 -1.085631 0 + 1 b e 1.2 1.3 0.997345 1 + 2 c f 0.7 0.1 0.282978 2 + >>> wide_to_long(df, ["A", "B"], i="id", j="year") + X A B + id year + 0 1970 -1.085631 a 2.5 + 1 1970 0.997345 b 1.2 + 2 1970 0.282978 c 0.7 + 0 1980 -1.085631 d 3.2 + 1 1980 0.997345 e 1.3 + 2 1980 0.282978 f 0.1 + + Notes + ----- + All extra variables are treated as extra id variables. This simply uses + `pandas.melt` under the hood, but is hard-coded to "do the right thing" + in a typicaly case. + """ + def get_var_names(df, regex): + return df.filter(regex=regex).columns.tolist() + + def melt_stub(df, stub, i, j): + varnames = get_var_names(df, "^"+stub) + newdf = melt(df, id_vars=i, value_vars=varnames, + value_name=stub, var_name=j) + newdf_j = newdf[j].str.replace(stub, "") + try: + newdf_j = newdf_j.astype(int) + except ValueError: + pass + newdf[j] = newdf_j + return newdf + + id_vars = get_var_names(df, "^(?!%s)" % "|".join(stubnames)) + if i not in id_vars: + id_vars += [i] + + stub = stubnames.pop(0) + newdf = melt_stub(df, stub, id_vars, j) + + for stub in stubnames: + new = melt_stub(df, stub, id_vars, j) + newdf = newdf.merge(new, how="outer", on=id_vars + [j], copy=False) + return newdf.set_index([i, j]) def convert_dummies(data, cat_variables, prefix_sep='_'): """ diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py index c6eb9739cf3d2..b48fb29de0289 100644 --- a/pandas/tests/test_reshape.py +++ b/pandas/tests/test_reshape.py @@ -15,7 +15,8 @@ from pandas.util.testing import assert_frame_equal from numpy.testing import assert_array_equal -from pandas.core.reshape import melt, convert_dummies, lreshape, get_dummies +from pandas.core.reshape import (melt, convert_dummies, lreshape, get_dummies, + wide_to_long) import pandas.util.testing as tm from pandas.compat import StringIO, cPickle, range @@ -296,6 +297,27 @@ def test_pairs(self): 'wt': ['wt%d' % i for i in range(1, 4)]} self.assertRaises(ValueError, lreshape, df, spec) +class TestWideToLong(tm.TestCase): + def test_simple(self): + np.random.seed(123) + x = np.random.randn(3) + df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"}, + "A1980" : {0 : "d", 1 : "e", 2 : "f"}, + "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7}, + "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1}, + "X" : dict(zip(range(3), x)) + }) + df["id"] = df.index + exp_data = {"X" : x.tolist() + x.tolist(), + "A" : ['a', 'b', 'c', 'd', 'e', 'f'], + "B" : [2.5, 1.2, 0.7, 3.2, 1.3, 0.1], + "year" : [1970, 1970, 1970, 1980, 1980, 1980], + "id" : [0, 1, 2, 0, 1, 2]} + exp_frame = DataFrame(exp_data) + exp_frame = exp_frame.set_index(['id', 'year'])[["X", "A", "B"]] + long_frame = wide_to_long(df, ["A", "B"], i="id", j="year") + tm.assert_frame_equal(long_frame, exp_frame) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
closes #4920 I thought I submitted this PR long ago.
https://api.github.com/repos/pandas-dev/pandas/pulls/5564
2013-11-21T12:46:25Z
2013-12-07T14:15:52Z
2013-12-07T14:15:52Z
2014-06-30T21:46:37Z
DOC: styling clean-up of docstrings (part 1, frame.py)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index a031b0550c734..6a1ecfed15896 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -170,11 +170,11 @@ class DataFrame(NDFrame): See also -------- - DataFrame.from_records: constructor from tuples, also record arrays - DataFrame.from_dict: from dicts of Series, arrays, or dicts - DataFrame.from_csv: from CSV files - DataFrame.from_items: from sequence of (key, value) pairs - read_csv / read_table / read_clipboard + DataFrame.from_records : constructor from tuples, also record arrays + DataFrame.from_dict : from dicts of Series, arrays, or dicts + DataFrame.from_csv : from CSV files + DataFrame.from_items : from sequence of (key, value) pairs + pandas.read_csv, pandas.read_table, pandas.read_clipboard """ _auto_consolidate = True @@ -728,7 +728,7 @@ def from_records(cls, data, index=None, exclude=None, columns=None, index : string, list of fields, array-like Field of array to use as the index, alternately a specific set of input labels to use - exclude: sequence, default None + exclude : sequence, default None Columns or fields to exclude columns : sequence, default None Column names to use. If the passed data do not have named @@ -1167,8 +1167,10 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', Column label for index column(s) if desired. If None is given, and `header` and `index` are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. - startow : upper left cell row to dump data frame - startcol : upper left cell column to dump data frame + startow : + upper left cell row to dump data frame + startcol : + upper left cell column to dump data frame engine : string, default None write engine to use - you can also set this via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and @@ -1180,7 +1182,7 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', ----- If passing an existing ExcelWriter object, then the sheet will be added to the existing workbook. This can be used to save different - DataFrames to one workbook + DataFrames to one workbook: >>> writer = ExcelWriter('output.xlsx') >>> df1.to_excel(writer,'Sheet1') @@ -1249,13 +1251,14 @@ def to_sql(self, name, con, flavor='sqlite', if_exists='fail', **kwargs): Parameters ---------- - name: name of SQL table - conn: an open SQL database connection object + name : str + Name of SQL table + conn : an open SQL database connection object flavor: {'sqlite', 'mysql', 'oracle'}, default 'sqlite' if_exists: {'fail', 'replace', 'append'}, default 'fail' - fail: If table exists, do nothing. - replace: If table exists, drop it, recreate it, and insert data. - append: If table exists, insert data. Create if does not exist. + - fail: If table exists, do nothing. + - replace: If table exists, drop it, recreate it, and insert data. + - append: If table exists, insert data. Create if does not exist. """ from pandas.io.sql import write_frame write_frame( @@ -1316,6 +1319,7 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, CSS class(es) to apply to the resulting html table escape : boolean, default True Convert the characters <, >, and & to HTML-safe sequences. + """ if force_unicode is not None: # pragma: no cover @@ -1355,6 +1359,7 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, bold_rows : boolean, default True Make the row labels bold in the output + """ if force_unicode is not None: # pragma: no cover @@ -1923,8 +1928,9 @@ def _set_item(self, key, value): def insert(self, loc, column, value, allow_duplicates=False): """ Insert column into DataFrame at specified location. - if allow_duplicates is False, Raises Exception if column is already - contained in the DataFrame + + If `allow_duplicates` is False, raises Exception if column + is already contained in the DataFrame. Parameters ---------- @@ -2010,7 +2016,7 @@ def xs(self, key, axis=0, level=None, copy=True, drop_level=True): which levels are used. Levels can be referred by label or position. copy : boolean, default True Whether to make a copy of the data - drop_level, default True + drop_level : boolean, default True If False, returns object with same levels as self. Examples @@ -2133,9 +2139,9 @@ def xs(self, key, axis=0, level=None, copy=True, drop_level=True): _xs = xs def lookup(self, row_labels, col_labels): - """Label-based "fancy indexing" function for DataFrame. Given - equal-length arrays of row and column labels, return an array of the - values corresponding to each (row, col) pair. + """Label-based "fancy indexing" function for DataFrame. + Given equal-length arrays of row and column labels, return an + array of the values corresponding to each (row, col) pair. Parameters ---------- @@ -2146,13 +2152,11 @@ def lookup(self, row_labels, col_labels): Notes ----- - Akin to - - .. code-block:: python + Akin to:: - result = [] - for row, col in zip(row_labels, col_labels): - result.append(df.get_value(row, col)) + result = [] + for row, col in zip(row_labels, col_labels): + result.append(df.get_value(row, col)) Examples -------- @@ -2467,14 +2471,14 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None, axis : {0, 1}, or tuple/list thereof Pass tuple or list to drop on multiple axes how : {'any', 'all'} - any : if any NA values are present, drop that label - all : if all values are NA, drop that label + * any : if any NA values are present, drop that label + * all : if all values are NA, drop that label thresh : int, default None int value : require that many non-NA values subset : array-like Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include - inplace : bool, defalt False + inplace : boolean, defalt False If True, do operation inplace and return None. Returns @@ -2725,7 +2729,7 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False): ---------- level : int axis : {0, 1} - ascending : bool, default True + ascending : boolean, default True inplace : boolean, default False Sort the DataFrame without creating a new instance @@ -2790,9 +2794,11 @@ def reorder_levels(self, order, axis=0): Parameters ---------- - order: list of int representing new level order. - (reference level by number not by key) - axis: where to reorder levels + order : list of int + List representing new level order. Reference level by number + not by key. + axis : int + Where to reorder levels. Returns ------- @@ -3057,8 +3063,10 @@ def combine_first(self, other): Examples -------- + a's values prioritized, use values from b to fill holes: + >>> a.combine_first(b) - a's values prioritized, use values from b to fill holes + Returns ------- @@ -3094,7 +3102,7 @@ def update(self, other, join='left', overwrite=True, filter_func=None, filter_func : callable(1d-array) -> 1d-array<boolean>, default None Can choose to replace values other than NA. Return True for values that should be updated - raise_conflict : bool + raise_conflict : boolean If True, will raise an error if the DataFrame and other both contain data in the same place. """ @@ -3322,22 +3330,24 @@ def diff(self, periods=1): def apply(self, func, axis=0, broadcast=False, raw=False, reduce=True, args=(), **kwds): """ - Applies function along input axis of DataFrame. Objects passed to - functions are Series objects having index either the DataFrame's index - (axis=0) or the columns (axis=1). Return type depends on whether passed - function aggregates + Applies function along input axis of DataFrame. + + Objects passed to functions are Series objects having index + either the DataFrame's index (axis=0) or the columns (axis=1). + Return type depends on whether passed function aggregates Parameters ---------- func : function - Function to apply to each column + Function to apply to each column/row axis : {0, 1} - 0 : apply function to each column - 1 : apply function to each row - broadcast : bool, default False + * 0 : apply function to each column + * 1 : apply function to each row + broadcast : boolean, default False For aggregation functions, return object of same size with values propagated - reduce : bool, default True, try to apply reduction procedures + reduce : boolean, default True + Try to apply reduction procedures raw : boolean, default False If False, convert each row or column into a Series. If raw=True the passed function will receive ndarray objects instead. If you are @@ -3529,6 +3539,11 @@ def applymap(self, func): Returns ------- applied : DataFrame + + See also + -------- + DataFrame.apply : For operations on rows/columns + """ # if we have a dtype == 'M8[ns]', provide boxed values @@ -3611,6 +3626,7 @@ def join(self, other, on=None, how='left', lsuffix='', rsuffix='', how : {'left', 'right', 'outer', 'inner'} How to handle indexes of the two objects. Default: 'left' for joining on index, None otherwise + * left: use calling frame's index * right: use input frame's index * outer: form union of indexes @@ -3698,9 +3714,9 @@ def corr(self, method='pearson', min_periods=1): Parameters ---------- method : {'pearson', 'kendall', 'spearman'} - pearson : standard correlation coefficient - kendall : Kendall Tau correlation coefficient - spearman : Spearman rank correlation + * pearson : standard correlation coefficient + * kendall : Kendall Tau correlation coefficient + * spearman : Spearman rank correlation min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson @@ -3756,7 +3772,9 @@ def cov(self, min_periods=None): ------- y : DataFrame - y contains the covariance matrix of the DataFrame's time series. + Notes + ----- + `y` contains the covariance matrix of the DataFrame's time series. The covariance is normalized by N-1 (unbiased estimator). """ numeric_df = self._get_numeric_data() @@ -4156,9 +4174,9 @@ def mode(self, axis=0, numeric_only=False): Parameters ---------- axis : {0, 1, 'index', 'columns'} (default 0) - 0/'index' : get mode of each column - 1/'columns' : get mode of each row - numeric_only : bool, default False + * 0/'index' : get mode of each column + * 1/'columns' : get mode of each row + numeric_only : boolean, default False if True, only apply to numeric columns Returns @@ -4213,14 +4231,14 @@ def rank(self, axis=0, numeric_only=None, method='average', numeric_only : boolean, default None Include only float, int, boolean data method : {'average', 'min', 'max', 'first'} - average: average rank of group - min: lowest rank in group - max: highest rank in group - first: ranks assigned in order they appear in the array + * average: average rank of group + * min: lowest rank in group + * max: highest rank in group + * first: ranks assigned in order they appear in the array na_option : {'keep', 'top', 'bottom'} - keep: leave NA values where they are - top: smallest rank if ascending - bottom: smallest rank if descending + * keep: leave NA values where they are + * top: smallest rank if ascending + * bottom: smallest rank if descending ascending : boolean, default True False for ranks by high (1) to low (N) @@ -4861,11 +4879,11 @@ def boxplot(self, column=None, by=None, ax=None, fontsize=None, Can be any valid input to groupby by : string or sequence Column in the DataFrame to group by - ax : matplotlib axis object, default None + ax : matplotlib axis object, default None fontsize : int or string - rot : int, default None + rot : int, default None Rotation for ticks - grid : boolean, default None (matlab style default) + grid : boolean, default None (matlab style default) Axis grid lines Returns
Some stylistic clean-up of the docstrings in frame.py. Things changed are mainly: - missing spaces aroung the `:` in `param : type` - explanation of param on second line (instead of on the same line -> there should be the type info) - add bullets to enumerations of possible values of a keyword (so they are rendered as a list in the html docstring instead of one continuous block of text) Eg the `method` keyword explanation in http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.corr.html
https://api.github.com/repos/pandas-dev/pandas/pulls/5560
2013-11-20T21:25:18Z
2013-11-23T10:10:45Z
2013-11-23T10:10:45Z
2014-06-23T21:39:28Z
DOC version added 0.13 to cumcount
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 03919caf6d101..705db807ddf8f 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -709,6 +709,8 @@ can be used as group keys. If so, the order of the levels will be preserved: Enumerate group items ~~~~~~~~~~~~~~~~~~~~~ +.. versionadded:: 0.13.0 + To see the order in which each row appears within its group, use the ``cumcount`` method:
@jreback is this what you mean? #4646
https://api.github.com/repos/pandas-dev/pandas/pulls/5559
2013-11-20T21:12:41Z
2013-11-20T21:15:16Z
2013-11-20T21:15:16Z
2014-06-14T21:14:01Z
BUG: repr of series fails repr in the presence of a Float64Index that is not monotonic (GH5557)
diff --git a/pandas/core/series.py b/pandas/core/series.py index cf704e9aef174..9a2eb9da3a6a4 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -870,12 +870,12 @@ def _tidy_repr(self, max_vals=20): Internal function, should always return unicode string """ num = max_vals // 2 - head = self[:num]._get_repr(print_header=True, length=False, - dtype=False, name=False) - tail = self[-(max_vals - num):]._get_repr(print_header=False, - length=False, - name=False, - dtype=False) + head = self.iloc[:num]._get_repr(print_header=True, length=False, + dtype=False, name=False) + tail = self.iloc[-(max_vals - num):]._get_repr(print_header=False, + length=False, + name=False, + dtype=False) result = head + '\n...\n' + tail result = '%s\n%s' % (result, self._repr_footer()) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 581513408c0a1..02b5812c3e653 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1797,6 +1797,15 @@ def f(): df2["B"] = df2["A"] df2["B"] = df2["A"] + def test_float64index_slicing_bug(self): + # GH 5557, related to slicing a float index + ser = {256: 2321.0, 1: 78.0, 2: 2716.0, 3: 0.0, 4: 369.0, 5: 0.0, 6: 269.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 3536.0, 11: 0.0, 12: 24.0, 13: 0.0, 14: 931.0, 15: 0.0, 16: 101.0, 17: 78.0, 18: 9643.0, 19: 0.0, 20: 0.0, 21: 0.0, 22: 63761.0, 23: 0.0, 24: 446.0, 25: 0.0, 26: 34773.0, 27: 0.0, 28: 729.0, 29: 78.0, 30: 0.0, 31: 0.0, 32: 3374.0, 33: 0.0, 34: 1391.0, 35: 0.0, 36: 361.0, 37: 0.0, 38: 61808.0, 39: 0.0, 40: 0.0, 41: 0.0, 42: 6677.0, 43: 0.0, 44: 802.0, 45: 0.0, 46: 2691.0, 47: 0.0, 48: 3582.0, 49: 0.0, 50: 734.0, 51: 0.0, 52: 627.0, 53: 70.0, 54: 2584.0, 55: 0.0, 56: 324.0, 57: 0.0, 58: 605.0, 59: 0.0, 60: 0.0, 61: 0.0, 62: 3989.0, 63: 10.0, 64: 42.0, 65: 0.0, 66: 904.0, 67: 0.0, 68: 88.0, 69: 70.0, 70: 8172.0, 71: 0.0, 72: 0.0, 73: 0.0, 74: 64902.0, 75: 0.0, 76: 347.0, 77: 0.0, 78: 36605.0, 79: 0.0, 80: 379.0, 81: 70.0, 82: 0.0, 83: 0.0, 84: 3001.0, 85: 0.0, 86: 1630.0, 87: 7.0, 88: 364.0, 89: 0.0, 90: 67404.0, 91: 9.0, 92: 0.0, 93: 0.0, 94: 7685.0, 95: 0.0, 96: 1017.0, 97: 0.0, 98: 2831.0, 99: 0.0, 100: 2963.0, 101: 0.0, 102: 854.0, 103: 0.0, 104: 0.0, 105: 0.0, 106: 0.0, 107: 0.0, 108: 0.0, 109: 0.0, 110: 0.0, 111: 0.0, 112: 0.0, 113: 0.0, 114: 0.0, 115: 0.0, 116: 0.0, 117: 0.0, 118: 0.0, 119: 0.0, 120: 0.0, 121: 0.0, 122: 0.0, 123: 0.0, 124: 0.0, 125: 0.0, 126: 67744.0, 127: 22.0, 128: 264.0, 129: 0.0, 260: 197.0, 268: 0.0, 265: 0.0, 269: 0.0, 261: 0.0, 266: 1198.0, 267: 0.0, 262: 2629.0, 258: 775.0, 257: 0.0, 263: 0.0, 259: 0.0, 264: 163.0, 250: 10326.0, 251: 0.0, 252: 1228.0, 253: 0.0, 254: 2769.0, 255: 0.0} + + # smoke test for the repr + s = Series(ser) + result = s.value_counts() + str(result) + def test_floating_index_doc_example(self): index = Index([1.5, 2, 3, 4.5, 5])
closes #5557 ``` In [4]: s.value_counts() Out[4]: 0 85 70 3 78 3 2584 1 3001 1 734 1 163 1 3374 1 9643 1 42 1 8172 1 931 1 802 1 67744 1 2716 1 ... 775 1 36605 1 1630 1 605 1 347 1 729 1 88 1 854 1 34773 1 2769 1 3536 1 67404 1 264 1 1228 1 324 1 Length: 61, dtype: int64 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5558
2013-11-20T19:52:12Z
2013-11-20T20:15:58Z
2013-11-20T20:15:58Z
2014-06-12T14:46:25Z
BUG: Bug in selecting from a non-unique index with loc (GH5553)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 1642324172e3b..ccc34a4051508 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -813,6 +813,7 @@ Bug Fixes - Bug in getitem with a multi-index and ``iloc`` (:issue:`5528`) - Bug in delitem on a Series (:issue:`5542`) - Bug fix in apply when using custom function and objects are not mutated (:issue:`5545`) + - Bug in selecting from a non-unique index with ``loc`` (:issue:`5553`) pandas 0.12.0 ------------- diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 6d0f57c6ddd57..fcb9a82b96211 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -826,8 +826,10 @@ def _reindex(keys, level=None): # a unique indexer if keyarr_is_unique: - new_indexer = (Index(cur_indexer) + - Index(missing_indexer)).values + + # see GH5553, make sure we use the right indexer + new_indexer = np.arange(len(indexer)) + new_indexer[cur_indexer] = np.arange(len(result._get_axis(axis))) new_indexer[missing_indexer] = -1 # we have a non_unique selector, need to use the original diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 86c3808781694..581513408c0a1 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -869,9 +869,9 @@ def test_dups_fancy_indexing(self): assert_frame_equal(df,result) # GH 3561, dups not in selected order - df = DataFrame({'test': [5,7,9,11]}, index=['A', 'A', 'B', 'C']) + df = DataFrame({'test': [5,7,9,11], 'test1': [4.,5,6,7], 'other': list('abcd') }, index=['A', 'A', 'B', 'C']) rows = ['C', 'B'] - expected = DataFrame({'test' : [11,9]},index=rows) + expected = DataFrame({'test' : [11,9], 'test1': [ 7., 6], 'other': ['d','c']},index=rows) result = df.ix[rows] assert_frame_equal(result, expected) @@ -879,7 +879,15 @@ def test_dups_fancy_indexing(self): assert_frame_equal(result, expected) rows = ['C','B','E'] - expected = DataFrame({'test' : [11,9,np.nan]},index=rows) + expected = DataFrame({'test' : [11,9,np.nan], 'test1': [7.,6,np.nan], 'other': ['d','c',np.nan]},index=rows) + result = df.ix[rows] + assert_frame_equal(result, expected) + + # see GH5553, make sure we use the right indexer + rows = ['F','G','H','C','B','E'] + expected = DataFrame({'test' : [np.nan,np.nan,np.nan,11,9,np.nan], + 'test1': [np.nan,np.nan,np.nan,7.,6,np.nan], + 'other': [np.nan,np.nan,np.nan,'d','c',np.nan]},index=rows) result = df.ix[rows] assert_frame_equal(result, expected)
closes #5553
https://api.github.com/repos/pandas-dev/pandas/pulls/5555
2013-11-20T15:25:57Z
2013-11-20T16:07:05Z
2013-11-20T16:07:05Z
2014-07-16T08:41:04Z
BUG: Bug fix in apply when using custom function and objects are not mutated (GH5545)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 6ba31fbc61a05..1642324172e3b 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -812,6 +812,7 @@ Bug Fixes length to the indexer (:issue:`5508`) - Bug in getitem with a multi-index and ``iloc`` (:issue:`5528`) - Bug in delitem on a Series (:issue:`5542`) + - Bug fix in apply when using custom function and objects are not mutated (:issue:`5545`) pandas 0.12.0 ------------- diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 20f17a7f42472..1d5691edb6313 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -543,13 +543,13 @@ def head(self, n=5): >>> df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) - >>> df.groupby('A', as_index=False).head(1) + >>> df.groupby('A', as_index=False).head(1) A B 0 1 2 2 5 6 >>> df.groupby('A').head(1) A B - A + A 1 0 1 2 5 2 5 6 @@ -572,16 +572,16 @@ def tail(self, n=5): >>> df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) - >>> df.groupby('A', as_index=False).tail(1) + >>> df.groupby('A', as_index=False).tail(1) A B 0 1 2 2 5 6 >>> df.groupby('A').head(1) A B - A + A 1 0 1 2 5 2 5 6 - + """ rng = np.arange(0, -self.grouper._max_groupsize, -1, dtype='int64') in_tail = self._cumcount_array(rng, ascending=False) > -n @@ -2149,6 +2149,12 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): keys, values, not_indexed_same=not_indexed_same ) + # still a series + # path added as of GH 5545 + elif all_indexed_same: + from pandas.tools.merge import concat + return concat(values) + if not all_indexed_same: return self._concat_objects( keys, values, not_indexed_same=not_indexed_same diff --git a/pandas/src/reduce.pyx b/pandas/src/reduce.pyx index cd010151f1ef7..13df983c45d53 100644 --- a/pandas/src/reduce.pyx +++ b/pandas/src/reduce.pyx @@ -541,7 +541,7 @@ def apply_frame_axis0(object frame, object f, object names, # I'm paying the price for index-sharing, ugh try: if piece.index is slider.dummy.index: - piece.index = piece.index.copy() + piece = piece.copy() else: mutated = True except AttributeError: diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index a1a7456f51140..1ee7268c0ca82 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1214,7 +1214,7 @@ def test_groupby_as_index_apply(self): res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index # apply doesn't maintain the original ordering - exp_not_as_apply = Index([0, 2, 1, 4]) + exp_not_as_apply = Index([0, 2, 1, 4]) exp_as_apply = MultiIndex.from_tuples([(1, 0), (1, 2), (2, 1), (3, 4)]) assert_index_equal(res_as_apply, exp_as_apply) @@ -1845,6 +1845,28 @@ def test_apply_corner(self): expected = self.tsframe * 2 assert_frame_equal(result, expected) + def test_apply_without_copy(self): + # GH 5545 + # returning a non-copy in an applied function fails + + data = DataFrame({'id_field' : [100, 100, 200, 300], 'category' : ['a','b','c','c'], 'value' : [1,2,3,4]}) + + def filt1(x): + if x.shape[0] == 1: + return x.copy() + else: + return x[x.category == 'c'] + + def filt2(x): + if x.shape[0] == 1: + return x + else: + return x[x.category == 'c'] + + expected = data.groupby('id_field').apply(filt1) + result = data.groupby('id_field').apply(filt2) + assert_frame_equal(result,expected) + def test_apply_use_categorical_name(self): from pandas import qcut cats = qcut(self.df.C, 4) @@ -2638,7 +2660,7 @@ def test_cumcount_mi(self): expected = Series([0, 1, 2, 0, 3], index=mi) assert_series_equal(expected, g.cumcount()) - assert_series_equal(expected, sg.cumcount()) + assert_series_equal(expected, sg.cumcount()) def test_cumcount_groupby_not_col(self): df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A'], index=[0] * 5) @@ -2895,7 +2917,7 @@ def test_filter_maintains_ordering(self): def test_filter_and_transform_with_non_unique_int_index(self): # GH4620 index = [1, 1, 1, 2, 1, 1, 0, 1] - df = DataFrame({'pid' : [1,1,1,2,2,3,3,3], + df = DataFrame({'pid' : [1,1,1,2,2,3,3,3], 'tag' : [23,45,62,24,45,34,25,62]}, index=index) grouped_df = df.groupby('tag') ser = df['pid'] @@ -2923,7 +2945,7 @@ def test_filter_and_transform_with_non_unique_int_index(self): # ^ made manually because this can get confusing! assert_series_equal(actual, expected) - # Transform Series + # Transform Series actual = grouped_ser.transform(len) expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index) assert_series_equal(actual, expected)
closes #5545
https://api.github.com/repos/pandas-dev/pandas/pulls/5554
2013-11-20T14:08:02Z
2013-11-20T14:28:49Z
2013-11-20T14:28:49Z
2014-06-22T05:16:05Z
HTML (and text) reprs for large dataframes.
diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index 08ef25b178af9..828797deff5cf 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -573,8 +573,9 @@ indexing semantics are quite different in places from a matrix. Console display ~~~~~~~~~~~~~~~ -For very large DataFrame objects, only a summary will be printed to the console -(here I am reading a CSV version of the **baseball** dataset from the **plyr** +Very large DataFrames will be truncated to display them in the console. +You can also get a summary using :meth:`~pandas.DataFrame.info`. +(Here I am reading a CSV version of the **baseball** dataset from the **plyr** R package): .. ipython:: python @@ -587,6 +588,7 @@ R package): baseball = read_csv('data/baseball.csv') print(baseball) + baseball.info() .. ipython:: python :suppress: @@ -622,19 +624,8 @@ option: reset_option('line_width') -You can also disable this feature via the ``expand_frame_repr`` option: - -.. ipython:: python - - set_option('expand_frame_repr', False) - - DataFrame(randn(3, 12)) - -.. ipython:: python - :suppress: - - reset_option('expand_frame_repr') - +You can also disable this feature via the ``expand_frame_repr`` option. +This will print the table in one block. DataFrame column attribute access and IPython completion ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/faq.rst b/doc/source/faq.rst index e5312e241ce47..21d581f12c53f 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -36,21 +36,25 @@ horizontal scrolling, auto-detection of width/height. To appropriately address all these environments, the display behavior is controlled by several options, which you're encouraged to tweak to suit your setup. -As of 0.12, these are the relevant options, all under the `display` namespace, -(e.g. display.width, etc'): +As of 0.13, these are the relevant options, all under the `display` namespace, +(e.g. ``display.width``, etc.): - notebook_repr_html: if True, IPython frontends with HTML support will display dataframes as HTML tables when possible. -- expand_repr (default True): when the frame width cannot fit within the screen, - the output will be broken into multiple pages to accomedate. This applies to - textual (as opposed to HTML) display only. -- max_columns: max dataframe columns to display. a wider frame will trigger - a summary view, unless `expand_repr` is True and HTML output is disabled. -- max_rows: max dataframe rows display. a longer frame will trigger a summary view. -- width: width of display screen in characters, used to determine the width of lines - when expand_repr is active, Setting this to None will trigger auto-detection of terminal - width, this only works for proper terminals, not IPython frontends such as ipnb. - width is ignored in IPython notebook, since the browser provides horizontal scrolling. +- large_repr (default 'truncate'): when a :class:`~pandas.DataFrame` + exceeds max_columns or max_rows, it can be displayed either as a + truncated table or, with this set to 'info', as a short summary view. +- max_columns (default 20): max dataframe columns to display. +- max_rows (default 60): max dataframe rows display. + +Two additional options only apply to displaying DataFrames in terminals, +not to the HTML view: + +- expand_repr (default True): when the frame width cannot fit within + the screen, the output will be broken into multiple pages. +- width: width of display screen in characters, used to determine the + width of lines when expand_repr is active. Setting this to None will + trigger auto-detection of terminal width. IPython users can use the IPython startup file to import pandas and set these options automatically when starting up. diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 207281caafeae..a39e415abe519 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -375,6 +375,22 @@ HDFStore API Changes via the option ``io.hdf.dropna_table`` (:issue:`4625`) - pass thru store creation arguments; can be used to support in-memory stores +DataFrame repr Changes +~~~~~~~~~~~~~~~~~~~~~~ + +The HTML and plain text representations of :class:`DataFrame` now show +a truncated view of the table once it exceeds a certain size, rather +than switching to the short info view (:issue:`4886`, :issue:`5550`). +This makes the representation more consistent as small DataFrames get +larger. + +.. image:: _static/df_repr_truncated.png + :alt: Truncated HTML representation of a DataFrame + +To get the info view, call :meth:`DataFrame.info`. If you prefer the +info view as the repr for large DataFrames, you can set this by running +``set_option('display.large_repr', 'info')``. + Enhancements ~~~~~~~~~~~~ diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 5502dc94e24c1..b7ec76522b60c 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -166,13 +166,19 @@ pc_max_info_rows_doc = """ : int or None - max_info_rows is the maximum number of rows for which a frame will - perform a null check on its columns when repr'ing To a console. - The default is 1,000,000 rows. So, if a DataFrame has more - 1,000,000 rows there will be no null check performed on the - columns and thus the representation will take much less time to - display in an interactive session. A value of None means always - perform a null check when repr'ing. + Deprecated. +""" + +pc_max_info_rows_deprecation_warning = """\ +max_info_rows has been deprecated, as reprs no longer use the info view. +""" + +pc_large_repr_doc = """ +: 'truncate'/'info' + + For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can + show a truncated table (the default from 0.13), or switch to the view from + df.info() (the behaviour in earlier versions of pandas). """ pc_mpl_style_doc = """ @@ -220,6 +226,8 @@ def mpl_style_cb(key): cf.register_option('max_colwidth', 50, max_colwidth_doc, validator=is_int) cf.register_option('max_columns', 20, pc_max_cols_doc, validator=is_instance_factory([type(None), int])) + cf.register_option('large_repr', 'truncate', pc_large_repr_doc, + validator=is_one_of_factory(['truncate', 'info'])) cf.register_option('max_info_columns', 100, pc_max_info_cols_doc, validator=is_int) cf.register_option('colheader_justify', 'right', colheader_justify_doc, @@ -258,6 +266,9 @@ def mpl_style_cb(key): msg=pc_height_deprecation_warning, rkey='display.max_rows') +cf.deprecate_option('display.max_info_rows', + msg=pc_max_info_rows_deprecation_warning) + tc_sim_interactive_doc = """ : boolean Whether to simulate interactive mode for purposes of testing diff --git a/pandas/core/format.py b/pandas/core/format.py index 7354600c78c67..1ca68b8d47e09 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -1,3 +1,4 @@ +#coding: utf-8 from __future__ import print_function # pylint: disable=W0141 @@ -263,7 +264,8 @@ class DataFrameFormatter(TableFormatter): def __init__(self, frame, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, justify=None, float_format=None, sparsify=None, - index_names=True, line_width=None, **kwds): + index_names=True, line_width=None, max_rows=None, max_cols=None, + show_dimensions=False, **kwds): self.frame = frame self.buf = buf if buf is not None else StringIO() self.show_index_names = index_names @@ -280,6 +282,9 @@ def __init__(self, frame, buf=None, columns=None, col_space=None, self.header = header self.index = index self.line_width = line_width + self.max_rows = max_rows + self.max_cols = max_cols + self.show_dimensions = show_dimensions if justify is None: self.justify = get_option("display.colheader_justify") @@ -303,12 +308,20 @@ def _to_str_columns(self): str_index = self._get_formatted_index() str_columns = self._get_formatted_column_labels() - stringified = [] - _strlen = _strlen_func() - for i, c in enumerate(self.columns): - if self.header: + cols_to_show = self.columns[:self.max_cols] + truncate_h = self.max_cols and (len(self.columns) > self.max_cols) + truncate_v = self.max_rows and (len(self.frame) > self.max_rows) + self.truncated_v = truncate_v + if truncate_h: + cols_to_show = self.columns[:self.max_cols] + else: + cols_to_show = self.columns + + if self.header: + stringified = [] + for i, c in enumerate(cols_to_show): fmt_values = self._format_col(i) cheader = str_columns[i] @@ -316,7 +329,7 @@ def _to_str_columns(self): *(_strlen(x) for x in cheader)) fmt_values = _make_fixed_width(fmt_values, self.justify, - minimum=max_colwidth) + minimum=max_colwidth, truncated=truncate_v) max_len = max(np.max([_strlen(x) for x in fmt_values]), max_colwidth) @@ -326,14 +339,17 @@ def _to_str_columns(self): cheader = [x.rjust(max_len) for x in cheader] stringified.append(cheader + fmt_values) - else: - stringified = [_make_fixed_width(self._format_col(i), - self.justify) - for i, c in enumerate(self.columns)] + else: + stringified = [_make_fixed_width(self._format_col(i), self.justify, + truncated=truncate_v) + for i, c in enumerate(cols_to_show)] strcols = stringified if self.index: strcols.insert(0, str_index) + if truncate_h: + strcols.append(([''] * len(str_columns[-1])) \ + + (['...'] * min(len(self.frame), self.max_rows)) ) return strcols @@ -364,6 +380,10 @@ def to_string(self, force_unicode=None): self.buf.writelines(text) + if self.show_dimensions: + self.buf.write("\n\n[%d rows x %d columns]" \ + % (len(frame), len(frame.columns)) ) + def _join_multiline(self, *strcols): lwidth = self.line_width adjoin_width = 1 @@ -378,6 +398,11 @@ def _join_multiline(self, *strcols): col_bins = _binify(col_widths, lwidth) nbins = len(col_bins) + if self.max_rows and len(self.frame) > self.max_rows: + nrows = self.max_rows + 1 + else: + nrows = len(self.frame) + str_lst = [] st = 0 for i, ed in enumerate(col_bins): @@ -385,9 +410,9 @@ def _join_multiline(self, *strcols): row.insert(0, idx) if nbins > 1: if ed <= len(strcols) and i < nbins - 1: - row.append([' \\'] + [' '] * (len(self.frame) - 1)) + row.append([' \\'] + [' '] * (nrows - 1)) else: - row.append([' '] * len(self.frame)) + row.append([' '] * nrows) str_lst.append(adjoin(adjoin_width, *row)) st = ed @@ -458,8 +483,8 @@ def write(buf, frame, column_format, strcols): def _format_col(self, i): formatter = self._get_formatter(i) - return format_array(self.frame.icol(i).get_values(), formatter, - float_format=self.float_format, + return format_array(self.frame.icol(i)[:self.max_rows].get_values(), + formatter, float_format=self.float_format, na_rep=self.na_rep, space=self.col_space) @@ -467,7 +492,9 @@ def to_html(self, classes=None): """ Render a DataFrame to a html table. """ - html_renderer = HTMLFormatter(self, classes=classes) + html_renderer = HTMLFormatter(self, classes=classes, + max_rows=self.max_rows, + max_cols=self.max_cols) if hasattr(self.buf, 'write'): html_renderer.write_result(self.buf) elif isinstance(self.buf, compat.string_types): @@ -483,8 +510,13 @@ def _get_formatted_column_labels(self): def is_numeric_dtype(dtype): return issubclass(dtype.type, np.number) - if isinstance(self.columns, MultiIndex): - fmt_columns = self.columns.format(sparsify=False, adjoin=False) + if self.max_cols: + columns = self.columns[:self.max_cols] + else: + columns = self.columns + + if isinstance(columns, MultiIndex): + fmt_columns = columns.format(sparsify=False, adjoin=False) fmt_columns = lzip(*fmt_columns) dtypes = self.frame.dtypes.values need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes))) @@ -496,14 +528,14 @@ def is_numeric_dtype(dtype): str_columns = [list(x) for x in zip(*str_columns)] else: - fmt_columns = self.columns.format() + fmt_columns = columns.format() dtypes = self.frame.dtypes need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes))) str_columns = [[' ' + x if not self._get_formatter(i) and need_leadsp[x] else x] for i, (col, x) in - enumerate(zip(self.columns, fmt_columns))] + enumerate(zip(columns, fmt_columns))] if self.show_index_names and self.has_index_names: for x in str_columns: @@ -521,7 +553,10 @@ def has_column_names(self): def _get_formatted_index(self): # Note: this is only used by to_string(), not by to_html(). - index = self.frame.index + if self.max_rows: + index = self.frame.index[:self.max_rows] + else: + index = self.frame.index columns = self.frame.columns show_index_names = self.show_index_names and self.has_index_names @@ -564,7 +599,7 @@ class HTMLFormatter(TableFormatter): indent_delta = 2 - def __init__(self, formatter, classes=None): + def __init__(self, formatter, classes=None, max_rows=None, max_cols=None): self.fmt = formatter self.classes = classes @@ -574,6 +609,9 @@ def __init__(self, formatter, classes=None): self.bold_rows = self.fmt.kwds.get('bold_rows', False) self.escape = self.fmt.kwds.get('escape', True) + self.max_rows = max_rows or len(self.fmt.frame) + self.max_cols = max_cols or len(self.fmt.columns) + def write(self, s, indent=0): rs = com.pprint_thing(s) self.elements.append(' ' * indent + rs) @@ -640,6 +678,8 @@ def write_result(self, buf): 'not %s') % type(self.classes)) _classes.extend(self.classes) + + self.write('<table border="1" class="%s">' % ' '.join(_classes), indent) @@ -656,6 +696,10 @@ def write_result(self, buf): indent = self._write_body(indent) self.write('</table>', indent) + if self.fmt.show_dimensions: + by = chr(215) if compat.PY3 else unichr(215) # × + self.write(u('<p>%d rows %s %d columns</p>') % + (len(frame), by, len(frame.columns)) ) _put_lines(buf, self.elements) def _write_header(self, indent): @@ -680,7 +724,9 @@ def _column_header(): else: if self.fmt.index: row.append(self.columns.name or '') - row.extend(self.columns) + row.extend(self.columns[:self.max_cols]) + if len(self.columns) > self.max_cols: + row.append('') return row self.write('<thead>', indent) @@ -695,6 +741,13 @@ def _column_header(): sentinal = com.sentinal_factory() levels = self.columns.format(sparsify=sentinal, adjoin=False, names=False) + # Truncate column names + if len(levels[0]) > self.max_cols: + levels = [lev[:self.max_cols] for lev in levels] + truncated = True + else: + truncated = False + level_lengths = _get_level_lengths(levels, sentinal) row_levels = self.frame.index.nlevels @@ -716,6 +769,9 @@ def _column_header(): j += 1 row.append(v) + if truncated: + row.append('') + self.write_tr(row, indent, self.indent_delta, tags=tags, header=True) else: @@ -726,8 +782,8 @@ def _column_header(): align=align) if self.fmt.has_index_names: - row = [x if x is not None else '' - for x in self.frame.index.names] + [''] * len(self.columns) + row = [x if x is not None else '' for x in self.frame.index.names] \ + + [''] * min(len(self.columns), self.max_cols) self.write_tr(row, indent, self.indent_delta, header=True) indent -= self.indent_delta @@ -740,15 +796,16 @@ def _write_body(self, indent): indent += self.indent_delta fmt_values = {} - for i in range(len(self.columns)): + for i in range(min(len(self.columns), self.max_cols)): fmt_values[i] = self.fmt._format_col(i) + truncated = (len(self.columns) > self.max_cols) # write values if self.fmt.index: if isinstance(self.frame.index, MultiIndex): self._write_hierarchical_rows(fmt_values, indent) else: - self._write_regular_rows(fmt_values, indent) + self._write_regular_rows(fmt_values, indent, truncated) else: for i in range(len(self.frame)): row = [fmt_values[j][i] for j in range(len(self.columns))] @@ -760,8 +817,8 @@ def _write_body(self, indent): return indent - def _write_regular_rows(self, fmt_values, indent): - ncols = len(self.columns) + def _write_regular_rows(self, fmt_values, indent, truncated): + ncols = min(len(self.columns), self.max_cols) fmt = self.fmt._get_formatter('__index__') if fmt is not None: @@ -769,10 +826,17 @@ def _write_regular_rows(self, fmt_values, indent): else: index_values = self.frame.index.format() - for i in range(len(self.frame)): + for i in range(min(len(self.frame), self.max_rows)): row = [] row.append(index_values[i]) row.extend(fmt_values[j][i] for j in range(ncols)) + if truncated: + row.append('...') + self.write_tr(row, indent, self.indent_delta, tags=None, + nindex_levels=1) + + if len(self.frame) > self.max_rows: + row = [''] + (['...'] * ncols) self.write_tr(row, indent, self.indent_delta, tags=None, nindex_levels=1) @@ -780,7 +844,8 @@ def _write_hierarchical_rows(self, fmt_values, indent): template = 'rowspan="%d" valign="top"' frame = self.frame - ncols = len(self.columns) + ncols = min(len(self.columns), self.max_cols) + truncate = (len(frame) > self.max_rows) idx_values = frame.index.format(sparsify=False, adjoin=False, names=False) @@ -792,9 +857,13 @@ def _write_hierarchical_rows(self, fmt_values, indent): sentinal = com.sentinal_factory() levels = frame.index.format(sparsify=sentinal, adjoin=False, names=False) + # Truncate row names + if truncate: + levels = [lev[:self.max_rows] for lev in levels] + level_lengths = _get_level_lengths(levels, sentinal) - for i in range(len(frame)): + for i in range(min(len(frame), self.max_rows)): row = [] tags = {} @@ -825,6 +894,11 @@ def _write_hierarchical_rows(self, fmt_values, indent): self.write_tr(row, indent, self.indent_delta, tags=None, nindex_levels=frame.index.nlevels) + # Truncation markers (...) + if truncate: + row = ([''] * frame.index.nlevels) + (['...'] * ncols) + self.write_tr(row, indent, self.indent_delta, tags=None) + def _get_level_lengths(levels, sentinal=''): from itertools import groupby @@ -1708,7 +1782,7 @@ def _format_timedelta64(x): return lib.repr_timedelta64(x) -def _make_fixed_width(strings, justify='right', minimum=None): +def _make_fixed_width(strings, justify='right', minimum=None, truncated=False): if len(strings) == 0: return strings @@ -1737,7 +1811,12 @@ def just(x): return justfunc(x, eff_len) - return [just(x) for x in strings] + result = [just(x) for x in strings] + + if truncated: + result.append(justfunc('...'[:max_len], max_len)) + + return result def _trim_zeros(str_floats, na_rep='NaN'): diff --git a/pandas/core/frame.py b/pandas/core/frame.py index e1dafc60e64d8..88cf898d354e9 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -429,6 +429,12 @@ def _repr_fits_horizontal_(self, ignore_width=False): return repr_width < width + def _info_repr(self): + """True if the repr should show the info view.""" + info_repr_option = (get_option("display.large_repr") == "info") + return info_repr_option and not \ + (self._repr_fits_horizontal_() and self._repr_fits_vertical_()) + def __unicode__(self): """ Return a string representation for a particular DataFrame @@ -437,30 +443,18 @@ def __unicode__(self): py2/py3. """ buf = StringIO(u("")) - fits_vertical = self._repr_fits_vertical_() - fits_horizontal = False - if fits_vertical: - # This needs to compute the entire repr - # so don't do it unless rownum is bounded - fits_horizontal = self._repr_fits_horizontal_() - - if fits_vertical and fits_horizontal: - self.to_string(buf=buf) - else: + if self._info_repr(): + self.info(buf=buf) + return buf.getvalue() + + max_rows = get_option("display.max_rows") + max_cols = get_option("display.max_columns") + if get_option("display.expand_frame_repr"): width, _ = fmt.get_console_size() - max_columns = get_option("display.max_columns") - expand_repr = get_option("display.expand_frame_repr") - # within max_cols and max_rows, but cols exceed width - # of terminal, then use expand_repr - if (fits_vertical and - expand_repr and - len(self.columns) <= max_columns): - self.to_string(buf=buf, line_width=width) - else: - max_info_rows = get_option('display.max_info_rows') - verbose = (max_info_rows is None or - self.shape[0] <= max_info_rows) - self.info(buf=buf, verbose=verbose) + else: + width = None + self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols, + line_width=width, show_dimensions=True) return buf.getvalue() @@ -480,28 +474,20 @@ def _repr_html_(self): if com.in_qtconsole(): raise ValueError('Disable HTML output in QtConsole') + if self._info_repr(): + buf = StringIO(u("")) + self.info(buf=buf) + return '<pre>' + buf.getvalue() + '</pre>' + if get_option("display.notebook_repr_html"): - fits_vertical = self._repr_fits_vertical_() - fits_horizontal = False - if fits_vertical: - fits_horizontal = self._repr_fits_horizontal_( - ignore_width=ipnbh) - - if fits_horizontal and fits_vertical: - return ('<div style="max-height:1000px;' - 'max-width:1500px;overflow:auto;">\n' + - self.to_html() + '\n</div>') - else: - buf = StringIO(u("")) - max_info_rows = get_option('display.max_info_rows') - verbose = (max_info_rows is None or - self.shape[0] <= max_info_rows) - self.info(buf=buf, verbose=verbose) - info = buf.getvalue() - info = info.replace('&', r'&amp;') - info = info.replace('<', r'&lt;') - info = info.replace('>', r'&gt;') - return ('<pre>\n' + info + '\n</pre>') + max_rows = get_option("display.max_rows") + max_cols = get_option("display.max_columns") + + return ('<div style="max-height:1000px;' + 'max-width:1500px;overflow:auto;">\n' + + self.to_html(max_rows=max_rows, max_cols=max_cols, + show_dimensions=True) \ + + '\n</div>') else: return None @@ -1269,7 +1255,8 @@ def to_string(self, buf=None, columns=None, col_space=None, colSpace=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, nanRep=None, index_names=True, justify=None, force_unicode=None, - line_width=None): + line_width=None, max_rows=None, max_cols=None, + show_dimensions=False): """ Render a DataFrame to a console-friendly tabular output. """ @@ -1295,7 +1282,9 @@ def to_string(self, buf=None, columns=None, col_space=None, colSpace=None, justify=justify, index_names=index_names, header=header, index=index, - line_width=line_width) + line_width=line_width, + max_rows=max_rows, max_cols=max_cols, + show_dimensions=show_dimensions) formatter.to_string() if buf is None: @@ -1307,7 +1296,8 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, force_unicode=None, bold_rows=True, - classes=None, escape=True): + classes=None, escape=True, max_rows=None, max_cols=None, + show_dimensions=False): """ Render a DataFrame as an HTML table. @@ -1318,7 +1308,12 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, classes : str or list or tuple, default None CSS class(es) to apply to the resulting html table escape : boolean, default True - Convert the characters <, >, and & to HTML-safe sequences. + Convert the characters <, >, and & to HTML-safe sequences.= + max_rows : int, optional + Maximum number of rows to show before truncating. If None, show all. + max_cols : int, optional + Maximum number of columns to show before truncating. If None, show + all. """ @@ -1340,7 +1335,9 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, index_names=index_names, header=header, index=index, bold_rows=bold_rows, - escape=escape) + escape=escape, + max_rows=max_rows, max_cols=max_cols, + show_dimensions=show_dimensions) formatter.to_html(classes=classes) if buf is None: @@ -1386,7 +1383,7 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, def info(self, verbose=True, buf=None, max_cols=None): """ - Concise summary of a DataFrame, used in __repr__ when very large. + Concise summary of a DataFrame. Parameters ---------- diff --git a/pandas/io/clipboard.py b/pandas/io/clipboard.py index 51142c9f52655..13135d255d9e2 100644 --- a/pandas/io/clipboard.py +++ b/pandas/io/clipboard.py @@ -1,5 +1,5 @@ """ io on the clipboard """ -from pandas import compat, get_option +from pandas import compat, get_option, DataFrame from pandas.compat import StringIO def read_clipboard(**kwargs): # pragma: no cover @@ -64,5 +64,10 @@ def to_clipboard(obj, excel=None, sep=None, **kwargs): # pragma: no cover except: pass - clipboard_set(str(obj)) + if isinstance(obj, DataFrame): + # str(df) has various unhelpful defaults, like truncation + objstr = obj.to_string() + else: + objstr = str(obj) + clipboard_set(objstr) diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py index 45b479ebb589e..6ee0afa1c8c07 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/io/tests/test_clipboard.py @@ -7,6 +7,7 @@ from pandas import DataFrame from pandas import read_clipboard +from pandas import get_option from pandas.util import testing as tm from pandas.util.testing import makeCustomDataframe as mkdf @@ -33,6 +34,11 @@ def setUpClass(cls): cls.data['mixed'] = DataFrame({'a': np.arange(1.0, 6.0) + 0.01, 'b': np.arange(1, 6), 'c': list('abcde')}) + # Test GH-5346 + max_rows = get_option('display.max_rows') + cls.data['longdf'] = mkdf(max_rows+1, 3, data_gen_f=lambda *args: randint(2), + c_idx_type='s', r_idx_type='i', + c_idx_names=[None], r_idx_names=[None]) cls.data_types = list(cls.data.keys()) @classmethod diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index d9bf8adb71298..8e23176e9d005 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -3,6 +3,7 @@ from pandas.compat import range, zip, lrange, StringIO, PY3, lzip, u import pandas.compat as compat +import itertools import os import sys import unittest @@ -34,6 +35,20 @@ def has_info_repr(df): r = repr(df) return r.split('\n')[0].startswith("<class") +def has_horizontally_truncated_repr(df): + r = repr(df) + return any(l.strip().endswith('...') for l in r.splitlines()) + +def has_vertically_truncated_repr(df): + r = repr(df) + return '..' in r.splitlines()[-3] + +def has_truncated_repr(df): + return has_horizontally_truncated_repr(df) or has_vertically_truncated_repr(df) + +def has_doubly_truncated_repr(df): + return has_horizontally_truncated_repr(df) and has_vertically_truncated_repr(df) + def has_expanded_repr(df): r = repr(df) for line in r.split('\n'): @@ -113,16 +128,16 @@ def test_repr_truncation(self): def test_repr_chop_threshold(self): df = DataFrame([[0.1, 0.5],[0.5, -0.1]]) pd.reset_option("display.chop_threshold") # default None - self.assertEqual(repr(df), ' 0 1\n0 0.1 0.5\n1 0.5 -0.1') + self.assertEqual(repr(df), ' 0 1\n0 0.1 0.5\n1 0.5 -0.1\n\n[2 rows x 2 columns]') with option_context("display.chop_threshold", 0.2 ): - self.assertEqual(repr(df), ' 0 1\n0 0.0 0.5\n1 0.5 0.0') + self.assertEqual(repr(df), ' 0 1\n0 0.0 0.5\n1 0.5 0.0\n\n[2 rows x 2 columns]') with option_context("display.chop_threshold", 0.6 ): - self.assertEqual(repr(df), ' 0 1\n0 0 0\n1 0 0') + self.assertEqual(repr(df), ' 0 1\n0 0 0\n1 0 0\n\n[2 rows x 2 columns]') with option_context("display.chop_threshold", None ): - self.assertEqual(repr(df), ' 0 1\n0 0.1 0.5\n1 0.5 -0.1') + self.assertEqual(repr(df), ' 0 1\n0 0.1 0.5\n1 0.5 -0.1\n\n[2 rows x 2 columns]') def test_repr_obeys_max_seq_limit(self): import pandas.core.common as com @@ -172,19 +187,19 @@ def test_expand_frame_repr(self): 'display.width',20, 'display.max_rows', 20): with option_context('display.expand_frame_repr', True): - self.assertFalse(has_info_repr(df_small)) + self.assertFalse(has_truncated_repr(df_small)) self.assertFalse(has_expanded_repr(df_small)) - self.assertFalse(has_info_repr(df_wide)) + self.assertFalse(has_truncated_repr(df_wide)) self.assertTrue(has_expanded_repr(df_wide)) - self.assertTrue(has_info_repr(df_tall)) - self.assertFalse(has_expanded_repr(df_tall)) + self.assertTrue(has_vertically_truncated_repr(df_tall)) + self.assertTrue(has_expanded_repr(df_tall)) with option_context('display.expand_frame_repr', False): - self.assertFalse(has_info_repr(df_small)) + self.assertFalse(has_truncated_repr(df_small)) self.assertFalse(has_expanded_repr(df_small)) - self.assertTrue(has_info_repr(df_wide)) + self.assertFalse(has_horizontally_truncated_repr(df_wide)) self.assertFalse(has_expanded_repr(df_wide)) - self.assertTrue(has_info_repr(df_tall)) + self.assertTrue(has_vertically_truncated_repr(df_tall)) self.assertFalse(has_expanded_repr(df_tall)) def test_repr_non_interactive(self): @@ -196,7 +211,7 @@ def test_repr_non_interactive(self): 'display.width', 0, 'display.height', 0, 'display.max_rows',5000): - self.assertFalse(has_info_repr(df)) + self.assertFalse(has_truncated_repr(df)) self.assertFalse(has_expanded_repr(df)) def test_repr_max_columns_max_rows(self): @@ -218,20 +233,20 @@ def mkframe(n): self.assertFalse(has_expanded_repr(mkframe(4))) self.assertFalse(has_expanded_repr(mkframe(5))) self.assertFalse(has_expanded_repr(df6)) - self.assertTrue(has_info_repr(df6)) + self.assertTrue(has_doubly_truncated_repr(df6)) with option_context('display.max_rows', 20, 'display.max_columns', 10): # Out off max_columns boundary, but no extending # since not exceeding width self.assertFalse(has_expanded_repr(df6)) - self.assertFalse(has_info_repr(df6)) + self.assertFalse(has_truncated_repr(df6)) with option_context('display.max_rows', 9, 'display.max_columns', 10): # out vertical bounds can not result in exanded repr self.assertFalse(has_expanded_repr(df10)) - self.assertTrue(has_info_repr(df10)) + self.assertTrue(has_vertically_truncated_repr(df10)) # width=None in terminal, auto detection with option_context('display.max_columns', 100, @@ -723,45 +738,6 @@ def test_frame_info_encoding(self): repr(df.T) fmt.set_option('display.max_rows', 200) - def test_large_frame_repr(self): - def wrap_rows_options(f): - def _f(*args, **kwargs): - old_max_rows = pd.get_option('display.max_rows') - old_max_info_rows = pd.get_option('display.max_info_rows') - o = f(*args, **kwargs) - pd.set_option('display.max_rows', old_max_rows) - pd.set_option('display.max_info_rows', old_max_info_rows) - return o - return _f - - @wrap_rows_options - def test_setting(value, nrows=3, ncols=2): - if value is None: - expected_difference = 0 - elif isinstance(value, int): - expected_difference = ncols - else: - raise ValueError("'value' must be int or None") - - with option_context('mode.sim_interactive', True): - pd.set_option('display.max_rows', nrows - 1) - pd.set_option('display.max_info_rows', value) - - smallx = DataFrame(np.random.rand(nrows, ncols)) - repr_small = repr(smallx) - - bigx = DataFrame(np.random.rand(nrows + 1, ncols)) - repr_big = repr(bigx) - - diff = len(repr_small.splitlines()) - len(repr_big.splitlines()) - - # the difference in line count is the number of columns - self.assertEqual(diff, expected_difference) - - test_setting(None) - test_setting(3) - self.assertRaises(ValueError, test_setting, 'string') - def test_pprint_thing(self): import nose from pandas.core.common import pprint_thing as pp_t @@ -799,6 +775,8 @@ def test_wide_repr(self): df = DataFrame([col(max_cols-1, 25) for _ in range(10)]) set_option('display.expand_frame_repr', False) rep_str = repr(df) + print(rep_str) + assert "10 rows x %d columns" % (max_cols-1) in rep_str set_option('display.expand_frame_repr', True) wide_repr = repr(df) self.assert_(rep_str != wide_repr) @@ -814,7 +792,7 @@ def test_wide_repr_wide_columns(self): df = DataFrame(randn(5, 3), columns=['a' * 90, 'b' * 90, 'c' * 90]) rep_str = repr(df) - self.assert_(len(rep_str.splitlines()) == 20) + self.assertEqual(len(rep_str.splitlines()), 22) def test_wide_repr_named(self): with option_context('mode.sim_interactive', True): @@ -1433,6 +1411,114 @@ def test_repr_html(self): fmt.reset_option('^display.') + def test_repr_html_wide(self): + row = lambda l, k: [tm.rands(k) for _ in range(l)] + max_cols = get_option('display.max_columns') + df = DataFrame([row(max_cols-1, 25) for _ in range(10)]) + reg_repr = df._repr_html_() + assert "..." not in reg_repr + + wide_df = DataFrame([row(max_cols+1, 25) for _ in range(10)]) + wide_repr = wide_df._repr_html_() + assert "..." in wide_repr + + def test_repr_html_wide_multiindex_cols(self): + row = lambda l, k: [tm.rands(k) for _ in range(l)] + max_cols = get_option('display.max_columns') + + tuples = list(itertools.product(np.arange(max_cols//2), ['foo', 'bar'])) + mcols = pandas.MultiIndex.from_tuples(tuples, names=['first', 'second']) + df = DataFrame([row(len(mcols), 25) for _ in range(10)], columns=mcols) + reg_repr = df._repr_html_() + assert '...' not in reg_repr + + + tuples = list(itertools.product(np.arange(1+(max_cols//2)), ['foo', 'bar'])) + mcols = pandas.MultiIndex.from_tuples(tuples, names=['first', 'second']) + df = DataFrame([row(len(mcols), 25) for _ in range(10)], columns=mcols) + wide_repr = df._repr_html_() + assert '...' in wide_repr + + def test_repr_html_long(self): + max_rows = get_option('display.max_rows') + h = max_rows - 1 + df = pandas.DataFrame({'A':np.arange(1,1+h), 'B':np.arange(41, 41+h)}) + reg_repr = df._repr_html_() + assert '...' not in reg_repr + assert str(40 + h) in reg_repr + + h = max_rows + 1 + df = pandas.DataFrame({'A':np.arange(1,1+h), 'B':np.arange(41, 41+h)}) + long_repr = df._repr_html_() + assert '...' in long_repr + assert str(40 + h) not in long_repr + assert u('%d rows ') % h in long_repr + assert u('2 columns') in long_repr + + def test_repr_html_long_multiindex(self): + max_rows = get_option('display.max_rows') + max_L1 = max_rows//2 + + tuples = list(itertools.product(np.arange(max_L1), ['foo', 'bar'])) + idx = pandas.MultiIndex.from_tuples(tuples, names=['first', 'second']) + df = DataFrame(np.random.randn(max_L1*2, 2), index=idx, + columns=['A', 'B']) + reg_repr = df._repr_html_() + assert '...' not in reg_repr + + tuples = list(itertools.product(np.arange(max_L1+1), ['foo', 'bar'])) + idx = pandas.MultiIndex.from_tuples(tuples, names=['first', 'second']) + df = DataFrame(np.random.randn((max_L1+1)*2, 2), index=idx, + columns=['A', 'B']) + long_repr = df._repr_html_() + assert '...' in long_repr + + def test_repr_html_long_and_wide(self): + max_cols = get_option('display.max_columns') + max_rows = get_option('display.max_rows') + + h, w = max_rows-1, max_cols-1 + df = pandas.DataFrame(dict((k,np.arange(1,1+h)) for k in np.arange(w))) + assert '...' not in df._repr_html_() + + h, w = max_rows+1, max_cols+1 + df = pandas.DataFrame(dict((k,np.arange(1,1+h)) for k in np.arange(w))) + assert '...' in df._repr_html_() + + def test_info_repr(self): + max_rows = get_option('display.max_rows') + max_cols = get_option('display.max_columns') + # Long + h, w = max_rows+1, max_cols-1 + df = pandas.DataFrame(dict((k,np.arange(1,1+h)) for k in np.arange(w))) + assert has_vertically_truncated_repr(df) + with option_context('display.large_repr', 'info'): + assert has_info_repr(df) + + # Wide + h, w = max_rows-1, max_cols+1 + df = pandas.DataFrame(dict((k,np.arange(1,1+h)) for k in np.arange(w))) + assert has_vertically_truncated_repr(df) + with option_context('display.large_repr', 'info'): + assert has_info_repr(df) + + def test_info_repr_html(self): + max_rows = get_option('display.max_rows') + max_cols = get_option('display.max_columns') + # Long + h, w = max_rows+1, max_cols-1 + df = pandas.DataFrame(dict((k,np.arange(1,1+h)) for k in np.arange(w))) + assert '<class' not in df._repr_html_() + with option_context('display.large_repr', 'info'): + assert '<class' in df._repr_html_() + + # Wide + h, w = max_rows-1, max_cols+1 + df = pandas.DataFrame(dict((k,np.arange(1,1+h)) for k in np.arange(w))) + assert '<class' not in df._repr_html_() + with option_context('display.large_repr', 'info'): + assert '<class' in df._repr_html_() + def test_fake_qtconsole_repr_html(self): def get_ipython(): return {'config': @@ -1483,7 +1569,7 @@ def test_float_trim_zeros(self): vals = [2.08430917305e+10, 3.52205017305e+10, 2.30674817305e+10, 2.03954217305e+10, 5.59897817305e+10] skip = True - for line in repr(DataFrame({'A': vals})).split('\n'): + for line in repr(DataFrame({'A': vals})).split('\n')[:-2]: if line.startswith('dtype:'): continue if _three_digit_exp():
As discussed in #4886, the HTML representation of DataFrames currently starts off as a table, but switches to the condensed info view if the table exceeds a certain size (by default, more than 60 rows or 20 columns). I've seen this confusing users, who think that they suddenly have a completely different kind of object, and don't understand why. With these changes, the HTML repr always displays the table, but truncates it when it exceeds a certain size. It reuses the same options, `display.max_rows` and `display.max_columns`. Before: ![pandas_long_repr_before](https://f.cloud.github.com/assets/327925/1575857/ae103a8c-5153-11e3-8a2c-f770cc76bdc2.png) ![pandas_wide_repr_before](https://f.cloud.github.com/assets/327925/1575858/ae40b22a-5153-11e3-8c1e-3d9a632a5136.png) After: ![pandas_long_repr_after](https://f.cloud.github.com/assets/327925/1575860/b35a2caa-5153-11e3-9887-69302703f545.png) ![pandas_wide_repr_after](https://f.cloud.github.com/assets/327925/1575861/b35c7bc2-5153-11e3-872c-6a85523f93db.png)
https://api.github.com/repos/pandas-dev/pandas/pulls/5550
2013-11-19T19:49:46Z
2013-11-26T01:04:01Z
2013-11-26T01:04:01Z
2014-06-12T17:07:25Z
DOC: mention new to_clipboard/paste into excel capability in release docs
diff --git a/doc/source/release.rst b/doc/source/release.rst index 4a4726c78a677..6ba31fbc61a05 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -57,6 +57,8 @@ New features the bandwidth, and to gkde.evaluate() to specify the indicies at which it is evaluated, respecttively. See scipy docs. (:issue:`4298`) - Added ``isin`` method to DataFrame (:issue:`4211`) + - ``df.to_clipboard()`` learned a new ``excel`` keyword that let's you + paste df data directly into excel (enabled by default). (:issue:`5070`). - Clipboard functionality now works with PySide (:issue:`4282`) - New ``extract`` string method returns regex matches more conveniently (:issue:`4685`) diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 70fc0572ad8fb..6e908ce739a1d 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -378,6 +378,8 @@ HDFStore API Changes Enhancements ~~~~~~~~~~~~ +- ``df.to_clipboard()`` learned a new ``excel`` keyword that let's you + paste df data directly into excel (enabled by default). (:issue:`5070`). - ``read_html`` now raises a ``URLError`` instead of catching and raising a ``ValueError`` (:issue:`4303`, :issue:`4305`) - Added a test for ``read_clipboard()`` and ``to_clipboard()`` (:issue:`4282`)
https://api.github.com/repos/pandas-dev/pandas/pulls/5548
2013-11-19T11:52:13Z
2013-11-19T11:52:30Z
2013-11-19T11:52:30Z
2014-07-16T08:40:50Z
DOC: Fixing a good number of spelling mistakes in 0.13 release notes
diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 70fc0572ad8fb..2d01f1fb49942 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -49,10 +49,10 @@ API changes index = index.set_levels([[1, 2, 3, 4], [1, 2, 4, 4]]) # similarly, for names, you can rename the object - # but setting names is not deprecated. + # but setting names is not deprecated index = index.set_names(["bob", "cranberry"]) - # and all methods take an inplace kwarg - but returns None + # and all methods take an inplace kwarg - but return None index.set_names(["bob", "cranberry"], inplace=True) - **All** division with ``NDFrame`` - likes is now truedivision, regardless @@ -80,7 +80,7 @@ API changes - ``__nonzero__`` for all NDFrame objects, will now raise a ``ValueError``, this reverts back to (:issue:`1073`, :issue:`4633`) behavior. See :ref:`gotchas<gotchas.truth>` for a more detailed discussion. - This prevents doing boolean comparision on *entire* pandas objects, which is inherently ambiguous. These all will raise a ``ValueError``. + This prevents doing boolean comparison on *entire* pandas objects, which is inherently ambiguous. These all will raise a ``ValueError``. .. code-block:: python @@ -106,7 +106,7 @@ API changes statistical mode(s) by axis/Series. (:issue:`5367`) - Chained assignment will now by default warn if the user is assigning to a copy. This can be changed - with he option ``mode.chained_assignment``, allowed options are ``raise/warn/None``. See :ref:`the docs<indexing.view_versus_copy>`. + with the option ``mode.chained_assignment``, allowed options are ``raise/warn/None``. See :ref:`the docs<indexing.view_versus_copy>`. .. ipython:: python @@ -158,7 +158,7 @@ Deprecated in 0.13.0 of ``match`` will change to become analogous to ``contains``, which returns a boolean indexer. (Their distinction is strictness: ``match`` relies on ``re.match`` while - ``contains`` relies on ``re.serach``.) In this release, the deprecated + ``contains`` relies on ``re.search``.) In this release, the deprecated behavior is the default, but the new behavior is available through the keyword argument ``as_indexer=True``. @@ -310,8 +310,8 @@ HDFStore API Changes os.remove(path) - the ``format`` keyword now replaces the ``table`` keyword; allowed values are ``fixed(f)`` or ``table(t)`` - the same defaults as prior < 0.13.0 remain, e.g. ``put`` implies ``fixed`` format - and ``append`` imples ``table`` format. This default format can be set as an option by setting ``io.hdf.default_format``. + the same defaults as prior < 0.13.0 remain, e.g. ``put`` implies ``fixed`` format and ``append`` implies + ``table`` format. This default format can be set as an option by setting ``io.hdf.default_format``. .. ipython:: python @@ -443,7 +443,7 @@ Enhancements td * -1 td * Series([1,2,3,4]) - Absolute ``DateOffset`` objects can act equivalenty to ``timedeltas`` + Absolute ``DateOffset`` objects can act equivalently to ``timedeltas`` .. ipython:: python @@ -467,8 +467,8 @@ Enhancements - ``plot(kind='kde')`` now accepts the optional parameters ``bw_method`` and ``ind``, passed to scipy.stats.gaussian_kde() (for scipy >= 0.11.0) to set - the bandwidth, and to gkde.evaluate() to specify the indicies at which it - is evaluated, respecttively. See scipy docs. (:issue:`4298`) + the bandwidth, and to gkde.evaluate() to specify the indices at which it + is evaluated, respectively. See scipy docs. (:issue:`4298`) - DataFrame constructor now accepts a numpy masked record array (:issue:`3478`) @@ -752,7 +752,7 @@ Experimental df3 = pandas.concat([df2.min(), df2.mean(), df2.max()], axis=1,keys=["Min Tem", "Mean Temp", "Max Temp"]) - The resulting dataframe is:: + The resulting DataFrame is:: > df3 Min Tem Mean Temp Max Temp @@ -842,7 +842,7 @@ to unify methods and behaviors. Series formerly subclassed directly from - ``swapaxes`` on a ``Panel`` with the same axes specified now return a copy - support attribute access for setting - - filter supports same api as original ``DataFrame`` filter + - filter supports the same API as the original ``DataFrame`` filter - Reindex called with no arguments will now return a copy of the input object @@ -870,11 +870,11 @@ to unify methods and behaviors. Series formerly subclassed directly from - enable setitem on ``SparseSeries`` for boolean/integer/slices - ``SparsePanels`` implementation is unchanged (e.g. not using BlockManager, needs work) -- added ``ftypes`` method to Series/DataFame, similar to ``dtypes``, but indicates +- added ``ftypes`` method to Series/DataFrame, similar to ``dtypes``, but indicates if the underlying is sparse/dense (as well as the dtype) -- All ``NDFrame`` objects now have a ``_prop_attributes``, which can be used to indcated various - values to propogate to a new object from an existing (e.g. name in ``Series`` will follow - more automatically now) +- All ``NDFrame`` objects can now use ``__finalize__()`` to specify various + values to propagate to new objects from an existing one (e.g. ``name`` in ``Series`` will + follow more automatically now) - Internal type checking is now done via a suite of generated classes, allowing ``isinstance(value, klass)`` without having to directly import the klass, courtesy of @jtratner - Bug in Series update where the parent frame is not updating its cache based on @@ -886,7 +886,7 @@ to unify methods and behaviors. Series formerly subclassed directly from - Refactor ``rename`` methods to core/generic.py; fixes ``Series.rename`` for (:issue:`4605`), and adds ``rename`` with the same signature for ``Panel`` - Refactor ``clip`` methods to core/generic.py (:issue:`4798`) -- Refactor of ``_get_numeric_data/_get_bool_data`` to core/generic.py, allowing Series/Panel functionaility +- Refactor of ``_get_numeric_data/_get_bool_data`` to core/generic.py, allowing Series/Panel functionality - ``Series`` (for index) / ``Panel`` (for items) now allow attribute access to its elements (:issue:`1903`) .. ipython:: python
https://api.github.com/repos/pandas-dev/pandas/pulls/5547
2013-11-19T10:58:32Z
2013-11-19T23:36:10Z
2013-11-19T23:36:10Z
2014-07-16T08:40:49Z
BUG: Bug in delitem on a Series (GH5542)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 8d39db39ad4a6..4a4726c78a677 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -809,6 +809,7 @@ Bug Fixes - Fixed various setitem with 1d ndarray that does not have a matching length to the indexer (:issue:`5508`) - Bug in getitem with a multi-index and ``iloc`` (:issue:`5528`) + - Bug in delitem on a Series (:issue:`5542`) pandas 0.12.0 ------------- diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 44a18ef3043b3..79667ecddc8a6 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1969,7 +1969,7 @@ def ndim(self): self._ndim = len(self.axes) return self._ndim - def set_axis(self, axis, value, maybe_rename=True, check_axis=True): + def _set_axis(self, axis, value, check_axis=True): cur_axis = self.axes[axis] value = _ensure_index(value) @@ -1980,6 +1980,10 @@ def set_axis(self, axis, value, maybe_rename=True, check_axis=True): self.axes[axis] = value self._shape = None + return cur_axis, value + + def set_axis(self, axis, value, maybe_rename=True, check_axis=True): + cur_axis, value = self._set_axis(axis, value, check_axis) if axis == 0: @@ -3473,24 +3477,22 @@ def reindex_axis0_with_method(self, new_axis, indexer=None, method=None, return self.reindex(new_axis, indexer=indexer, method=method, fill_value=fill_value, limit=limit, copy=copy) + def _delete_from_block(self, i, item): + super(SingleBlockManager, self)._delete_from_block(i, item) + + # reset our state + self._block = self.blocks[0] if len(self.blocks) else make_block(np.array([],dtype=self._block.dtype),[],[]) + self._values = self._block.values + def get_slice(self, slobj, raise_on_error=False): if raise_on_error: _check_slice_bounds(slobj, self.index) return self.__class__(self._block._slice(slobj), self.index._getitem_slice(slobj), fastpath=True) - def set_axis(self, axis, value): - cur_axis = self.axes[axis] - value = _ensure_index(value) - - if len(value) != len(cur_axis): - raise ValueError('Length mismatch: Expected axis has %d elements, ' - 'new values have %d elements' % (len(cur_axis), - len(value))) - - self.axes[axis] = value - self._shape = None - self._block.set_ref_items(self.items, maybe_rename=True) + def set_axis(self, axis, value, maybe_rename=True, check_axis=True): + cur_axis, value = self._set_axis(axis, value, check_axis) + self._block.set_ref_items(self.items, maybe_rename=maybe_rename) def set_ref_items(self, ref_items, maybe_rename=True): """ we can optimize and our ref_locs are always equal to ref_items """ diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index b6f0387835c22..c44ede057adb2 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -113,6 +113,35 @@ def test_combine_first_dt64(self): xp = Series([datetime(2010, 1, 1), '2011']) assert_series_equal(rs, xp) + def test_delitem(self): + + # GH 5542 + # should delete the item inplace + s = Series(lrange(5)) + del s[0] + + expected = Series(lrange(1,5),index=lrange(1,5)) + assert_series_equal(s, expected) + + del s[1] + expected = Series(lrange(2,5),index=lrange(2,5)) + assert_series_equal(s, expected) + + # empty + s = Series() + def f(): + del s[0] + self.assertRaises(KeyError, f) + + # only 1 left, del, add, del + s = Series(1) + del s[0] + assert_series_equal(s, Series(dtype='int64')) + s[0] = 1 + assert_series_equal(s, Series(1)) + del s[0] + assert_series_equal(s, Series(dtype='int64')) + def test_getitem_preserve_name(self): result = self.ts[self.ts > 0] self.assertEquals(result.name, self.ts.name)
closes #5542
https://api.github.com/repos/pandas-dev/pandas/pulls/5544
2013-11-19T01:00:56Z
2013-11-19T01:49:54Z
2013-11-19T01:49:54Z
2014-06-23T11:03:02Z
TST: fix cumcount test on 32-bit env
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 9df5541615cee..da20dadb062fc 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2574,7 +2574,7 @@ def test_cumcount_empty(self): ge = DataFrame().groupby() se = Series().groupby() - e = Series(dtype='int') # edge case, as this is usually considered float + e = Series(dtype='int64') # edge case, as this is usually considered float assert_series_equal(e, ge.cumcount()) assert_series_equal(e, se.cumcount())
Fix `test_groupby.test_cumcount_empty()` on 32-bit platforms by specifying expected dtype of 'int64'. ``` ====================================================================== FAIL: test_cumcount_empty (pandas.tests.test_groupby.TestGroupBy) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/gmd/github_shadow/pandas/pandas/tests/test_groupby.py", line 2579, in test_cumcount_empty assert_series_equal(e, ge.cumcount()) File "/home/gmd/github_shadow/pandas/pandas/util/testing.py", line 423, in assert_series_equal assert_attr_equal('dtype', left, right) File "/home/gmd/github_shadow/pandas/pandas/util/testing.py", line 407, in assert_attr_equal assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr)) File "/home/gmd/github_shadow/pandas/pandas/util/testing.py", line 390, in assert_equal assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b) AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64') ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5540
2013-11-18T01:29:16Z
2013-11-18T01:48:18Z
2013-11-18T01:48:18Z
2014-06-12T16:56:31Z
TST: correctly predict locale setability
diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index 614f5ecc39e9d..66ae52983b692 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -65,7 +65,7 @@ def test_set_locale(self): enc = codecs.lookup(enc).name new_locale = lang, enc - if not tm._can_set_locale('.'.join(new_locale)): + if not tm._can_set_locale(new_locale): with tm.assertRaises(locale.Error): with tm.set_locale(new_locale): pass
Don't join the lang and encoding when performing the trial set, because ``` locale.setlocale(locale.LC_ALL, ('it_CH', 'utf-8')) ``` succeeds, but ``` locale.setlocale(locale.LC_ALL, 'it_CH.utf-8') ``` throws locale.Error. Closes #5537.
https://api.github.com/repos/pandas-dev/pandas/pulls/5538
2013-11-17T14:24:40Z
2013-11-17T15:00:14Z
2013-11-17T15:00:14Z
2014-06-19T22:50:55Z
PERF faster head, tail and size groupby methods
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index e763700d08cf4..20f17a7f42472 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -52,7 +52,6 @@ _apply_whitelist = frozenset(['last', 'first', 'mean', 'sum', 'min', 'max', - 'head', 'tail', 'cumsum', 'cumprod', 'cummin', 'cummax', 'resample', 'describe', @@ -482,13 +481,19 @@ def picker(arr): return np.nan return self.agg(picker) - def cumcount(self): - """Number each item in each group from 0 to the length of that group. + def cumcount(self, **kwargs): + """ + Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: Series(np.arange(len(x)), x.index)) + Parameters + ---------- + ascending : bool, default True + If False, number in reverse, from length of group - 1 to 0. + Example ------- @@ -510,14 +515,111 @@ def cumcount(self): 4 1 5 3 dtype: int64 + >>> df.groupby('A').cumcount(ascending=False) + 0 3 + 1 2 + 2 1 + 3 1 + 4 0 + 5 0 + dtype: int64 """ + ascending = kwargs.pop('ascending', True) + index = self.obj.index - cumcounts = np.zeros(len(index), dtype='int64') - for v in self.indices.values(): - cumcounts[v] = np.arange(len(v), dtype='int64') + rng = np.arange(self.grouper._max_groupsize, dtype='int64') + cumcounts = self._cumcount_array(rng, ascending=ascending) return Series(cumcounts, index) + def head(self, n=5): + """ + Returns first n rows of each group. + + Essentially equivalent to ``.apply(lambda x: x.head(n))`` + + Example + ------- + + >>> df = DataFrame([[1, 2], [1, 4], [5, 6]], + columns=['A', 'B']) + >>> df.groupby('A', as_index=False).head(1) + A B + 0 1 2 + 2 5 6 + >>> df.groupby('A').head(1) + A B + A + 1 0 1 2 + 5 2 5 6 + + """ + rng = np.arange(self.grouper._max_groupsize, dtype='int64') + in_head = self._cumcount_array(rng) < n + head = self.obj[in_head] + if self.as_index: + head.index = self._index_with_as_index(in_head) + return head + + def tail(self, n=5): + """ + Returns last n rows of each group + + Essentially equivalent to ``.apply(lambda x: x.tail(n))`` + + Example + ------- + + >>> df = DataFrame([[1, 2], [1, 4], [5, 6]], + columns=['A', 'B']) + >>> df.groupby('A', as_index=False).tail(1) + A B + 0 1 2 + 2 5 6 + >>> df.groupby('A').head(1) + A B + A + 1 0 1 2 + 5 2 5 6 + + """ + rng = np.arange(0, -self.grouper._max_groupsize, -1, dtype='int64') + in_tail = self._cumcount_array(rng, ascending=False) > -n + tail = self.obj[in_tail] + if self.as_index: + tail.index = self._index_with_as_index(in_tail) + return tail + + def _cumcount_array(self, arr, **kwargs): + ascending = kwargs.pop('ascending', True) + + len_index = len(self.obj.index) + cumcounts = np.zeros(len_index, dtype='int64') + if ascending: + for v in self.indices.values(): + cumcounts[v] = arr[:len(v)] + else: + for v in self.indices.values(): + cumcounts[v] = arr[len(v)-1::-1] + return cumcounts + + def _index_with_as_index(self, b): + """ + Take boolean mask of index to be returned from apply, if as_index=True + + """ + # TODO perf, it feels like this should already be somewhere... + from itertools import chain + original = self.obj.index + gp = self.grouper + levels = chain((gp.levels[i][gp.labels[i][b]] + for i in range(len(gp.groupings))), + (original.get_level_values(i)[b] + for i in range(original.nlevels))) + new = MultiIndex.from_arrays(list(levels)) + new.names = gp.names + original.names + return new + def _try_cast(self, result, obj): """ try to cast the result to our obj original type, @@ -758,14 +860,28 @@ def names(self): def size(self): """ Compute group sizes + """ # TODO: better impl labels, _, ngroups = self.group_info - bin_counts = Series(labels).value_counts() + bin_counts = algos.value_counts(labels, sort=False) bin_counts = bin_counts.reindex(np.arange(ngroups)) bin_counts.index = self.result_index return bin_counts + @cache_readonly + def _max_groupsize(self): + ''' + Compute size of largest group + + ''' + # For many items in each group this is much faster than + # self.size().max(), in worst case marginally slower + if self.indices: + return max(len(v) for v in self.indices.values()) + else: + return 0 + @cache_readonly def groups(self): if len(self.groupings) == 1: diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 9df5541615cee..9c636168114c7 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1203,24 +1203,64 @@ def test_groupby_as_index_apply(self): g_not_as = df.groupby('user_id', as_index=False) res_as = g_as.head(2).index - exp_as = MultiIndex.from_tuples([(1, 0), (1, 2), (2, 1), (3, 4)]) + exp_as = MultiIndex.from_tuples([(1, 0), (2, 1), (1, 2), (3, 4)]) assert_index_equal(res_as, exp_as) res_not_as = g_not_as.head(2).index - exp_not_as = Index([0, 2, 1, 4]) + exp_not_as = Index([0, 1, 2, 4]) assert_index_equal(res_not_as, exp_not_as) - res_as = g_as.apply(lambda x: x.head(2)).index - assert_index_equal(res_not_as, exp_not_as) + res_as_apply = g_as.apply(lambda x: x.head(2)).index + res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index - res_not_as = g_not_as.apply(lambda x: x.head(2)).index - assert_index_equal(res_not_as, exp_not_as) + # apply doesn't maintain the original ordering + exp_not_as_apply = Index([0, 2, 1, 4]) + exp_as_apply = MultiIndex.from_tuples([(1, 0), (1, 2), (2, 1), (3, 4)]) + + assert_index_equal(res_as_apply, exp_as_apply) + assert_index_equal(res_not_as_apply, exp_not_as_apply) ind = Index(list('abcde')) df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind) res = df.groupby(0, as_index=False).apply(lambda x: x).index assert_index_equal(res, ind) + def test_groupby_head_tail(self): + df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) + g_as = df.groupby('A', as_index=True) + g_not_as = df.groupby('A', as_index=False) + + # as_index= False, much easier + assert_frame_equal(df.loc[[0, 2]], g_not_as.head(1)) + assert_frame_equal(df.loc[[1, 2]], g_not_as.tail(1)) + + empty_not_as = DataFrame(columns=df.columns) + assert_frame_equal(empty_not_as, g_not_as.head(0)) + assert_frame_equal(empty_not_as, g_not_as.tail(0)) + assert_frame_equal(empty_not_as, g_not_as.head(-1)) + assert_frame_equal(empty_not_as, g_not_as.tail(-1)) + + assert_frame_equal(df, g_not_as.head(7)) # contains all + assert_frame_equal(df, g_not_as.tail(7)) + + # as_index=True, yuck + # prepend the A column as an index, in a roundabout way + df_as = df.copy() + df_as.index = df.set_index('A', append=True, + drop=False).index.swaplevel(0, 1) + + assert_frame_equal(df_as.loc[[0, 2]], g_as.head(1)) + assert_frame_equal(df_as.loc[[1, 2]], g_as.tail(1)) + + empty_as = DataFrame(index=df_as.index[:0], columns=df.columns) + assert_frame_equal(empty_as, g_as.head(0)) + assert_frame_equal(empty_as, g_as.tail(0)) + assert_frame_equal(empty_as, g_as.head(-1)) + assert_frame_equal(empty_as, g_as.tail(-1)) + + assert_frame_equal(df_as, g_as.head(7)) # contains all + assert_frame_equal(df_as, g_as.tail(7)) + def test_groupby_multiple_key(self): df = tm.makeTimeDataFrame() grouped = df.groupby([lambda x: x.year,
Try again with #5518. Massive gains in groupby head and tail, adds more tests for these, slight speed improvement in size (not as much as I'd hoped, basically iterating through grouper.indices is slow :( ). _As mentioned before, I added a helper function to prepend as_index to index (if that makes **any** sense), I think it could be faster, and also using it in apply may fix some bugs there..._
https://api.github.com/repos/pandas-dev/pandas/pulls/5533
2013-11-17T04:47:17Z
2013-11-20T01:42:36Z
2013-11-20T01:42:36Z
2014-07-16T08:40:40Z
sphinxext py3 compatibility
diff --git a/doc/sphinxext/numpydoc/docscrape.py b/doc/sphinxext/numpydoc/docscrape.py index 4ee0f2e400d0e..2c49ed84ad224 100755 --- a/doc/sphinxext/numpydoc/docscrape.py +++ b/doc/sphinxext/numpydoc/docscrape.py @@ -499,10 +499,14 @@ def splitlines_x(s): for field, items in [('Methods', self.methods), ('Attributes', self.properties)]: if not self[field]: - self[field] = [ - (name, '', - splitlines_x(pydoc.getdoc(getattr(self._cls, name)))) - for name in sorted(items)] + doc_list = [] + for name in sorted(items): + try: + doc_item = pydoc.getdoc(getattr(self._cls, name)) + doc_list.append((name, '', splitlines_x(doc_item))) + except AttributeError: + pass # method doesn't exist + self[field] = doc_list @property def methods(self):
Temporary sphinxext py3 compatibility fix as discussed in #5479 before #5221 is resolved. So far test_docscrape.py passes on both Python 2.7 and 3.3 (which should be the typical developer interpreters).
https://api.github.com/repos/pandas-dev/pandas/pulls/5530
2013-11-16T20:43:28Z
2014-01-30T10:49:25Z
2014-01-30T10:49:25Z
2014-06-12T19:56:32Z
BUG: Bug in getitem with a multi-index and iloc (GH5528)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 24811bc7b1b45..8d39db39ad4a6 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -808,6 +808,7 @@ Bug Fixes - performance improvements in ``isnull`` on larger size pandas objects - Fixed various setitem with 1d ndarray that does not have a matching length to the indexer (:issue:`5508`) + - Bug in getitem with a multi-index and ``iloc`` (:issue:`5528`) pandas 0.12.0 ------------- diff --git a/pandas/core/index.py b/pandas/core/index.py index 1f2e823833810..096aff548dc9c 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -2826,6 +2826,11 @@ def reindex(self, target, method=None, level=None, limit=None, ------- (new_index, indexer, mask) : (MultiIndex, ndarray, ndarray) """ + + # a direct takeable + if takeable: + return self.take(target), target + if level is not None: if method is not None: raise TypeError('Fill method not supported if level passed') diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index e01fd6a763ceb..86c3808781694 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -389,6 +389,37 @@ def test_iloc_getitem_slice_dups(self): assert_frame_equal(df.iloc[10:,:2],df2) assert_frame_equal(df.iloc[10:,2:],df1) + def test_iloc_getitem_multiindex(self): + + df = DataFrame(np.random.randn(3, 3), + columns=[[2,2,4],[6,8,10]], + index=[[4,4,8],[8,10,12]]) + + rs = df.iloc[2] + xp = df.irow(2) + assert_series_equal(rs, xp) + + rs = df.iloc[:,2] + xp = df.icol(2) + assert_series_equal(rs, xp) + + rs = df.iloc[2,2] + xp = df.values[2,2] + self.assert_(rs == xp) + + # for multiple items + # GH 5528 + rs = df.iloc[[0,1]] + xp = df.xs(4,drop_level=False) + assert_frame_equal(rs,xp) + + tup = zip(*[['a','a','b','b'],['x','y','x','y']]) + index = MultiIndex.from_tuples(tup) + df = DataFrame(np.random.randn(4, 4), index=index) + rs = df.iloc[[2, 3]] + xp = df.xs('b',drop_level=False) + assert_frame_equal(rs,xp) + def test_iloc_getitem_out_of_bounds(self): # out-of-bounds slice @@ -409,23 +440,6 @@ def test_iloc_setitem(self): result = df.iloc[:,2:3] assert_frame_equal(result, expected) - def test_iloc_multiindex(self): - df = DataFrame(np.random.randn(3, 3), - columns=[[2,2,4],[6,8,10]], - index=[[4,4,8],[8,10,12]]) - - rs = df.iloc[2] - xp = df.irow(2) - assert_series_equal(rs, xp) - - rs = df.iloc[:,2] - xp = df.icol(2) - assert_series_equal(rs, xp) - - rs = df.iloc[2,2] - xp = df.values[2,2] - self.assert_(rs == xp) - def test_loc_getitem_int(self): # int label @@ -704,7 +718,7 @@ def test_iloc_setitem_series(self): result = s.iloc[:4] assert_series_equal(result, expected) - def test_iloc_multiindex(self): + def test_iloc_getitem_multiindex(self): mi_labels = DataFrame(np.random.randn(4, 3), columns=[['i', 'i', 'j'], ['A', 'A', 'B']], index=[['i', 'i', 'j', 'k'], ['X', 'X', 'Y','Y']])
closes #5528
https://api.github.com/repos/pandas-dev/pandas/pulls/5529
2013-11-16T17:27:53Z
2013-11-16T19:13:47Z
2013-11-16T19:13:47Z
2014-06-18T13:18:30Z
StataWriter: Replace non-isalnum characters in variable names by _ inste...
diff --git a/doc/source/release.rst b/doc/source/release.rst index 3f148748081b9..df0a27169e6df 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -221,6 +221,7 @@ Improvements to existing features MultiIndex and Hierarchical Rows. Set the ``merge_cells`` to ``False`` to restore the previous behaviour. (:issue:`5254`) - The FRED DataReader now accepts multiple series (:issue`3413`) + - StataWriter adjusts variable names to Stata's limitations API Changes ~~~~~~~~~~~ diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 1d0d1d17ec631..52c1ba463fc00 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -957,11 +957,49 @@ def _write_descriptors(self, typlist=None, varlist=None, srtlist=None, self._write(typ) # varlist, length 33*nvar, char array, null terminated + converted_names = [] + duplicate_var_id = 0 + for j, name in enumerate(self.varlist): + orig_name = name + # Replaces all characters disallowed in .dta format by their integral representation. + for c in name: + if (c < 'A' or c > 'Z') and (c < 'a' or c > 'z') and (c < '0' or c > '9') and c != '_': + name = name.replace(c, '_') + + # Variable name may not start with a number + if name[0] > '0' and name[0] < '9': + name = '_' + name + + name = name[:min(len(name), 32)] + + if not name == orig_name: + # check for duplicates + while self.varlist.count(name) > 0: + # prepend ascending number to avoid duplicates + name = '_' + str(duplicate_var_id) + name + name = name[:min(len(name), 32)] + duplicate_var_id += 1 + + converted_names.append('{0} -> {1}'.format(orig_name, name)) + self.varlist[j] = name + for name in self.varlist: name = self._null_terminate(name, True) name = _pad_bytes(name[:32], 33) self._write(name) + if converted_names: + from warnings import warn + warn("""Not all pandas column names were valid Stata variable names. + Made the following replacements: + + {0} + + If this is not what you expect, please make sure you have Stata-compliant + column names in your DataFrame (max 32 characters, only alphanumerics and + underscores)/ + """.format('\n '.join(converted_names))) + # srtlist, 2*(nvar+1), int array, encoded by byteorder srtlist = _pad_bytes("", (2*(nvar+1))) self._write(srtlist) diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 76dae396c04ed..24eb2dfbc0372 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -231,6 +231,42 @@ def test_encoding(self): self.assert_(result == expected) self.assert_(isinstance(result, unicode)) + def test_read_write_dta11(self): + original = DataFrame([(1, 2, 3, 4)], + columns=['good', 'bäd', '8number', 'astringwithmorethan32characters______']) + if compat.PY3: + formatted = DataFrame([(1, 2, 3, 4)], + columns=['good', 'b_d', '_8number', 'astringwithmorethan32characters_']) + else: + formatted = DataFrame([(1, 2, 3, 4)], + columns=['good', 'b__d', '_8number', 'astringwithmorethan32characters_']) + formatted.index.name = 'index' + + with tm.ensure_clean() as path: + with warnings.catch_warnings(record=True) as w: + original.to_stata(path, None, False) + np.testing.assert_equal( + len(w), 1) # should get a warning for that format. + + written_and_read_again = self.read_dta(path) + tm.assert_frame_equal(written_and_read_again.set_index('index'), formatted) + + def test_read_write_dta12(self): + original = DataFrame([(1, 2, 3, 4)], + columns=['astringwithmorethan32characters_1', 'astringwithmorethan32characters_2', '+', '-']) + formatted = DataFrame([(1, 2, 3, 4)], + columns=['astringwithmorethan32characters_', '_0astringwithmorethan32character', '_', '_1_']) + formatted.index.name = 'index' + + with tm.ensure_clean() as path: + with warnings.catch_warnings(record=True) as w: + original.to_stata(path, None, False) + np.testing.assert_equal( + len(w), 1) # should get a warning for that format. + + written_and_read_again = self.read_dta(path) + tm.assert_frame_equal(written_and_read_again.set_index('index'), formatted) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False)
...ad of integral represantation of replaced character. Eliminate duplicates created by replacement.
https://api.github.com/repos/pandas-dev/pandas/pulls/5525
2013-11-15T19:43:26Z
2013-12-04T13:02:57Z
2013-12-04T13:02:57Z
2014-07-09T17:56:15Z
BUG/TST: Fixes isnull behavior on NaT in array. Closes #5443
diff --git a/pandas/lib.pyx b/pandas/lib.pyx index 56ef9a4fcb160..afd8ac87589be 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -170,7 +170,6 @@ cdef inline int64_t get_timedelta64_value(val): cdef double INF = <double> np.inf cdef double NEGINF = -INF - cpdef checknull(object val): if util.is_float_object(val) or util.is_complex_object(val): return val != val # and val != INF and val != NEGINF @@ -183,7 +182,7 @@ cpdef checknull(object val): elif is_array(val): return False else: - return util._checknull(val) + return _checknull(val) cpdef checknull_old(object val): if util.is_float_object(val) or util.is_complex_object(val): @@ -213,7 +212,8 @@ def isnullobj(ndarray[object] arr): n = len(arr) result = np.zeros(n, dtype=np.uint8) for i from 0 <= i < n: - result[i] = util._checknull(arr[i]) + arobj = arr[i] + result[i] = arobj is NaT or _checknull(arobj) return result.view(np.bool_) @cython.wraparound(False) diff --git a/pandas/src/util.pxd b/pandas/src/util.pxd index 7a30f018e623e..d6f3c4caea306 100644 --- a/pandas/src/util.pxd +++ b/pandas/src/util.pxd @@ -67,7 +67,7 @@ cdef inline is_array(object o): cdef inline bint _checknull(object val): try: - return val is None or (cpython.PyFloat_Check(val) and val != val) + return val is None or (cpython.PyFloat_Check(val) and val != val) except ValueError: return False diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 7b4ea855f2f9d..9be485b143d5f 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -5,7 +5,7 @@ import nose from nose.tools import assert_equal import numpy as np -from pandas.tslib import iNaT +from pandas.tslib import iNaT, NaT from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp from pandas import compat @@ -114,6 +114,14 @@ def test_isnull_lists(): result = isnull([u('foo'), u('bar')]) assert(not result.any()) +def test_isnull_nat(): + result = isnull([NaT]) + exp = np.array([True]) + assert(np.array_equal(result, exp)) + + result = isnull(np.array([NaT], dtype=object)) + exp = np.array([True]) + assert(np.array_equal(result, exp)) def test_isnull_datetime(): assert (not isnull(datetime.now()))
I added a test case test_isnull_nat() to test_common.py and a check for NaT in lib.isnullobj. pd.isnull(np.array([pd.NaT])) now yields the correct results ([True]). closes #5443
https://api.github.com/repos/pandas-dev/pandas/pulls/5524
2013-11-15T16:31:06Z
2014-01-06T23:44:31Z
2014-01-06T23:44:31Z
2014-06-25T08:45:15Z
Ensure that we can get a commit number from git
diff --git a/setup.py b/setup.py index 6ab478bfc2541..44229fa64d4cd 100755 --- a/setup.py +++ b/setup.py @@ -201,12 +201,12 @@ def build_extensions(self): try: import subprocess try: - pipe = subprocess.Popen(["git", "describe", "HEAD"], + pipe = subprocess.Popen(["git", "describe", "--always"], stdout=subprocess.PIPE).stdout except OSError: # msysgit compatibility pipe = subprocess.Popen( - ["git.cmd", "describe", "HEAD"], + ["git.cmd", "describe", "--always"], stdout=subprocess.PIPE).stdout rev = pipe.read().strip() # makes distutils blow up on Python 2.7
Currently, git describe fails with "fatal: No names found, cannot describe anything."
https://api.github.com/repos/pandas-dev/pandas/pulls/5520
2013-11-15T09:52:20Z
2013-11-16T10:36:14Z
2013-11-16T10:36:13Z
2014-07-16T08:40:30Z
ENH: Have pivot and pivot_table take similar arguments
diff --git a/doc/source/release.rst b/doc/source/release.rst index 12a83f48706e5..f81369c60fdfd 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -124,6 +124,11 @@ API Changes DataFrame returned by ``GroupBy.apply`` (:issue:`6124`). This facilitates ``DataFrame.stack`` operations where the name of the column index is used as the name of the inserted column containing the pivoted data. + +- The :func:`pivot_table`/:meth:`DataFrame.pivot_table` and :func:`crosstab` functions + now take arguments ``index`` and ``columns`` instead of ``rows`` and ``cols``. A + ``FutureWarning`` is raised to alert that the old ``rows`` and ``cols`` arguments + will not be supported in a future release (:issue:`5505`) Experimental Features ~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/v0.14.0.txt b/doc/source/v0.14.0.txt index cfee48d62928b..8937b94be2b85 100644 --- a/doc/source/v0.14.0.txt +++ b/doc/source/v0.14.0.txt @@ -165,6 +165,11 @@ These are out-of-bounds selections # New output, 4-level MultiIndex df_multi.set_index([df_multi.index, df_multi.index]) +- The :func:`pivot_table`/:meth:`DataFrame.pivot_table` and :func:`crosstab` functions + now take arguments ``index`` and ``columns`` instead of ``rows`` and ``cols``. A + ``FutureWarning`` is raised to alert that the old ``rows`` and ``cols`` arguments + will not be supported in a future release (:issue:`5505`) + MultiIndexing Using Slicers ~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index a4b229e98ada9..59f1bf3453b1b 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -1,5 +1,7 @@ # pylint: disable=E1103 +import warnings + from pandas import Series, DataFrame from pandas.core.index import MultiIndex from pandas.tools.merge import concat @@ -10,8 +12,8 @@ import numpy as np -def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', - fill_value=None, margins=False, dropna=True): +def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', + fill_value=None, margins=False, dropna=True, **kwarg): """ Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on @@ -21,9 +23,9 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', ---------- data : DataFrame values : column to aggregate, optional - rows : list of column names or arrays to group on + index : list of column names or arrays to group on Keys to group on the x-axis of the pivot table - cols : list of column names or arrays to group on + columns : list of column names or arrays to group on Keys to group on the y-axis of the pivot table aggfunc : function, default numpy.mean, or list of functions If list of functions passed, the resulting pivot table will have @@ -35,6 +37,8 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', Add all row / columns (e.g. for subtotal / grand totals) dropna : boolean, default True Do not include columns whose entries are all NaN + rows : kwarg only alias of index [deprecated] + cols : kwarg only alias of columns [deprecated] Examples -------- @@ -50,8 +54,8 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', 7 bar two small 6 8 bar two large 7 - >>> table = pivot_table(df, values='D', rows=['A', 'B'], - ... cols=['C'], aggfunc=np.sum) + >>> table = pivot_table(df, values='D', index=['A', 'B'], + ... columns=['C'], aggfunc=np.sum) >>> table small large foo one 1 4 @@ -63,21 +67,43 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', ------- table : DataFrame """ - rows = _convert_by(rows) - cols = _convert_by(cols) + # Parse old-style keyword arguments + rows = kwarg.pop('rows', None) + if rows is not None: + warnings.warn("rows is deprecated, use index", FutureWarning) + if index is None: + index = rows + else: + msg = "Can only specify either 'rows' or 'index'" + raise TypeError(msg) + + cols = kwarg.pop('cols', None) + if cols is not None: + warnings.warn("cols is deprecated, use columns", FutureWarning) + if columns is None: + columns = cols + else: + msg = "Can only specify either 'cols' or 'columns'" + raise TypeError(msg) + + if kwarg: + raise TypeError("Unexpected argument(s): %s" % kwarg.keys()) + + index = _convert_by(index) + columns = _convert_by(columns) if isinstance(aggfunc, list): pieces = [] keys = [] for func in aggfunc: - table = pivot_table(data, values=values, rows=rows, cols=cols, + table = pivot_table(data, values=values, index=index, columns=columns, fill_value=fill_value, aggfunc=func, margins=margins) pieces.append(table) keys.append(func.__name__) return concat(pieces, keys=keys, axis=1) - keys = rows + cols + keys = index + columns values_passed = values is not None if values_passed: @@ -106,7 +132,7 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', table = agged if table.index.nlevels > 1: to_unstack = [agged.index.names[i] - for i in range(len(rows), len(keys))] + for i in range(len(index), len(keys))] table = agged.unstack(to_unstack) if not dropna: @@ -132,14 +158,14 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', table = table.fillna(value=fill_value, downcast='infer') if margins: - table = _add_margins(table, data, values, rows=rows, - cols=cols, aggfunc=aggfunc) + table = _add_margins(table, data, values, rows=index, + cols=columns, aggfunc=aggfunc) # discard the top level if values_passed and not values_multi: table = table[values[0]] - if len(rows) == 0 and len(cols) > 0: + if len(index) == 0 and len(columns) > 0: table = table.T return table @@ -299,8 +325,8 @@ def _convert_by(by): return by -def crosstab(rows, cols, values=None, rownames=None, colnames=None, - aggfunc=None, margins=False, dropna=True): +def crosstab(index, columns, values=None, rownames=None, colnames=None, + aggfunc=None, margins=False, dropna=True, **kwarg): """ Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an @@ -308,9 +334,9 @@ def crosstab(rows, cols, values=None, rownames=None, colnames=None, Parameters ---------- - rows : array-like, Series, or list of arrays/Series + index : array-like, Series, or list of arrays/Series Values to group by in the rows - cols : array-like, Series, or list of arrays/Series + columns : array-like, Series, or list of arrays/Series Values to group by in the columns values : array-like, optional Array of values to aggregate according to the factors @@ -324,6 +350,8 @@ def crosstab(rows, cols, values=None, rownames=None, colnames=None, Add row/column margins (subtotals) dropna : boolean, default True Do not include columns whose entries are all NaN + rows : kwarg only alias of index [deprecated] + cols : kwarg only alias of columns [deprecated] Notes ----- @@ -353,26 +381,48 @@ def crosstab(rows, cols, values=None, rownames=None, colnames=None, ------- crosstab : DataFrame """ - rows = com._maybe_make_list(rows) - cols = com._maybe_make_list(cols) + # Parse old-style keyword arguments + rows = kwarg.pop('rows', None) + if rows is not None: + warnings.warn("rows is deprecated, use index", FutureWarning) + if index is None: + index = rows + else: + msg = "Can only specify either 'rows' or 'index'" + raise TypeError(msg) + + cols = kwarg.pop('cols', None) + if cols is not None: + warnings.warn("cols is deprecated, use columns", FutureWarning) + if columns is None: + columns = cols + else: + msg = "Can only specify either 'cols' or 'columns'" + raise TypeError(msg) + + if kwarg: + raise TypeError("Unexpected argument(s): %s" % kwarg.keys()) + + index = com._maybe_make_list(index) + columns = com._maybe_make_list(columns) - rownames = _get_names(rows, rownames, prefix='row') - colnames = _get_names(cols, colnames, prefix='col') + rownames = _get_names(index, rownames, prefix='row') + colnames = _get_names(columns, colnames, prefix='col') data = {} - data.update(zip(rownames, rows)) - data.update(zip(colnames, cols)) + data.update(zip(rownames, index)) + data.update(zip(colnames, columns)) if values is None: df = DataFrame(data) df['__dummy__'] = 0 - table = df.pivot_table('__dummy__', rows=rownames, cols=colnames, + table = df.pivot_table('__dummy__', index=rownames, columns=colnames, aggfunc=len, margins=margins, dropna=dropna) return table.fillna(0).astype(np.int64) else: data['__dummy__'] = values df = DataFrame(data) - table = df.pivot_table('__dummy__', rows=rownames, cols=colnames, + table = df.pivot_table('__dummy__', index=rownames, columns=colnames, aggfunc=aggfunc, margins=margins, dropna=dropna) return table diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 2843433fc61e3..12f0ffa6e8aa5 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -1,4 +1,6 @@ import datetime +import unittest +import warnings import numpy as np from numpy.testing import assert_equal @@ -30,39 +32,52 @@ def setUp(self): 'F': np.random.randn(11)}) def test_pivot_table(self): - rows = ['A', 'B'] - cols = 'C' - table = pivot_table(self.data, values='D', rows=rows, cols=cols) + index = ['A', 'B'] + columns = 'C' + table = pivot_table(self.data, values='D', index=index, columns=columns) - table2 = self.data.pivot_table(values='D', rows=rows, cols=cols) + table2 = self.data.pivot_table(values='D', index=index, columns=columns) tm.assert_frame_equal(table, table2) # this works - pivot_table(self.data, values='D', rows=rows) + pivot_table(self.data, values='D', index=index) - if len(rows) > 1: - self.assertEqual(table.index.names, tuple(rows)) + if len(index) > 1: + self.assertEqual(table.index.names, tuple(index)) else: - self.assertEqual(table.index.name, rows[0]) + self.assertEqual(table.index.name, index[0]) - if len(cols) > 1: - self.assertEqual(table.columns.names, cols) + if len(columns) > 1: + self.assertEqual(table.columns.names, columns) else: - self.assertEqual(table.columns.name, cols[0]) + self.assertEqual(table.columns.name, columns[0]) - expected = self.data.groupby(rows + [cols])['D'].agg(np.mean).unstack() + expected = self.data.groupby(index + [columns])['D'].agg(np.mean).unstack() tm.assert_frame_equal(table, expected) + def test_pivot_table_warnings(self): + index = ['A', 'B'] + columns = 'C' + with tm.assert_produces_warning(FutureWarning): + table = pivot_table(self.data, values='D', rows=index, + cols=columns) + + with tm.assert_produces_warning(False): + table2 = pivot_table(self.data, values='D', index=index, + columns=columns) + + tm.assert_frame_equal(table, table2) + def test_pivot_table_nocols(self): df = DataFrame({'rows': ['a', 'b', 'c'], 'cols': ['x', 'y', 'z'], 'values': [1,2,3]}) - rs = df.pivot_table(cols='cols', aggfunc=np.sum) - xp = df.pivot_table(rows='cols', aggfunc=np.sum).T + rs = df.pivot_table(columns='cols', aggfunc=np.sum) + xp = df.pivot_table(index='cols', aggfunc=np.sum).T tm.assert_frame_equal(rs, xp) - rs = df.pivot_table(cols='cols', aggfunc={'values': 'mean'}) - xp = df.pivot_table(rows='cols', aggfunc={'values': 'mean'}).T + rs = df.pivot_table(columns='cols', aggfunc={'values': 'mean'}) + xp = df.pivot_table(index='cols', aggfunc={'values': 'mean'}).T tm.assert_frame_equal(rs, xp) def test_pivot_table_dropna(self): @@ -92,22 +107,22 @@ def test_pivot_table_dropna(self): def test_pass_array(self): - result = self.data.pivot_table('D', rows=self.data.A, cols=self.data.C) - expected = self.data.pivot_table('D', rows='A', cols='C') + result = self.data.pivot_table('D', index=self.data.A, columns=self.data.C) + expected = self.data.pivot_table('D', index='A', columns='C') tm.assert_frame_equal(result, expected) def test_pass_function(self): - result = self.data.pivot_table('D', rows=lambda x: x // 5, - cols=self.data.C) - expected = self.data.pivot_table('D', rows=self.data.index // 5, - cols='C') + result = self.data.pivot_table('D', index=lambda x: x // 5, + columns=self.data.C) + expected = self.data.pivot_table('D', index=self.data.index // 5, + columns='C') tm.assert_frame_equal(result, expected) def test_pivot_table_multiple(self): - rows = ['A', 'B'] - cols = 'C' - table = pivot_table(self.data, rows=rows, cols=cols) - expected = self.data.groupby(rows + [cols]).agg(np.mean).unstack() + index = ['A', 'B'] + columns = 'C' + table = pivot_table(self.data, index=index, columns=columns) + expected = self.data.groupby(index + [columns]).agg(np.mean).unstack() tm.assert_frame_equal(table, expected) def test_pivot_dtypes(self): @@ -116,7 +131,7 @@ def test_pivot_dtypes(self): f = DataFrame({'a' : ['cat', 'bat', 'cat', 'bat'], 'v' : [1,2,3,4], 'i' : ['a','b','a','b']}) self.assertEqual(f.dtypes['v'], 'int64') - z = pivot_table(f, values='v', rows=['a'], cols=['i'], fill_value=0, aggfunc=np.sum) + z = pivot_table(f, values='v', index=['a'], columns=['i'], fill_value=0, aggfunc=np.sum) result = z.get_dtype_counts() expected = Series(dict(int64 = 2)) tm.assert_series_equal(result, expected) @@ -125,21 +140,21 @@ def test_pivot_dtypes(self): f = DataFrame({'a' : ['cat', 'bat', 'cat', 'bat'], 'v' : [1.5,2.5,3.5,4.5], 'i' : ['a','b','a','b']}) self.assertEqual(f.dtypes['v'], 'float64') - z = pivot_table(f, values='v', rows=['a'], cols=['i'], fill_value=0, aggfunc=np.mean) + z = pivot_table(f, values='v', index=['a'], columns=['i'], fill_value=0, aggfunc=np.mean) result = z.get_dtype_counts() expected = Series(dict(float64 = 2)) tm.assert_series_equal(result, expected) def test_pivot_multi_values(self): result = pivot_table(self.data, values=['D', 'E'], - rows='A', cols=['B', 'C'], fill_value=0) + index='A', columns=['B', 'C'], fill_value=0) expected = pivot_table(self.data.drop(['F'], axis=1), - rows='A', cols=['B', 'C'], fill_value=0) + index='A', columns=['B', 'C'], fill_value=0) tm.assert_frame_equal(result, expected) def test_pivot_multi_functions(self): f = lambda func: pivot_table(self.data, values=['D', 'E'], - rows=['A', 'B'], cols='C', + index=['A', 'B'], columns='C', aggfunc=func) result = f([np.mean, np.std]) means = f(np.mean) @@ -149,7 +164,7 @@ def test_pivot_multi_functions(self): # margins not supported?? f = lambda func: pivot_table(self.data, values=['D', 'E'], - rows=['A', 'B'], cols='C', + index=['A', 'B'], columns='C', aggfunc=func, margins=True) result = f([np.mean, np.std]) means = f(np.mean) @@ -169,14 +184,14 @@ def test_pivot_index_with_nan(self): tm.assert_frame_equal(result, expected) def test_margins(self): - def _check_output(res, col, rows=['A', 'B'], cols=['C']): + def _check_output(res, col, index=['A', 'B'], columns=['C']): cmarg = res['All'][:-1] - exp = self.data.groupby(rows)[col].mean() + exp = self.data.groupby(index)[col].mean() tm.assert_series_equal(cmarg, exp) res = res.sortlevel() rmarg = res.xs(('All', ''))[:-1] - exp = self.data.groupby(cols)[col].mean() + exp = self.data.groupby(columns)[col].mean() tm.assert_series_equal(rmarg, exp) gmarg = res['All']['All', ''] @@ -184,12 +199,12 @@ def _check_output(res, col, rows=['A', 'B'], cols=['C']): self.assertEqual(gmarg, exp) # column specified - table = self.data.pivot_table('D', rows=['A', 'B'], cols='C', + table = self.data.pivot_table('D', index=['A', 'B'], columns='C', margins=True, aggfunc=np.mean) _check_output(table, 'D') # no column specified - table = self.data.pivot_table(rows=['A', 'B'], cols='C', + table = self.data.pivot_table(index=['A', 'B'], columns='C', margins=True, aggfunc=np.mean) for valcol in table.columns.levels[0]: _check_output(table[valcol], valcol) @@ -198,18 +213,18 @@ def _check_output(res, col, rows=['A', 'B'], cols=['C']): # to help with a buglet self.data.columns = [k * 2 for k in self.data.columns] - table = self.data.pivot_table(rows=['AA', 'BB'], margins=True, + table = self.data.pivot_table(index=['AA', 'BB'], margins=True, aggfunc=np.mean) for valcol in table.columns: gmarg = table[valcol]['All', ''] self.assertEqual(gmarg, self.data[valcol].mean()) # this is OK - table = self.data.pivot_table(rows=['AA', 'BB'], margins=True, + table = self.data.pivot_table(index=['AA', 'BB'], margins=True, aggfunc='mean') # no rows - rtable = self.data.pivot_table(cols=['AA', 'BB'], margins=True, + rtable = self.data.pivot_table(columns=['AA', 'BB'], margins=True, aggfunc=np.mean) tm.assert_isinstance(rtable, Series) for item in ['DD', 'EE', 'FF']: @@ -223,10 +238,10 @@ def test_pivot_integer_columns(self): data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'], [d + datetime.timedelta(i) for i in range(20)], [1.0])) df = pandas.DataFrame(data) - table = df.pivot_table(values=4, rows=[0, 1, 3], cols=[2]) + table = df.pivot_table(values=4, index=[0, 1, 3], columns=[2]) df2 = df.rename(columns=str) - table2 = df2.pivot_table(values='4', rows=['0', '1', '3'], cols=['2']) + table2 = df2.pivot_table(values='4', index=['0', '1', '3'], columns=['2']) tm.assert_frame_equal(table, table2, check_names=False) @@ -238,7 +253,7 @@ def test_pivot_no_level_overlap(self): 'c': (['foo'] * 4 + ['bar'] * 4) * 2, 'value': np.random.randn(16)}) - table = data.pivot_table('value', rows='a', cols=['b', 'c']) + table = data.pivot_table('value', index='a', columns=['b', 'c']) grouped = data.groupby(['a', 'b', 'c'])['value'].mean() expected = grouped.unstack('b').unstack('c').dropna(axis=1, how='all') @@ -283,8 +298,8 @@ def test_pivot_columns_lexsorted(self): df = DataFrame(items) - pivoted = df.pivot_table('Price', rows=['Month', 'Day'], - cols=['Index', 'Symbol', 'Year'], + pivoted = df.pivot_table('Price', index=['Month', 'Day'], + columns=['Index', 'Symbol', 'Year'], aggfunc='mean') self.assert_(pivoted.columns.is_monotonic) @@ -292,30 +307,30 @@ def test_pivot_columns_lexsorted(self): def test_pivot_complex_aggfunc(self): f = {'D': ['std'], 'E': ['sum']} expected = self.data.groupby(['A', 'B']).agg(f).unstack('B') - result = self.data.pivot_table(rows='A', cols='B', aggfunc=f) + result = self.data.pivot_table(index='A', columns='B', aggfunc=f) tm.assert_frame_equal(result, expected) def test_margins_no_values_no_cols(self): # Regression test on pivot table: no values or cols passed. - result = self.data[['A', 'B']].pivot_table(rows=['A', 'B'], aggfunc=len, margins=True) + result = self.data[['A', 'B']].pivot_table(index=['A', 'B'], aggfunc=len, margins=True) result_list = result.tolist() self.assertEqual(sum(result_list[:-1]), result_list[-1]) def test_margins_no_values_two_rows(self): # Regression test on pivot table: no values passed but rows are a multi-index - result = self.data[['A', 'B', 'C']].pivot_table(rows=['A', 'B'], cols='C', aggfunc=len, margins=True) + result = self.data[['A', 'B', 'C']].pivot_table(index=['A', 'B'], columns='C', aggfunc=len, margins=True) self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0]) def test_margins_no_values_one_row_one_col(self): # Regression test on pivot table: no values passed but row and col defined - result = self.data[['A', 'B']].pivot_table(rows='A', cols='B', aggfunc=len, margins=True) + result = self.data[['A', 'B']].pivot_table(index='A', columns='B', aggfunc=len, margins=True) self.assertEqual(result.All.tolist(), [4.0, 7.0, 11.0]) def test_margins_no_values_two_row_two_cols(self): # Regression test on pivot table: no values passed but rows and cols are multi-indexed self.data['D'] = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k'] - result = self.data[['A', 'B', 'C', 'D']].pivot_table(rows=['A', 'B'], cols=['C', 'D'], aggfunc=len, margins=True) + result = self.data[['A', 'B', 'C', 'D']].pivot_table(index=['A', 'B'], columns=['C', 'D'], aggfunc=len, margins=True) self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0]) @@ -415,7 +430,7 @@ def test_crosstab_pass_values(self): df = DataFrame({'foo': a, 'bar': b, 'baz': c, 'values': values}) - expected = df.pivot_table('values', rows=['foo', 'bar'], cols='baz', + expected = df.pivot_table('values', index=['foo', 'bar'], cols='baz', aggfunc=np.sum) tm.assert_frame_equal(table, expected)
Right now, there is ``` df.pivot(index=foo, columns=bar, values=baz) ``` and ``` df.pivot_table(rows=foo, cols=bar, values=baz) ``` Because of dirtyness in my data I occasionally have to go back and forth between the two forms (and use `df.pivot_table(..., aggfunc='count')` to see who had the bad data). It would be nice if I didn't have to change the keyword arguments each time and one had an alias of the other. It would also be easier to remember, especially in the trivial case of `cols` vs `columns`. Finally, the two are related operations and having an option for consistent keyword parameters makes sense. I could make a PR to solve this easy enough, if this is an enhancement you are interested in. I'm not really sure what your stance is on redundant keyword arguments.
https://api.github.com/repos/pandas-dev/pandas/pulls/5505
2013-11-14T23:27:59Z
2014-03-14T22:41:31Z
2014-03-14T22:41:31Z
2014-06-12T15:38:56Z
BUG: Fixed various setitem with iterable that does not have a matching length to the indexer (GH5508)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 59ff48887269e..9516b67d4ca47 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -805,6 +805,8 @@ Bug Fixes - ``pd.to_timedelta`` of a scalar returns a scalar (:issue:`5410`) - ``pd.to_timedelta`` accepts ``NaN`` and ``NaT``, returning ``NaT`` instead of raising (:issue:`5437`) - performance improvements in ``isnull`` on larger size pandas objects + - Fixed various setitem with 1d ndarray that does not have a matching + length to the indexer (:issue:`5508`) pandas 0.12.0 ------------- diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index c854e0b086994..b462624dde1f5 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -365,6 +365,10 @@ def can_do_equal_len(): # per label values else: + if len(labels) != len(value): + raise ValueError('Must have equal len keys and value when' + ' setting with an iterable') + for item, v in zip(labels, value): setter(item, v) else: diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index dacac6ef64b29..e01fd6a763ceb 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -650,6 +650,35 @@ def test_iloc_getitem_frame(self): # trying to use a label self.assertRaises(ValueError, df.iloc.__getitem__, tuple(['j','D'])) + def test_setitem_ndarray_1d(self): + # GH5508 + + # len of indexer vs length of the 1d ndarray + df = DataFrame(index=Index(lrange(1,11))) + df['foo'] = np.zeros(10, dtype=np.float64) + df['bar'] = np.zeros(10, dtype=np.complex) + + # invalid + def f(): + df.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2]) + self.assertRaises(ValueError, f) + + # valid + df.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2, 1.0]) + + result = df.ix[2:5, 'bar'] + expected = Series([2.33j, 1.23+0.1j, 2.2, 1.0],index=[2,3,4,5]) + assert_series_equal(result,expected) + + # dtype getting changed? + df = DataFrame(index=Index(lrange(1,11))) + df['foo'] = np.zeros(10, dtype=np.float64) + df['bar'] = np.zeros(10, dtype=np.complex) + + def f(): + df[2:5] = np.arange(1,4)*1j + self.assertRaises(ValueError, f) + def test_iloc_setitem_series(self): """ originally from test_series.py """ df = DataFrame(np.random.randn(10, 4), index=list('abcdefghij'), columns=list('ABCD'))
closes #5508
https://api.github.com/repos/pandas-dev/pandas/pulls/5512
2013-11-14T00:32:11Z
2013-11-14T01:06:31Z
2013-11-14T01:06:30Z
2014-06-24T20:52:21Z
ENH add cumcount groupby method
diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 7b769eeccbe68..e666bad2317df 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -705,3 +705,16 @@ can be used as group keys. If so, the order of the levels will be preserved: factor = qcut(data, [0, .25, .5, .75, 1.]) data.groupby(factor).mean() + +Enumerate group items +~~~~~~~~~~~~~~~~~~~~~ + +To see the order in which each row appears within its group, use the +``cumcount`` method: + +.. ipython:: python + + df = pd.DataFrame(list('aaabba'), columns=['A']) + df + + df.groupby('A').cumcount() \ No newline at end of file diff --git a/doc/source/release.rst b/doc/source/release.rst index 59ff48887269e..36bc02da3e68d 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -64,6 +64,7 @@ New features - ``to_csv()`` now outputs datetime objects according to a specified format string via the ``date_format`` keyword (:issue:`4313`) - Added ``LastWeekOfMonth`` DateOffset (:issue:`4637`) + - Added ``cumcount`` groupby method (:issue:`4646`) - Added ``FY5253``, and ``FY5253Quarter`` DateOffsets (:issue:`4511`) - Added ``mode()`` method to ``Series`` and ``DataFrame`` to get the statistical mode(s) of a column/series. (:issue:`5367`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 668c665613c0d..f37b94cd7f689 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -468,6 +468,7 @@ def ohlc(self): Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex + """ return self._cython_agg_general('ohlc') @@ -480,9 +481,49 @@ def picker(arr): return np.nan return self.agg(picker) + def cumcount(self): + ''' + Number each item in each group from 0 to the length of that group. + + Essentially this is equivalent to + + >>> self.apply(lambda x: Series(np.arange(len(x)), x.index)). + + Example + ------- + + >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], columns=['A']) + >>> df + A + 0 a + 1 a + 2 a + 3 b + 4 b + 5 a + >>> df.groupby('A').cumcount() + 0 0 + 1 1 + 2 2 + 3 0 + 4 1 + 5 3 + dtype: int64 + + ''' + index = self.obj.index + cumcounts = np.zeros(len(index), dtype='int64') + for v in self.indices.values(): + cumcounts[v] = np.arange(len(v), dtype='int64') + return Series(cumcounts, index) + + def _try_cast(self, result, obj): - """ try to cast the result to our obj original type, - we may have roundtripped thru object in the mean-time """ + """ + try to cast the result to our obj original type, + we may have roundtripped thru object in the mean-time + + """ if obj.ndim > 1: dtype = obj.values.dtype else: diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index ca74f46122d88..9df5541615cee 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2560,6 +2560,57 @@ def test_groupby_with_empty(self): grouped = series.groupby(grouper) assert next(iter(grouped), None) is None + def test_cumcount(self): + df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A']) + g = df.groupby('A') + sg = g.A + + expected = Series([0, 1, 2, 0, 3]) + + assert_series_equal(expected, g.cumcount()) + assert_series_equal(expected, sg.cumcount()) + + def test_cumcount_empty(self): + ge = DataFrame().groupby() + se = Series().groupby() + + e = Series(dtype='int') # edge case, as this is usually considered float + + assert_series_equal(e, ge.cumcount()) + assert_series_equal(e, se.cumcount()) + + def test_cumcount_dupe_index(self): + df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A'], index=[0] * 5) + g = df.groupby('A') + sg = g.A + + expected = Series([0, 1, 2, 0, 3], index=[0] * 5) + + assert_series_equal(expected, g.cumcount()) + assert_series_equal(expected, sg.cumcount()) + + def test_cumcount_mi(self): + mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]]) + df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A'], index=mi) + g = df.groupby('A') + sg = g.A + + expected = Series([0, 1, 2, 0, 3], index=mi) + + assert_series_equal(expected, g.cumcount()) + assert_series_equal(expected, sg.cumcount()) + + def test_cumcount_groupby_not_col(self): + df = DataFrame([['a'], ['a'], ['a'], ['b'], ['a']], columns=['A'], index=[0] * 5) + g = df.groupby([0, 0, 0, 1, 0]) + sg = g.A + + expected = Series([0, 1, 2, 0, 3], index=[0] * 5) + + assert_series_equal(expected, g.cumcount()) + assert_series_equal(expected, sg.cumcount()) + + def test_filter_series(self): import pandas as pd s = pd.Series([1, 3, 20, 5, 22, 24, 7]) @@ -3180,7 +3231,7 @@ def test_tab_completion(self): 'min','name','ngroups','nth','ohlc','plot', 'prod', 'size','std','sum','transform','var', 'count', 'head', 'describe', 'cummax', 'dtype', 'quantile', 'rank', 'cumprod', 'tail', - 'resample', 'cummin', 'fillna', 'cumsum']) + 'resample', 'cummin', 'fillna', 'cumsum', 'cumcount']) self.assertEqual(results, expected) def assert_fp_equal(a, b):
closes #4646 Happy to hear ways to make this faster, but I think this is a good convenience function as is. Update: made significantly faster...
https://api.github.com/repos/pandas-dev/pandas/pulls/5510
2013-11-14T00:13:44Z
2013-11-14T22:04:00Z
2013-11-14T22:04:00Z
2014-06-14T09:29:13Z
BUG: bug in to_msgpack for timezone aware datetime index
diff --git a/doc/source/release.rst b/doc/source/release.rst index 17fe4be734f4d..59ff48887269e 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -85,7 +85,7 @@ Experimental Features (:issue:`4897`). - Add msgpack support via ``pd.read_msgpack()`` and ``pd.to_msgpack()`` / ``df.to_msgpack()`` for serialization of arbitrary pandas (and python - objects) in a lightweight portable binary format (:issue:`686`) + objects) in a lightweight portable binary format (:issue:`686`, :issue:`5506`) - Added PySide support for the qtpandas DataFrameModel and DataFrameWidget. - Added :mod:`pandas.io.gbq` for reading from (and writing to) Google BigQuery into a DataFrame. (:issue:`4140`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index ba2ba1b482dee..efa083e239f63 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -842,7 +842,7 @@ def to_hdf(self, path_or_buf, key, **kwargs): from pandas.io import pytables return pytables.to_hdf(path_or_buf, key, self, **kwargs) - def to_msgpack(self, path_or_buf, **kwargs): + def to_msgpack(self, path_or_buf=None, **kwargs): """ msgpack (serialize) object to input file path diff --git a/pandas/io/packers.py b/pandas/io/packers.py index adb70a92b8a54..08299738f31a2 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -100,13 +100,14 @@ def to_msgpack(path_or_buf, *args, **kwargs): def writer(fh): for a in args: fh.write(pack(a, **kwargs)) - return fh if isinstance(path_or_buf, compat.string_types): with open(path_or_buf, mode) as fh: writer(fh) elif path_or_buf is None: - return writer(compat.BytesIO()) + buf = compat.BytesIO() + writer(buf) + return buf.getvalue() else: writer(path_or_buf) @@ -263,17 +264,23 @@ def encode(obj): return {'typ': 'period_index', 'klass': obj.__class__.__name__, 'name': getattr(obj, 'name', None), - 'freq': obj.freqstr, + 'freq': getattr(obj,'freqstr',None), 'dtype': obj.dtype.num, 'data': convert(obj.asi8)} elif isinstance(obj, DatetimeIndex): + tz = getattr(obj,'tz',None) + + # store tz info and data as UTC + if tz is not None: + tz = tz.zone + obj = obj.tz_convert('UTC') return {'typ': 'datetime_index', 'klass': obj.__class__.__name__, 'name': getattr(obj, 'name', None), 'dtype': obj.dtype.num, 'data': convert(obj.asi8), - 'freq': obj.freqstr, - 'tz': obj.tz} + 'freq': getattr(obj,'freqstr',None), + 'tz': tz } elif isinstance(obj, MultiIndex): return {'typ': 'multi_index', 'klass': obj.__class__.__name__, @@ -440,7 +447,13 @@ def decode(obj): return globals()[obj['klass']](data, name=obj['name'], freq=obj['freq']) elif typ == 'datetime_index': data = unconvert(obj['data'], np.int64, obj.get('compress')) - return globals()[obj['klass']](data, freq=obj['freq'], tz=obj['tz'], name=obj['name']) + result = globals()[obj['klass']](data, freq=obj['freq'], name=obj['name']) + tz = obj['tz'] + + # reverse tz conversion + if tz is not None: + result = result.tz_localize('UTC').tz_convert(tz) + return result elif typ == 'series': dtype = dtype_for(obj['dtype']) index = obj['index'] diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index e5938ecf87b68..6b986fa87ccce 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -61,18 +61,26 @@ def test_string_io(self): df = DataFrame(np.random.randn(10,2)) s = df.to_msgpack(None) - result = read_msgpack(s.getvalue()) + result = read_msgpack(s) + tm.assert_frame_equal(result,df) + + s = df.to_msgpack() + result = read_msgpack(s) + tm.assert_frame_equal(result,df) + + s = df.to_msgpack() + result = read_msgpack(compat.BytesIO(s)) tm.assert_frame_equal(result,df) s = to_msgpack(None,df) - result = read_msgpack(s.getvalue()) + result = read_msgpack(s) tm.assert_frame_equal(result, df) with ensure_clean(self.path) as p: - s = df.to_msgpack(None) + s = df.to_msgpack() fh = open(p,'wb') - fh.write(s.getvalue()) + fh.write(s) fh.close() result = read_msgpack(p) tm.assert_frame_equal(result, df) @@ -80,10 +88,6 @@ def test_string_io(self): def test_iterator_with_string_io(self): dfs = [ DataFrame(np.random.randn(10,2)) for i in range(5) ] - s = to_msgpack(None,*dfs) - for i, result in enumerate(read_msgpack(s.getvalue(),iterator=True)): - tm.assert_frame_equal(result,dfs[i]) - s = to_msgpack(None,*dfs) for i, result in enumerate(read_msgpack(s,iterator=True)): tm.assert_frame_equal(result,dfs[i]) @@ -98,7 +102,7 @@ def test_numpy_scalar_float(self): def test_numpy_scalar_complex(self): x = np.complex64(np.random.rand() + 1j * np.random.rand()) x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(np.allclose(x, x_rec)) def test_scalar_float(self): x = np.random.rand() @@ -108,10 +112,9 @@ def test_scalar_float(self): def test_scalar_complex(self): x = np.random.rand() + 1j * np.random.rand() x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(np.allclose(x, x_rec)) def test_list_numpy_float(self): - raise nose.SkipTest('buggy test') x = [np.float32(np.random.rand()) for i in range(5)] x_rec = self.encode_decode(x) tm.assert_almost_equal(x,x_rec) @@ -120,13 +123,11 @@ def test_list_numpy_float_complex(self): if not hasattr(np, 'complex128'): raise nose.SkipTest('numpy cant handle complex128') - # buggy test - raise nose.SkipTest('buggy test') x = [np.float32(np.random.rand()) for i in range(5)] + \ [np.complex128(np.random.rand() + 1j * np.random.rand()) for i in range(5)] x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(np.allclose(x, x_rec)) def test_list_float(self): x = [np.random.rand() for i in range(5)] @@ -137,7 +138,7 @@ def test_list_float_complex(self): x = [np.random.rand() for i in range(5)] + \ [(np.random.rand() + 1j * np.random.rand()) for i in range(5)] x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(np.allclose(x, x_rec)) def test_dict_float(self): x = {'foo': 1.0, 'bar': 2.0} @@ -147,7 +148,8 @@ def test_dict_float(self): def test_dict_complex(self): x = {'foo': 1.0 + 1.0j, 'bar': 2.0 + 2.0j} x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(all(map(lambda x, y: x == y, x.values(), x_rec.values())) and + all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values()))) def test_dict_numpy_float(self): x = {'foo': np.float32(1.0), 'bar': np.float32(2.0)} @@ -158,7 +160,9 @@ def test_dict_numpy_complex(self): x = {'foo': np.complex128( 1.0 + 1.0j), 'bar': np.complex128(2.0 + 2.0j)} x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(all(map(lambda x, y: x == y, x.values(), x_rec.values())) and + all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values()))) + def test_numpy_array_float(self): @@ -173,7 +177,8 @@ def test_numpy_array_float(self): def test_numpy_array_complex(self): x = (np.random.rand(5) + 1j * np.random.rand(5)).astype(np.complex128) x_rec = self.encode_decode(x) - tm.assert_almost_equal(x,x_rec) + self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and + x.dtype == x_rec.dtype) def test_list_mixed(self): x = [1.0, np.float32(3.5), np.complex128(4.25), u('foo')] @@ -235,6 +240,16 @@ def test_basic_index(self): i_rec = self.encode_decode(i) self.assert_(i.equals(i_rec)) + # datetime with no freq (GH5506) + i = Index([Timestamp('20130101'),Timestamp('20130103')]) + i_rec = self.encode_decode(i) + self.assert_(i.equals(i_rec)) + + # datetime with timezone + i = Index([Timestamp('20130101 9:00:00'),Timestamp('20130103 11:00:00')]).tz_localize('US/Eastern') + i_rec = self.encode_decode(i) + self.assert_(i.equals(i_rec)) + def test_multi_index(self): for s, i in self.mi.items():
BUG: bug in to_msgpack for no freqstr datetime index (GH5506) closes #5506
https://api.github.com/repos/pandas-dev/pandas/pulls/5507
2013-11-13T18:48:54Z
2013-11-13T19:53:52Z
2013-11-13T19:53:52Z
2014-07-16T08:40:20Z
API/ENH: pass thru store creation arguments for HDFStore; can be used to support in-memory stores
diff --git a/doc/source/release.rst b/doc/source/release.rst index 6ae7493fb4b72..17fe4be734f4d 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -289,6 +289,7 @@ API Changes - ``flush`` now accepts an ``fsync`` parameter, which defaults to ``False`` (:issue:`5364`) - ``unicode`` indices not supported on ``table`` formats (:issue:`5386`) + - pass thru store creation arguments; can be used to support in-memory stores - ``JSON`` - added ``date_unit`` parameter to specify resolution of timestamps. diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 73800c06669f5..70fc0572ad8fb 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -373,6 +373,7 @@ HDFStore API Changes - add the keyword ``dropna=True`` to ``append`` to change whether ALL nan rows are not written to the store (default is ``True``, ALL nan rows are NOT written), also settable via the option ``io.hdf.dropna_table`` (:issue:`4625`) +- pass thru store creation arguments; can be used to support in-memory stores Enhancements ~~~~~~~~~~~~ diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index bc2f41502614d..49f60a7051ba9 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -376,7 +376,7 @@ def __init__(self, path, mode=None, complevel=None, complib=None, self._complib = complib self._fletcher32 = fletcher32 self._filters = None - self.open(mode=mode) + self.open(mode=mode, **kwargs) @property def root(self): @@ -465,7 +465,7 @@ def items(self): iteritems = items - def open(self, mode='a'): + def open(self, mode='a', **kwargs): """ Open the file in the specified mode @@ -502,11 +502,11 @@ def open(self, mode='a'): fletcher32=self._fletcher32) try: - self._handle = tables.openFile(self._path, self._mode) + self._handle = tables.openFile(self._path, self._mode, **kwargs) except (IOError) as e: # pragma: no cover if 'can not be written' in str(e): print('Opening %s in read-only mode' % self._path) - self._handle = tables.openFile(self._path, 'r') + self._handle = tables.openFile(self._path, 'r', **kwargs) else: raise except (Exception) as e: diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 737acef209a50..759f63a962e57 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -489,6 +489,28 @@ def test_reopen_handle(self): store.close() self.assert_(not store.is_open) + def test_open_args(self): + + with ensure_clean_path(self.path) as path: + + df = tm.makeDataFrame() + + # create an in memory store + store = HDFStore(path,mode='a',driver='H5FD_CORE',driver_core_backing_store=0) + store['df'] = df + store.append('df2',df) + + tm.assert_frame_equal(store['df'],df) + tm.assert_frame_equal(store['df2'],df) + + store.close() + + # only supported on pytable >= 3.0.0 + if LooseVersion(tables.__version__) >= '3.0.0': + + # the file should not have actually been written + self.assert_(os.path.exists(path) is False) + def test_flush(self): with ensure_clean_store(self.path) as store:
https://api.github.com/repos/pandas-dev/pandas/pulls/5499
2013-11-12T17:25:52Z
2013-11-12T18:09:27Z
2013-11-12T18:09:27Z
2014-07-16T08:40:12Z
PERF: msgpack encoding changes to use to/from string for speed boosts
diff --git a/pandas/core/common.py b/pandas/core/common.py index 7f1fe50048599..42964c9d48537 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1871,8 +1871,12 @@ def _asarray_tuplesafe(values, dtype=None): else: # Making a 1D array that safely contains tuples is a bit tricky # in numpy, leading to the following - result = np.empty(len(values), dtype=object) - result[:] = values + try: + result = np.empty(len(values), dtype=object) + result[:] = values + except (ValueError): + # we have a list-of-list + result[:] = [ tuple(x) for x in values ] return result diff --git a/pandas/core/generic.py b/pandas/core/generic.py index bc80988eb612c..ba2ba1b482dee 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -851,8 +851,8 @@ def to_msgpack(self, path_or_buf, **kwargs): Parameters ---------- - path : string File path - args : an object or objects to serialize + path : string File path, buffer-like, or None + if None, return generated string append : boolean whether to append to an existing msgpack (default is False) compress : type of compressor (zlib or blosc), default to None (no compression) diff --git a/pandas/io/packers.py b/pandas/io/packers.py index d6aa1ebeb896a..adb70a92b8a54 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -40,12 +40,13 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ +import os from datetime import datetime, date, timedelta from dateutil.parser import parse import numpy as np from pandas import compat -from pandas.compat import u +from pandas.compat import u, PY3 from pandas import ( Timestamp, Period, Series, DataFrame, Panel, Panel4D, Index, MultiIndex, Int64Index, PeriodIndex, DatetimeIndex, Float64Index, NaT @@ -54,6 +55,7 @@ from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame from pandas.core.common import needs_i8_conversion +from pandas.io.common import get_filepath_or_buffer from pandas.core.internals import BlockManager, make_block import pandas.core.internals as internals @@ -71,7 +73,7 @@ compressor = None -def to_msgpack(path, *args, **kwargs): +def to_msgpack(path_or_buf, *args, **kwargs): """ msgpack (serialize) object to input file path @@ -80,7 +82,8 @@ def to_msgpack(path, *args, **kwargs): Parameters ---------- - path : string File path + path_or_buf : string File path, buffer-like, or None + if None, return generated string args : an object or objects to serialize append : boolean whether to append to an existing msgpack (default is False) @@ -90,17 +93,24 @@ def to_msgpack(path, *args, **kwargs): compressor = kwargs.pop('compress', None) append = kwargs.pop('append', None) if append: - f = open(path, 'a+b') + mode = 'a+b' else: - f = open(path, 'wb') - try: - for a in args: - f.write(pack(a, **kwargs)) - finally: - f.close() + mode = 'wb' + def writer(fh): + for a in args: + fh.write(pack(a, **kwargs)) + return fh + + if isinstance(path_or_buf, compat.string_types): + with open(path_or_buf, mode) as fh: + writer(fh) + elif path_or_buf is None: + return writer(compat.BytesIO()) + else: + writer(path_or_buf) -def read_msgpack(path, iterator=False, **kwargs): +def read_msgpack(path_or_buf, iterator=False, **kwargs): """ Load msgpack pandas object from the specified file path @@ -110,8 +120,7 @@ def read_msgpack(path, iterator=False, **kwargs): Parameters ---------- - path : string - File path + path_or_buf : string File path, BytesIO like or string iterator : boolean, if True, return an iterator to the unpacker (default is False) @@ -120,15 +129,40 @@ def read_msgpack(path, iterator=False, **kwargs): obj : type of object stored in file """ + path_or_buf, _ = get_filepath_or_buffer(path_or_buf) if iterator: - return Iterator(path) + return Iterator(path_or_buf) - with open(path, 'rb') as fh: + def read(fh): l = list(unpack(fh)) if len(l) == 1: return l[0] return l + # see if we have an actual file + if isinstance(path_or_buf, compat.string_types): + + try: + path_exists = os.path.exists(path_or_buf) + except (TypeError): + path_exists = False + + if path_exists: + with open(path_or_buf, 'rb') as fh: + return read(fh) + + # treat as a string-like + if not hasattr(path_or_buf,'read'): + + try: + fh = compat.BytesIO(path_or_buf) + return read(fh) + finally: + fh.close() + + # a buffer like + return read(path_or_buf) + dtype_dict = {21: np.dtype('M8[ns]'), u('datetime64[ns]'): np.dtype('M8[ns]'), u('datetime64[us]'): np.dtype('M8[us]'), @@ -168,6 +202,10 @@ def convert(values): values = values.view('i8') v = values.ravel() + # convert object + if dtype == np.object_: + return v.tolist() + if compressor == 'zlib': # return string arrays like they are @@ -189,12 +227,7 @@ def convert(values): return blosc.compress(v, typesize=dtype.itemsize) # ndarray (on original dtype) - if dtype == 'float64' or dtype == 'int64': - return v - - # as a list - return v.tolist() - + return v.tostring() def unconvert(values, dtype, compress=None): @@ -216,9 +249,8 @@ def unconvert(values, dtype, compress=None): return np.frombuffer(values, dtype=dtype) - # as a list - return np.array(values, dtype=dtype) - + # from a string + return np.fromstring(values.encode('latin1'),dtype=dtype) def encode(obj): """ @@ -253,19 +285,20 @@ def encode(obj): 'klass': obj.__class__.__name__, 'name': getattr(obj, 'name', None), 'dtype': obj.dtype.num, - 'data': obj.tolist()} + 'data': convert(obj.values)} elif isinstance(obj, Series): if isinstance(obj, SparseSeries): - d = {'typ': 'sparse_series', - 'klass': obj.__class__.__name__, - 'dtype': obj.dtype.num, - 'index': obj.index, - 'sp_index': obj.sp_index, - 'sp_values': convert(obj.sp_values), - 'compress': compressor} - for f in ['name', 'fill_value', 'kind']: - d[f] = getattr(obj, f, None) - return d + raise NotImplementedError("msgpack sparse series is not implemented") + #d = {'typ': 'sparse_series', + # 'klass': obj.__class__.__name__, + # 'dtype': obj.dtype.num, + # 'index': obj.index, + # 'sp_index': obj.sp_index, + # 'sp_values': convert(obj.sp_values), + # 'compress': compressor} + #for f in ['name', 'fill_value', 'kind']: + # d[f] = getattr(obj, f, None) + #return d else: return {'typ': 'series', 'klass': obj.__class__.__name__, @@ -276,23 +309,25 @@ def encode(obj): 'compress': compressor} elif issubclass(tobj, NDFrame): if isinstance(obj, SparseDataFrame): - d = {'typ': 'sparse_dataframe', - 'klass': obj.__class__.__name__, - 'columns': obj.columns} - for f in ['default_fill_value', 'default_kind']: - d[f] = getattr(obj, f, None) - d['data'] = dict([(name, ss) - for name, ss in compat.iteritems(obj)]) - return d + raise NotImplementedError("msgpack sparse frame is not implemented") + #d = {'typ': 'sparse_dataframe', + # 'klass': obj.__class__.__name__, + # 'columns': obj.columns} + #for f in ['default_fill_value', 'default_kind']: + # d[f] = getattr(obj, f, None) + #d['data'] = dict([(name, ss) + # for name, ss in compat.iteritems(obj)]) + #return d elif isinstance(obj, SparsePanel): - d = {'typ': 'sparse_panel', - 'klass': obj.__class__.__name__, - 'items': obj.items} - for f in ['default_fill_value', 'default_kind']: - d[f] = getattr(obj, f, None) - d['data'] = dict([(name, df) - for name, df in compat.iteritems(obj)]) - return d + raise NotImplementedError("msgpack sparse frame is not implemented") + #d = {'typ': 'sparse_panel', + # 'klass': obj.__class__.__name__, + # 'items': obj.items} + #for f in ['default_fill_value', 'default_kind']: + # d[f] = getattr(obj, f, None) + #d['data'] = dict([(name, df) + # for name, df in compat.iteritems(obj)]) + #return d else: data = obj._data @@ -354,7 +389,7 @@ def encode(obj): 'klass': obj.__class__.__name__, 'indices': obj.indices, 'length': obj.length} - elif isinstance(obj, np.ndarray) and obj.dtype not in ['float64', 'int64']: + elif isinstance(obj, np.ndarray): return {'typ': 'ndarray', 'shape': obj.shape, 'ndim': obj.ndim, @@ -394,14 +429,18 @@ def decode(obj): return Period(ordinal=obj['ordinal'], freq=obj['freq']) elif typ == 'index': dtype = dtype_for(obj['dtype']) - data = obj['data'] + data = unconvert(obj['data'], np.typeDict[obj['dtype']], obj.get('compress')) return globals()[obj['klass']](data, dtype=dtype, name=obj['name']) elif typ == 'multi_index': - return globals()[obj['klass']].from_tuples(obj['data'], names=obj['names']) + data = unconvert(obj['data'], np.typeDict[obj['dtype']], obj.get('compress')) + data = [ tuple(x) for x in data ] + return globals()[obj['klass']].from_tuples(data, names=obj['names']) elif typ == 'period_index': - return globals()[obj['klass']](obj['data'], name=obj['name'], freq=obj['freq']) + data = unconvert(obj['data'], np.int64, obj.get('compress')) + return globals()[obj['klass']](data, name=obj['name'], freq=obj['freq']) elif typ == 'datetime_index': - return globals()[obj['klass']](obj['data'], freq=obj['freq'], tz=obj['tz'], name=obj['name']) + data = unconvert(obj['data'], np.int64, obj.get('compress')) + return globals()[obj['klass']](data, freq=obj['freq'], tz=obj['tz'], name=obj['name']) elif typ == 'series': dtype = dtype_for(obj['dtype']) index = obj['index'] @@ -425,17 +464,17 @@ def create_block(b): return timedelta(*obj['data']) elif typ == 'timedelta64': return np.timedelta64(int(obj['data'])) - elif typ == 'sparse_series': - dtype = dtype_for(obj['dtype']) - return globals( - )[obj['klass']](unconvert(obj['sp_values'], dtype, obj['compress']), sparse_index=obj['sp_index'], - index=obj['index'], fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name']) - elif typ == 'sparse_dataframe': - return globals()[obj['klass']](obj['data'], - columns=obj['columns'], default_fill_value=obj['default_fill_value'], default_kind=obj['default_kind']) - elif typ == 'sparse_panel': - return globals()[obj['klass']](obj['data'], - items=obj['items'], default_fill_value=obj['default_fill_value'], default_kind=obj['default_kind']) + #elif typ == 'sparse_series': + # dtype = dtype_for(obj['dtype']) + # return globals( + # )[obj['klass']](unconvert(obj['sp_values'], dtype, obj['compress']), sparse_index=obj['sp_index'], + # index=obj['index'], fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name']) + #elif typ == 'sparse_dataframe': + # return globals()[obj['klass']](obj['data'], + # columns=obj['columns'], default_fill_value=obj['default_fill_value'], default_kind=obj['default_kind']) + #elif typ == 'sparse_panel': + # return globals()[obj['klass']](obj['data'], + # items=obj['items'], default_fill_value=obj['default_fill_value'], default_kind=obj['default_kind']) elif typ == 'block_index': return globals()[obj['klass']](obj['length'], obj['blocs'], obj['blengths']) elif typ == 'int_index': @@ -460,7 +499,7 @@ def create_block(b): def pack(o, default=encode, - encoding='utf-8', unicode_errors='strict', use_single_float=False): + encoding='latin1', unicode_errors='strict', use_single_float=False): """ Pack an object and return the packed bytes. """ @@ -471,7 +510,7 @@ def pack(o, default=encode, def unpack(packed, object_hook=decode, - list_hook=None, use_list=False, encoding='utf-8', + list_hook=None, use_list=False, encoding='latin1', unicode_errors='strict', object_pairs_hook=None): """ Unpack a packed object, return an iterator @@ -488,7 +527,7 @@ def unpack(packed, object_hook=decode, class Packer(_Packer): def __init__(self, default=encode, - encoding='utf-8', + encoding='latin1', unicode_errors='strict', use_single_float=False): super(Packer, self).__init__(default=default, @@ -501,7 +540,7 @@ class Unpacker(_Unpacker): def __init__(self, file_like=None, read_size=0, use_list=False, object_hook=decode, - object_pairs_hook=None, list_hook=None, encoding='utf-8', + object_pairs_hook=None, list_hook=None, encoding='latin1', unicode_errors='strict', max_buffer_size=0): super(Unpacker, self).__init__(file_like=file_like, read_size=read_size, @@ -525,10 +564,36 @@ def __init__(self, path, **kwargs): def __iter__(self): + needs_closing = True try: - fh = open(self.path, 'rb') + + # see if we have an actual file + if isinstance(self.path, compat.string_types): + + try: + path_exists = os.path.exists(self.path) + except (TypeError): + path_exists = False + + if path_exists: + fh = open(self.path, 'rb') + else: + fh = compat.BytesIO(self.path) + + else: + + if not hasattr(self.path,'read'): + fh = compat.BytesIO(self.path) + + else: + + # a file-like + needs_closing = False + fh = self.path + unpacker = unpack(fh) for o in unpacker: yield o finally: - fh.close() + if needs_closing: + fh.close() diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 79b421ff7b047..e5938ecf87b68 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -55,36 +55,66 @@ def encode_decode(self, x, **kwargs): to_msgpack(p, x, **kwargs) return read_msgpack(p, **kwargs) +class TestAPI(Test): + + def test_string_io(self): + + df = DataFrame(np.random.randn(10,2)) + s = df.to_msgpack(None) + result = read_msgpack(s.getvalue()) + tm.assert_frame_equal(result,df) + + s = to_msgpack(None,df) + result = read_msgpack(s.getvalue()) + tm.assert_frame_equal(result, df) + + with ensure_clean(self.path) as p: + + s = df.to_msgpack(None) + fh = open(p,'wb') + fh.write(s.getvalue()) + fh.close() + result = read_msgpack(p) + tm.assert_frame_equal(result, df) + + def test_iterator_with_string_io(self): + + dfs = [ DataFrame(np.random.randn(10,2)) for i in range(5) ] + s = to_msgpack(None,*dfs) + for i, result in enumerate(read_msgpack(s.getvalue(),iterator=True)): + tm.assert_frame_equal(result,dfs[i]) + + s = to_msgpack(None,*dfs) + for i, result in enumerate(read_msgpack(s,iterator=True)): + tm.assert_frame_equal(result,dfs[i]) class TestNumpy(Test): def test_numpy_scalar_float(self): x = np.float32(np.random.rand()) x_rec = self.encode_decode(x) - self.assert_(np.allclose(x, x_rec) and type(x) == type(x_rec)) + tm.assert_almost_equal(x,x_rec) def test_numpy_scalar_complex(self): x = np.complex64(np.random.rand() + 1j * np.random.rand()) x_rec = self.encode_decode(x) - self.assert_(np.allclose(x, x_rec) and type(x) == type(x_rec)) + tm.assert_almost_equal(x,x_rec) def test_scalar_float(self): x = np.random.rand() x_rec = self.encode_decode(x) - self.assert_(np.allclose(x, x_rec) and type(x) == type(x_rec)) + tm.assert_almost_equal(x,x_rec) def test_scalar_complex(self): x = np.random.rand() + 1j * np.random.rand() x_rec = self.encode_decode(x) - self.assert_(np.allclose(x, x_rec) and type(x) == type(x_rec)) + tm.assert_almost_equal(x,x_rec) def test_list_numpy_float(self): raise nose.SkipTest('buggy test') x = [np.float32(np.random.rand()) for i in range(5)] x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: - x == y, x, x_rec)) and - all(map(lambda x, y: type(x) == type(y), x, x_rec))) + tm.assert_almost_equal(x,x_rec) def test_list_numpy_float_complex(self): if not hasattr(np, 'complex128'): @@ -96,65 +126,59 @@ def test_list_numpy_float_complex(self): [np.complex128(np.random.rand() + 1j * np.random.rand()) for i in range(5)] x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and - all(map(lambda x, y: type(x) == type(y), x, x_rec))) + tm.assert_almost_equal(x,x_rec) def test_list_float(self): x = [np.random.rand() for i in range(5)] x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and - all(map(lambda x, y: type(x) == type(y), x, x_rec))) + tm.assert_almost_equal(x,x_rec) def test_list_float_complex(self): x = [np.random.rand() for i in range(5)] + \ [(np.random.rand() + 1j * np.random.rand()) for i in range(5)] x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and - all(map(lambda x, y: type(x) == type(y), x, x_rec))) + tm.assert_almost_equal(x,x_rec) def test_dict_float(self): x = {'foo': 1.0, 'bar': 2.0} x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x.values(), x_rec.values())) and - all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values()))) + tm.assert_almost_equal(x,x_rec) def test_dict_complex(self): x = {'foo': 1.0 + 1.0j, 'bar': 2.0 + 2.0j} x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x.values(), x_rec.values())) and - all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values()))) + tm.assert_almost_equal(x,x_rec) def test_dict_numpy_float(self): x = {'foo': np.float32(1.0), 'bar': np.float32(2.0)} x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x.values(), x_rec.values())) and - all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values()))) + tm.assert_almost_equal(x,x_rec) def test_dict_numpy_complex(self): x = {'foo': np.complex128( 1.0 + 1.0j), 'bar': np.complex128(2.0 + 2.0j)} x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x.values(), x_rec.values())) and - all(map(lambda x, y: type(x) == type(y), x.values(), x_rec.values()))) + tm.assert_almost_equal(x,x_rec) def test_numpy_array_float(self): - x = np.random.rand(5).astype(np.float32) - x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and - x.dtype == x_rec.dtype) + + # run multiple times + for n in range(10): + x = np.random.rand(10) + for dtype in ['float32','float64']: + x = x.astype(dtype) + x_rec = self.encode_decode(x) + tm.assert_almost_equal(x,x_rec) def test_numpy_array_complex(self): x = (np.random.rand(5) + 1j * np.random.rand(5)).astype(np.complex128) x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and - x.dtype == x_rec.dtype) + tm.assert_almost_equal(x,x_rec) def test_list_mixed(self): x = [1.0, np.float32(3.5), np.complex128(4.25), u('foo')] x_rec = self.encode_decode(x) - self.assert_(all(map(lambda x, y: x == y, x, x_rec)) and - all(map(lambda x, y: type(x) == type(y), x, x_rec))) - + tm.assert_almost_equal(x,x_rec) class TestBasic(Test): @@ -219,8 +243,12 @@ def test_multi_index(self): def test_unicode(self): i = tm.makeUnicodeIndex(100) - i_rec = self.encode_decode(i) - self.assert_(i.equals(i_rec)) + + # this currently fails + self.assertRaises(UnicodeEncodeError, self.encode_decode, i) + + #i_rec = self.encode_decode(i) + #self.assert_(i.equals(i_rec)) class TestSeries(Test): @@ -255,9 +283,11 @@ def setUp(self): def test_basic(self): - for s, i in self.d.items(): - i_rec = self.encode_decode(i) - assert_series_equal(i, i_rec) + # run multiple times here + for n in range(10): + for s, i in self.d.items(): + i_rec = self.encode_decode(i) + assert_series_equal(i, i_rec) class TestNDFrame(Test): @@ -326,8 +356,10 @@ class TestSparse(Test): def _check_roundtrip(self, obj, comparator, **kwargs): - i_rec = self.encode_decode(obj) - comparator(obj, i_rec, **kwargs) + # currently these are not implemetned + #i_rec = self.encode_decode(obj) + #comparator(obj, i_rec, **kwargs) + self.assertRaises(NotImplementedError, self.encode_decode, obj) def test_sparse_series(self): diff --git a/pandas/msgpack.pyx b/pandas/msgpack.pyx index 2c8d7fd014b94..4413e2c0986ab 100644 --- a/pandas/msgpack.pyx +++ b/pandas/msgpack.pyx @@ -172,10 +172,6 @@ cdef class Packer(object): cdef object dtype cdef int n,i - cdef double f8val - cdef int64_t i8val - cdef ndarray[float64_t,ndim=1] array_double - cdef ndarray[int64_t,ndim=1] array_int if nest_limit < 0: raise PackValueError("recursion limit exceeded.") @@ -241,44 +237,6 @@ cdef class Packer(object): ret = self._pack(v, nest_limit-1) if ret != 0: break - # ndarray support ONLY (and float64/int64) for now - elif isinstance(o, np.ndarray) and not hasattr(o,'values') and (o.dtype == 'float64' or o.dtype == 'int64'): - - ret = msgpack_pack_map(&self.pk, 5) - if ret != 0: return -1 - - dtype = o.dtype - self.pack_pair('typ', 'ndarray', nest_limit) - self.pack_pair('shape', o.shape, nest_limit) - self.pack_pair('ndim', o.ndim, nest_limit) - self.pack_pair('dtype', dtype.num, nest_limit) - - ret = self._pack('data', nest_limit-1) - if ret != 0: return ret - - if dtype == 'float64': - array_double = o.ravel() - n = len(array_double) - ret = msgpack_pack_array(&self.pk, n) - if ret != 0: return ret - - for i in range(n): - - f8val = array_double[i] - ret = msgpack_pack_double(&self.pk, f8val) - if ret != 0: break - elif dtype == 'int64': - array_int = o.ravel() - n = len(array_int) - ret = msgpack_pack_array(&self.pk, n) - if ret != 0: return ret - - for i in range(n): - - i8val = array_int[i] - ret = msgpack_pack_long_long(&self.pk, i8val) - if ret != 0: break - elif self._default: o = self._default(o) ret = self._pack(o, nest_limit-1)
API: disable sparse structure encodings and unicode indexes ``` ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- packers_read_pack | 3.5113 | 17.8316 | 0.1969 | packers_read_pickle | 0.6769 | 0.6770 | 0.9999 | packers_write_pack | 1.8814 | 3.5230 | 0.5340 | packers_write_pickle | 1.5387 | 1.4664 | 1.0493 | packers_write_hdf_store | 12.1900 | 12.2033 | 0.9989 | packers_read_csv | 52.3347 | 52.2310 | 1.0020 | packers_write_csv | 536.2056 | 526.5187 | 1.0184 | packers_write_hdf_table | 33.3436 | 32.4137 | 1.0287 | packers_read_hdf_store | 8.3120 | 8.0493 | 1.0326 | packers_read_hdf_table | 13.9607 | 12.9707 | 1.0763 | ------------------------------------------------------------------------------- Test name | head[ms] | base[ms] | ratio | ------------------------------------------------------------------------------- Ratio < 1.0 means the target commit is faster then the baseline. Seed used: 1234 Target [381e86b] : PERF: msgpack encoding changnes to use to/from string for speed boosts API: disable sparse structure encodings and unicode indexes Base [46008ec] : DOC: add cookbook entry ``` ``` In [1]: from pandas.io.packers import pack In [2]: import cPickle as pkl In [3]: df = pd.DataFrame(np.random.rand(1000, 100)) In [6]: %timeit buf = pack(df) 1000 loops, best of 3: 492 ﾵs per loop In [7]: %timeit buf = pkl.dumps(df,pkl.HIGHEST_PROTOCOL) 1000 loops, best of 3: 681 ﾵs per loop In [8]: df = pd.DataFrame(np.random.rand(100000, 100)) In [11]: %timeit buf = pack(df) 1 loops, best of 3: 184 ms per loop In [12]: %timeit buf = pkl.dumps(df,pkl.HIGHEST_PROTOCOL) 10 loops, best of 3: 111 ms per loop ``` now pretty competitive with pickle note on bigger frames, writing an in-memory hdf file is quite fast ``` In [3]: def f(x): ...: store = pd.HDFStore('test.h5',mode='w',driver='H5FD_CORE',driver_core_backing_store=0) ...: store['df'] = x ...: store.close() ...: In [11]: df = pd.DataFrame(np.random.rand(100000, 100)) In [13]: %timeit -n 5 buf = pack(df) 5 loops, best of 3: 202 ms per loop In [14]: %timeit -n 5 buf = pkl.dumps(df,pkl.HIGHEST_PROTOCOL) 5 loops, best of 3: 115 ms per loop In [15]: %timeit -n 5 f(df) 5 loops, best of 3: 53.9 ms per loop ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5498
2013-11-12T15:39:50Z
2013-11-13T14:13:45Z
2013-11-13T14:13:45Z
2021-10-26T16:20:50Z
ENH: accept mutliple series for FRED DataReader
diff --git a/doc/source/release.rst b/doc/source/release.rst index ccc34a4051508..07efacc0bf641 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -219,6 +219,7 @@ Improvements to existing features option it is no longer possible to round trip Excel files with merged MultiIndex and Hierarchical Rows. Set the ``merge_cells`` to ``False`` to restore the previous behaviour. (:issue:`5254`) + - The FRED DataReader now accepts multiple series (:issue`3413`) API Changes ~~~~~~~~~~~ diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index b950876738852..1c9893ec7bd02 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -80,7 +80,9 @@ FRED gdp=web.DataReader("GDP", "fred", start, end) gdp.ix['2013-01-01'] - + # Multiple series: + inflation = web.DataReader(["CPIAUCSL", "CPILFESL"], "fred", start, end) + inflation.head() .. _remote_data.ff: Fama/French diff --git a/pandas/io/data.py b/pandas/io/data.py index cb9f096a1d07a..cf49515cac576 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -17,7 +17,7 @@ ) import pandas.compat as compat from pandas import Panel, DataFrame, Series, read_csv, concat -from pandas.core.common import PandasError +from pandas.core.common import is_list_like, PandasError from pandas.io.parsers import TextParser from pandas.io.common import urlopen, ZipFile, urlencode from pandas.util.testing import _network_error_classes @@ -41,8 +41,9 @@ def DataReader(name, data_source=None, start=None, end=None, Parameters ---------- - name : str - the name of the dataset + name : str or list of strs + the name of the dataset. Some data sources (yahoo, google, fred) will + accept a list of names. data_source: str the data source ("yahoo", "google", "fred", or "ff") start : {datetime, None} @@ -436,24 +437,37 @@ def get_data_fred(name, start=dt.datetime(2010, 1, 1), Date format is datetime Returns a DataFrame. + + If multiple names are passed for "series" then the index of the + DataFrame is the outer join of the indicies of each series. """ start, end = _sanitize_dates(start, end) fred_URL = "http://research.stlouisfed.org/fred2/series/" - url = fred_URL + '%s' % name + '/downloaddata/%s' % name + '.csv' - with urlopen(url) as resp: - data = read_csv(resp, index_col=0, parse_dates=True, - header=None, skiprows=1, names=["DATE", name], - na_values='.') - try: - return data.truncate(start, end) - except KeyError: - if data.ix[3].name[7:12] == 'Error': - raise IOError("Failed to get the data. Check that {0!r} is " - "a valid FRED series.".format(name)) - raise + if not is_list_like(name): + names = [name] + else: + names = name + urls = [fred_URL + '%s' % n + '/downloaddata/%s' % n + '.csv' for + n in names] + + def fetch_data(url, name): + with urlopen(url) as resp: + data = read_csv(resp, index_col=0, parse_dates=True, + header=None, skiprows=1, names=["DATE", name], + na_values='.') + try: + return data.truncate(start, end) + except KeyError: + if data.ix[3].name[7:12] == 'Error': + raise IOError("Failed to get the data. Check that {0!r} is " + "a valid FRED series.".format(name)) + raise + df = concat([fetch_data(url, n) for url, n in zip(urls, names)], + axis=1, join='outer') + return df def get_data_famafrench(name): # path of zip files diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index 4e2331f05001d..8ba770aa31939 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -16,6 +16,10 @@ import pandas.util.testing as tm from numpy.testing import assert_array_equal +if compat.PY3: + from urllib.error import HTTPError +else: + from urllib2 import HTTPError def _skip_if_no_lxml(): try: @@ -422,6 +426,24 @@ def test_invalid_series(self): name = "NOT A REAL SERIES" self.assertRaises(Exception, web.get_data_fred, name) + @network + def test_fred_multi(self): + names = ['CPIAUCSL', 'CPALTT01USQ661S', 'CPILFESL'] + start = datetime(2010, 1, 1) + end = datetime(2013, 1, 27) + + received = web.DataReader(names, "fred", start, end).head(1) + expected = DataFrame([[217.478, 0.99701529, 220.544]], columns=names, + index=[pd.tslib.Timestamp('2010-01-01 00:00:00')]) + expected.index.rename('DATE', inplace=True) + assert_frame_equal(received, expected, check_less_precise=True) + + @network + def test_fred_multi_bad_series(self): + + names = ['NOTAREALSERIES', 'CPIAUCSL', "ALSO FAKE"] + with tm.assertRaises(HTTPError): + DataReader(names, data_source="fred") if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
closes #3413 Let me know if you want this for .13 or .14 and I'll fill in the release notes appropriately.
https://api.github.com/repos/pandas-dev/pandas/pulls/5492
2013-11-11T15:22:23Z
2013-11-27T18:31:21Z
2013-11-27T18:31:21Z
2016-11-03T12:37:37Z
print_versions.py fixes
diff --git a/ci/print_versions.py b/ci/print_versions.py index 900c7c15db42e..463d31ac1d1d3 100755 --- a/ci/print_versions.py +++ b/ci/print_versions.py @@ -2,15 +2,15 @@ def show_versions(): - import subprocess + import imp import os fn = __file__ this_dir = os.path.dirname(fn) - pandas_dir = os.path.dirname(this_dir) - sv_path = os.path.join(pandas_dir, 'pandas', 'util', - 'print_versions.py') - return subprocess.check_call(['python', sv_path]) + pandas_dir = os.path.abspath(os.path.join(this_dir,"..")) + sv_path = os.path.join(pandas_dir, 'pandas','util') + mod = imp.load_module('pvmod', *imp.find_module('print_versions', [sv_path])) + return mod.show_versions() if __name__ == '__main__': - show_versions() + return show_versions()
#4760 #4901 1. Path resolution was broken, so running the script outside the pandas root directory didn't work. 2. The use of `subprocess` to invoke the script from the source tree relied on system python, ignoring the interpreter version actually used to invoke the scripts. Travis was unaffected since the system python there always matches the python version of the env. 3. Using subprocess meant Ctrl-C didn't interrupt the script (meh). cc @cpcloud
https://api.github.com/repos/pandas-dev/pandas/pulls/5486
2013-11-10T21:48:16Z
2013-11-10T21:48:36Z
2013-11-10T21:48:36Z
2014-07-16T08:40:00Z
PERF: perf regression with mixed-type ops using numexpr (GH5481)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index d8a722ff1439e..1222b5b93799d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2810,11 +2810,28 @@ def _arith_op(left, right): return func(left, right) if this._is_mixed_type or other._is_mixed_type: - # XXX no good for duplicate columns - # but cannot outer join in align if dups anyways? - result = {} - for col in this: - result[col] = _arith_op(this[col].values, other[col].values) + + # unique + if this.columns.is_unique: + + def f(col): + r = _arith_op(this[col].values, other[col].values) + return self._constructor_sliced(r,index=new_index,dtype=r.dtype) + + result = dict([ (col, f(col)) for col in this ]) + + # non-unique + else: + + def f(i): + r = _arith_op(this.iloc[:,i].values, other.iloc[:,i].values) + return self._constructor_sliced(r,index=new_index,dtype=r.dtype) + + result = dict([ (i,f(i)) for i, col in enumerate(this.columns) ]) + result = self._constructor(result, index=new_index, copy=False) + result.columns = new_columns + return result + else: result = _arith_op(this.values, other.values) @@ -2890,10 +2907,12 @@ def _compare(a, b): # non-unique else: def _compare(a, b): - return [func(a.iloc[:,i], b.iloc[:,i]) for i, col in enumerate(a.columns)] + return dict([(i,func(a.iloc[:,i], b.iloc[:,i])) for i, col in enumerate(a.columns)]) new_data = expressions.evaluate(_compare, str_rep, self, other) - return self._constructor(data=new_data, index=self.columns, - columns=self.index, copy=False).T + result = self._constructor(data=new_data, index=self.index, + copy=False) + result.columns = self.columns + return result def _compare_frame(self, other, func, str_rep): if not self._indexed_same(other): diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index ffc40ffbadc39..5762171b38918 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -3217,6 +3217,15 @@ def check(result, expected=None): this_df['A'] = index check(this_df, expected_df) + # operations + for op in ['__add__','__mul__','__sub__','__truediv__']: + df = DataFrame(dict(A = np.arange(10), B = np.random.rand(10))) + expected = getattr(df,op)(df) + expected.columns = ['A','A'] + df.columns = ['A','A'] + result = getattr(df,op)(df) + check(result,expected) + def test_column_dups_indexing(self): def check(result, expected=None):
closes #5481 BUG: non-unique ops not aligning correctly these are bascially a trivial op in numpy, so numexpr is slightly slower (but the the dtype inference issue is fixed). Essentially the recreation of an int64 ndarray had to check if its a datetime-like. In this case just passing in the dtype on the reconstructed series fixes it. Also handles non-unique columns now (no tests before, and it would fail). ``` In [1]: df = pd.DataFrame({"A": np.arange(1000000), "B": np.arange(1000000, 0, -1), "C": np.random.randn(1000000)}) In [2]: pd.computation.expressions.set_use_numexpr(False) In [3]: %timeit df*df 100 loops, best of 3: 11 ms per loop In [4]: pd.computation.expressions.set_use_numexpr(True) In [5]: %timeit df*df 100 loops, best of 3: 15.7 ms per loop In [6]: df = df.astype(float) In [7]: pd.computation.expressions.set_use_numexpr(False) In [8]: %timeit df*df 100 loops, best of 3: 5.16 ms per loop In [9]: pd.computation.expressions.set_use_numexpr(True) In [10]: %timeit df*df 100 loops, best of 3: 5.37 ms per loop ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5482
2013-11-10T17:44:26Z
2013-11-10T18:47:39Z
2013-11-10T18:47:39Z
2014-06-18T10:04:21Z
VIS/ENH Hexbin plot
diff --git a/doc/source/release.rst b/doc/source/release.rst index be6d5d464cb36..35ce6c9359d56 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -53,6 +53,8 @@ pandas 0.14.0 New features ~~~~~~~~~~~~ +- Hexagonal bin plots from ``DataFrame.plot`` with ``kind='hexbin'`` (:issue:`5478`) + API Changes ~~~~~~~~~~~ diff --git a/doc/source/v0.14.0.txt b/doc/source/v0.14.0.txt index d181df4c6b89b..ea9fbadeeaf4e 100644 --- a/doc/source/v0.14.0.txt +++ b/doc/source/v0.14.0.txt @@ -154,6 +154,7 @@ Enhancements - ``plot(legend='reverse')`` will now reverse the order of legend labels for most plot kinds. (:issue:`6014`) - improve performance of slice indexing on Series with string keys (:issue:`6341`) +- Hexagonal bin plots from ``DataFrame.plot`` with ``kind='hexbin'`` (:issue:`5478`) Performance ~~~~~~~~~~~ diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 3af83b4d80c8c..081dfd0292cdc 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -414,6 +414,59 @@ setting `kind='kde'`: @savefig kde_plot.png ser.plot(kind='kde') +.. _visualization.hexbin + +Hexagonal Bin plot +~~~~~~~~~~~~~~~~~~ +*New in .14* You can create hexagonal bin plots with ``DataFrame.plot`` and +``kind='hexbin'``. +Hexbin plots can be a useful alternative to scatter plots if your data are +too dense to plot each point individually. + +.. ipython:: python + :suppress: + + plt.figure(); + +.. ipython:: python + + df = DataFrame(randn(1000, 2), columns=['a', 'b']) + df['b'] = df['b'] = df['b'] + np.arange(1000) + + @savefig hexbin_plot.png + df.plot(kind='hexbin', x='a', y='b', gridsize=25) + + +A useful keyword argument is ``gridsize``; it controls the number of hexagons +in the x-direction, and defaults to 100. A larger ``gridsize`` means more, smaller +bins. + +By default, a histogram of the counts around each ``(x, y)`` point is computed. +You can specify alternative aggregations by passing values to the ``C`` and +``reduce_C_function`` arguments. ``C`` specifies the value at each ``(x, y)`` point +and ``reduce_C_function`` is a function of one argument that reduces all the +values in a bin to a single number (e.g. ``mean``, ``max``, ``sum``, ``std``). In this +example the positions are given by columns ``a`` and ``b``, while the value is +given by column ``z``. The bins are aggregated with numpy's ``max`` function. + +.. ipython:: python + :suppress: + + plt.figure(); + +.. ipython:: python + + df = DataFrame(randn(1000, 2), columns=['a', 'b']) + df['b'] = df['b'] = df['b'] + np.arange(1000) + df['z'] = np.random.uniform(0, 3, 1000) + + @savefig hexbin_plot_agg.png + df.plot(kind='hexbin', x='a', y='b', C='z', reduce_C_function=np.max, + gridsize=25) + + +See the `matplotlib hexbin documenation <http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hexbin>`__ for more. + .. _visualization.andrews_curves: Andrews Curves diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 2902621a1e944..041920e1de6ea 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -956,6 +956,65 @@ def test_invalid_kind(self): with tm.assertRaises(ValueError): df.plot(kind='aasdf') + @slow + def test_hexbin_basic(self): + df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform(size=20)}) + + ax = df.plot(kind='hexbin', x='A', y='B', gridsize=10) + # TODO: need better way to test. This just does existence. + self.assert_(len(ax.collections) == 1) + + @slow + def test_hexbin_with_c(self): + df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform(size=20)}) + + ax = df.plot(kind='hexbin', x='A', y='B', C='C') + self.assert_(len(ax.collections) == 1) + + ax = df.plot(kind='hexbin', x='A', y='B', C='C', + reduce_C_function=np.std) + self.assert_(len(ax.collections) == 1) + + @slow + def test_hexbin_cmap(self): + df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform(size=20)}) + + # Default to BuGn + ax = df.plot(kind='hexbin', x='A', y='B') + self.assertEquals(ax.collections[0].cmap.name, 'BuGn') + + cm = 'cubehelix' + ax = df.plot(kind='hexbin', x='A', y='B', colormap=cm) + self.assertEquals(ax.collections[0].cmap.name, cm) + + @slow + def test_no_color_bar(self): + df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform(size=20)}) + + ax = df.plot(kind='hexbin', x='A', y='B', colorbar=None) + self.assertIs(ax.collections[0].colorbar, None) + + @slow + def test_allow_cmap(self): + df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform(size=20)}) + + ax = df.plot(kind='hexbin', x='A', y='B', cmap='YlGn') + self.assertEquals(ax.collections[0].cmap.name, 'YlGn') + + with tm.assertRaises(TypeError): + df.plot(kind='hexbin', x='A', y='B', cmap='YlGn', + colormap='BuGn') + @tm.mplskip class TestDataFrameGroupByPlots(tm.TestCase): diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index c11e2a891ec7a..7038284b6c2a0 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -835,7 +835,14 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=True, secondary_y = [secondary_y] self.secondary_y = secondary_y - self.colormap = colormap + # ugly TypeError if user passes matplotlib's `cmap` name. + # Probably better to accept either. + if 'cmap' in kwds and colormap: + raise TypeError("Only specify one of `cmap` and `colormap`.") + elif 'cmap' in kwds: + self.colormap = kwds.pop('cmap') + else: + self.colormap = colormap self.kwds = kwds @@ -1263,6 +1270,52 @@ def _post_plot_logic(self): ax.set_xlabel(com.pprint_thing(x)) +class HexBinPlot(MPLPlot): + def __init__(self, data, x, y, C=None, **kwargs): + MPLPlot.__init__(self, data, **kwargs) + + if x is None or y is None: + raise ValueError('hexbin requires and x and y column') + if com.is_integer(x) and not self.data.columns.holds_integer(): + x = self.data.columns[x] + if com.is_integer(y) and not self.data.columns.holds_integer(): + y = self.data.columns[y] + + if com.is_integer(C) and not self.data.columns.holds_integer(): + C = self.data.columns[C] + + self.x = x + self.y = y + self.C = C + + def _make_plot(self): + import matplotlib.pyplot as plt + + x, y, data, C = self.x, self.y, self.data, self.C + ax = self.axes[0] + # pandas uses colormap, matplotlib uses cmap. + cmap = self.colormap or 'BuGn' + cmap = plt.cm.get_cmap(cmap) + cb = self.kwds.pop('colorbar', True) + + if C is None: + c_values = None + else: + c_values = data[C].values + + ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap, + **self.kwds) + if cb: + img = ax.collections[0] + self.fig.colorbar(img, ax=ax) + + def _post_plot_logic(self): + ax = self.axes[0] + x, y = self.x, self.y + ax.set_ylabel(com.pprint_thing(y)) + ax.set_xlabel(com.pprint_thing(x)) + + class LinePlot(MPLPlot): def __init__(self, data, **kwargs): @@ -1663,11 +1716,12 @@ def plot_frame(frame=None, x=None, y=None, subplots=False, sharex=True, ax : matplotlib axis object, default None style : list or dict matplotlib line style per column - kind : {'line', 'bar', 'barh', 'kde', 'density', 'scatter'} + kind : {'line', 'bar', 'barh', 'kde', 'density', 'scatter', 'hexbin'} bar : vertical bar plot barh : horizontal bar plot kde/density : Kernel Density Estimation plot scatter: scatter plot + hexbin: hexbin plot logx : boolean, default False For line plots, use log scaling on x axis logy : boolean, default False @@ -1695,6 +1749,17 @@ def plot_frame(frame=None, x=None, y=None, subplots=False, sharex=True, Returns ------- ax_or_axes : matplotlib.AxesSubplot or list of them + + Notes + ----- + + If `kind`='hexbin', you can control the size of the bins with the + `gridsize` argument. By default, a histogram of the counts around each + `(x, y)` point is computed. You can specify alternative aggregations + by passing values to the `C` and `reduce_C_function` arguments. + `C` specifies the value at each `(x, y)` point and `reduce_C_function` + is a function of one argument that reduces all the values in a bin to + a single number (e.g. `mean`, `max`, `sum`, `std`). """ kind = _get_standard_kind(kind.lower().strip()) if kind == 'line': @@ -1705,6 +1770,8 @@ def plot_frame(frame=None, x=None, y=None, subplots=False, sharex=True, klass = KdePlot elif kind == 'scatter': klass = ScatterPlot + elif kind == 'hexbin': + klass = HexBinPlot else: raise ValueError('Invalid chart type given %s' % kind) @@ -1717,6 +1784,16 @@ def plot_frame(frame=None, x=None, y=None, subplots=False, sharex=True, figsize=figsize, logx=logx, logy=logy, sort_columns=sort_columns, secondary_y=secondary_y, **kwds) + elif kind == 'hexbin': + C = kwds.pop('C', None) # remove from kwargs so we can set default + plot_obj = klass(frame, x=x, y=y, kind=kind, subplots=subplots, + rot=rot,legend=legend, ax=ax, style=style, + fontsize=fontsize, use_index=use_index, sharex=sharex, + sharey=sharey, xticks=xticks, yticks=yticks, + xlim=xlim, ylim=ylim, title=title, grid=grid, + figsize=figsize, logx=logx, logy=logy, + sort_columns=sort_columns, secondary_y=secondary_y, + C=C, **kwds) else: if x is not None: if com.is_integer(x) and not frame.columns.holds_integer():
This is just 10 minutes of copy-paste cargo-culting to gauge interest, I haven't tested anything yet. ``` python In [1]: df = pd.DataFrame(np.random.randn(1000, 2)) In [2]: df.plot(kind='hexbin', x=0, y=1) Out[2]: <matplotlib.axes.AxesSubplot at 0x10eb76e10> ``` ![hexbin](https://f.cloud.github.com/assets/1312546/1506888/ef198d6a-4953-11e3-8ecc-891d22def090.png) It's not terribly difficult to do this on your own, so my feeling wouldn't be hurt at all if people are -1 on this :) EDIT: oops I branched from my `to_frame` branch. I'll clean up the commits.
https://api.github.com/repos/pandas-dev/pandas/pulls/5478
2013-11-09T15:31:46Z
2014-02-14T14:46:40Z
2014-02-14T14:46:40Z
2016-11-03T12:37:49Z
API: make sure _is_copy on NDFrame is always available (GH5475)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 3c1942e300729..bc80988eb612c 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -74,6 +74,7 @@ class NDFrame(PandasObject): '_data', 'name', '_cacher', '_is_copy', '_subtyp', '_index', '_default_kind', '_default_fill_value'] _internal_names_set = set(_internal_names) _metadata = [] + _is_copy = None def __init__(self, data, axes=None, copy=False, dtype=None, fastpath=False): diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index d6d340a695231..dacac6ef64b29 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1736,6 +1736,16 @@ def f(): df = DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]}) df.loc[0]['A'] = 111 + # make sure that _is_copy is picked up reconstruction + # GH5475 + df = DataFrame({"A": [1,2]}) + self.assert_(df._is_copy is False) + with tm.ensure_clean('__tmp__pickle') as path: + df.to_pickle(path) + df2 = pd.read_pickle(path) + df2["B"] = df2["A"] + df2["B"] = df2["A"] + def test_floating_index_doc_example(self): index = Index([1.5, 2, 3, 4.5, 5])
closes #5475
https://api.github.com/repos/pandas-dev/pandas/pulls/5477
2013-11-09T01:43:33Z
2013-11-09T01:56:27Z
2013-11-09T01:56:27Z
2014-07-04T19:23:41Z
TST: win32 fixes
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index b8de54ade31db..44e560b86a683 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -180,6 +180,11 @@ def test_floor_division(self): self.check_floor_division(lhs, '//', rhs) def test_pow(self): + import platform + if platform.system() == 'Windows': + raise nose.SkipTest('not testing pow on Windows') + + # odd failure on win32 platform, so skip for lhs, rhs in product(self.lhses, self.rhses): self.check_pow(lhs, '**', rhs) @@ -867,8 +872,13 @@ def testit(r_idx_type, c_idx_type, index_name): expected = s + df assert_frame_equal(res, expected) - args = product(self.lhs_index_types, self.index_types, - ('index', 'columns')) + # only test dt with dt, otherwise weird joins result + args = product(['i','u','s'],['i','u','s'],('index', 'columns')) + for r_idx_type, c_idx_type, index_name in args: + testit(r_idx_type, c_idx_type, index_name) + + # dt with dt + args = product(['dt'],['dt'],('index', 'columns')) for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 5a370b71e3511..885ec2714c47a 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -537,7 +537,7 @@ def __setitem__(self, key, value): if value.shape != shape[1:]: raise ValueError('shape of value must be {0}, shape of given ' 'object was {1}'.format(shape[1:], - value.shape)) + tuple(map(int, value.shape)))) mat = np.asarray(value) elif np.isscalar(value): dtype, value = _infer_dtype_from_scalar(value) diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 2a3f85b550a7c..dce46c972fb3b 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -16,15 +16,14 @@ _TYPE_MAP = { np.string_: 'string', np.unicode_: 'unicode', np.bool_: 'boolean', - np.datetime64 : 'datetime64' + np.datetime64 : 'datetime64', + np.timedelta64 : 'timedelta64' } try: _TYPE_MAP[np.float128] = 'floating' _TYPE_MAP[np.complex256] = 'complex' _TYPE_MAP[np.float16] = 'floating' - _TYPE_MAP[np.datetime64] = 'datetime64' - _TYPE_MAP[np.timedelta64] = 'timedelta64' except AttributeError: pass diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index a634cbd0ca2ed..9a18e3c8562f6 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -20,6 +20,7 @@ from pandas.util.testing import (assert_almost_equal, assertRaisesRegexp, assert_copy) from pandas import compat +from pandas.compat import long import pandas.util.testing as tm import pandas.core.config as cf @@ -1344,7 +1345,7 @@ def test_inplace_mutation_resets_values(self): # make sure label setting works too labels2 = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]] - exp_values = np.array([(1, 'a')] * 6, dtype=object) + exp_values = np.array([(long(1), 'a')] * 6, dtype=object) new_values = mi2.set_labels(labels2).values # not inplace shouldn't change assert_almost_equal(mi2._tuples, vals2) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 5732d2ad56a4f..d6d340a695231 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1687,7 +1687,7 @@ def test_detect_chained_assignment(self): # work with the chain expected = DataFrame([[-5,1],[-6,3]],columns=list('AB')) - df = DataFrame(np.arange(4).reshape(2,2),columns=list('AB')) + df = DataFrame(np.arange(4).reshape(2,2),columns=list('AB'),dtype='int64') self.assert_(not df._is_copy) df['A'][0] = -5 @@ -1695,7 +1695,7 @@ def test_detect_chained_assignment(self): assert_frame_equal(df, expected) expected = DataFrame([[-5,2],[np.nan,3.]],columns=list('AB')) - df = DataFrame({ 'A' : np.arange(2), 'B' : np.array(np.arange(2,4),dtype=np.float64)}) + df = DataFrame({ 'A' : Series(range(2),dtype='int64'), 'B' : np.array(np.arange(2,4),dtype=np.float64)}) self.assert_(not df._is_copy) df['A'][0] = -5 df['A'][1] = np.nan @@ -1703,7 +1703,7 @@ def test_detect_chained_assignment(self): self.assert_(not df['A']._is_copy) # using a copy (the chain), fails - df = DataFrame({ 'A' : np.arange(2), 'B' : np.array(np.arange(2,4),dtype=np.float64)}) + df = DataFrame({ 'A' : Series(range(2),dtype='int64'), 'B' : np.array(np.arange(2,4),dtype=np.float64)}) def f(): df.loc[0]['A'] = -5 self.assertRaises(com.SettingWithCopyError, f) @@ -1711,7 +1711,7 @@ def f(): # doc example df = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'], - 'c' : np.arange(7) }) + 'c' : Series(range(7),dtype='int64') }) self.assert_(not df._is_copy) expected = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'], diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 5ff4718d97a92..2ea570d6f8e94 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -177,8 +177,14 @@ def get_locales(prefix=None, normalize=True, For example:: locale.setlocale(locale.LC_ALL, locale_string) + + On error will return None (no locale available, e.g. Windows) + """ - raw_locales = locale_getter() + try: + raw_locales = locale_getter() + except: + return None try: raw_locales = str(raw_locales, encoding=pd.options.display.encoding)
TST: test_indexing dtype comps, closes #5466 TST don't compare invalid indices in eval operations, closes #5467 TST: invalid assignment for np.timedelta64 closes #5465 TST: skip locale checks on windows cloes #5464 TST: dtype comp issue on windows in test_indexing (GH5466) TST: skip test_pow on windows (GH5467)
https://api.github.com/repos/pandas-dev/pandas/pulls/5474
2013-11-08T16:04:19Z
2013-11-08T18:18:25Z
2013-11-08T18:18:25Z
2014-07-16T08:39:49Z
BUG: DatetimeIndex.normalize now accounts for nanoseconds (#5461).
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 51939ac22e956..a7bd2250f95e3 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1420,6 +1420,11 @@ def test_normalize(self): result = rng.normalize() expected = date_range('1/1/2000', periods=10, freq='D') self.assert_(result.equals(expected)) + + rng_ns = pd.DatetimeIndex(np.array([1380585623454345752, 1380585612343234312]).astype("datetime64[ns]")) + rng_ns_normalized = rng_ns.normalize() + expected = pd.DatetimeIndex(np.array([1380585600000000000, 1380585600000000000]).astype("datetime64[ns]")) + self.assert_(rng_ns_normalized.equals(expected)) self.assert_(result.is_normalized) self.assert_(not rng.is_normalized) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 11b86db8b8d92..96391f703d1d7 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -2180,6 +2180,7 @@ cdef inline int64_t _normalized_stamp(pandas_datetimestruct *dts): dts.min = 0 dts.sec = 0 dts.us = 0 + dts.ps = 0 return pandas_datetimestruct_to_datetime(PANDAS_FR_ns, dts)
closes #5461 datetime64(ns) uses the dts->ps which was not being set to zero in the normalize function.
https://api.github.com/repos/pandas-dev/pandas/pulls/5472
2013-11-08T12:04:03Z
2013-11-08T12:46:21Z
2013-11-08T12:46:21Z
2014-07-15T19:20:42Z
TST: Fix test case to use int64 and explicit float
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index f866c1b229fe5..b6f0387835c22 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -1724,7 +1724,7 @@ def test_mode(self): exp = Series([11, 12]) assert_series_equal(s.mode(), exp) - assert_series_equal(Series([1, 2, 3]).mode(), Series([], dtype=int)) + assert_series_equal(Series([1, 2, 3]).mode(), Series([], dtype='int64')) lst = [5] * 20 + [1] * 10 + [6] * 25 np.random.shuffle(lst) @@ -1736,7 +1736,7 @@ def test_mode(self): s = Series(lst) s[0] = np.nan - assert_series_equal(s.mode(), Series([6], dtype=float)) + assert_series_equal(s.mode(), Series([6.])) s = Series(list('adfasbasfwewefwefweeeeasdfasnbam')) assert_series_equal(s.mode(), Series(['e']))
#5468
https://api.github.com/repos/pandas-dev/pandas/pulls/5469
2013-11-08T03:59:58Z
2013-11-08T04:38:59Z
2013-11-08T04:38:59Z
2014-07-16T08:39:46Z
PERF: performance improvements on isnull/notnull for large pandas objects
diff --git a/doc/source/release.rst b/doc/source/release.rst index 053f4b1f1b66e..6ae7493fb4b72 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -803,6 +803,7 @@ Bug Fixes - Make tests create temp files in temp directory by default. (:issue:`5419`) - ``pd.to_timedelta`` of a scalar returns a scalar (:issue:`5410`) - ``pd.to_timedelta`` accepts ``NaN`` and ``NaT``, returning ``NaT`` instead of raising (:issue:`5437`) + - performance improvements in ``isnull`` on larger size pandas objects pandas 0.12.0 ------------- diff --git a/pandas/core/common.py b/pandas/core/common.py index 453227aec6e23..7f1fe50048599 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -128,7 +128,7 @@ def _isnull_new(obj): elif isinstance(obj, (ABCSeries, np.ndarray)): return _isnull_ndarraylike(obj) elif isinstance(obj, ABCGeneric): - return obj.apply(isnull) + return obj._constructor(obj._data.apply(lambda x: isnull(x.values))) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike(np.asarray(obj)) else: @@ -155,7 +155,7 @@ def _isnull_old(obj): elif isinstance(obj, (ABCSeries, np.ndarray)): return _isnull_ndarraylike_old(obj) elif isinstance(obj, ABCGeneric): - return obj.apply(_isnull_old) + return obj._constructor(obj._data.apply(lambda x: _isnull_old(x.values))) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike_old(np.asarray(obj)) else: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 30dccb971ae18..3c1942e300729 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2208,13 +2208,13 @@ def isnull(self): """ Return a boolean same-sized object indicating if the values are null """ - return self.__class__(isnull(self),**self._construct_axes_dict()).__finalize__(self) + return isnull(self).__finalize__(self) def notnull(self): """ Return a boolean same-sized object indicating if the values are not null """ - return self.__class__(notnull(self),**self._construct_axes_dict()).__finalize__(self) + return notnull(self).__finalize__(self) def clip(self, lower=None, upper=None, out=None): """ diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 51fe09a805903..c5e245d2e320c 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -2153,6 +2153,13 @@ def apply(self, f, *args, **kwargs): continue if callable(f): applied = f(blk, *args, **kwargs) + + # if we are no a block, try to coerce + if not isinstance(applied, Block): + applied = make_block(applied, + blk.items, + blk.ref_items) + else: applied = getattr(blk, f)(*args, **kwargs) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index dfedfd629f736..7b4ea855f2f9d 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -75,17 +75,28 @@ def test_isnull(): assert not isnull(np.inf) assert not isnull(-np.inf) + # series for s in [tm.makeFloatSeries(),tm.makeStringSeries(), tm.makeObjectSeries(),tm.makeTimeSeries(),tm.makePeriodSeries()]: assert(isinstance(isnull(s), Series)) - # call on DataFrame - df = DataFrame(np.random.randn(10, 5)) - df['foo'] = 'bar' - result = isnull(df) - expected = result.apply(isnull) - tm.assert_frame_equal(result, expected) - + # frame + for df in [tm.makeTimeDataFrame(),tm.makePeriodFrame(),tm.makeMixedDataFrame()]: + result = isnull(df) + expected = df.apply(isnull) + tm.assert_frame_equal(result, expected) + + # panel + for p in [ tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) ]: + result = isnull(p) + expected = p.apply(isnull) + tm.assert_panel_equal(result, expected) + + # panel 4d + for p in [ tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D()) ]: + result = isnull(p) + expected = p.apply(isnull) + tm.assert_panel4d_equal(result, expected) def test_isnull_lists(): result = isnull([[False]]) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 895c651c0bfe7..5ff4718d97a92 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -531,6 +531,9 @@ def assert_copy(iter1, iter2, **eql_kwargs): def getCols(k): return string.ascii_uppercase[:k] +def getArangeMat(): + return np.arange(N * K).reshape((N, K)) + # make index def makeStringIndex(k=10): @@ -601,24 +604,20 @@ def getTimeSeriesData(nper=None): return dict((c, makeTimeSeries(nper)) for c in getCols(K)) +def getPeriodData(nper=None): + return dict((c, makePeriodSeries(nper)) for c in getCols(K)) + +# make frame def makeTimeDataFrame(nper=None): data = getTimeSeriesData(nper) return DataFrame(data) -def getPeriodData(nper=None): - return dict((c, makePeriodSeries(nper)) for c in getCols(K)) - -# make frame def makeDataFrame(): data = getSeriesData() return DataFrame(data) -def getArangeMat(): - return np.arange(N * K).reshape((N, K)) - - def getMixedTypeDict(): index = Index(['a', 'b', 'c', 'd', 'e']) @@ -631,6 +630,8 @@ def getMixedTypeDict(): return index, data +def makeMixedDataFrame(): + return DataFrame(getMixedTypeDict()[1]) def makePeriodFrame(nper=None): data = getPeriodData(nper) @@ -827,13 +828,13 @@ def add_nans(panel): dm = panel[item] for j, col in enumerate(dm.columns): dm[col][:i + j] = np.NaN - + return panel def add_nans_panel4d(panel4d): for l, label in enumerate(panel4d.labels): panel = panel4d[label] add_nans(panel) - + return panel4d class TestSubDict(dict): diff --git a/vb_suite/frame_methods.py b/vb_suite/frame_methods.py index 3567ee2b09f99..b7754e28629d0 100644 --- a/vb_suite/frame_methods.py +++ b/vb_suite/frame_methods.py @@ -221,6 +221,9 @@ def f(K=100): frame_xs_col = Benchmark('df.xs(50000,axis = 1)', setup) +#---------------------------------------------------------------------- +# nulls/masking + ## masking setup = common_setup + """ data = np.random.randn(1000, 500) @@ -230,8 +233,17 @@ def f(K=100): mask = isnull(df) """ -mask_bools = Benchmark('bools.mask(mask)', setup, - start_date=datetime(2013,1,1)) +frame_mask_bools = Benchmark('bools.mask(mask)', setup, + start_date=datetime(2013,1,1)) + +frame_mask_floats = Benchmark('bools.astype(float).mask(mask)', setup, + start_date=datetime(2013,1,1)) + +## isnull +setup = common_setup + """ +data = np.random.randn(1000, 1000) +df = DataFrame(data) +""" +frame_isnull = Benchmark('isnull(df)', setup, + start_date=datetime(2012,1,1)) -mask_floats = Benchmark('bools.astype(float).mask(mask)', setup, - start_date=datetime(2013,1,1))
``` In [1]: df = DataFrame(np.random.randn(1000,1000)) In [2]: %timeit df.apply(pd.isnull) 10 loops, best of 3: 121 ms per loop In [3]: %timeit pd.isnull(df) 100 loops, best of 3: 3.15 ms per loop ``` from 0.12 ``` In [2]: %timeit df.apply(pd.isnull) 10 loops, best of 3: 74.3 ms per loop In [3]: %timeit pd.isnull(df) 10 loops, best of 3: 81.8 ms per loop ``` now evaluates block-by-block rather than column-by-column (via apply)
https://api.github.com/repos/pandas-dev/pandas/pulls/5462
2013-11-07T13:06:16Z
2013-11-07T14:44:58Z
2013-11-07T14:44:58Z
2014-07-16T08:39:41Z
DOC: add basic documentation to some of the GroupBy properties and methods
diff --git a/doc/source/api.rst b/doc/source/api.rst index c2c2bc8710af2..4ecde7e05256a 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1112,6 +1112,42 @@ Conversion DatetimeIndex.to_pydatetime +GroupBy +------- +.. currentmodule:: pandas.core.groupby + +GroupBy objects are returned by groupby calls: :func:`pandas.DataFrame.groupby`, :func:`pandas.Series.groupby`, etc. + +Indexing, iteration +~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: generated/ + + GroupBy.__iter__ + GroupBy.groups + GroupBy.indices + GroupBy.get_group + +Function application +~~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: generated/ + + GroupBy.apply + GroupBy.aggregate + GroupBy.transform + +Computations / Descriptive Stats +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: generated/ + + GroupBy.mean + GroupBy.median + GroupBy.std + GroupBy.var + GroupBy.ohlc + .. HACK - see github issue #4539. To ensure old links remain valid, include here the autosummaries with previous currentmodules as a comment and add diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 1d5691edb6313..5183ff6484d02 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -226,6 +226,7 @@ def __unicode__(self): @property def groups(self): + """ dict {group name -> group labels} """ return self.grouper.groups @property @@ -234,6 +235,7 @@ def ngroups(self): @property def indices(self): + """ dict {group name -> group indices} """ return self.grouper.indices @property @@ -310,6 +312,22 @@ def curried(x): return wrapper def get_group(self, name, obj=None): + """ + Constructs NDFrame from group with provided name + + Parameters + ---------- + name : object + the name of the group to get as a DataFrame + obj : NDFrame, default None + the NDFrame to take the DataFrame out of. If + it is None, the object groupby was called on will + be used + + Returns + ------- + group : type of obj + """ if obj is None: obj = self.obj @@ -838,6 +856,7 @@ def apply(self, f, data, axis=0): @cache_readonly def indices(self): + """ dict {group name -> group indices} """ if len(self.groupings) == 1: return self.groupings[0].indices else: @@ -884,6 +903,7 @@ def _max_groupsize(self): @cache_readonly def groups(self): + """ dict {group name -> group labels} """ if len(self.groupings) == 1: return self.groupings[0].groups else:
I added some simple documentation to the GroupBy and Grouper classes
https://api.github.com/repos/pandas-dev/pandas/pulls/5459
2013-11-07T01:45:06Z
2013-11-27T20:00:21Z
2013-11-27T20:00:21Z
2014-06-14T22:38:30Z
DOC: small error in cookbook (doc build warning)
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 84cca1c4b7ae9..9a295865fbd82 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -335,7 +335,7 @@ The :ref:`CSV <io.read_csv_table>` docs <http://stackoverflow.com/questions/11622652/large-persistent-dataframe-in-pandas/12193309#12193309>`__ `Reading only certain rows of a csv chunk-by-chunk -<http://stackoverflow.com/questions/19674212/pandas-data-frame-select-rows-and-clear-memory>``__ +<http://stackoverflow.com/questions/19674212/pandas-data-frame-select-rows-and-clear-memory>`__ `Reading the first few lines of a frame <http://stackoverflow.com/questions/15008970/way-to-read-first-few-lines-for-pandas-dataframe>`__
https://api.github.com/repos/pandas-dev/pandas/pulls/5454
2013-11-06T16:23:22Z
2013-11-06T16:34:26Z
2013-11-06T16:34:26Z
2014-07-16T08:39:33Z
DOC: remove docstring of flags attribute during doc building process (#5331)
diff --git a/doc/source/conf.py b/doc/source/conf.py index a500289b27ab1..695a954f78cfb 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -246,3 +246,12 @@ 'GH'), 'wiki': ('https://github.com/pydata/pandas/wiki/%s', 'wiki ')} + +# remove the docstring of the flags attribute (inherited from numpy ndarray) +# because these give doc build errors (see GH issue 5331) +def remove_flags_docstring(app, what, name, obj, options, lines): + if what == "attribute" and name.endswith(".flags"): + del lines[:] + +def setup(app): + app.connect("autodoc-process-docstring", remove_flags_docstring)
Further fixes #5331 with @jtratner 's suggestion (https://github.com/pydata/pandas/issues/5142#issuecomment-27871973). This removes the docstring of all attributes with the name `flags` during the doc building process.
https://api.github.com/repos/pandas-dev/pandas/pulls/5450
2013-11-06T14:05:13Z
2013-11-06T15:31:36Z
2013-11-06T15:31:36Z
2014-07-09T18:47:42Z
PERF: Fixed decoding perf issue on py3 (GH5441)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 926e8f1d0c5ea..e137a93af07a9 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -531,6 +531,7 @@ Bug Fixes names weren't strings (:issue:`4956`) - A zero length series written in Fixed format not deserializing properly. (:issue:`4708`) + - Fixed decoding perf issue on pyt3 (:issue:`5441`) - Fixed bug in tslib.tz_convert(vals, tz1, tz2): it could raise IndexError exception while trying to access trans[pos + 1] (:issue:`4496`) - The ``by`` argument now works correctly with the ``layout`` argument diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 975d04c185d51..bc2f41502614d 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -3972,8 +3972,11 @@ def _unconvert_string_array(data, nan_rep=None, encoding=None): # where the passed encoding is actually None) encoding = _ensure_encoding(encoding) if encoding is not None and len(data): - f = np.vectorize(lambda x: x.decode(encoding), otypes=[np.object]) - data = f(data) + try: + data = data.astype(str).astype(object) + except: + f = np.vectorize(lambda x: x.decode(encoding), otypes=[np.object]) + data = f(data) if nan_rep is None: nan_rep = 'nan'
closes #5441
https://api.github.com/repos/pandas-dev/pandas/pulls/5448
2013-11-06T12:39:07Z
2013-11-06T13:59:15Z
2013-11-06T13:59:15Z
2014-06-16T19:05:47Z
DOC: fix build error in to_latex and release notes
diff --git a/doc/source/release.rst b/doc/source/release.rst index 136829e692c8f..d9924a2457e7c 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -386,6 +386,7 @@ API Changes division. .. code-block:: python + In [3]: arr = np.array([1, 2, 3, 4]) In [4]: arr2 = np.array([5, 3, 2, 1]) diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index c736a52cd1e71..acd6baf4d6551 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -60,6 +60,7 @@ API changes division. .. code-block:: python + In [3]: arr = np.array([1, 2, 3, 4]) In [4]: arr2 = np.array([5, 3, 2, 1]) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 60bd36d58f0de..89219e76444a5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1295,7 +1295,9 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, justify=None, force_unicode=None, bold_rows=True, classes=None, escape=True): """ - to_html-specific options + Render a DataFrame as an HTML table. + + `to_html`-specific options: bold_rows : boolean, default True Make the row labels bold in the output @@ -1303,8 +1305,6 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None, CSS class(es) to apply to the resulting html table escape : boolean, default True Convert the characters <, >, and & to HTML-safe sequences. - - Render a DataFrame as an HTML table. """ if force_unicode is not None: # pragma: no cover @@ -1337,12 +1337,13 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, float_format=None, sparsify=None, index_names=True, bold_rows=True, force_unicode=None): """ - to_latex-specific options - bold_rows : boolean, default True - Make the row labels bold in the output - Render a DataFrame to a tabular environment table. You can splice this into a LaTeX document. + + `to_latex`-specific options: + + bold_rows : boolean, default True + Make the row labels bold in the output """ if force_unicode is not None: # pragma: no cover
This partly fixes #5331 (the `to_latex` part, not the 'Malformed table' one). And in the same time, fixes a build error in the newly added docs on `truediv` in the release notes.
https://api.github.com/repos/pandas-dev/pandas/pulls/5447
2013-11-06T10:25:16Z
2013-11-06T12:58:55Z
2013-11-06T12:58:55Z
2014-07-16T08:39:27Z
PERF/ENH: Use xlsxwriter by default (and then fall back on openpyxl).
diff --git a/doc/source/io.rst b/doc/source/io.rst index 1a879866c5516..29478d1fd7841 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1825,8 +1825,10 @@ written. For example: df.to_excel('path_to_file.xlsx', sheet_name='Sheet1') -Files with a ``.xls`` extension will be written using ``xlwt`` and those with -a ``.xlsx`` extension will be written using ``openpyxl``. +Files with a ``.xls`` extension will be written using ``xlwt`` and those with a +``.xlsx`` extension will be written using ``xlsxwriter`` (if available) or +``openpyxl``. + The Panel class also has a ``to_excel`` instance method, which writes each DataFrame in the Panel to a separate sheet. @@ -1858,14 +1860,19 @@ Excel writer engines 1. the ``engine`` keyword argument 2. the filename extension (via the default specified in config options) -By default, ``pandas`` uses `openpyxl <http://packages.python.org/openpyxl/>`__ -for ``.xlsx`` and ``.xlsm`` files and `xlwt <http://www.python-excel.org/>`__ -for ``.xls`` files. If you have multiple engines installed, you can set the -default engine through :ref:`setting the config options <basics.working_with_options>` -``io.excel.xlsx.writer`` and ``io.excel.xls.writer``. +By default, ``pandas`` uses the `XlsxWriter`_ for ``.xlsx`` and `openpyxl`_ +for ``.xlsm`` files and `xlwt`_ for ``.xls`` files. If you have multiple +engines installed, you can set the default engine through :ref:`setting the +config options <basics.working_with_options>` ``io.excel.xlsx.writer`` and +``io.excel.xls.writer``. pandas will fall back on `openpyxl`_ for ``.xlsx`` +files if `Xlsxwriter`_ is not available. + +.. _XlsxWriter: http://xlsxwriter.readthedocs.org +.. _openpyxl: http://packages.python.org/openpyxl/ +.. _xlwt: http://www.python-excel.org -For example if the `XlsxWriter <http://xlsxwriter.readthedocs.org>`__ -module is installed you can use it as a xlsx writer engine as follows: +To specify which writer you want to use, you can pass an engine keyword +argument to ``to_excel`` and to ``ExcelWriter``. .. code-block:: python diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 13f7a3dbe7d4a..51eee0a5d6327 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -290,8 +290,7 @@ def use_inf_as_null_cb(key): with cf.config_prefix('io.excel'): # going forward, will be additional writers for ext, options in [('xls', ['xlwt']), - ('xlsm', ['openpyxl']), - ('xlsx', ['openpyxl'])]: + ('xlsm', ['openpyxl'])]: default = options.pop(0) if options: options = " " + ", ".join(options) @@ -300,3 +299,17 @@ def use_inf_as_null_cb(key): doc = writer_engine_doc.format(ext=ext, default=default, others=options) cf.register_option(ext + '.writer', default, doc, validator=str) + def _register_xlsx(engine, other): + cf.register_option('xlsx.writer', engine, + writer_engine_doc.format(ext='xlsx', + default=engine, + others=", '%s'" % other), + validator=str) + + try: + # better memory footprint + import xlsxwriter + _register_xlsx('xlsxwriter', 'openpyxl') + except ImportError: + # fallback + _register_xlsx('openpyxl', 'xlsxwriter') diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 8bcf5e461ce7c..1b94d1b02e741 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -14,7 +14,7 @@ from pandas.io.parsers import read_csv from pandas.io.excel import ( ExcelFile, ExcelWriter, read_excel, _XlwtWriter, _OpenpyxlWriter, - register_writer + register_writer, _XlsxWriter ) from pandas.util.testing import ensure_clean from pandas.core.config import set_option, get_option @@ -1026,9 +1026,14 @@ def test_ExcelWriter_dispatch(self): with tm.assertRaisesRegexp(ValueError, 'No engine'): ExcelWriter('nothing') - _skip_if_no_openpyxl() + try: + import xlsxwriter + writer_klass = _XlsxWriter + except ImportError: + _skip_if_no_openpyxl() + writer_klass = _OpenpyxlWriter writer = ExcelWriter('apple.xlsx') - tm.assert_isinstance(writer, _OpenpyxlWriter) + tm.assert_isinstance(writer, writer_klass) _skip_if_no_xlwt() writer = ExcelWriter('apple.xls')
Since xlsxwriter has a smaller install base (because it's newer), reasonable to assume that if you have it installed, you want xlsxwriter. Plus, it has better performance characteristics in general (esp. in terms of memory usage)
https://api.github.com/repos/pandas-dev/pandas/pulls/5446
2013-11-06T02:34:52Z
2013-11-07T03:23:10Z
2013-11-07T03:23:10Z
2014-07-16T08:39:26Z
ENH: Always do true division on Python 2.X
diff --git a/doc/source/release.rst b/doc/source/release.rst index 5fef061b9e447..926e8f1d0c5ea 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -376,6 +376,25 @@ API Changes dates are given (:issue:`5242`) - ``Timestamp`` now supports ``now/today/utcnow`` class methods (:issue:`5339`) + - **All** division with ``NDFrame`` - likes is now truedivision, regardless + of the future import. You can use ``//`` and ``floordiv`` to do integer + division. + + .. code-block:: python + In [3]: arr = np.array([1, 2, 3, 4]) + + In [4]: arr2 = np.array([5, 3, 2, 1]) + + In [5]: arr / arr2 + Out[5]: array([0, 0, 1, 4]) + + In [6]: pd.Series(arr) / pd.Series(arr2) # no future import required + Out[6]: + 0 0.200000 + 1 0.666667 + 2 1.500000 + 3 4.000000 + dtype: float64 Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 9b958d59d5a74..c736a52cd1e71 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -55,6 +55,26 @@ API changes # and all methods take an inplace kwarg - but returns None index.set_names(["bob", "cranberry"], inplace=True) +- **All** division with ``NDFrame`` - likes is now truedivision, regardless + of the future import. You can use ``//`` and ``floordiv`` to do integer + division. + + .. code-block:: python + In [3]: arr = np.array([1, 2, 3, 4]) + + In [4]: arr2 = np.array([5, 3, 2, 1]) + + In [5]: arr / arr2 + Out[5]: array([0, 0, 1, 4]) + + In [6]: pd.Series(arr) / pd.Series(arr2) # no future import required + Out[6]: + 0 0.200000 + 1 0.666667 + 2 1.500000 + 3 4.000000 + dtype: float64 + - Infer and downcast dtype if ``downcast='infer'`` is passed to ``fillna/ffill/bfill`` (:issue:`4604`) - ``__nonzero__`` for all NDFrame objects, will now raise a ``ValueError``, this reverts back to (:issue:`1073`, :issue:`4633`) behavior. See :ref:`gotchas<gotchas.truth>` for a more detailed discussion. diff --git a/pandas/computation/expressions.py b/pandas/computation/expressions.py index 3c1fb091ab823..f1007cbc81eb7 100644 --- a/pandas/computation/expressions.py +++ b/pandas/computation/expressions.py @@ -61,6 +61,7 @@ def _evaluate_standard(op, op_str, a, b, raise_on_error=True, **eval_kwargs): _store_test_result(False) return op(a, b) + def _can_use_numexpr(op, op_str, a, b, dtype_check): """ return a boolean if we WILL be using numexpr """ if op_str is not None: @@ -86,7 +87,8 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check): return False -def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, **eval_kwargs): +def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, truediv=True, + **eval_kwargs): result = None if _can_use_numexpr(op, op_str, a, b, 'evaluate'): @@ -96,7 +98,8 @@ def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, **eval_kwargs): result = ne.evaluate('a_value %s b_value' % op_str, local_dict={'a_value': a_value, 'b_value': b_value}, - casting='safe', **eval_kwargs) + casting='safe', truediv=truediv, + **eval_kwargs) except (ValueError) as detail: if 'unknown type object' in str(detail): pass @@ -112,10 +115,12 @@ def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, **eval_kwargs): return result + def _where_standard(cond, a, b, raise_on_error=True): return np.where(_values_from_object(cond), _values_from_object(a), _values_from_object(b)) + def _where_numexpr(cond, a, b, raise_on_error=False): result = None @@ -190,10 +195,10 @@ def where(cond, a, b, raise_on_error=False, use_numexpr=True): return _where_standard(cond, a, b, raise_on_error=raise_on_error) -def set_test_mode(v = True): +def set_test_mode(v=True): """ - Keeps track of whether numexpr was used. Stores an additional ``True`` for - every successful use of evaluate with numexpr since the last + Keeps track of whether numexpr was used. Stores an additional ``True`` + for every successful use of evaluate with numexpr since the last ``get_test_result`` """ global _TEST_MODE, _TEST_RESULT diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 6ab6f15aaab15..2699dd0a25a2b 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -2,7 +2,7 @@ Generic data algorithms. This module is experimental at the moment and not intended for public consumption """ - +from __future__ import division from warnings import warn import numpy as np diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 1cb0c4adcf5d4..1e843e40037b1 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -8,7 +8,7 @@ alignment and a host of useful data manipulation methods having to do with the labeling information """ - +from __future__ import division # pylint: disable=E1101,E1103 # pylint: disable=W0212,W0231,W0703,W0622 diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 0c647bb6ee7eb..9d227f0afffc6 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -3,6 +3,8 @@ This is not a public API. """ +# necessary to enforce truediv in Python 2.X +from __future__ import division import operator import numpy as np import pandas as pd @@ -80,24 +82,10 @@ def names(x): rmod=arith_method(lambda x, y: y % x, names('rmod'), default_axis=default_axis), ) - if not compat.PY3: - new_methods["div"] = arith_method(operator.div, names('div'), op('/'), - truediv=False, fill_zeros=np.inf, - default_axis=default_axis) - new_methods["rdiv"] = arith_method(lambda x, y: operator.div(y, x), - names('rdiv'), truediv=False, - fill_zeros=np.inf, - default_axis=default_axis) - else: - new_methods["div"] = arith_method(operator.truediv, names('div'), - op('/'), truediv=True, - fill_zeros=np.inf, - default_axis=default_axis) - new_methods["rdiv"] = arith_method(lambda x, y: operator.truediv(y, x), - names('rdiv'), truediv=False, - fill_zeros=np.inf, - default_axis=default_axis) - # Comp methods never had a default axis set + new_methods['div'] = new_methods['truediv'] + new_methods['rdiv'] = new_methods['rtruediv'] + + # Comp methods never had a default axis set if comp_method: new_methods.update(dict( eq=comp_method(operator.eq, names('eq'), op('==')), diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 6b50bfb76a3ea..5a370b71e3511 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -2,7 +2,7 @@ Contains data structures designed for manipulating panel (3-dimensional) data """ # pylint: disable=E1103,W0231,W0212,W0621 - +from __future__ import division from pandas.compat import map, zip, range, lrange, lmap, u, OrderedDict, OrderedDefaultdict from pandas import compat import sys diff --git a/pandas/core/series.py b/pandas/core/series.py index 3fe9540ba3fe0..e62bf2f36d209 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1,6 +1,7 @@ """ Data structure for 1-dimensional cross-sectional and time series data """ +from __future__ import division # pylint: disable=E1101,E1103 # pylint: disable=W0703,W0622,W0613,W0201 diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index bed4ede6ce5f3..141aef3051b23 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -1,7 +1,7 @@ """ SparseArray data structure """ - +from __future__ import division # pylint: disable=E1101,E1103,W0231 from numpy import nan, ndarray diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index b577f5ba8f5ec..deb9065a2b5a6 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -2,7 +2,7 @@ Data structures for sparse float data. Life is made simpler by dealing only with float64 data """ - +from __future__ import division # pylint: disable=E1101,E1103,W0231,E0202 from numpy import nan diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 85f5ba1f08b1d..6284e4551e167 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -72,23 +72,21 @@ def run_arithmetic_test(self, df, other, assert_func, check_dtype=False, if not compat.PY3: operations.append('div') for arith in operations: - if test_flex: - op = getattr(df, arith) - else: - op = getattr(operator, arith) + operator_name = arith + if arith == 'div': + operator_name = 'truediv' + if test_flex: op = lambda x, y: getattr(df, arith)(y) op.__name__ = arith else: - op = getattr(operator, arith) + op = getattr(operator, operator_name) expr.set_use_numexpr(False) expected = op(df, other) expr.set_use_numexpr(True) result = op(df, other) try: if check_dtype: - if arith == 'div': - assert expected.dtype.kind == df.dtype.kind if arith == 'truediv': assert expected.dtype.kind == 'f' assert_func(expected, result) @@ -103,7 +101,7 @@ def test_integer_arithmetic(self): assert_series_equal, check_dtype=True) @nose.tools.nottest - def run_binary_test(self, df, other, assert_func, check_dtype=False, + def run_binary_test(self, df, other, assert_func, test_flex=False, numexpr_ops=set(['gt', 'lt', 'ge', 'le', 'eq', 'ne'])): """ @@ -127,11 +125,6 @@ def run_binary_test(self, df, other, assert_func, check_dtype=False, result = op(df, other) used_numexpr = expr.get_test_result() try: - if check_dtype: - if arith == 'div': - assert expected.dtype.kind == result.dtype.kind - if arith == 'truediv': - assert result.dtype.kind == 'f' if arith in numexpr_ops: assert used_numexpr, "Did not use numexpr as expected." else: @@ -267,8 +260,10 @@ def testit(): for f, f2 in [ (self.frame, self.frame2), (self.mixed, self.mixed2) ]: for op, op_str in [('add','+'),('sub','-'),('mul','*'),('div','/'),('pow','**')]: - - op = getattr(operator,op,None) + if op == 'div': + op = getattr(operator, 'truediv', None) + else: + op = getattr(operator, op, None) if op is not None: result = expr._can_use_numexpr(op, op_str, f, f, 'evaluate') self.assert_(result == (not f._is_mixed_type)) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 91fa1f1a19ffc..f866c1b229fe5 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -2032,7 +2032,12 @@ def check(series, other, check_reverse=False): for opname in simple_ops: op = getattr(Series, opname) - alt = getattr(operator, opname) + + if op == 'div': + alt = operator.truediv + else: + alt = getattr(operator, opname) + result = op(series, other) expected = alt(series, other) tm.assert_almost_equal(result, expected) @@ -2079,11 +2084,11 @@ def test_modulo(self): def test_div(self): - # integer div, but deal with the 0's + # no longer do integer div for any ops, but deal with the 0's p = DataFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]}) result = p['first'] / p['second'] expected = Series( - p['first'].values / p['second'].values, dtype='float64') + p['first'].values.astype(float) / p['second'].values, dtype='float64') expected.iloc[0:3] = np.inf assert_series_equal(result, expected) @@ -2098,10 +2103,7 @@ def test_div(self): p = DataFrame({'first': [3, 4, 5, 8], 'second': [1, 1, 1, 1]}) result = p['first'] / p['second'] - if compat.PY3: - assert_series_equal(result, p['first'].astype('float64')) - else: - assert_series_equal(result, p['first']) + assert_series_equal(result, p['first'].astype('float64')) self.assertFalse(np.array_equal(result, p['second'] / p['first'])) def test_operators(self):
Close #5356. Note: this version actually forces `a / b` to always do truediv as well. It's less complicated to maintain if we do it that way, but it's also possible to have this `a / b` use div and `a.div(b)` use truediv, just might be strange. This is definitely different from numpy behavior. We also may need to add the division future import to more places.
https://api.github.com/repos/pandas-dev/pandas/pulls/5439
2013-11-05T11:46:16Z
2013-11-05T22:53:10Z
2013-11-05T22:53:10Z
2014-06-23T22:30:35Z
BUG: pd.to_timedelta handles missing data
diff --git a/doc/source/release.rst b/doc/source/release.rst index 4b33c20424b33..fde13941d0266 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -771,6 +771,7 @@ Bug Fixes - Fix empty series not printing name in repr (:issue:`4651`) - Make tests create temp files in temp directory by default. (:issue:`5419`) - ``pd.to_timedelta`` of a scalar returns a scalar (:issue:`5410`) + - ``pd.to_timedelta`` accepts ``NaN`` and ``NaT``, returning ``NaT`` instead of raising (:issue:`5437`) pandas 0.12.0 ------------- diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 62aa95d270924..3ce9b9288e8c7 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1162,15 +1162,25 @@ def _try_fill(self, value): def _try_coerce_args(self, values, other): """ provide coercion to our input arguments - we are going to compare vs i8, so coerce to integer - values is always ndarra like, other may not be """ - values = values.view('i8') + we are going to compare vs i8, so coerce to floats + repring NaT with np.nan so nans propagate + values is always ndarray like, other may not be """ + def masker(v): + mask = isnull(v) + v = v.view('i8').astype('float64') + v[mask] = np.nan + return v + + values = masker(values) + if isnull(other) or (np.isscalar(other) and other == tslib.iNaT): - other = tslib.iNaT + other = np.nan elif isinstance(other, np.timedelta64): other = _coerce_scalar_to_timedelta_type(other,unit='s').item() + if other == tslib.iNaT: + other = np.nan else: - other = other.view('i8') + other = masker(other) return values, other diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 0c647bb6ee7eb..5e800ffd82306 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -255,7 +255,7 @@ def __init__(self, left, right, name): self.name = name lvalues = self._convert_to_array(left, name=name) - rvalues = self._convert_to_array(right, name=name) + rvalues = self._convert_to_array(right, name=name, other=lvalues) self.is_timedelta_lhs = com.is_timedelta64_dtype(left) self.is_datetime_lhs = com.is_datetime64_dtype(left) @@ -317,7 +317,7 @@ def _validate(self): 'of a series/ndarray of type datetime64[ns] ' 'or a timedelta') - def _convert_to_array(self, values, name=None): + def _convert_to_array(self, values, name=None, other=None): """converts values to ndarray""" from pandas.tseries.timedeltas import _possibly_cast_to_timedelta @@ -325,9 +325,16 @@ def _convert_to_array(self, values, name=None): if not is_list_like(values): values = np.array([values]) inferred_type = lib.infer_dtype(values) + if inferred_type in ('datetime64', 'datetime', 'date', 'time'): + # if we have a other of timedelta, but use pd.NaT here we + # we are in the wrong path + if other is not None and other.dtype == 'timedelta64[ns]' and all(isnull(v) for v in values): + values = np.empty(values.shape,dtype=other.dtype) + values[:] = tslib.iNaT + # a datetlike - if not (isinstance(values, (pa.Array, pd.Series)) and + elif not (isinstance(values, (pa.Array, pd.Series)) and com.is_datetime64_dtype(values)): values = tslib.array_to_datetime(values) elif isinstance(values, pd.DatetimeIndex): @@ -354,6 +361,15 @@ def _convert_to_array(self, values, name=None): ', '.join([com.pprint_thing(v) for v in values[mask]]))) values = _possibly_cast_to_timedelta(os, coerce=coerce) + elif inferred_type == 'floating': + + # all nan, so ok, use the other dtype (e.g. timedelta or datetime) + if isnull(values).all(): + values = np.empty(values.shape,dtype=other.dtype) + values[:] = tslib.iNaT + else: + raise TypeError("incompatible type [{0}] for a datetime/timedelta" + " operation".format(pa.array(values).dtype)) else: raise TypeError("incompatible type [{0}] for a datetime/timedelta" " operation".format(pa.array(values).dtype)) @@ -452,6 +468,8 @@ def na_op(x, y): def wrapper(left, right, name=name): + if isinstance(right, pd.DataFrame): + return NotImplemented time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name) if time_converted is None: @@ -488,8 +506,6 @@ def wrapper(left, right, name=name): return left._constructor(wrap_results(arr), index=index, name=name, dtype=dtype) - elif isinstance(right, pd.DataFrame): - return NotImplemented else: # scalars if hasattr(lvalues, 'values'): diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 199ad19986b39..df03851ca4ddb 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -195,6 +195,122 @@ def test_timedelta_ops(self): expected = to_timedelta('00:00:08') tm.assert_almost_equal(result, expected) + def test_to_timedelta_on_missing_values(self): + _skip_if_numpy_not_friendly() + + # GH5438 + timedelta_NaT = np.timedelta64('NaT') + + actual = pd.to_timedelta(Series(['00:00:01', np.nan])) + expected = Series([np.timedelta64(1000000000, 'ns'), timedelta_NaT], dtype='<m8[ns]') + assert_series_equal(actual, expected) + + actual = pd.to_timedelta(Series(['00:00:01', pd.NaT])) + assert_series_equal(actual, expected) + + actual = pd.to_timedelta(np.nan) + self.assert_(actual == timedelta_NaT) + + actual = pd.to_timedelta(pd.NaT) + self.assert_(actual == timedelta_NaT) + + def test_timedelta_ops_with_missing_values(self): + _skip_if_numpy_not_friendly() + + # setup + s1 = pd.to_timedelta(Series(['00:00:01'])) + s2 = pd.to_timedelta(Series(['00:00:02'])) + sn = pd.to_timedelta(Series([pd.NaT])) + df1 = DataFrame(['00:00:01']).apply(pd.to_timedelta) + df2 = DataFrame(['00:00:02']).apply(pd.to_timedelta) + dfn = DataFrame([pd.NaT]).apply(pd.to_timedelta) + scalar1 = pd.to_timedelta('00:00:01') + scalar2 = pd.to_timedelta('00:00:02') + timedelta_NaT = pd.to_timedelta('NaT') + NA = np.nan + + actual = scalar1 + scalar1 + self.assert_(actual == scalar2) + actual = scalar2 - scalar1 + self.assert_(actual == scalar1) + + actual = s1 + s1 + assert_series_equal(actual, s2) + actual = s2 - s1 + assert_series_equal(actual, s1) + + actual = s1 + scalar1 + assert_series_equal(actual, s2) + actual = s2 - scalar1 + assert_series_equal(actual, s1) + + actual = s1 + timedelta_NaT + assert_series_equal(actual, sn) + actual = s1 - timedelta_NaT + assert_series_equal(actual, sn) + + actual = s1 + NA + assert_series_equal(actual, sn) + actual = s1 - NA + assert_series_equal(actual, sn) + + actual = s1 + pd.NaT # NaT is datetime, not timedelta + assert_series_equal(actual, sn) + actual = s2 - pd.NaT + assert_series_equal(actual, sn) + + actual = s1 + df1 + assert_frame_equal(actual, df2) + actual = s2 - df1 + assert_frame_equal(actual, df1) + actual = df1 + s1 + assert_frame_equal(actual, df2) + actual = df2 - s1 + assert_frame_equal(actual, df1) + + actual = df1 + df1 + assert_frame_equal(actual, df2) + actual = df2 - df1 + assert_frame_equal(actual, df1) + + actual = df1 + scalar1 + assert_frame_equal(actual, df2) + actual = df2 - scalar1 + assert_frame_equal(actual, df1) + + actual = df1 + timedelta_NaT + assert_frame_equal(actual, dfn) + actual = df1 - timedelta_NaT + assert_frame_equal(actual, dfn) + + actual = df1 + NA + assert_frame_equal(actual, dfn) + actual = df1 - NA + assert_frame_equal(actual, dfn) + + actual = df1 + pd.NaT # NaT is datetime, not timedelta + assert_frame_equal(actual, dfn) + actual = df1 - pd.NaT + assert_frame_equal(actual, dfn) + + def test_apply_to_timedelta(self): + _skip_if_numpy_not_friendly() + + timedelta_NaT = pd.to_timedelta('NaT') + + list_of_valid_strings = ['00:00:01', '00:00:02'] + a = pd.to_timedelta(list_of_valid_strings) + b = Series(list_of_valid_strings).apply(pd.to_timedelta) + # Can't compare until apply on a Series gives the correct dtype + # assert_series_equal(a, b) + + list_of_strings = ['00:00:01', np.nan, pd.NaT, timedelta_NaT] + a = pd.to_timedelta(list_of_strings) + b = Series(list_of_strings).apply(pd.to_timedelta) + # Can't compare until apply on a Series gives the correct dtype + # assert_series_equal(a, b) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 862dc8d410996..835401a13403f 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -9,7 +9,7 @@ import pandas.tslib as tslib from pandas import compat, _np_version_under1p7 from pandas.core.common import (ABCSeries, is_integer, is_timedelta64_dtype, - _values_from_object, is_list_like) + _values_from_object, is_list_like, isnull) repr_timedelta = tslib.repr_timedelta64 repr_timedelta64 = tslib.repr_timedelta64 @@ -84,6 +84,8 @@ def conv(v): r = conv(r) elif r == tslib.iNaT: return r + elif isnull(r): + return np.timedelta64('NaT') elif isinstance(r, np.timedelta64): r = r.astype("m8[{0}]".format(unit.lower())) elif is_integer(r):
closes #5437 **Updated**: Mostly following @jreback's suggestion, but we need `np.timedelta64('NaT')`, not `pd.tslib.iNaT`, because numpy cannot cast `np.datetime64('NaT')` as `np.timedelta64` type, and throws an `AssertionError`. ``` In [1]: pd.to_timedelta(Series(['00:00:01', '00:00:02'])) Out[1]: 0 00:00:01 1 00:00:02 dtype: timedelta64[ns] In [2]: pd.to_timedelta(Series(['00:00:01', np.nan])) Out[2]: 0 00:00:01 1 NaT dtype: timedelta64[ns] In [3]: pd.to_timedelta(Series(['00:00:01', pd.NaT])) Out[3]: 0 00:00:01 1 NaT dtype: timedelta64[ns] ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5438
2013-11-05T03:27:15Z
2013-11-06T23:51:20Z
2013-11-06T23:51:20Z
2014-06-24T21:15:32Z
BUG/TST: bug in the test apparatus when dtypes are equal in a Series but the values are not equal
diff --git a/pandas/src/testing.pyx b/pandas/src/testing.pyx index 404b97879e2be..c573d8b2afbad 100644 --- a/pandas/src/testing.pyx +++ b/pandas/src/testing.pyx @@ -88,9 +88,10 @@ cpdef assert_almost_equal(a, b, bint check_less_precise=False): return True except: pass - else: - for i in xrange(na): - assert_almost_equal(a[i], b[i], check_less_precise) + + for i in xrange(na): + assert_almost_equal(a[i], b[i], check_less_precise) + return True elif isiterable(b): assert False, ( diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py index 8431d91a8fff6..c3c1c4f5977e6 100644 --- a/pandas/tests/test_testing.py +++ b/pandas/tests/test_testing.py @@ -6,9 +6,9 @@ import nose import numpy as np import sys - +from pandas import Series from pandas.util.testing import ( - assert_almost_equal, assertRaisesRegexp, raise_with_traceback + assert_almost_equal, assertRaisesRegexp, raise_with_traceback, assert_series_equal ) # let's get meta. @@ -127,3 +127,29 @@ def test_raise_with_traceback(self): e = LookupError("error_text") _, _, traceback = sys.exc_info() raise_with_traceback(e, traceback) + +class TestAssertSeriesEqual(unittest.TestCase): + _multiprocess_can_split_ = True + + def _assert_equal(self, x, y, **kwargs): + assert_series_equal(x,y,**kwargs) + assert_series_equal(y,x,**kwargs) + + def _assert_not_equal(self, a, b, **kwargs): + self.assertRaises(AssertionError, assert_series_equal, a, b, **kwargs) + self.assertRaises(AssertionError, assert_series_equal, b, a, **kwargs) + + def test_equal(self): + self._assert_equal(Series(range(3)),Series(range(3))) + self._assert_equal(Series(list('abc')),Series(list('abc'))) + + def test_not_equal(self): + self._assert_not_equal(Series(range(3)),Series(range(3))+1) + self._assert_not_equal(Series(list('abc')),Series(list('xyz'))) + self._assert_not_equal(Series(range(3)),Series(range(4))) + self._assert_not_equal(Series(range(3)),Series(range(3),dtype='float64')) + self._assert_not_equal(Series(range(3)),Series(range(3),index=[1,2,4])) + + # ATM meta data is not checked in assert_series_equal + # self._assert_not_equal(Series(range(3)),Series(range(3),name='foo'),check_names=True) +
noticed in #5429
https://api.github.com/repos/pandas-dev/pandas/pulls/5434
2013-11-04T22:01:12Z
2013-11-04T22:49:49Z
2013-11-04T22:49:49Z
2014-07-11T22:41:28Z
BUG: fix Resampling a Series with a timezone using kind='period' (GH5430)
diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 96ff8c47abc1e..5377543ac8c54 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -192,7 +192,9 @@ def _get_time_period_bins(self, axis): labels = binner = PeriodIndex(start=axis[0], end=axis[-1], freq=self.freq) - end_stamps = (labels + 1).asfreq('D', 's').to_timestamp() + end_stamps = (labels + 1).asfreq(self.freq, 's').to_timestamp() + if axis.tzinfo: + end_stamps = end_stamps.tz_localize(axis.tzinfo) bins = axis.searchsorted(end_stamps, side='left') return binner, bins, labels diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index b8144c2b5eab9..51939ac22e956 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1836,6 +1836,37 @@ def test_concat_datetime_datetime64_frame(self): # it works! pd.concat([df1, df2_obj]) + def test_period_resample(self): + # GH3609 + s = Series(range(100),index=date_range('20130101', freq='s', periods=100), dtype='float') + s[10:30] = np.nan + expected = Series([34.5, 79.5], index=[Period('2013-01-01 00:00', 'T'), Period('2013-01-01 00:01', 'T')]) + result = s.to_period().resample('T', kind='period') + assert_series_equal(result, expected) + result2 = s.resample('T', kind='period') + assert_series_equal(result2, expected) + + def test_period_resample_with_local_timezone(self): + # GH5430 + _skip_if_no_pytz() + import pytz + + local_timezone = pytz.timezone('America/Los_Angeles') + + start = datetime(year=2013, month=11, day=1, hour=0, minute=0, tzinfo=pytz.utc) + # 1 day later + end = datetime(year=2013, month=11, day=2, hour=0, minute=0, tzinfo=pytz.utc) + + index = pd.date_range(start, end, freq='H') + + series = pd.Series(1, index=index) + series = series.tz_convert(local_timezone) + result = series.resample('D', kind='period') + # Create the expected series + expected_index = (pd.period_range(start=start, end=end, freq='D') - 1) # Index is moved back a day with the timezone conversion from UTC to Pacific + expected = pd.Series(1, index=expected_index) + assert_series_equal(result, expected) + def _simple_ts(start, end, freq='D'): rng = date_range(start, end, freq=freq)
There's a failing test case and the patch in subsequent commits. closes #5430 closes #3609
https://api.github.com/repos/pandas-dev/pandas/pulls/5432
2013-11-04T20:11:40Z
2013-11-06T00:23:40Z
2013-11-06T00:23:40Z
2014-07-16T08:39:13Z
BUG: Excel writer doesn't handle "cols" option correctly
diff --git a/pandas/core/format.py b/pandas/core/format.py index 5062fd9be6357..ae0d95b1c3074 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -1393,8 +1393,12 @@ def _format_regular_rows(self): for idx, idxval in enumerate(index_values): yield ExcelCell(self.rowcounter + idx, 0, idxval, header_style) + # Get a frame that will account for any duplicates in the column names. + col_mapped_frame = self.df.loc[:, self.columns] + + # Write the body of the frame data series by series. for colidx in range(len(self.columns)): - series = self.df.iloc[:, colidx] + series = col_mapped_frame.iloc[:, colidx] for i, val in enumerate(series): yield ExcelCell(self.rowcounter + i, colidx + coloffset, val) @@ -1461,8 +1465,12 @@ def _format_hierarchical_rows(self): header_style) gcolidx += 1 + # Get a frame that will account for any duplicates in the column names. + col_mapped_frame = self.df.loc[:, self.columns] + + # Write the body of the frame data series by series. for colidx in range(len(self.columns)): - series = self.df.iloc[:, colidx] + series = col_mapped_frame.iloc[:, colidx] for i, val in enumerate(series): yield ExcelCell(self.rowcounter + i, gcolidx + colidx, val) diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 8bcf5e461ce7c..a126bd965e4b6 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -922,11 +922,25 @@ def test_duplicated_columns(self): write_frame.columns = colnames write_frame.to_excel(path, 'test1') - read_frame = read_excel(path, 'test1').astype(np.int64) + read_frame = read_excel(path, 'test1') read_frame.columns = colnames tm.assert_frame_equal(write_frame, read_frame) + def test_swapped_columns(self): + # Test for issue #5427. + _skip_if_no_xlrd() + + with ensure_clean(self.ext) as path: + write_frame = DataFrame({'A': [1, 1, 1], + 'B': [2, 2, 2]}) + write_frame.to_excel(path, 'test1', cols=['B', 'A']) + + read_frame = read_excel(path, 'test1', header=0) + + tm.assert_series_equal(write_frame['A'], read_frame['A']) + tm.assert_series_equal(write_frame['B'], read_frame['B']) + class OpenpyxlTests(ExcelWriterBase, unittest.TestCase): ext = '.xlsx'
closes #5427 Notes on this PR: - This fix addresses an issue introduced in #5235 (Issue with Excel writers when column names are duplicated). - Basically it needs to handle 2 different use cases: - The user uses duplicate column names - The user specifies a different order via the `cols` option ``` python # Case 1 df = pd.DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) df.columns = ['A', 'B', 'B'] ``` ``` python # Case 2 df = pd.DataFrame({'A': ['a', 'a', 'a'], 'B': ['b', 'b', 'b']}) df.to_excel('frame.xlsx', sheet_name='Sheet1', cols=['B', 'A']) ``` The proposed solution is to iterate through self.columns in the default or user supplied order. If a duplicate column name is encountered (i.e. if `df['B']` returns more than one series) then we select the first series and keep track of that index. If the duplicate column name is encountered again then we select the next available series from `df['B']` up to the last series available. If the duplicate name is encountered again then we return the last series again. The patch is slightly kludgy. I didn't know how to check if `df[col]` contained more than one series so I used `len(self.df[col_name].columns)` in a `try:catch`. Hopefully there is something cleaner. The change is repeated twice in the code. There is a new test for this issue and an existing test for the previous issue. I don't think this needs a release note since it is a fix for an issue that was never released.
https://api.github.com/repos/pandas-dev/pandas/pulls/5429
2013-11-04T01:38:29Z
2013-11-06T22:16:21Z
2013-11-06T22:16:21Z
2014-07-02T22:44:20Z
BUG: not clearing the cache when reindexing issue when partial setting (GH5424)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 6e10bd651d90a..19639e2e759e1 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -660,7 +660,7 @@ Bug Fixes the ``by`` argument was passed (:issue:`4112`, :issue:`4113`). - Fixed a bug in ``convert_objects`` for > 2 ndims (:issue:`4937`) - Fixed a bug in DataFrame/Panel cache insertion and subsequent indexing - (:issue:`4939`) + (:issue:`4939`, :issue:`5424`) - Fixed string methods for ``FrozenNDArray`` and ``FrozenList`` (:issue:`4929`) - Fixed a bug with setting invalid or out-of-range values in indexing diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 0bc0afaf255f2..c854e0b086994 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -189,9 +189,13 @@ def _setitem_with_indexer(self, indexer, value): return self.obj # reindex the axis + # make sure to clear the cache because we are + # just replacing the block manager here + # so the object is the same index = self.obj._get_axis(i) labels = _safe_append_to_index(index, key) self.obj._data = self.obj.reindex_axis(labels,i)._data + self.obj._maybe_update_cacher(clear=True) if isinstance(labels,MultiIndex): self.obj.sortlevel(inplace=True) @@ -223,7 +227,8 @@ def _setitem_with_indexer(self, indexer, value): if len(self.obj.values): new_values = np.concatenate([self.obj.values, new_values]) - self.obj._data = self.obj._constructor(new_values, index=new_index, name=self.obj.name) + self.obj._data = self.obj._constructor(new_values, + index=new_index, name=self.obj.name)._data self.obj._maybe_update_cacher(clear=True) return self.obj diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index b73c7cdbb8f87..6b487cb006a80 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -3539,24 +3539,6 @@ def test_operators_timedelta64(self): self.assertTrue(df['off1'].dtype == 'timedelta64[ns]') self.assertTrue(df['off2'].dtype == 'timedelta64[ns]') - def test__slice_consolidate_invalidate_item_cache(self): - # #3970 - df = DataFrame({ "aa":lrange(5), "bb":[2.2]*5}) - - # Creates a second float block - df["cc"] = 0.0 - - # caches a reference to the 'bb' series - df["bb"] - - # repr machinery triggers consolidation - repr(df) - - # Assignment to wrong series - df['bb'].iloc[0] = 0.17 - df._clear_item_cache() - self.assertAlmostEqual(df['bb'][0], 0.17) - def test_new_empty_index(self): df1 = DataFrame(randn(0, 3)) df2 = DataFrame(randn(0, 3)) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 2cb26804ea4be..2ad9f10d1b990 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1645,6 +1645,41 @@ def test_cache_updating(self): result = df.loc[(0,0),'z'] self.assert_(result == 2) + def test_slice_consolidate_invalidate_item_cache(self): + # #3970 + df = DataFrame({ "aa":lrange(5), "bb":[2.2]*5}) + + # Creates a second float block + df["cc"] = 0.0 + + # caches a reference to the 'bb' series + df["bb"] + + # repr machinery triggers consolidation + repr(df) + + # Assignment to wrong series + df['bb'].iloc[0] = 0.17 + df._clear_item_cache() + self.assertAlmostEqual(df['bb'][0], 0.17) + + def test_setitem_cache_updating(self): + # GH 5424 + cont = ['one', 'two','three', 'four', 'five', 'six', 'seven'] + + for do_ref in [False,False]: + df = DataFrame({'a' : cont, "b":cont[3:]+cont[:3] ,'c' : np.arange(7)}) + + # ref the cache + if do_ref: + df.ix[0,"c"] + + # set it + df.ix[7,'c'] = 1 + + self.assert_(df.ix[0,'c'] == 0.0) + self.assert_(df.ix[7,'c'] == 1.0) + def test_floating_index_doc_example(self): index = Index([1.5, 2, 3, 4.5, 5])
closes #5424
https://api.github.com/repos/pandas-dev/pandas/pulls/5426
2013-11-04T00:13:08Z
2013-11-04T00:40:53Z
2013-11-04T00:40:53Z
2014-06-23T22:17:11Z
TST: Use tempfiles in all tests.
diff --git a/doc/source/release.rst b/doc/source/release.rst index 6e10bd651d90a..04642c358d000 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -769,6 +769,7 @@ Bug Fixes - The GroupBy methods ``transform`` and ``filter`` can be used on Series and DataFrames that have repeated (non-unique) indices. (:issue:`4620`) - Fix empty series not printing name in repr (:issue:`4651`) + - Make tests create temp files in temp directory by default. (:issue:`5419`) pandas 0.12.0 ------------- diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 97dc8dcdec73a..975d04c185d51 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -225,11 +225,6 @@ def _tables(): return _table_mod -def h5_open(path, mode): - tables = _tables() - return tables.openFile(path, mode) - - @contextmanager def get_store(path, **kwargs): """ @@ -389,6 +384,10 @@ def root(self): self._check_if_open() return self._handle.root + @property + def filename(self): + return self._path + def __getitem__(self, key): return self.get(key) @@ -475,6 +474,8 @@ def open(self, mode='a'): mode : {'a', 'w', 'r', 'r+'}, default 'a' See HDFStore docstring or tables.openFile for info about modes """ + tables = _tables() + if self._mode != mode: # if we are chaning a write mode to read, ok @@ -501,13 +502,20 @@ def open(self, mode='a'): fletcher32=self._fletcher32) try: - self._handle = h5_open(self._path, self._mode) - except IOError as e: # pragma: no cover + self._handle = tables.openFile(self._path, self._mode) + except (IOError) as e: # pragma: no cover if 'can not be written' in str(e): print('Opening %s in read-only mode' % self._path) - self._handle = h5_open(self._path, 'r') + self._handle = tables.openFile(self._path, 'r') else: raise + except (Exception) as e: + + # trying to read from a non-existant file causes an error which + # is not part of IOError, make it one + if self._mode == 'r' and 'Unable to open/create file' in str(e): + raise IOError(str(e)) + raise def close(self): """ diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 311a0953f1c02..6eb3cbf1a3903 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -261,10 +261,9 @@ def test_read_xlrd_Book(self): import xlrd - pth = '__tmp_excel_read_worksheet__.xls' df = self.frame - with ensure_clean(pth) as pth: + with ensure_clean('.xls') as pth: df.to_excel(pth, "SheetA") book = xlrd.open_workbook(pth) @@ -303,7 +302,7 @@ def test_reader_closes_file(self): f = open(pth, 'rb') with ExcelFile(f) as xlsx: # parses okay - df = xlsx.parse('Sheet1', index_col=0) + xlsx.parse('Sheet1', index_col=0) self.assertTrue(f.closed) @@ -364,12 +363,12 @@ class ExcelWriterBase(SharedItems): # 1. A check_skip function that skips your tests if your writer isn't # installed. # 2. Add a property ext, which is the file extension that your writer - # writes to. + # writes to. (needs to start with '.' so it's a valid path) # 3. Add a property engine_name, which is the name of the writer class. def setUp(self): self.check_skip() super(ExcelWriterBase, self).setUp() - self.option_name = 'io.excel.%s.writer' % self.ext + self.option_name = 'io.excel.%s.writer' % self.ext.strip('.') self.prev_engine = get_option(self.option_name) set_option(self.option_name, self.engine_name) @@ -380,10 +379,7 @@ def test_excel_sheet_by_name_raise(self): _skip_if_no_xlrd() import xlrd - ext = self.ext - pth = os.path.join(self.dirpath, 'testit.{0}'.format(ext)) - - with ensure_clean(pth) as pth: + with ensure_clean(self.ext) as pth: gt = DataFrame(np.random.randn(10, 2)) gt.to_excel(pth) xl = ExcelFile(pth) @@ -394,10 +390,8 @@ def test_excel_sheet_by_name_raise(self): def test_excelwriter_contextmanager(self): _skip_if_no_xlrd() - ext = self.ext - pth = os.path.join(self.dirpath, 'testit.{0}'.format(ext)) - with ensure_clean(pth) as pth: + with ensure_clean(self.ext) as pth: with ExcelWriter(pth) as writer: self.frame.to_excel(writer, 'Data1') self.frame2.to_excel(writer, 'Data2') @@ -410,10 +404,8 @@ def test_excelwriter_contextmanager(self): def test_roundtrip(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.frame['A'][:5] = nan self.frame.to_excel(path, 'test1') @@ -446,10 +438,8 @@ def test_roundtrip(self): def test_mixed(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_mixed__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.mixed_frame.to_excel(path, 'test1') reader = ExcelFile(path) recons = reader.parse('test1', index_col=0) @@ -457,12 +447,10 @@ def test_mixed(self): def test_tsframe(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_tsframe__.' + ext df = tm.makeTimeDataFrame()[:5] - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: df.to_excel(path, 'test1') reader = ExcelFile(path) recons = reader.parse('test1') @@ -470,22 +458,19 @@ def test_tsframe(self): def test_basics_with_nan(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_int_types__.' + ext - self.frame['A'][:5] = nan - self.frame.to_excel(path, 'test1') - self.frame.to_excel(path, 'test1', cols=['A', 'B']) - self.frame.to_excel(path, 'test1', header=False) - self.frame.to_excel(path, 'test1', index=False) + with ensure_clean(self.ext) as path: + self.frame['A'][:5] = nan + self.frame.to_excel(path, 'test1') + self.frame.to_excel(path, 'test1', cols=['A', 'B']) + self.frame.to_excel(path, 'test1', header=False) + self.frame.to_excel(path, 'test1', index=False) def test_int_types(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_int_types__.' + ext for np_type in (np.int8, np.int16, np.int32, np.int64): - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: # Test np.int values read come back as int (rather than float # which is Excel's format). frame = DataFrame(np.random.randint(-10, 10, size=(10, 2)), @@ -505,11 +490,9 @@ def test_int_types(self): def test_float_types(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_float_types__.' + ext for np_type in (np.float16, np.float32, np.float64): - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: # Test np.float values read come back as float. frame = DataFrame(np.random.random_sample(10), dtype=np_type) frame.to_excel(path, 'test1') @@ -519,11 +502,9 @@ def test_float_types(self): def test_bool_types(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_bool_types__.' + ext for np_type in (np.bool8, np.bool_): - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: # Test np.bool values read come back as float. frame = (DataFrame([1, 0, True, False], dtype=np_type)) frame.to_excel(path, 'test1') @@ -533,10 +514,8 @@ def test_bool_types(self): def test_sheets(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_sheets__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.frame['A'][:5] = nan self.frame.to_excel(path, 'test1') @@ -560,10 +539,8 @@ def test_sheets(self): def test_colaliases(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_aliases__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.frame['A'][:5] = nan self.frame.to_excel(path, 'test1') @@ -582,10 +559,8 @@ def test_colaliases(self): def test_roundtrip_indexlabels(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_indexlabels__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.frame['A'][:5] = nan @@ -617,10 +592,7 @@ def test_roundtrip_indexlabels(self): frame.index.names = ['test'] self.assertEqual(frame.index.names, recons.index.names) - # test index_labels in same row as column names - path = '%s.%s' % (tm.rands(10), ext) - - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.frame.to_excel(path, 'test1', cols=['A', 'B', 'C', 'D'], index=False) @@ -636,12 +608,10 @@ def test_roundtrip_indexlabels(self): def test_excel_roundtrip_indexname(self): _skip_if_no_xlrd() - path = '%s.%s' % (tm.rands(10), self.ext) - df = DataFrame(np.random.randn(10, 4)) df.index.name = 'foo' - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: df.to_excel(path) xf = ExcelFile(path) @@ -656,7 +626,7 @@ def test_excel_roundtrip_datetime(self): # datetime.date, not sure what to test here exactly path = '__tmp_excel_roundtrip_datetime__.' + self.ext tsf = self.tsframe.copy() - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: tsf.index = [x.date() for x in self.tsframe.index] tsf.to_excel(path, 'test1') @@ -670,7 +640,7 @@ def test_to_excel_periodindex(self): frame = self.tsframe xp = frame.resample('M', kind='period') - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: xp.to_excel(path, 'sht1') reader = ExcelFile(path) @@ -679,8 +649,6 @@ def test_to_excel_periodindex(self): def test_to_excel_multiindex(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_multiindex__' + ext + '__.' + ext frame = self.frame old_index = frame.index @@ -689,7 +657,7 @@ def test_to_excel_multiindex(self): names=['first', 'second']) frame.index = new_index - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: frame.to_excel(path, 'test1', header=False) frame.to_excel(path, 'test1', cols=['A', 'B']) @@ -703,8 +671,6 @@ def test_to_excel_multiindex(self): def test_to_excel_multiindex_dates(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_multiindex_dates__' + ext + '__.' + ext # try multiindex with dates tsframe = self.tsframe @@ -712,7 +678,7 @@ def test_to_excel_multiindex_dates(self): new_index = [old_index, np.arange(len(old_index))] tsframe.index = MultiIndex.from_arrays(new_index) - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: tsframe.to_excel(path, 'test1', index_label=['time', 'foo']) reader = ExcelFile(path) recons = reader.parse('test1', index_col=[0, 1]) @@ -736,7 +702,7 @@ def test_to_excel_float_format(self): [12.32112, 123123.2, 321321.2]], index=['A', 'B'], columns=['X', 'Y', 'Z']) - with ensure_clean(filename) as filename: + with ensure_clean(self.ext) as filename: df.to_excel(filename, 'test1', float_format='%.2f') reader = ExcelFile(filename) @@ -748,21 +714,18 @@ def test_to_excel_float_format(self): def test_to_excel_unicode_filename(self): _skip_if_no_xlrd() - ext = self.ext - filename = u('\u0192u.') + ext - - try: - f = open(filename, 'wb') - except UnicodeEncodeError: - raise nose.SkipTest('no unicode file names on this system') - else: - f.close() - - df = DataFrame([[0.123456, 0.234567, 0.567567], - [12.32112, 123123.2, 321321.2]], - index=['A', 'B'], columns=['X', 'Y', 'Z']) + with ensure_clean(u('\u0192u.') + self.ext) as filename: + try: + f = open(filename, 'wb') + except UnicodeEncodeError: + raise nose.SkipTest('no unicode file names on this system') + else: + f.close() + + df = DataFrame([[0.123456, 0.234567, 0.567567], + [12.32112, 123123.2, 321321.2]], + index=['A', 'B'], columns=['X', 'Y', 'Z']) - with ensure_clean(filename) as filename: df.to_excel(filename, 'test1', float_format='%.2f') reader = ExcelFile(filename) @@ -879,10 +842,9 @@ def test_excel_010_hemstring(self): # override of #2370 until sorted out in 0.11 def roundtrip(df, header=True, parser_hdr=0): - path = '__tmp__test_xl_010_%s__.%s' % (np.random.randint(1, 10000), self.ext) - df.to_excel(path, header=header) - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: + df.to_excel(path, header=header) xf = pd.ExcelFile(path) res = xf.parse(xf.sheet_names[0], header=parser_hdr) return res @@ -926,10 +888,8 @@ def roundtrip(df, header=True, parser_hdr=0): def test_duplicated_columns(self): # Test for issue #5235. _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_duplicated_columns__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: write_frame = DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) colnames = ['A', 'B', 'B'] @@ -943,7 +903,7 @@ def test_duplicated_columns(self): class OpenpyxlTests(ExcelWriterBase, unittest.TestCase): - ext = 'xlsx' + ext = '.xlsx' engine_name = 'openpyxl' check_skip = staticmethod(_skip_if_no_openpyxl) @@ -974,7 +934,7 @@ def test_to_excel_styleconverter(self): class XlwtTests(ExcelWriterBase, unittest.TestCase): - ext = 'xls' + ext = '.xls' engine_name = 'xlwt' check_skip = staticmethod(_skip_if_no_xlwt) @@ -999,7 +959,7 @@ def test_to_excel_styleconverter(self): class XlsxWriterTests(ExcelWriterBase, unittest.TestCase): - ext = 'xlsx' + ext = '.xlsx' engine_name = 'xlsxwriter' check_skip = staticmethod(_skip_if_no_xlsxwriter) @@ -1007,10 +967,8 @@ class XlsxWriterTests(ExcelWriterBase, unittest.TestCase): # floating point values read back in from the output XlsxWriter file. def test_roundtrip_indexlabels(self): _skip_if_no_xlrd() - ext = self.ext - path = '__tmp_to_excel_from_excel_indexlabels__.' + ext - with ensure_clean(path) as path: + with ensure_clean(self.ext) as path: self.frame['A'][:5] = nan diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 598f374e0fcf7..737acef209a50 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -3,6 +3,7 @@ import sys import os import warnings +import tempfile from contextlib import contextmanager import datetime @@ -54,18 +55,41 @@ def safe_close(store): pass +def create_tempfile(path): + """ create an unopened named temporary file """ + return os.path.join(tempfile.gettempdir(),path) + @contextmanager -def ensure_clean(path, mode='a', complevel=None, complib=None, +def ensure_clean_store(path, mode='a', complevel=None, complib=None, fletcher32=False): - store = HDFStore(path, mode=mode, complevel=complevel, - complib=complib, fletcher32=False) + try: + + # put in the temporary path if we don't have one already + if not len(os.path.dirname(path)): + path = create_tempfile(path) + + store = HDFStore(path, mode=mode, complevel=complevel, + complib=complib, fletcher32=False) yield store finally: safe_close(store) if mode == 'w' or mode == 'a': safe_remove(path) +@contextmanager +def ensure_clean_path(path): + """ + return essentially a named temporary file that is not opened + and deleted on existing + """ + + try: + filename = create_tempfile(path) + yield filename + finally: + safe_remove(filename) + # set these parameters so we don't have file sharing tables.parameters.MAX_NUMEXPR_THREADS = 1 tables.parameters.MAX_BLOSC_THREADS = 1 @@ -94,7 +118,7 @@ class TestHDFStore(unittest.TestCase): def setUp(self): warnings.filterwarnings(action='ignore', category=FutureWarning) - self.path = '__%s__.h5' % tm.rands(10) + self.path = 'tmp.__%s__.h5' % tm.rands(10) def tearDown(self): pass @@ -151,7 +175,7 @@ def test_api(self): # GH4584 # API issue when to_hdf doesn't acdept append AND format args - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeDataFrame() df.iloc[:10].to_hdf(path,'df',append=True,format='table') @@ -163,7 +187,7 @@ def test_api(self): df.iloc[10:].to_hdf(path,'df',append=True,format='table') assert_frame_equal(read_hdf(path,'df'),df) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeDataFrame() df.iloc[:10].to_hdf(path,'df',append=True) @@ -175,7 +199,7 @@ def test_api(self): df.iloc[10:].to_hdf(path,'df',append=True) assert_frame_equal(read_hdf(path,'df'),df) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeDataFrame() df.to_hdf(path,'df',append=False,format='fixed') @@ -190,19 +214,24 @@ def test_api(self): df.to_hdf(path,'df') assert_frame_equal(read_hdf(path,'df'),df) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: + path = store._path df = tm.makeDataFrame() + + _maybe_remove(store,'df') store.append('df',df.iloc[:10],append=True,format='table') store.append('df',df.iloc[10:],append=True,format='table') assert_frame_equal(read_hdf(path,'df'),df) # append to False + _maybe_remove(store,'df') store.append('df',df.iloc[:10],append=False,format='table') store.append('df',df.iloc[10:],append=True,format='table') assert_frame_equal(read_hdf(path,'df'),df) # formats + _maybe_remove(store,'df') store.append('df',df.iloc[:10],append=False,format='table') store.append('df',df.iloc[10:],append=True,format='table') assert_frame_equal(read_hdf(path,'df'),df) @@ -212,7 +241,7 @@ def test_api(self): store.append('df',df.iloc[10:],append=True,format=None) assert_frame_equal(read_hdf(path,'df'),df) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: # invalid df = tm.makeDataFrame() @@ -226,7 +255,7 @@ def test_api(self): def test_api_default_format(self): # default_format option - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeDataFrame() pandas.set_option('io.hdf.default_format','fixed') @@ -245,7 +274,7 @@ def test_api_default_format(self): pandas.set_option('io.hdf.default_format',None) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeDataFrame() @@ -267,7 +296,7 @@ def test_api_default_format(self): def test_keys(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['a'] = tm.makeTimeSeries() store['b'] = tm.makeStringSeries() store['c'] = tm.makeDataFrame() @@ -279,7 +308,7 @@ def test_keys(self): def test_repr(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: repr(store) store['a'] = tm.makeTimeSeries() store['b'] = tm.makeStringSeries() @@ -314,7 +343,7 @@ def test_repr(self): str(store) # storers - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeDataFrame() store.append('df',df) @@ -325,7 +354,7 @@ def test_repr(self): def test_contains(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['a'] = tm.makeTimeSeries() store['b'] = tm.makeDataFrame() store['foo/bar'] = tm.makeDataFrame() @@ -344,7 +373,7 @@ def test_contains(self): def test_versioning(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['a'] = tm.makeTimeSeries() store['b'] = tm.makeDataFrame() df = tm.makeTimeDataFrame() @@ -370,7 +399,7 @@ def test_mode(self): def check(mode): - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: # constructor if mode in ['r','r+']: @@ -381,7 +410,7 @@ def check(mode): self.assert_(store._handle.mode == mode) store.close() - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: # context if mode in ['r','r+']: @@ -393,7 +422,7 @@ def f(): with get_store(path,mode=mode) as store: self.assert_(store._handle.mode == mode) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: # conv write if mode in ['r','r+']: @@ -416,7 +445,7 @@ def f(): def test_reopen_handle(self): - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: store = HDFStore(path,mode='a') store['a'] = tm.makeTimeSeries() @@ -462,14 +491,14 @@ def test_reopen_handle(self): def test_flush(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['a'] = tm.makeTimeSeries() store.flush() store.flush(fsync=True) def test_get(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['a'] = tm.makeTimeSeries() left = store.get('a') right = store['a'] @@ -483,7 +512,7 @@ def test_get(self): def test_getattr(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: s = tm.makeTimeSeries() store['a'] = s @@ -511,7 +540,7 @@ def test_getattr(self): def test_put(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: ts = tm.makeTimeSeries() df = tm.makeTimeDataFrame() @@ -540,7 +569,7 @@ def test_put(self): def test_put_string_index(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: index = Index( ["I am a very long string index: %s" % i for i in range(20)]) @@ -565,7 +594,7 @@ def test_put_string_index(self): def test_put_compression(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeTimeDataFrame() store.put('c', df, format='table', complib='zlib') @@ -579,7 +608,7 @@ def test_put_compression_blosc(self): tm.skip_if_no_package('tables', '2.2', app='blosc support') df = tm.makeTimeDataFrame() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # can't compress if format='fixed' self.assertRaises(ValueError, store.put, 'b', df, @@ -609,7 +638,7 @@ def test_put_mixed_type(self): df.ix[3:6, ['obj1']] = np.nan df = df.consolidate().convert_objects() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: _maybe_remove(store, 'df') # cannot use assert_produces_warning here for some reason @@ -623,7 +652,7 @@ def test_put_mixed_type(self): def test_append(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeTimeDataFrame() _maybe_remove(store, 'df1') store.append('df1', df[:10]) @@ -711,7 +740,7 @@ def test_append(self): def test_append_series(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # basic ss = tm.makeStringSeries() @@ -759,7 +788,7 @@ def test_store_index_types(self): # GH5386 # test storing various index types - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: def check(format,index): df = DataFrame(np.random.randn(10,2),columns=list('AB')) @@ -792,7 +821,7 @@ def test_encoding(self): if sys.byteorder != 'little': raise nose.SkipTest('system byteorder is not little, skipping test_encoding!') - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame(dict(A='foo',B='bar'),index=range(5)) df.loc[2,'A'] = np.nan df.loc[3,'B'] = np.nan @@ -806,7 +835,7 @@ def test_encoding(self): def test_append_some_nans(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame({'A' : Series(np.random.randn(20)).astype('int32'), 'A1' : np.random.randn(20), 'A2' : np.random.randn(20), @@ -845,7 +874,7 @@ def test_append_some_nans(self): def test_append_all_nans(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame({'A1' : np.random.randn(20), 'A2' : np.random.randn(20)}, @@ -916,7 +945,7 @@ def test_append_all_nans(self): def test_append_frame_column_oriented(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # column oriented df = tm.makeTimeDataFrame() @@ -942,7 +971,7 @@ def test_append_frame_column_oriented(self): def test_append_with_different_block_ordering(self): #GH 4096; using same frames, but different block orderings - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: for i in range(10): @@ -964,7 +993,7 @@ def test_append_with_different_block_ordering(self): store.append('df',df) # test a different ordering but with more fields (like invalid combinate) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame(np.random.randn(10,2),columns=list('AB'), dtype='float64') df['int64'] = Series([1]*len(df),dtype='int64') @@ -982,7 +1011,7 @@ def test_append_with_different_block_ordering(self): def test_ndim_indexables(self): """ test using ndim tables in new ways""" - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: p4d = tm.makePanel4D() @@ -1049,7 +1078,7 @@ def check_indexers(key, indexers): def test_append_with_strings(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: wp = tm.makePanel() wp2 = wp.rename_axis( dict([(x, "%s_extra" % x) for x in wp.minor_axis]), axis=2) @@ -1118,7 +1147,7 @@ def check_col(key,name,size): result = store.select('df') tm.assert_frame_equal(result, df) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: def check_col(key,name,size): self.assert_(getattr(store.get_storer(key).table.description,name).itemsize == size) @@ -1157,7 +1186,7 @@ def check_col(key,name,size): def test_append_with_data_columns(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeTimeDataFrame() df.loc[:,'B'].iloc[0] = 1. _maybe_remove(store, 'df') @@ -1196,7 +1225,7 @@ def test_append_with_data_columns(self): def check_col(key,name,size): self.assert_(getattr(store.get_storer(key).table.description,name).itemsize == size) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: _maybe_remove(store, 'df') store.append('df', df_new, data_columns=['string'], min_itemsize={'string': 30}) @@ -1210,7 +1239,7 @@ def check_col(key,name,size): min_itemsize={'values': 30}) check_col('df', 'string', 30) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df_new['string2'] = 'foobarbah' df_new['string_block1'] = 'foobarbah1' df_new['string_block2'] = 'foobarbah2' @@ -1220,7 +1249,7 @@ def check_col(key,name,size): check_col('df', 'string2', 40) check_col('df', 'values_block_1', 50) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # multiple data columns df_new = df.copy() df_new.loc[:,'A'].iloc[0] = 1. @@ -1247,7 +1276,7 @@ def check_col(key,name,size): df_new.string2 == 'cool')] tm.assert_frame_equal(result, expected) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # doc example df_dc = df.copy() df_dc['string'] = 'foo' @@ -1274,7 +1303,7 @@ def check_col(key,name,size): def test_create_table_index(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: def col(t,column): return getattr(store.get_storer(t).table.cols,column) @@ -1365,7 +1394,7 @@ def test_big_table_frame(self): import time x = time.time() - with ensure_clean(self.path,mode='w') as store: + with ensure_clean_store(self.path,mode='w') as store: store.append('df', df) rows = store.root.df.table.nrows recons = store.select('df') @@ -1393,7 +1422,7 @@ def test_big_table2_frame(self): % (len(df.index), time.time() - start_time)) def f(chunksize): - with ensure_clean(self.path,mode='w') as store: + with ensure_clean_store(self.path,mode='w') as store: store.append('df', df, chunksize=chunksize) r = store.root.df.table.nrows return r @@ -1421,7 +1450,7 @@ def test_big_put_frame(self): print("\nbig_put frame (creation of df) [rows->%s] -> %5.2f" % (len(df.index), time.time() - start_time)) - with ensure_clean(self.path, mode='w') as store: + with ensure_clean_store(self.path, mode='w') as store: start_time = time.time() store = HDFStore(self.path, mode='w') store.put('df', df) @@ -1447,7 +1476,7 @@ def test_big_table_panel(self): x = time.time() - with ensure_clean(self.path, mode='w') as store: + with ensure_clean_store(self.path, mode='w') as store: store.append('wp', wp) rows = store.root.wp.table.nrows recons = store.select('wp') @@ -1461,7 +1490,7 @@ def test_append_diff_item_order(self): wp1 = wp.ix[:, :10, :] wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :] - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('panel', wp1, format='table') self.assertRaises(ValueError, store.put, 'panel', wp2, append=True) @@ -1475,7 +1504,7 @@ def test_append_hierarchical(self): df = DataFrame(np.random.randn(10, 3), index=index, columns=['A', 'B', 'C']) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('mi', df) result = store.select('mi') tm.assert_frame_equal(result, df) @@ -1485,7 +1514,7 @@ def test_append_hierarchical(self): expected = df.reindex(columns=['A','B']) tm.assert_frame_equal(result,expected) - with tm.ensure_clean('test.hdf') as path: + with ensure_clean_path('test.hdf') as path: df.to_hdf(path,'df',format='table') result = read_hdf(path,'df',columns=['A','B']) expected = df.reindex(columns=['A','B']) @@ -1498,7 +1527,7 @@ def test_column_multiindex(self): index = MultiIndex.from_tuples([('A','a'), ('A','b'), ('B','a'), ('B','b')], names=['first','second']) df = DataFrame(np.arange(12).reshape(3,4), columns=index) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('df',df) tm.assert_frame_equal(store['df'],df,check_index_type=True,check_column_type=True) @@ -1512,7 +1541,7 @@ def test_column_multiindex(self): # non_index_axes name df = DataFrame(np.arange(12).reshape(3,4), columns=Index(list('ABCD'),name='foo')) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('df1',df,format='table') tm.assert_frame_equal(store['df1'],df,check_index_type=True,check_column_type=True) @@ -1521,14 +1550,14 @@ def test_pass_spec_to_storer(self): df = tm.makeDataFrame() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('df',df) self.assertRaises(TypeError, store.select, 'df', columns=['A']) self.assertRaises(TypeError, store.select, 'df',where=[('columns=A')]) def test_append_misc(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # unsuported data types for non-tables p4d = tm.makePanel4D() @@ -1552,7 +1581,7 @@ def test_append_misc(self): # more chunksize in append tests def check(obj, comparator): for c in [10, 200, 1000]: - with ensure_clean(self.path,mode='w') as store: + with ensure_clean_store(self.path,mode='w') as store: store.append('obj', obj, chunksize=c) result = store.select('obj') comparator(result,obj) @@ -1573,7 +1602,7 @@ def check(obj, comparator): check(p4d, assert_panel4d_equal) # empty frame, GH4273 - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # 0 len df_empty = DataFrame(columns=list('ABC')) @@ -1610,7 +1639,7 @@ def check(obj, comparator): def test_append_raise(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # test append with invalid input to get good error messages @@ -1652,14 +1681,14 @@ def test_table_index_incompatible_dtypes(self): df2 = DataFrame({'a': [4, 5, 6]}, index=date_range('1/1/2000', periods=3)) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('frame', df1, format='table') self.assertRaises(TypeError, store.put, 'frame', df2, format='table', append=True) def test_table_values_dtypes_roundtrip(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df1 = DataFrame({'a': [1, 2, 3]}, dtype='f8') store.append('df_f8', df1) assert_series_equal(df1.dtypes,store['df_f8'].dtypes) @@ -1714,7 +1743,7 @@ def test_table_mixed_dtypes(self): df.ix[3:6, ['obj1']] = np.nan df = df.consolidate().convert_objects() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('df1_mixed', df) tm.assert_frame_equal(store.select('df1_mixed'), df) @@ -1728,7 +1757,7 @@ def test_table_mixed_dtypes(self): wp['int2'] = 2 wp = wp.consolidate() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('p1_mixed', wp) assert_panel_equal(store.select('p1_mixed'), wp) @@ -1742,13 +1771,13 @@ def test_table_mixed_dtypes(self): wp['int2'] = 2 wp = wp.consolidate() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('p4d_mixed', wp) assert_panel4d_equal(store.select('p4d_mixed'), wp) def test_unimplemented_dtypes_table_columns(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: l = [('date', datetime.date(2001, 1, 2))] @@ -1770,7 +1799,7 @@ def test_unimplemented_dtypes_table_columns(self): df['datetime1'] = datetime.date(2001, 1, 2) df = df.consolidate().convert_objects() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # this fails because we have a date in the object block...... self.assertRaises(TypeError, store.append, 'df_unimplemented', df) @@ -1790,7 +1819,7 @@ def compare(a,b): raise AssertionError("invalid tz comparsion [%s] [%s]" % (a_e,b_e)) # as columns - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: _maybe_remove(store, 'df_tz') df = DataFrame(dict(A = [ Timestamp('20130102 2:00:00',tz='US/Eastern') + timedelta(hours=1)*i for i in range(5) ])) @@ -1825,7 +1854,7 @@ def compare(a,b): self.assertRaises(ValueError, store.append, 'df_tz', df) # as index - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # GH 4098 example df = DataFrame(dict(A = Series(lrange(3), index=date_range('2000-1-1',periods=3,freq='H', tz='US/Eastern')))) @@ -1853,7 +1882,7 @@ def test_store_timezone(self): import os # original method - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: today = datetime.date(2013,9,10) df = DataFrame([1,2,3], index = [today, today, today]) @@ -1876,7 +1905,7 @@ def setTZ(tz): try: - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: setTZ('EST5EDT') today = datetime.date(2013,9,10) @@ -1903,7 +1932,7 @@ def test_append_with_timedelta(self): df['C'] = df['A']-df['B'] df.ix[3:5,'C'] = np.nan - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # table _maybe_remove(store, 'df') @@ -1938,7 +1967,7 @@ def test_append_with_timedelta(self): def test_remove(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: ts = tm.makeTimeSeries() df = tm.makeDataFrame() @@ -1975,7 +2004,7 @@ def test_remove(self): def test_remove_where(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # non-existance crit1 = Term('index>foo') @@ -2011,7 +2040,7 @@ def test_remove_where(self): def test_remove_crit(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: wp = tm.makePanel() @@ -2077,7 +2106,7 @@ def test_remove_crit(self): def test_invalid_terms(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeTimeDataFrame() df['string'] = 'foo' @@ -2100,7 +2129,7 @@ def test_invalid_terms(self): self.assertRaises(ValueError, store.select, 'wp', "major_axis<'20000108' & minor_axis['A', 'B']") # from the docs - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: dfq = DataFrame(np.random.randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10)) dfq.to_hdf(path,'dfq',format='table',data_columns=True) @@ -2109,7 +2138,7 @@ def test_invalid_terms(self): read_hdf(path,'dfq',where="A>0 or C>0") # catch the invalid reference - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: dfq = DataFrame(np.random.randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10)) dfq.to_hdf(path,'dfq',format='table') @@ -2117,7 +2146,7 @@ def test_invalid_terms(self): def test_terms(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: wp = tm.makePanel() p4d = tm.makePanel4D() @@ -2184,7 +2213,7 @@ def test_terms(self): store.select('p4d', t) def test_term_compat(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: wp = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'], major_axis=date_range('1/1/2000', periods=5), @@ -2203,7 +2232,7 @@ def test_term_compat(self): def test_same_name_scoping(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: import pandas as pd df = DataFrame(np.random.randn(20, 2),index=pd.date_range('20130101',periods=20)) @@ -2378,7 +2407,7 @@ def test_frame(self): self._check_roundtrip(tdf, tm.assert_frame_equal, compression=True) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # not consolidated df['foo'] = np.random.randn(len(df)) store['df'] = df @@ -2417,7 +2446,7 @@ def test_timezones(self): rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern') frame = DataFrame(np.random.randn(len(rng), 4), index=rng) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['frame'] = frame recons = store['frame'] self.assert_(recons.index.equals(rng)) @@ -2427,7 +2456,7 @@ def test_fixed_offset_tz(self): rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00') frame = DataFrame(np.random.randn(len(rng), 4), index=rng) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['frame'] = frame recons = store['frame'] self.assert_(recons.index.equals(rng)) @@ -2447,7 +2476,7 @@ def test_store_hierarchical(self): self._check_roundtrip(frame['A'], tm.assert_series_equal) # check that the names are stored - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['frame'] = frame recons = store['frame'] assert(recons.index.names == ('foo', 'bar')) @@ -2456,7 +2485,7 @@ def test_store_index_name(self): df = tm.makeDataFrame() df.index.name = 'foo' - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['frame'] = df recons = store['frame'] assert(recons.index.name == 'foo') @@ -2465,7 +2494,7 @@ def test_store_series_name(self): df = tm.makeDataFrame() series = df['A'] - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['series'] = series recons = store['series'] assert(recons.name == 'A') @@ -2488,7 +2517,7 @@ def _make_one(): self._check_roundtrip(df1, tm.assert_frame_equal) self._check_roundtrip(df2, tm.assert_frame_equal) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['obj'] = df1 tm.assert_frame_equal(store['obj'], df1) store['obj'] = df2 @@ -2525,7 +2554,7 @@ def test_select_with_dups(self): df = DataFrame(np.random.randn(10,4),columns=['A','A','B','B']) df.index = date_range('20130101 9:30',periods=10,freq='T') - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('df',df) result = store.select('df') @@ -2546,7 +2575,7 @@ def test_select_with_dups(self): axis=1) df.index = date_range('20130101 9:30',periods=10,freq='T') - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('df',df) result = store.select('df') @@ -2566,7 +2595,7 @@ def test_select_with_dups(self): assert_frame_equal(result,expected,by_blocks=True) # duplicates on both index and columns - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.append('df',df) store.append('df',df) @@ -2577,7 +2606,7 @@ def test_select_with_dups(self): def test_wide_table_dups(self): wp = tm.makePanel() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('panel', wp, format='table') store.put('panel', wp, format='table', append=True) @@ -2601,7 +2630,7 @@ def test_longpanel(self): def test_overwrite_node(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store['a'] = tm.makeTimeDataFrame() ts = tm.makeTimeSeries() store['a'] = ts @@ -2641,7 +2670,7 @@ def test_sparse_with_compression(self): def test_select(self): wp = tm.makePanel() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # put/select ok _maybe_remove(store, 'wp') @@ -2705,7 +2734,7 @@ def test_select(self): def test_select_dtypes(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # with a Timestamp data column (GH #2637) df = DataFrame(dict(ts=bdate_range('2012-01-01', periods=300), A=np.random.randn(300))) @@ -2753,7 +2782,7 @@ def test_select_dtypes(self): expected = df.reindex(index=list(df.index)[0:10],columns=['A']) tm.assert_frame_equal(expected, result) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # floats w/o NaN df = DataFrame(dict(cols = range(11), values = range(11)),dtype='float64') @@ -2795,7 +2824,7 @@ def test_select_dtypes(self): def test_select_with_many_inputs(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame(dict(ts=bdate_range('2012-01-01', periods=300), A=np.random.randn(300), @@ -2836,7 +2865,7 @@ def test_select_with_many_inputs(self): def test_select_iterator(self): # single table - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeTimeDataFrame(500) _maybe_remove(store, 'df') @@ -2862,14 +2891,14 @@ def test_select_iterator(self): result = concat(results) tm.assert_frame_equal(result, expected) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeTimeDataFrame(500) df.to_hdf(path,'df_non_table') self.assertRaises(TypeError, read_hdf, path,'df_non_table',chunksize=100) self.assertRaises(TypeError, read_hdf, path,'df_non_table',iterator=True) - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeTimeDataFrame(500) df.to_hdf(path,'df',format='table') @@ -2885,7 +2914,7 @@ def test_select_iterator(self): # multiple - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df1 = tm.makeTimeDataFrame(500) store.append('df1',df1,data_columns=True) @@ -2921,7 +2950,7 @@ def test_retain_index_attributes(self): df = DataFrame(dict(A = Series(lrange(3), index=date_range('2000-1-1',periods=3,freq='H')))) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: _maybe_remove(store,'data') store.put('data', df, format='table') @@ -2951,7 +2980,7 @@ def test_retain_index_attributes(self): def test_retain_index_attributes2(self): - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: with tm.assert_produces_warning(expected_warning=AttributeConflictWarning): @@ -2980,7 +3009,7 @@ def test_panel_select(self): wp = tm.makePanel() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('wp', wp, format='table') date = wp.major_axis[len(wp.major_axis) // 2] @@ -3000,7 +3029,7 @@ def test_frame_select(self): df = tm.makeTimeDataFrame() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('frame', df,format='table') date = df.index[len(df) // 2] @@ -3034,7 +3063,7 @@ def test_frame_select_complex(self): df['string'] = 'foo' df.loc[df.index[0:4],'string'] = 'bar' - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('df', df, table=True, data_columns=['string']) # empty @@ -3079,7 +3108,7 @@ def test_invalid_filtering(self): df = tm.makeTimeDataFrame() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('df', df, table=True) # not implemented @@ -3091,7 +3120,7 @@ def test_invalid_filtering(self): def test_string_select(self): # GH 2973 - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = tm.makeTimeDataFrame() @@ -3140,7 +3169,7 @@ def test_read_column(self): df = tm.makeTimeDataFrame() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: _maybe_remove(store, 'df') store.append('df', df) @@ -3178,7 +3207,7 @@ def f(): def test_coordinates(self): df = tm.makeTimeDataFrame() - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: _maybe_remove(store, 'df') store.append('df', df) @@ -3223,7 +3252,7 @@ def test_coordinates(self): tm.assert_frame_equal(result, expected) # pass array/mask as the coordinates - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000)) store.append('df',df) @@ -3265,7 +3294,7 @@ def test_append_to_multiple(self): df2['foo'] = 'bar' df = concat([df1, df2], axis=1) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # exceptions self.assertRaises(ValueError, store.append_to_multiple, @@ -3289,7 +3318,7 @@ def test_append_to_multiple_dropna(self): df1.ix[1, ['A', 'B']] = np.nan df = concat([df1, df2], axis=1) - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # dropna=True should guarantee rows are synchronized store.append_to_multiple( {'df1': ['A', 'B'], 'df2': None}, df, selector='df1', @@ -3315,7 +3344,7 @@ def test_select_as_multiple(self): df2 = tm.makeTimeDataFrame().rename(columns=lambda x: "%s_2" % x) df2['foo'] = 'bar' - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: # no tables stored self.assertRaises(Exception, store.select_as_multiple, @@ -3366,7 +3395,7 @@ def test_select_as_multiple(self): def test_start_stop(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: df = DataFrame(dict(A=np.random.rand(20), B=np.random.rand(20))) store.append('df', df) @@ -3388,7 +3417,7 @@ def test_select_filter_corner(self): df.index = ['%.3d' % c for c in df.index] df.columns = ['%.3d' % c for c in df.columns] - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: store.put('frame', df, format='table') crit = Term('columns=df.columns[:75]') @@ -3401,7 +3430,7 @@ def _check_roundtrip(self, obj, comparator, compression=False, **kwargs): if compression: options['complib'] = _default_compressor - with ensure_clean(self.path, 'w', **options) as store: + with ensure_clean_store(self.path, 'w', **options) as store: store['obj'] = obj retrieved = store['obj'] comparator(retrieved, obj, **kwargs) @@ -3412,7 +3441,7 @@ def _check_double_roundtrip(self, obj, comparator, compression=False, if compression: options['complib'] = compression or _default_compressor - with ensure_clean(self.path, 'w', **options) as store: + with ensure_clean_store(self.path, 'w', **options) as store: store['obj'] = obj retrieved = store['obj'] comparator(retrieved, obj, **kwargs) @@ -3425,7 +3454,7 @@ def _check_roundtrip_table(self, obj, comparator, compression=False): if compression: options['complib'] = _default_compressor - with ensure_clean(self.path, 'w', **options) as store: + with ensure_clean_store(self.path, 'w', **options) as store: store.put('obj', obj, format='table') retrieved = store['obj'] # sorted_obj = _test_sort(obj) @@ -3434,7 +3463,7 @@ def _check_roundtrip_table(self, obj, comparator, compression=False): def test_multiple_open_close(self): # GH 4409, open & close multiple times - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeDataFrame() df.to_hdf(path,'df',mode='w',format='table') @@ -3496,7 +3525,7 @@ def test_multiple_open_close(self): self.assert_(not store2.is_open) # ops on a closed store - with tm.ensure_clean(self.path) as path: + with ensure_clean_path(self.path) as path: df = tm.makeDataFrame() df.to_hdf(path,'df',mode='w',format='table') @@ -3681,7 +3710,7 @@ def test_legacy_table_write(self): def test_store_datetime_fractional_secs(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: dt = datetime.datetime(2012, 1, 2, 3, 4, 5, 123456) series = Series([0], [dt]) store['a'] = series @@ -3689,7 +3718,7 @@ def test_store_datetime_fractional_secs(self): def test_tseries_indices_series(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: idx = tm.makeDateIndex(10) ser = Series(np.random.randn(len(idx)), idx) store['a'] = ser @@ -3710,7 +3739,7 @@ def test_tseries_indices_series(self): def test_tseries_indices_frame(self): - with ensure_clean(self.path) as store: + with ensure_clean_store(self.path) as store: idx = tm.makeDateIndex(10) df = DataFrame(np.random.randn(len(idx), 3), index=idx) store['a'] = df @@ -3761,7 +3790,7 @@ def test_append_with_diff_col_name_types_raises_value_error(self): df4 = DataFrame({('1', 2): np.random.randn(10)}) df5 = DataFrame({('1', 2, object): np.random.randn(10)}) - with ensure_clean('__%s__.h5' % tm.rands(20)) as store: + with ensure_clean_store(self.path) as store: name = 'df_%s' % tm.rands(10) store.append(name, df) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index b73c7cdbb8f87..22f5fc527d8f5 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -5477,12 +5477,13 @@ def make_dtnat_arr(n,nnat=None): # N=35000 s1=make_dtnat_arr(chunksize+5) s2=make_dtnat_arr(chunksize+5,0) + path = '1.csv' - # s3=make_dtnat_arr(chunksize+5,0) - with ensure_clean('1.csv') as path: + # s3=make_dtnjat_arr(chunksize+5,0) + with ensure_clean('.csv') as pth: df=DataFrame(dict(a=s1,b=s2)) - df.to_csv(path,chunksize=chunksize) - recons = DataFrame.from_csv(path).convert_objects('coerce') + df.to_csv(pth,chunksize=chunksize) + recons = DataFrame.from_csv(pth).convert_objects('coerce') assert_frame_equal(df, recons,check_names=False,check_less_precise=True) for ncols in [4]: @@ -5491,7 +5492,6 @@ def make_dtnat_arr(n,nnat=None): base-1,base,base+1]: _do_test(mkdf(nrows, ncols,r_idx_type='dt', c_idx_type='s'),path, 'dt','s') - pass for ncols in [4]: diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 2e4d1f3e8df74..895c651c0bfe7 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -313,34 +313,35 @@ def ensure_clean(filename=None, return_filelike=False): ---------- filename : str (optional) if None, creates a temporary file which is then removed when out of - scope. - return_filelike: bool (default False) + scope. if passed, creates temporary file with filename as ending. + return_filelike : bool (default False) if True, returns a file-like which is *always* cleaned. Necessary for - savefig and other functions which want to append extensions. Ignores - filename if True. + savefig and other functions which want to append extensions. """ + filename = filename or '' if return_filelike: - f = tempfile.TemporaryFile() + f = tempfile.TemporaryFile(suffix=filename) try: yield f finally: f.close() else: - # if we are not passed a filename, generate a temporary - if filename is None: - filename = tempfile.mkstemp()[1] + + # don't generate tempfile if using a path with directory specified + if len(os.path.dirname(filename)): + raise ValueError("Can't pass a qualified name to ensure_clean()") try: + filename = tempfile.mkstemp(suffix=filename)[1] yield filename finally: try: if os.path.exists(filename): os.remove(filename) except Exception as e: - print(e) - + print("Exception on removing file: %s" % e) def get_data_path(f=''): """Return the path of a data file, these are relative to the current test
related #5419 Includes @jreback's commits from #5422 and hdf_temp: - TST: make pytables tests go thru a temporary dir and file - TST/BUG: incorrect way of testing for r+ modes TST: fix temporary files by using mktemp (rather than mkstemp) which opens them
https://api.github.com/repos/pandas-dev/pandas/pulls/5425
2013-11-03T23:42:01Z
2013-11-04T01:10:47Z
2013-11-04T01:10:47Z
2014-07-16T08:38:56Z
ENH: Better handling of MultiIndex with Excel
diff --git a/ci/requirements-2.7.txt b/ci/requirements-2.7.txt index 705aa9e3cc922..dc6e2d9f55977 100644 --- a/ci/requirements-2.7.txt +++ b/ci/requirements-2.7.txt @@ -8,7 +8,7 @@ numexpr==2.1 tables==2.3.1 matplotlib==1.1.1 openpyxl==1.6.2 -xlsxwriter==0.4.3 +xlsxwriter==0.4.6 xlrd==0.9.2 patsy==0.1.0 html5lib==1.0b2 diff --git a/ci/requirements-2.7_LOCALE.txt b/ci/requirements-2.7_LOCALE.txt index b18bff6797840..06574cdd6b299 100644 --- a/ci/requirements-2.7_LOCALE.txt +++ b/ci/requirements-2.7_LOCALE.txt @@ -2,7 +2,7 @@ python-dateutil pytz==2013b xlwt==0.7.5 openpyxl==1.6.2 -xlsxwriter==0.4.3 +xlsxwriter==0.4.6 xlrd==0.9.2 numpy==1.6.1 cython==0.19.1 diff --git a/ci/requirements-3.2.txt b/ci/requirements-3.2.txt index 0f3bdcbac38cb..136b5cf12cbc0 100644 --- a/ci/requirements-3.2.txt +++ b/ci/requirements-3.2.txt @@ -1,7 +1,7 @@ python-dateutil==2.1 pytz==2013b openpyxl==1.6.2 -xlsxwriter==0.4.3 +xlsxwriter==0.4.6 xlrd==0.9.2 numpy==1.7.1 cython==0.19.1 diff --git a/ci/requirements-3.3.txt b/ci/requirements-3.3.txt index 3ca888d1623e3..480fde477d88b 100644 --- a/ci/requirements-3.3.txt +++ b/ci/requirements-3.3.txt @@ -1,7 +1,7 @@ python-dateutil==2.2 pytz==2013b openpyxl==1.6.2 -xlsxwriter==0.4.3 +xlsxwriter==0.4.6 xlrd==0.9.2 html5lib==1.0b2 numpy==1.8.0 diff --git a/doc/source/release.rst b/doc/source/release.rst index 4b33c20424b33..77d78b2892b90 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -209,6 +209,11 @@ Improvements to existing features by color as expected. - ``read_excel()`` now tries to convert integral floats (like ``1.0``) to int by default. (:issue:`5394`) + - Excel writers now have a default option ``merge_cells`` in ``to_excel()`` + to merge cells in MultiIndex and Hierarchical Rows. Note: using this + option it is no longer possible to round trip Excel files with merged + MultiIndex and Hierarchical Rows. Set the ``merge_cells`` to ``False`` to + restore the previous behaviour. (:issue:`5254`) API Changes ~~~~~~~~~~~ diff --git a/pandas/core/format.py b/pandas/core/format.py index 75069297360d6..5062fd9be6357 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -1213,7 +1213,7 @@ def __init__(self, row, col, val, "right": "thin", "bottom": "thin", "left": "thin"}, - "alignment": {"horizontal": "center"}} + "alignment": {"horizontal": "center", "vertical": "top"}} class ExcelFormatter(object): @@ -1237,10 +1237,12 @@ class ExcelFormatter(object): Column label for index column(s) if desired. If None is given, and `header` and `index` are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. + merge_cells : boolean, default False + Format MultiIndex and Hierarchical Rows as merged cells. """ def __init__(self, df, na_rep='', float_format=None, cols=None, - header=True, index=True, index_label=None): + header=True, index=True, index_label=None, merge_cells=False): self.df = df self.rowcounter = 0 self.na_rep = na_rep @@ -1251,6 +1253,7 @@ def __init__(self, df, na_rep='', float_format=None, cols=None, self.index = index self.index_label = index_label self.header = header + self.merge_cells = merge_cells def _format_value(self, val): if lib.checknull(val): @@ -1264,29 +1267,44 @@ def _format_header_mi(self): if not(has_aliases or self.header): return - levels = self.columns.format(sparsify=True, adjoin=False, - names=False) - # level_lenghts = _get_level_lengths(levels) - coloffset = 1 - if isinstance(self.df.index, MultiIndex): - coloffset = len(self.df.index[0]) - - # for lnum, (records, values) in enumerate(zip(level_lenghts, - # levels)): - # name = self.columns.names[lnum] - # yield ExcelCell(lnum, coloffset, name, header_style) - # for i in records: - # if records[i] > 1: - # yield ExcelCell(lnum,coloffset + i + 1, values[i], - # header_style, lnum, coloffset + i + records[i]) - # else: - # yield ExcelCell(lnum, coloffset + i + 1, values[i], header_style) - - # self.rowcounter = lnum + columns = self.columns + level_strs = columns.format(sparsify=True, adjoin=False, names=False) + level_lengths = _get_level_lengths(level_strs) + coloffset = 0 lnum = 0 - for i, values in enumerate(zip(*levels)): - v = ".".join(map(com.pprint_thing, values)) - yield ExcelCell(lnum, coloffset + i, v, header_style) + + if isinstance(self.df.index, MultiIndex): + coloffset = len(self.df.index[0]) - 1 + + if self.merge_cells: + # Format multi-index as a merged cells. + for lnum in range(len(level_lengths)): + name = columns.names[lnum] + yield ExcelCell(lnum, coloffset, name, header_style) + + for lnum, (spans, levels, labels) in enumerate(zip(level_lengths, + columns.levels, + columns.labels) + ): + values = levels.take(labels) + for i in spans: + if spans[i] > 1: + yield ExcelCell(lnum, + coloffset + i + 1, + values[i], + header_style, + lnum, + coloffset + i + spans[i]) + else: + yield ExcelCell(lnum, + coloffset + i + 1, + values[i], + header_style) + else: + # Format in legacy format with dots to indicate levels. + for i, values in enumerate(zip(*level_strs)): + v = ".".join(map(com.pprint_thing, values)) + yield ExcelCell(lnum, coloffset + i + 1, v, header_style) self.rowcounter = lnum @@ -1354,14 +1372,17 @@ def _format_regular_rows(self): index_label = self.df.index.names[0] if index_label and self.header is not False: - # add to same level as column names - # if isinstance(self.df.columns, MultiIndex): - # yield ExcelCell(self.rowcounter, 0, - # index_label, header_style) - # self.rowcounter += 1 - # else: - yield ExcelCell(self.rowcounter - 1, 0, - index_label, header_style) + if self.merge_cells: + yield ExcelCell(self.rowcounter, + 0, + index_label, + header_style) + self.rowcounter += 1 + else: + yield ExcelCell(self.rowcounter - 1, + 0, + index_label, + header_style) # write index_values index_values = self.df.index @@ -1383,7 +1404,7 @@ def _format_hierarchical_rows(self): self.rowcounter += 1 gcolidx = 0 - # output index and index_label? + if self.index: index_labels = self.df.index.names # check for aliases @@ -1394,20 +1415,51 @@ def _format_hierarchical_rows(self): # if index labels are not empty go ahead and dump if (any(x is not None for x in index_labels) and self.header is not False): - # if isinstance(self.df.columns, MultiIndex): - # self.rowcounter += 1 - # else: - self.rowcounter -= 1 + + if not self.merge_cells: + self.rowcounter -= 1 + for cidx, name in enumerate(index_labels): - yield ExcelCell(self.rowcounter, cidx, - name, header_style) + yield ExcelCell(self.rowcounter, + cidx, + name, + header_style) self.rowcounter += 1 - for indexcolvals in zip(*self.df.index): - for idx, indexcolval in enumerate(indexcolvals): - yield ExcelCell(self.rowcounter + idx, gcolidx, - indexcolval, header_style) - gcolidx += 1 + if self.merge_cells: + # Format hierarchical rows as merged cells. + level_strs = self.df.index.format(sparsify=True, adjoin=False, + names=False) + level_lengths = _get_level_lengths(level_strs) + + for spans, levels, labels in zip(level_lengths, + self.df.index.levels, + self.df.index.labels): + values = levels.take(labels) + for i in spans: + if spans[i] > 1: + yield ExcelCell(self.rowcounter + i, + gcolidx, + values[i], + header_style, + self.rowcounter + i + spans[i] - 1, + gcolidx) + else: + yield ExcelCell(self.rowcounter + i, + gcolidx, + values[i], + header_style) + gcolidx += 1 + + else: + # Format hierarchical rows with non-merged values. + for indexcolvals in zip(*self.df.index): + for idx, indexcolval in enumerate(indexcolvals): + yield ExcelCell(self.rowcounter + idx, + gcolidx, + indexcolval, + header_style) + gcolidx += 1 for colidx in range(len(self.columns)): series = self.df.iloc[:, colidx] @@ -1415,8 +1467,8 @@ def _format_hierarchical_rows(self): yield ExcelCell(self.rowcounter + i, gcolidx + colidx, val) def get_formatted_cells(self): - for cell in itertools.chain(self._format_header(), self._format_body() - ): + for cell in itertools.chain(self._format_header(), + self._format_body()): cell.val = self._format_value(cell.val) yield cell diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 0a5306de9bbb5..18fba179f0654 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1130,7 +1130,8 @@ def to_csv(self, path_or_buf, sep=",", na_rep='', float_format=None, def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, cols=None, header=True, index=True, - index_label=None, startrow=0, startcol=0, engine=None): + index_label=None, startrow=0, startcol=0, engine=None, + merge_cells=True): """ Write DataFrame to a excel sheet @@ -1161,13 +1162,15 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', write engine to use - you can also set this via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and ``io.excel.xlsm.writer``. - + merge_cells : boolean, default True + Write MultiIndex and Hierarchical Rows as merged cells. Notes ----- If passing an existing ExcelWriter object, then the sheet will be added to the existing workbook. This can be used to save different DataFrames to one workbook + >>> writer = ExcelWriter('output.xlsx') >>> df1.to_excel(writer,'Sheet1') >>> df2.to_excel(writer,'Sheet2') @@ -1185,7 +1188,8 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', header=header, float_format=float_format, index=index, - index_label=index_label) + index_label=index_label, + merge_cells=merge_cells) formatted_cells = formatter.get_formatted_cells() excel_writer.write_cells(formatted_cells, sheet_name, startrow=startrow, startcol=startcol) diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 42c212caf41ca..b97c9da0b0d18 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -146,7 +146,7 @@ def __init__(self, io, **kwds): def parse(self, sheetname, header=0, skiprows=None, skip_footer=0, index_col=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, chunksize=None, - convert_float=True, **kwds): + convert_float=True, has_index_names=False, **kwds): """Read an Excel table into DataFrame Parameters @@ -169,25 +169,29 @@ def parse(self, sheetname, header=0, skiprows=None, skip_footer=0, parsed * If string then indicates comma separated list of column names and column ranges (e.g. "A:E" or "A,C,E:F") + parse_dates : boolean, default False + Parse date Excel values, + date_parser : function default None + Date parsing function na_values : list-like, default None List of additional strings to recognize as NA/NaN - keep_default_na : bool, default True - If na_values are specified and keep_default_na is False the default - NaN values are overridden, otherwise they're appended to - verbose : boolean, default False - Indicate number of NA values placed in non-numeric columns + thousands : str, default None + Thousands separator + chunksize : int, default None + Size of file chunk to read for lazy evaluation. convert_float : boolean, default True convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric data will be read in as floats: Excel stores all numbers as floats internally. + has_index_names : boolean, default False + True if the cols defined in index_col have an index name and are + not in the header Returns ------- parsed : DataFrame DataFrame parsed from the Excel file """ - has_index_names = False # removed as new argument of API function - skipfooter = kwds.pop('skipfooter', None) if skipfooter is not None: skip_footer = skipfooter @@ -506,6 +510,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): colletter = get_column_letter(startcol + cell.col + 1) xcell = wks.cell("%s%s" % (colletter, startrow + cell.row + 1)) xcell.value = _conv_value(cell.val) + style = None if cell.style: style = self._convert_to_style(cell.style) for field in style.__fields__: @@ -517,8 +522,6 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): elif isinstance(cell.val, datetime.date): xcell.style.number_format.format_code = "YYYY-MM-DD" - # merging requires openpyxl latest (works on 1.6.1) - # todo add version check if cell.mergestart is not None and cell.mergeend is not None: cletterstart = get_column_letter(startcol + cell.col + 1) cletterend = get_column_letter(startcol + cell.mergeend + 1) @@ -528,6 +531,25 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): cletterend, startrow + cell.mergestart + 1)) + # Excel requires that the format of the first cell in a merged + # range is repeated in the rest of the merged range. + if style: + first_row = startrow + cell.row + 1 + last_row = startrow + cell.mergestart + 1 + first_col = startcol + cell.col + 1 + last_col = startcol + cell.mergeend + 1 + + for row in range(first_row, last_row + 1): + for col in range(first_col, last_col + 1): + if row == first_row and col == first_col: + # Ignore first cell. It is already handled. + continue + colletter = get_column_letter(col) + xcell = wks.cell("%s%s" % (colletter, row)) + for field in style.__fields__: + xcell.style.__setattr__(field, \ + style.__getattribute__(field)) + @classmethod def _convert_to_style(cls, style_dict): """ @@ -723,8 +745,8 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): if cell.mergestart is not None and cell.mergeend is not None: wks.merge_range(startrow + cell.row, - startrow + cell.mergestart, startcol + cell.col, + startrow + cell.mergestart, startcol + cell.mergeend, cell.val, style) else: @@ -752,6 +774,16 @@ def _convert_to_style(self, style_dict, num_format_str=None): if font.get('bold'): xl_format.set_bold() + # Map the alignment to XlsxWriter alignment properties. + alignment = style_dict.get('alignment') + if alignment: + if (alignment.get('horizontal') + and alignment['horizontal'] == 'center'): + xl_format.set_align('center') + if (alignment.get('vertical') + and alignment['vertical'] == 'top'): + xl_format.set_align('top') + # Map the cell borders to XlsxWriter border properties. if style_dict.get('borders'): xl_format.set_border() diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 6eb3cbf1a3903..8bcf5e461ce7c 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -102,7 +102,8 @@ def test_parse_cols_int(self): df2 = df2.reindex(columns=['A', 'B', 'C']) df3 = xls.parse('Sheet2', skiprows=[1], index_col=0, parse_dates=True, parse_cols=3) - tm.assert_frame_equal(df, df2, check_names=False) # TODO add index to xls file) + # TODO add index to xls file) + tm.assert_frame_equal(df, df2, check_names=False) tm.assert_frame_equal(df3, df2, check_names=False) def test_parse_cols_list(self): @@ -121,7 +122,8 @@ def test_parse_cols_list(self): df3 = xls.parse('Sheet2', skiprows=[1], index_col=0, parse_dates=True, parse_cols=[0, 2, 3]) - tm.assert_frame_equal(df, df2, check_names=False) # TODO add index to xls file + # TODO add index to xls file) + tm.assert_frame_equal(df, df2, check_names=False) tm.assert_frame_equal(df3, df2, check_names=False) def test_parse_cols_str(self): @@ -141,7 +143,8 @@ def test_parse_cols_str(self): df2 = df2.reindex(columns=['A', 'B', 'C']) df3 = xls.parse('Sheet2', skiprows=[1], index_col=0, parse_dates=True, parse_cols='A:D') - tm.assert_frame_equal(df, df2, check_names=False) # TODO add index to xls, read xls ignores index name ? + # TODO add index to xls, read xls ignores index name ? + tm.assert_frame_equal(df, df2, check_names=False) tm.assert_frame_equal(df3, df2, check_names=False) del df, df2, df3 @@ -152,7 +155,8 @@ def test_parse_cols_str(self): df3 = xls.parse('Sheet2', skiprows=[1], index_col=0, parse_dates=True, parse_cols='A,C,D') - tm.assert_frame_equal(df, df2, check_names=False) # TODO add index to xls file + # TODO add index to xls file + tm.assert_frame_equal(df, df2, check_names=False) tm.assert_frame_equal(df3, df2, check_names=False) del df, df2, df3 @@ -284,7 +288,8 @@ def test_xlsx_table(self): df2 = self.read_csv(self.csv1, index_col=0, parse_dates=True) df3 = xlsx.parse('Sheet2', skiprows=[1], index_col=0, parse_dates=True) - tm.assert_frame_equal(df, df2, check_names=False) # TODO add index to xlsx file + # TODO add index to xlsx file + tm.assert_frame_equal(df, df2, check_names=False) tm.assert_frame_equal(df3, df2, check_names=False) df4 = xlsx.parse('Sheet1', index_col=0, parse_dates=True, @@ -365,6 +370,10 @@ class ExcelWriterBase(SharedItems): # 2. Add a property ext, which is the file extension that your writer # writes to. (needs to start with '.' so it's a valid path) # 3. Add a property engine_name, which is the name of the writer class. + + # Test with MultiIndex and Hierarchical Rows as merged cells. + merge_cells = True + def setUp(self): self.check_skip() super(ExcelWriterBase, self).setUp() @@ -433,7 +442,8 @@ def test_roundtrip(self): tm.assert_frame_equal(self.frame, recons) self.frame.to_excel(path, 'test1', na_rep='88') - recons = read_excel(path, 'test1', index_col=0, na_values=[88, 88.0]) + recons = read_excel(path, 'test1', index_col=0, + na_values=[88, 88.0]) tm.assert_frame_equal(self.frame, recons) def test_mixed(self): @@ -571,39 +581,56 @@ def test_roundtrip_indexlabels(self): # test index_label frame = (DataFrame(np.random.randn(10, 2)) >= 0) - frame.to_excel(path, 'test1', index_label=['test']) + frame.to_excel(path, 'test1', + index_label=['test'], + merge_cells=self.merge_cells) reader = ExcelFile(path) - recons = reader.parse('test1', index_col=0).astype(np.int64) + recons = reader.parse('test1', + index_col=0, + has_index_names=self.merge_cells + ).astype(np.int64) frame.index.names = ['test'] self.assertEqual(frame.index.names, recons.index.names) frame = (DataFrame(np.random.randn(10, 2)) >= 0) - frame.to_excel( - path, 'test1', index_label=['test', 'dummy', 'dummy2']) + frame.to_excel(path, + 'test1', + index_label=['test', 'dummy', 'dummy2'], + merge_cells=self.merge_cells) reader = ExcelFile(path) - recons = reader.parse('test1', index_col=0).astype(np.int64) + recons = reader.parse('test1', + index_col=0, + has_index_names=self.merge_cells + ).astype(np.int64) frame.index.names = ['test'] self.assertEqual(frame.index.names, recons.index.names) frame = (DataFrame(np.random.randn(10, 2)) >= 0) - frame.to_excel(path, 'test1', index_label='test') + frame.to_excel(path, + 'test1', + index_label='test', + merge_cells=self.merge_cells) reader = ExcelFile(path) - recons = reader.parse('test1', index_col=0).astype(np.int64) + recons = reader.parse('test1', + index_col=0, + has_index_names=self.merge_cells + ).astype(np.int64) frame.index.names = ['test'] - self.assertEqual(frame.index.names, recons.index.names) + self.assertAlmostEqual(frame.index.names, recons.index.names) with ensure_clean(self.ext) as path: - self.frame.to_excel(path, 'test1', - cols=['A', 'B', 'C', 'D'], index=False) - # take 'A' and 'B' as indexes (they are in same row as cols 'C', - # 'D') + self.frame.to_excel(path, + 'test1', + cols=['A', 'B', 'C', 'D'], + index=False, merge_cells=self.merge_cells) + # take 'A' and 'B' as indexes (same row as cols 'C', 'D') df = self.frame.copy() df = df.set_index(['A', 'B']) reader = ExcelFile(path) recons = reader.parse('test1', index_col=[0, 1]) - tm.assert_frame_equal(df, recons) + tm.assert_frame_equal(df, recons, check_less_precise=True) def test_excel_roundtrip_indexname(self): _skip_if_no_xlrd() @@ -612,10 +639,12 @@ def test_excel_roundtrip_indexname(self): df.index.name = 'foo' with ensure_clean(self.ext) as path: - df.to_excel(path) + df.to_excel(path, merge_cells=self.merge_cells) xf = ExcelFile(path) - result = xf.parse(xf.sheet_names[0], index_col=0) + result = xf.parse(xf.sheet_names[0], + index_col=0, + has_index_names=self.merge_cells) tm.assert_frame_equal(result, df) self.assertEqual(result.index.name, 'foo') @@ -624,19 +653,18 @@ def test_excel_roundtrip_datetime(self): _skip_if_no_xlrd() # datetime.date, not sure what to test here exactly - path = '__tmp_excel_roundtrip_datetime__.' + self.ext tsf = self.tsframe.copy() with ensure_clean(self.ext) as path: tsf.index = [x.date() for x in self.tsframe.index] - tsf.to_excel(path, 'test1') + tsf.to_excel(path, 'test1', merge_cells=self.merge_cells) reader = ExcelFile(path) recons = reader.parse('test1') tm.assert_frame_equal(self.tsframe, recons) def test_to_excel_periodindex(self): _skip_if_no_xlrd() - path = '__tmp_to_excel_periodindex__.' + self.ext + frame = self.tsframe xp = frame.resample('M', kind='period') @@ -651,8 +679,7 @@ def test_to_excel_multiindex(self): _skip_if_no_xlrd() frame = self.frame - old_index = frame.index - arrays = np.arange(len(old_index) * 2).reshape(2, -1) + arrays = np.arange(len(frame.index) * 2).reshape(2, -1) new_index = MultiIndex.from_arrays(arrays, names=['first', 'second']) frame.index = new_index @@ -662,42 +689,36 @@ def test_to_excel_multiindex(self): frame.to_excel(path, 'test1', cols=['A', 'B']) # round trip - frame.to_excel(path, 'test1') + frame.to_excel(path, 'test1', merge_cells=self.merge_cells) reader = ExcelFile(path) - df = reader.parse('test1', index_col=[0, 1], parse_dates=False) + df = reader.parse('test1', index_col=[0, 1], + parse_dates=False, + has_index_names=self.merge_cells) tm.assert_frame_equal(frame, df) self.assertEqual(frame.index.names, df.index.names) - self.frame.index = old_index # needed if setUP becomes a classmethod def test_to_excel_multiindex_dates(self): _skip_if_no_xlrd() # try multiindex with dates - tsframe = self.tsframe - old_index = tsframe.index - new_index = [old_index, np.arange(len(old_index))] + tsframe = self.tsframe.copy() + new_index = [tsframe.index, np.arange(len(tsframe.index))] tsframe.index = MultiIndex.from_arrays(new_index) with ensure_clean(self.ext) as path: - tsframe.to_excel(path, 'test1', index_label=['time', 'foo']) + tsframe.index.names = ['time', 'foo'] + tsframe.to_excel(path, 'test1', merge_cells=self.merge_cells) reader = ExcelFile(path) - recons = reader.parse('test1', index_col=[0, 1]) - - tm.assert_frame_equal(tsframe, recons, check_names=False) - self.assertEquals(recons.index.names, ('time', 'foo')) + recons = reader.parse('test1', + index_col=[0, 1], + has_index_names=self.merge_cells) - # infer index - tsframe.to_excel(path, 'test1') - reader = ExcelFile(path) - recons = reader.parse('test1') tm.assert_frame_equal(tsframe, recons) - - self.tsframe.index = old_index # needed if setUP becomes classmethod + self.assertEquals(recons.index.names, ('time', 'foo')) def test_to_excel_float_format(self): _skip_if_no_xlrd() - ext = self.ext - filename = '__tmp_to_excel_float_format__.' + ext + df = DataFrame([[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], index=['A', 'B'], columns=['X', 'Y', 'Z']) @@ -835,8 +856,13 @@ def test_to_excel_unicode_filename(self): # for maddr in mergedcells_addrs: # self.assertTrue(ws.cell(maddr).merged) # os.remove(filename) + def test_excel_010_hemstring(self): _skip_if_no_xlrd() + + if self.merge_cells: + raise nose.SkipTest('Skip tests for merged MI format.') + from pandas.util.testing import makeCustomDataframe as mkdf # ensure limited functionality in 0.10 # override of #2370 until sorted out in 0.11 @@ -844,7 +870,7 @@ def test_excel_010_hemstring(self): def roundtrip(df, header=True, parser_hdr=0): with ensure_clean(self.ext) as path: - df.to_excel(path, header=header) + df.to_excel(path, header=header, merge_cells=self.merge_cells) xf = pd.ExcelFile(path) res = xf.parse(xf.sheet_names[0], header=parser_hdr) return res @@ -917,7 +943,7 @@ def test_to_excel_styleconverter(self): "right": "thin", "bottom": "thin", "left": "thin"}, - "alignment": {"horizontal": "center"}} + "alignment": {"horizontal": "center", "vertical": "top"}} xlsx_style = _OpenpyxlWriter._convert_to_style(hstyle) self.assertTrue(xlsx_style.font.bold) @@ -931,6 +957,8 @@ def test_to_excel_styleconverter(self): xlsx_style.borders.left.border_style) self.assertEquals(openpyxl.style.Alignment.HORIZONTAL_CENTER, xlsx_style.alignment.horizontal) + self.assertEquals(openpyxl.style.Alignment.VERTICAL_TOP, + xlsx_style.alignment.vertical) class XlwtTests(ExcelWriterBase, unittest.TestCase): @@ -948,7 +976,8 @@ def test_to_excel_styleconverter(self): "right": "thin", "bottom": "thin", "left": "thin"}, - "alignment": {"horizontal": "center"}} + "alignment": {"horizontal": "center", "vertical": "top"}} + xls_style = _XlwtWriter._convert_to_style(hstyle) self.assertTrue(xls_style.font.bold) self.assertEquals(xlwt.Borders.THIN, xls_style.borders.top) @@ -956,6 +985,7 @@ def test_to_excel_styleconverter(self): self.assertEquals(xlwt.Borders.THIN, xls_style.borders.bottom) self.assertEquals(xlwt.Borders.THIN, xls_style.borders.left) self.assertEquals(xlwt.Alignment.HORZ_CENTER, xls_style.alignment.horz) + self.assertEquals(xlwt.Alignment.VERT_TOP, xls_style.alignment.vert) class XlsxWriterTests(ExcelWriterBase, unittest.TestCase): @@ -963,48 +993,38 @@ class XlsxWriterTests(ExcelWriterBase, unittest.TestCase): engine_name = 'xlsxwriter' check_skip = staticmethod(_skip_if_no_xlsxwriter) - # Override test from the Superclass to use assertAlmostEqual on the - # floating point values read back in from the output XlsxWriter file. - def test_roundtrip_indexlabels(self): - _skip_if_no_xlrd() - with ensure_clean(self.ext) as path: +class OpenpyxlTests_NoMerge(ExcelWriterBase, unittest.TestCase): + ext = '.xlsx' + engine_name = 'openpyxl' + check_skip = staticmethod(_skip_if_no_openpyxl) - self.frame['A'][:5] = nan + # Test < 0.13 non-merge behaviour for MultiIndex and Hierarchical Rows. + merge_cells = False - self.frame.to_excel(path, 'test1') - self.frame.to_excel(path, 'test1', cols=['A', 'B']) - self.frame.to_excel(path, 'test1', header=False) - self.frame.to_excel(path, 'test1', index=False) - # test index_label - frame = (DataFrame(np.random.randn(10, 2)) >= 0) - frame.to_excel(path, 'test1', index_label=['test']) - reader = ExcelFile(path) - recons = reader.parse('test1', index_col=0).astype(np.int64) - frame.index.names = ['test'] - self.assertEqual(frame.index.names, recons.index.names) +class XlwtTests_NoMerge(ExcelWriterBase, unittest.TestCase): + ext = '.xls' + engine_name = 'xlwt' + check_skip = staticmethod(_skip_if_no_xlwt) - frame = (DataFrame(np.random.randn(10, 2)) >= 0) - frame.to_excel( - path, 'test1', index_label=['test', 'dummy', 'dummy2']) - reader = ExcelFile(path) - recons = reader.parse('test1', index_col=0).astype(np.int64) - frame.index.names = ['test'] - self.assertEqual(frame.index.names, recons.index.names) + # Test < 0.13 non-merge behaviour for MultiIndex and Hierarchical Rows. + merge_cells = False - frame = (DataFrame(np.random.randn(10, 2)) >= 0) - frame.to_excel(path, 'test1', index_label='test') - reader = ExcelFile(path) - recons = reader.parse('test1', index_col=0).astype(np.int64) - frame.index.names = ['test'] - self.assertAlmostEqual(frame.index.names, recons.index.names) + +class XlsxWriterTests_NoMerge(ExcelWriterBase, unittest.TestCase): + ext = '.xlsx' + engine_name = 'xlsxwriter' + check_skip = staticmethod(_skip_if_no_xlsxwriter) + + # Test < 0.13 non-merge behaviour for MultiIndex and Hierarchical Rows. + merge_cells = False class ExcelWriterEngineTests(unittest.TestCase): def test_ExcelWriter_dispatch(self): with tm.assertRaisesRegexp(ValueError, 'No engine'): - writer = ExcelWriter('nothing') + ExcelWriter('nothing') _skip_if_no_openpyxl() writer = ExcelWriter('apple.xlsx') @@ -1046,7 +1066,6 @@ def check_called(func): func = lambda: df.to_excel('something.test') check_called(func) check_called(lambda: panel.to_excel('something.test')) - from pandas import set_option, get_option val = get_option('io.excel.xlsx.writer') set_option('io.excel.xlsx.writer', 'dummy') check_called(lambda: df.to_excel('something.xlsx'))
Allow optional formatting of MultiIndex and Hierarchical Rows as merged cells. closes #5254. Some notes on this PR: - The required xlsxwriter version was up-revved in `ci/requirements*.txt`to pick up a fix in that module that makes working with charts from pandas easier. - Added a comment to `release.rst`. - Modified `format.py` to format MultiIndex and Hierarchical Rows as merged cells in Excel. The old code path/option is still available. The new formatting must be explicitly invoked via the `merge_cells` option in `to_excel`: ``` python df.to_excel('file.xlsx', merge_cells=True) ``` - The `merge_cells` option can be renamed if required. During development I also used `multi_index` and `merge_range`. - Updated the API and docs in `frame.py` to reflect the new option. - Fixed openpyxl merge handling in `excel.py`. - Modified the `test_excel.py` test cases so that they could be used to test the existing `dot notation` or the new `merge_cells` options for MultiIndex and Hierarchical Rows handling. - Did some minor PEP8 formatting to the `test_excel.py`.
https://api.github.com/repos/pandas-dev/pandas/pulls/5423
2013-11-03T20:42:24Z
2013-11-06T01:42:23Z
2013-11-06T01:42:23Z
2014-06-16T22:40:15Z