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ENH: Add categorical support for Stata export
diff --git a/doc/source/io.rst b/doc/source/io.rst index 066a9af472c24..1d83e06a13567 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3626,12 +3626,18 @@ outside of this range, the data is cast to ``int16``. if ``int64`` values are larger than 2**53. .. warning:: + :class:`~pandas.io.stata.StataWriter`` and :func:`~pandas.core.frame.DataFrame.to_stata` only support fixed width strings containing up to 244 characters, a limitation imposed by the version 115 dta file format. Attempting to write *Stata* dta files with strings longer than 244 characters raises a ``ValueError``. +.. warning:: + + *Stata* data files only support text labels for categorical data. Exporting + data frames containing categorical data will convert non-string categorical values + to strings. .. _io.stata_reader: diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index 97cbecd4bb0e7..8b104d54ed778 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -41,6 +41,7 @@ API changes Enhancements ~~~~~~~~~~~~ +- Added ability to export Categorical data to Stata (:issue:`8633`). .. _whatsnew_0152.performance: diff --git a/pandas/io/stata.py b/pandas/io/stata.py index c2542594861c4..ab9d330b48988 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -15,13 +15,13 @@ import struct from dateutil.relativedelta import relativedelta from pandas.core.base import StringMixin +from pandas.core.categorical import Categorical from pandas.core.frame import DataFrame from pandas.core.series import Series -from pandas.core.categorical import Categorical import datetime from pandas import compat, to_timedelta, to_datetime, isnull, DatetimeIndex from pandas.compat import lrange, lmap, lzip, text_type, string_types, range, \ - zip + zip, BytesIO import pandas.core.common as com from pandas.io.common import get_filepath_or_buffer from pandas.lib import max_len_string_array, infer_dtype @@ -336,6 +336,15 @@ class PossiblePrecisionLoss(Warning): conversion range. This may result in a loss of precision in the saved data. """ +class ValueLabelTypeMismatch(Warning): + pass + +value_label_mismatch_doc = """ +Stata value labels (pandas categories) must be strings. Column {0} contains +non-string labels which will be converted to strings. Please check that the +Stata data file created has not lost information due to duplicate labels. +""" + class InvalidColumnName(Warning): pass @@ -425,6 +434,131 @@ def _cast_to_stata_types(data): return data +class StataValueLabel(object): + """ + Parse a categorical column and prepare formatted output + + Parameters + ----------- + value : int8, int16, int32, float32 or float64 + The Stata missing value code + + Attributes + ---------- + string : string + String representation of the Stata missing value + value : int8, int16, int32, float32 or float64 + The original encoded missing value + + Methods + ------- + generate_value_label + + """ + + def __init__(self, catarray): + + self.labname = catarray.name + + categories = catarray.cat.categories + self.value_labels = list(zip(np.arange(len(categories)), categories)) + self.value_labels.sort(key=lambda x: x[0]) + self.text_len = np.int32(0) + self.off = [] + self.val = [] + self.txt = [] + self.n = 0 + + # Compute lengths and setup lists of offsets and labels + for vl in self.value_labels: + category = vl[1] + if not isinstance(category, string_types): + category = str(category) + import warnings + warnings.warn(value_label_mismatch_doc.format(catarray.name), + ValueLabelTypeMismatch) + + self.off.append(self.text_len) + self.text_len += len(category) + 1 # +1 for the padding + self.val.append(vl[0]) + self.txt.append(category) + self.n += 1 + + if self.text_len > 32000: + raise ValueError('Stata value labels for a single variable must ' + 'have a combined length less than 32,000 ' + 'characters.') + + # Ensure int32 + self.off = np.array(self.off, dtype=np.int32) + self.val = np.array(self.val, dtype=np.int32) + + # Total length + self.len = 4 + 4 + 4 * self.n + 4 * self.n + self.text_len + + def _encode(self, s): + """ + Python 3 compatability shim + """ + if compat.PY3: + return s.encode(self._encoding) + else: + return s + + def generate_value_label(self, byteorder, encoding): + """ + Parameters + ---------- + byteorder : str + Byte order of the output + encoding : str + File encoding + + Returns + ------- + value_label : bytes + Bytes containing the formatted value label + """ + + self._encoding = encoding + bio = BytesIO() + null_string = '\x00' + null_byte = b'\x00' + + # len + bio.write(struct.pack(byteorder + 'i', self.len)) + + # labname + labname = self._encode(_pad_bytes(self.labname[:32], 33)) + bio.write(labname) + + # padding - 3 bytes + for i in range(3): + bio.write(struct.pack('c', null_byte)) + + # value_label_table + # n - int32 + bio.write(struct.pack(byteorder + 'i', self.n)) + + # textlen - int32 + bio.write(struct.pack(byteorder + 'i', self.text_len)) + + # off - int32 array (n elements) + for offset in self.off: + bio.write(struct.pack(byteorder + 'i', offset)) + + # val - int32 array (n elements) + for value in self.val: + bio.write(struct.pack(byteorder + 'i', value)) + + # txt - Text labels, null terminated + for text in self.txt: + bio.write(self._encode(text + null_string)) + + bio.seek(0) + return bio.read() + + class StataMissingValue(StringMixin): """ An observation's missing value. @@ -477,25 +611,31 @@ class StataMissingValue(StringMixin): for i in range(1, 27): MISSING_VALUES[i + b] = '.' + chr(96 + i) - base = b'\x00\x00\x00\x7f' + float32_base = b'\x00\x00\x00\x7f' increment = struct.unpack('<i', b'\x00\x08\x00\x00')[0] for i in range(27): - value = struct.unpack('<f', base)[0] + value = struct.unpack('<f', float32_base)[0] MISSING_VALUES[value] = '.' if i > 0: MISSING_VALUES[value] += chr(96 + i) int_value = struct.unpack('<i', struct.pack('<f', value))[0] + increment - base = struct.pack('<i', int_value) + float32_base = struct.pack('<i', int_value) - base = b'\x00\x00\x00\x00\x00\x00\xe0\x7f' + float64_base = b'\x00\x00\x00\x00\x00\x00\xe0\x7f' increment = struct.unpack('q', b'\x00\x00\x00\x00\x00\x01\x00\x00')[0] for i in range(27): - value = struct.unpack('<d', base)[0] + value = struct.unpack('<d', float64_base)[0] MISSING_VALUES[value] = '.' if i > 0: MISSING_VALUES[value] += chr(96 + i) int_value = struct.unpack('q', struct.pack('<d', value))[0] + increment - base = struct.pack('q', int_value) + float64_base = struct.pack('q', int_value) + + BASE_MISSING_VALUES = {'int8': 101, + 'int16': 32741, + 'int32': 2147483621, + 'float32': struct.unpack('<f', float32_base)[0], + 'float64': struct.unpack('<d', float64_base)[0]} def __init__(self, value): self._value = value @@ -518,6 +658,22 @@ def __eq__(self, other): return (isinstance(other, self.__class__) and self.string == other.string and self.value == other.value) + @classmethod + def get_base_missing_value(cls, dtype): + if dtype == np.int8: + value = cls.BASE_MISSING_VALUES['int8'] + elif dtype == np.int16: + value = cls.BASE_MISSING_VALUES['int16'] + elif dtype == np.int32: + value = cls.BASE_MISSING_VALUES['int32'] + elif dtype == np.float32: + value = cls.BASE_MISSING_VALUES['float32'] + elif dtype == np.float64: + value = cls.BASE_MISSING_VALUES['float64'] + else: + raise ValueError('Unsupported dtype') + return value + class StataParser(object): _default_encoding = 'cp1252' @@ -1111,10 +1267,10 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None, umissing, umissing_loc = np.unique(series[missing], return_inverse=True) replacement = Series(series, dtype=np.object) - for i, um in enumerate(umissing): + for j, um in enumerate(umissing): missing_value = StataMissingValue(um) - loc = missing_loc[umissing_loc == i] + loc = missing_loc[umissing_loc == j] replacement.iloc[loc] = missing_value else: # All replacements are identical dtype = series.dtype @@ -1390,6 +1546,45 @@ def _write(self, to_write): else: self._file.write(to_write) + def _prepare_categoricals(self, data): + """Check for categorigal columns, retain categorical information for + Stata file and convert categorical data to int""" + + is_cat = [com.is_categorical_dtype(data[col]) for col in data] + self._is_col_cat = is_cat + self._value_labels = [] + if not any(is_cat): + return data + + get_base_missing_value = StataMissingValue.get_base_missing_value + index = data.index + data_formatted = [] + for col, col_is_cat in zip(data, is_cat): + if col_is_cat: + self._value_labels.append(StataValueLabel(data[col])) + dtype = data[col].cat.codes.dtype + if dtype == np.int64: + raise ValueError('It is not possible to export int64-based ' + 'categorical data to Stata.') + values = data[col].cat.codes.values.copy() + + # Upcast if needed so that correct missing values can be set + if values.max() >= get_base_missing_value(dtype): + if dtype == np.int8: + dtype = np.int16 + elif dtype == np.int16: + dtype = np.int32 + else: + dtype = np.float64 + values = np.array(values, dtype=dtype) + + # Replace missing values with Stata missing value for type + values[values == -1] = get_base_missing_value(dtype) + data_formatted.append((col, values, index)) + + else: + data_formatted.append((col, data[col])) + return DataFrame.from_items(data_formatted) def _replace_nans(self, data): # return data @@ -1480,27 +1675,26 @@ def _check_column_names(self, data): def _prepare_pandas(self, data): #NOTE: we might need a different API / class for pandas objects so # we can set different semantics - handle this with a PR to pandas.io - class DataFrameRowIter(object): - def __init__(self, data): - self.data = data - - def __iter__(self): - for row in data.itertuples(): - # First element is index, so remove - yield row[1:] if self._write_index: data = data.reset_index() - # Check columns for compatibility with stata - data = _cast_to_stata_types(data) + # Ensure column names are strings data = self._check_column_names(data) + + # Check columns for compatibility with stata, upcast if necessary + data = _cast_to_stata_types(data) + # Replace NaNs with Stata missing values data = self._replace_nans(data) - self.datarows = DataFrameRowIter(data) + + # Convert categoricals to int data, and strip labels + data = self._prepare_categoricals(data) + self.nobs, self.nvar = data.shape self.data = data self.varlist = data.columns.tolist() + dtypes = data.dtypes if self._convert_dates is not None: self._convert_dates = _maybe_convert_to_int_keys( @@ -1515,6 +1709,7 @@ def __iter__(self): self.fmtlist = [] for col, dtype in dtypes.iteritems(): self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, data[col])) + # set the given format for the datetime cols if self._convert_dates is not None: for key in self._convert_dates: @@ -1529,8 +1724,14 @@ def write_file(self): self._write(_pad_bytes("", 5)) self._prepare_data() self._write_data() + self._write_value_labels() self._file.close() + def _write_value_labels(self): + for vl in self._value_labels: + self._file.write(vl.generate_value_label(self._byteorder, + self._encoding)) + def _write_header(self, data_label=None, time_stamp=None): byteorder = self._byteorder # ds_format - just use 114 @@ -1585,9 +1786,15 @@ def _write_descriptors(self, typlist=None, varlist=None, srtlist=None, self._write(_pad_bytes(fmt, 49)) # lbllist, 33*nvar, char array - #NOTE: this is where you could get fancy with pandas categorical type for i in range(nvar): - self._write(_pad_bytes("", 33)) + # Use variable name when categorical + if self._is_col_cat[i]: + name = self.varlist[i] + name = self._null_terminate(name, True) + name = _pad_bytes(name[:32], 33) + self._write(name) + else: # Default is empty label + self._write(_pad_bytes("", 33)) def _write_variable_labels(self, labels=None): nvar = self.nvar @@ -1624,9 +1831,6 @@ def _prepare_data(self): data_cols.append(data[col].values) dtype = np.dtype(dtype) - # 3. Convert to record array - - # data.to_records(index=False, convert_datetime64=False) if has_strings: self.data = np.fromiter(zip(*data_cols), dtype=dtype) else: diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 2cb7809166be5..d97feaea2658a 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -164,8 +164,9 @@ def test_read_dta2(self): parsed_117 = self.read_dta(self.dta2_117) # 113 is buggy due ot limits date format support in Stata # parsed_113 = self.read_dta(self.dta2_113) - tm.assert_equal( - len(w), 1) # should get a warning for that format. + + # should get a warning for that format. + tm.assert_equal(len(w), 1) # buggy test because of the NaT comparison on certain platforms # Format 113 test fails since it does not support tc and tC formats @@ -214,7 +215,7 @@ def test_read_dta4(self): 'labeled_with_missings', 'float_labelled']) # these are all categoricals - expected = pd.concat([ Series(pd.Categorical(value)) for col, value in compat.iteritems(expected)],axis=1) + expected = pd.concat([expected[col].astype('category') for col in expected], axis=1) tm.assert_frame_equal(parsed_113, expected) tm.assert_frame_equal(parsed_114, expected) @@ -744,6 +745,78 @@ def test_drop_column(self): columns = ['byte_', 'int_', 'long_', 'not_found'] read_stata(self.dta15_117, convert_dates=True, columns=columns) + def test_categorical_writing(self): + original = DataFrame.from_records( + [ + ["one", "ten", "one", "one", "one", 1], + ["two", "nine", "two", "two", "two", 2], + ["three", "eight", "three", "three", "three", 3], + ["four", "seven", 4, "four", "four", 4], + ["five", "six", 5, np.nan, "five", 5], + ["six", "five", 6, np.nan, "six", 6], + ["seven", "four", 7, np.nan, "seven", 7], + ["eight", "three", 8, np.nan, "eight", 8], + ["nine", "two", 9, np.nan, "nine", 9], + ["ten", "one", "ten", np.nan, "ten", 10] + ], + columns=['fully_labeled', 'fully_labeled2', 'incompletely_labeled', + 'labeled_with_missings', 'float_labelled', 'unlabeled']) + expected = original.copy() + + # these are all categoricals + original = pd.concat([original[col].astype('category') for col in original], axis=1) + + expected['incompletely_labeled'] = expected['incompletely_labeled'].apply(str) + expected['unlabeled'] = expected['unlabeled'].apply(str) + expected = pd.concat([expected[col].astype('category') for col in expected], axis=1) + expected.index.name = 'index' + + with tm.ensure_clean() as path: + with warnings.catch_warnings(record=True) as w: + # Silence warnings + original.to_stata(path) + written_and_read_again = self.read_dta(path) + tm.assert_frame_equal(written_and_read_again.set_index('index'), expected) + + + def test_categorical_warnings_and_errors(self): + # Warning for non-string labels + # Error for labels too long + original = pd.DataFrame.from_records( + [['a' * 10000], + ['b' * 10000], + ['c' * 10000], + ['d' * 10000]], + columns=['Too_long']) + + original = pd.concat([original[col].astype('category') for col in original], axis=1) + with tm.ensure_clean() as path: + tm.assertRaises(ValueError, original.to_stata, path) + + original = pd.DataFrame.from_records( + [['a'], + ['b'], + ['c'], + ['d'], + [1]], + columns=['Too_long']) + original = pd.concat([original[col].astype('category') for col in original], axis=1) + + with warnings.catch_warnings(record=True) as w: + original.to_stata(path) + tm.assert_equal(len(w), 1) # should get a warning for mixed content + + def test_categorical_with_stata_missing_values(self): + values = [['a' + str(i)] for i in range(120)] + values.append([np.nan]) + original = pd.DataFrame.from_records(values, columns=['many_labels']) + original = pd.concat([original[col].astype('category') for col in original], axis=1) + original.index.name = 'index' + with tm.ensure_clean() as path: + original.to_stata(path) + written_and_read_again = self.read_dta(path) + tm.assert_frame_equal(written_and_read_again.set_index('index'), original) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False)
Add support for exporting DataFrames containing categorical data. closes #8633 xref #7621
https://api.github.com/repos/pandas-dev/pandas/pulls/8767
2014-11-10T00:13:09Z
2014-11-13T11:15:40Z
2014-11-13T11:15:40Z
2014-11-13T16:51:41Z
BUG: fontsize parameter of plot only affects one axis.
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index b06342e8ce3c3..b36b79b54b125 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1230,7 +1230,7 @@ def test_subplots_timeseries(self): self._check_visible(ax.get_xticklabels(minor=True)) self._check_visible(ax.xaxis.get_label()) self._check_visible(ax.get_yticklabels()) - self._check_ticks_props(ax, xlabelsize=7, xrot=45) + self._check_ticks_props(ax, xlabelsize=7, xrot=45, ylabelsize=7) def test_subplots_layout(self): # GH 6667 @@ -1691,13 +1691,13 @@ def test_plot_bar(self): self._check_ticks_props(ax, xrot=90) ax = df.plot(kind='bar', rot=35, fontsize=10) - self._check_ticks_props(ax, xrot=35, xlabelsize=10) + self._check_ticks_props(ax, xrot=35, xlabelsize=10, ylabelsize=10) ax = _check_plot_works(df.plot, kind='barh') self._check_ticks_props(ax, yrot=0) ax = df.plot(kind='barh', rot=55, fontsize=11) - self._check_ticks_props(ax, yrot=55, ylabelsize=11) + self._check_ticks_props(ax, yrot=55, ylabelsize=11, xlabelsize=11) def _check_bar_alignment(self, df, kind='bar', stacked=False, subplots=False, align='center', @@ -2061,7 +2061,7 @@ def test_kde_df(self): self._check_ticks_props(ax, xrot=0) ax = df.plot(kind='kde', rot=20, fontsize=5) - self._check_ticks_props(ax, xrot=20, xlabelsize=5) + self._check_ticks_props(ax, xrot=20, xlabelsize=5, ylabelsize=5) axes = _check_plot_works(df.plot, kind='kde', subplots=True) self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index b9a96ee262101..c302458b9e0d4 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -2397,9 +2397,9 @@ def _plot(data, x=None, y=None, subplots=False, xlim : 2-tuple/list ylim : 2-tuple/list rot : int, default None - Rotation for ticks + Rotation for ticks (xticks for vertical, yticks for horizontal plots) fontsize : int, default None - Font size for ticks + Font size for xticks and yticks colormap : str or matplotlib colormap object, default None Colormap to select colors from. If string, load colormap with that name from matplotlib.
fixes #8765 ``` python import pandas as pd df = pd.DataFrame(np.random.randn(10, 9), index=range(10)) df.plot(fontsize=7) ``` ![fig_fonts_all_same_size](https://cloud.githubusercontent.com/assets/5957850/4969745/86e42902-686b-11e4-959c-e22b544e63e2.png)
https://api.github.com/repos/pandas-dev/pandas/pulls/8766
2014-11-09T23:54:51Z
2014-12-10T09:07:40Z
2014-12-10T09:07:40Z
2014-12-10T09:07:53Z
TST: Raise remote data error if no expiry dates are found (Options)
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index 66b839ed01a29..8f8220961b0f4 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -43,3 +43,4 @@ Experimental Bug Fixes ~~~~~~~~~ - Bug in ``groupby`` signatures that didn't include *args or **kwargs (:issue:`8733`). +- ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo (:issue:`8761`). \ No newline at end of file diff --git a/pandas/io/data.py b/pandas/io/data.py index 982755a854e36..d69343817a63f 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -1132,10 +1132,13 @@ def _get_expiry_dates_and_links(self): expiry_dates = [dt.datetime.strptime(element.text, "%B %d, %Y").date() for element in links] links = [element.attrib['data-selectbox-link'] for element in links] + + if len(expiry_dates) == 0: + raise RemoteDataError('Data not available') + expiry_links = dict(zip(expiry_dates, links)) self._expiry_links = expiry_links self._expiry_dates = expiry_dates - return expiry_dates, expiry_links def _parse_url(self, url):
Fixes #8761.
https://api.github.com/repos/pandas-dev/pandas/pulls/8763
2014-11-09T20:32:31Z
2014-11-09T22:04:09Z
2014-11-09T22:04:09Z
2014-11-10T01:18:40Z
Update tutorials.rst
diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index 421304bb89541..2c913f8911066 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -109,6 +109,21 @@ For more resources, please visit the main `repository <https://bitbucket.org/hro - Combining data from various sources +Practical data analysis with Python +----------------------------------- + +This `guide <http://wavedatalab.github.io/datawithpython>`_ is a comprehensive introduction to the data analysis process using the Python data ecosystem and an interesting open dataset. +There are four sections covering selected topics as follows: + +- `Munging Data <http://wavedatalab.github.io/datawithpython/munge.html>`_ + +- `Aggregating Data <http://wavedatalab.github.io/datawithpython/aggregate.html>`_ + +- `Visualizing Data <http://wavedatalab.github.io/datawithpython/visualize.html>`_ + +- `Time Series <http://wavedatalab.github.io/datawithpython/timeseries.html>`_ + + Excel charts with pandas, vincent and xlsxwriter ------------------------------------------------
Add a pandas tutorial to the tutorial links.
https://api.github.com/repos/pandas-dev/pandas/pulls/8759
2014-11-08T22:57:28Z
2014-11-09T14:01:12Z
2014-11-09T14:01:12Z
2014-11-09T21:35:09Z
BUG: Fix groupby methods to include *args and **kwds if applicable.
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index e6843f4a71f72..66b839ed01a29 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -42,3 +42,4 @@ Experimental Bug Fixes ~~~~~~~~~ +- Bug in ``groupby`` signatures that didn't include *args or **kwargs (:issue:`8733`). diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index b0b521141c92c..ef3fc03fc8d22 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -4415,6 +4415,26 @@ def test_regression_whitelist_methods(self) : expected = getattr(frame,op)(level=level,axis=axis) assert_frame_equal(result, expected) + def test_regression_kwargs_whitelist_methods(self): + # GH8733 + + index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], + ['one', 'two', 'three']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + raw_frame = DataFrame(np.random.randn(10, 3), index=index, + columns=Index(['A', 'B', 'C'], name='exp')) + + grouped = raw_frame.groupby(level=0, axis=1) + grouped.all(test_kwargs='Test kwargs') + grouped.any(test_kwargs='Test kwargs') + grouped.cumcount(test_kwargs='Test kwargs') + grouped.mad(test_kwargs='Test kwargs') + grouped.cummin(test_kwargs='Test kwargs') + grouped.skew(test_kwargs='Test kwargs') + grouped.cumprod(test_kwargs='Test kwargs') + def test_groupby_blacklist(self): from string import ascii_lowercase letters = np.array(list(ascii_lowercase)) @@ -4460,6 +4480,9 @@ def test_series_groupby_plotting_nominally_works(self): tm.close() height.groupby(gender).hist() tm.close() + #Regression test for GH8733 + height.groupby(gender).plot(alpha=0.5) + tm.close() def test_plotting_with_float_index_works(self): _skip_if_mpl_not_installed() diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py index e88bb906dc966..d839437a6fe33 100644 --- a/pandas/util/decorators.py +++ b/pandas/util/decorators.py @@ -282,5 +282,9 @@ def make_signature(func) : args = [] for i, (var, default) in enumerate(zip(spec.args, defaults)) : args.append(var if default=='' else var+'='+repr(default)) + if spec.varargs: + args.append('*' + spec.varargs) + if spec.keywords: + args.append('**' + spec.keywords) return args, spec.args
Fixes #8733 (and any other bug that would result from trying to send an arbitrary keyword to a groupby method).
https://api.github.com/repos/pandas-dev/pandas/pulls/8758
2014-11-08T20:14:45Z
2014-11-08T23:18:50Z
2014-11-08T23:18:50Z
2014-11-10T01:18:32Z
DOC: remove unused matplotlib directives from conf.py
diff --git a/doc/source/conf.py b/doc/source/conf.py index cc7f0e54b0a20..dd225dba7079a 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -44,12 +44,9 @@ 'ipython_sphinxext.ipython_directive', 'ipython_sphinxext.ipython_console_highlighting', 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.pngmath', 'sphinx.ext.ifconfig', - 'matplotlib.sphinxext.only_directives', - 'matplotlib.sphinxext.plot_directive', ]
I don't think we use the matplotlib directives anywhere (and the todo was twice in the list), so removed them from conf.py
https://api.github.com/repos/pandas-dev/pandas/pulls/8756
2014-11-08T17:31:27Z
2014-11-13T09:19:40Z
2014-11-13T09:19:40Z
2014-11-13T09:19:40Z
DOC: clean-up v0.15.1 whatsnew file
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 2ef12825f07aa..bd878db08a3ed 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -16,29 +16,32 @@ users upgrade to this version. API changes ~~~~~~~~~~~ -- Represent ``MultiIndex`` labels with a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the *reported* memory usage was incorrect as it didn't show the usage for the memory occupied by the underling data array. (:issue:`8456`) +- ``s.dt.hour`` and other ``.dt`` accessors will now return ``np.nan`` for missing values (rather than previously -1), (:issue:`8689`) .. ipython:: python - dfi = DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']) + s = Series(date_range('20130101',periods=5,freq='D')) + s.iloc[2] = np.nan + s previous behavior: .. code-block:: python - # this was underreported in prior versions - In [1]: dfi.memory_usage(index=True) - Out[1]: - Index 8000 # took about 24008 bytes in < 0.15.1 - A 8000 + In [6]: s.dt.hour + Out[6]: + 0 0 + 1 0 + 2 -1 + 3 0 + 4 0 dtype: int64 - current behavior: .. ipython:: python - dfi.memory_usage(index=True) + s.dt.hour - ``groupby`` with ``as_index=False`` will not add erroneous extra columns to result (:issue:`8582`): @@ -95,56 +98,7 @@ API changes gr.apply(sum) -- ``concat`` permits a wider variety of iterables of pandas objects to be - passed as the first parameter (:issue:`8645`): - - .. ipython:: python - - from collections import deque - df1 = pd.DataFrame([1, 2, 3]) - df2 = pd.DataFrame([4, 5, 6]) - - previous behavior: - - .. code-block:: python - - In [7]: pd.concat(deque((df1, df2))) - TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque" - - current behavior: - - .. ipython:: python - - pd.concat(deque((df1, df2))) - -- ``s.dt.hour`` and other ``.dt`` accessors will now return ``np.nan`` for missing values (rather than previously -1), (:issue:`8689`) - - .. ipython:: python - - s = Series(date_range('20130101',periods=5,freq='D')) - s.iloc[2] = np.nan - s - - previous behavior: - - .. code-block:: python - - In [6]: s.dt.hour - Out[6]: - 0 0 - 1 0 - 2 -1 - 3 0 - 4 0 - dtype: int64 - - current behavior: - - .. ipython:: python - - s.dt.hour - -- support for slicing with monotonic decreasing indexes, even if ``start`` or ``stop`` is +- Support for slicing with monotonic decreasing indexes, even if ``start`` or ``stop`` is not found in the index (:issue:`7860`): .. ipython:: python @@ -165,14 +119,14 @@ API changes s.loc[3.5:1.5] -- added Index properties `is_monotonic_increasing` and `is_monotonic_decreasing` (:issue:`8680`). +- ``io.data.Options`` has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`), (:issue:`8741`) -.. note:: io.data.Options has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`), (:issue:`8741`) + .. note:: - As a result of a change in Yahoo's option page layout, when an expiry date is given, - ``Options`` methods now return data for a single expiry date. Previously, methods returned all - data for the selected month. + As a result of a change in Yahoo's option page layout, when an expiry date is given, + ``Options`` methods now return data for a single expiry date. Previously, methods returned all + data for the selected month. The ``month`` and ``year`` parameters have been undeprecated and can be used to get all options data for a given month. @@ -185,11 +139,11 @@ API changes New features: - The expiry parameter can now be a single date or a list-like object containing dates. + - The expiry parameter can now be a single date or a list-like object containing dates. - A new property ``expiry_dates`` was added, which returns all available expiry dates. + - A new property ``expiry_dates`` was added, which returns all available expiry dates. - current behavior: + Current behavior: .. ipython:: python @@ -215,16 +169,78 @@ API changes Enhancements ~~~~~~~~~~~~ +- ``concat`` permits a wider variety of iterables of pandas objects to be + passed as the first parameter (:issue:`8645`): + + .. ipython:: python + + from collections import deque + df1 = pd.DataFrame([1, 2, 3]) + df2 = pd.DataFrame([4, 5, 6]) + + previous behavior: + + .. code-block:: python + + In [7]: pd.concat(deque((df1, df2))) + TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque" + + current behavior: + + .. ipython:: python + + pd.concat(deque((df1, df2))) + +- Represent ``MultiIndex`` labels with a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the *reported* memory usage was incorrect as it didn't show the usage for the memory occupied by the underling data array. (:issue:`8456`) + + .. ipython:: python + + dfi = DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']) + + previous behavior: + + .. code-block:: python + + # this was underreported in prior versions + In [1]: dfi.memory_usage(index=True) + Out[1]: + Index 8000 # took about 24008 bytes in < 0.15.1 + A 8000 + dtype: int64 + + + current behavior: + + .. ipython:: python + + dfi.memory_usage(index=True) + +- Added Index properties `is_monotonic_increasing` and `is_monotonic_decreasing` (:issue:`8680`). + - Added option to select columns when importing Stata files (:issue:`7935`) + - Qualify memory usage in ``DataFrame.info()`` by adding ``+`` if it is a lower bound (:issue:`8578`) + - Raise errors in certain aggregation cases where an argument such as ``numeric_only`` is not handled (:issue:`8592`). +- Added support for 3-character ISO and non-standard country codes in :func:`io.wb.download()` (:issue:`8482`) + +- :ref:`World Bank data requests <remote_data.wb>` now will warn/raise based + on an ``errors`` argument, as well as a list of hard-coded country codes and + the World Bank's JSON response. In prior versions, the error messages + didn't look at the World Bank's JSON response. Problem-inducing input were + simply dropped prior to the request. The issue was that many good countries + were cropped in the hard-coded approach. All countries will work now, but + some bad countries will raise exceptions because some edge cases break the + entire response. (:issue:`8482`) -- Added support for 3-character ISO and non-standard country codes in :func:``io.wb.download()`` (:issue:`8482`) -- :ref:`World Bank data requests <remote_data.wb>` now will warn/raise based on an ``errors`` argument, as well as a list of hard-coded country codes and the World Bank's JSON response. In prior versions, the error messages didn't look at the World Bank's JSON response. Problem-inducing input were simply dropped prior to the request. The issue was that many good countries were cropped in the hard-coded approach. All countries will work now, but some bad countries will raise exceptions because some edge cases break the entire response. (:issue:`8482`) - Added option to ``Series.str.split()`` to return a ``DataFrame`` rather than a ``Series`` (:issue:`8428`) + - Added option to ``df.info(null_counts=None|True|False)`` to override the default display options and force showing of the null-counts (:issue:`8701`) + +.. _whatsnew_0151.bug_fixes: + Bug Fixes ~~~~~~~~~ @@ -243,48 +259,19 @@ Bug Fixes - Compat issue is ``DataFrame.dtypes`` when ``options.mode.use_inf_as_null`` is True (:issue:`8722`) - Bug in ``read_csv``, ``dialect`` parameter would not take a string (:issue: `8703`) - Bug in slicing a multi-index level with an empty-list (:issue:`8737`) - - - - - - Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) - Bug in setitem with empty indexer and unwanted coercion of dtypes (:issue:`8669`) - - - - - - - - Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, e.g. datetime64) (:issue:`8607`) - - - Bug when doing label based indexing with integers not found in the index for non-unique but monotonic indexes (:issue:`8680`). - Bug when indexing a Float64Index with ``np.nan`` on numpy 1.7 (:issue:`8980`). - - - - - - - - - - - Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`) - Bug in ``GroupBy`` where a name conflict between the grouper and columns would break ``groupby`` operations (:issue:`7115`, :issue:`8112`) - - - - Fixed a bug where plotting a column ``y`` and specifying a label would mutate the index name of the original DataFrame (:issue:`8494`) - Fix regression in plotting of a DatetimeIndex directly with matplotlib (:issue:`8614`). - - Bug in ``date_range`` where partially-specified dates would incorporate current date (:issue:`6961`) - - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue:`8702`) - - Bug where ``DataReader``'s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (:issue:`8494`) - Bug in ``get_quote_yahoo`` that wouldn't allow non-float return values (:issue:`5229`). +
@jreback fixed the link to the bug fixes and reorderd it a bit (this is already included in the online docs)
https://api.github.com/repos/pandas-dev/pandas/pulls/8755
2014-11-08T17:29:35Z
2014-11-09T12:13:49Z
2014-11-09T12:13:49Z
2014-11-09T12:13:49Z
API: allow negative steps for label-based indexing
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index 61d18da45e5f0..eb2446d2a50d3 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -70,7 +70,30 @@ Bug Fixes - ``Timedelta`` kwargs may now be numpy ints and floats (:issue:`8757`). - ``sql_schema`` now generates dialect appropriate ``CREATE TABLE`` statements (:issue:`8697`) - ``slice`` string method now takes step into account (:issue:`8754`) +- Fix negative step support for label-based slices (:issue:`8753`) + Old behavior: + + .. code-block:: python + + In [1]: s = pd.Series(np.arange(3), ['a', 'b', 'c']) + Out[1]: + a 0 + b 1 + c 2 + dtype: int64 + + In [2]: s.loc['c':'a':-1] + Out[2]: + c 2 + dtype: int64 + + New behavior: + + .. ipython:: python + + s = pd.Series(np.arange(3), ['a', 'b', 'c']) + s.loc['c':'a':-1] diff --git a/pandas/core/index.py b/pandas/core/index.py index 6702a21167850..3f0b45ae10988 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -1959,23 +1959,99 @@ def slice_indexer(self, start=None, end=None, step=None): ----- This function assumes that the data is sorted, so use at your own peril """ - start_slice, end_slice = self.slice_locs(start, end) + start_slice, end_slice = self.slice_locs(start, end, step=step) # return a slice - if np.isscalar(start_slice) and np.isscalar(end_slice): + if not lib.isscalar(start_slice): + raise AssertionError("Start slice bound is non-scalar") + if not lib.isscalar(end_slice): + raise AssertionError("End slice bound is non-scalar") - # degenerate cases - if start is None and end is None: - return slice(None, None, step) + return slice(start_slice, end_slice, step) - return slice(start_slice, end_slice, step) + def _maybe_cast_slice_bound(self, label, side): + """ + This function should be overloaded in subclasses that allow non-trivial + casting on label-slice bounds, e.g. datetime-like indices allowing + strings containing formatted datetimes. - # loc indexers - return (Index(start_slice) & Index(end_slice)).values + Parameters + ---------- + label : object + side : {'left', 'right'} + + Notes + ----- + Value of `side` parameter should be validated in caller. - def slice_locs(self, start=None, end=None): """ - For an ordered Index, compute the slice locations for input labels + return label + + def get_slice_bound(self, label, side): + """ + Calculate slice bound that corresponds to given label. + + Returns leftmost (one-past-the-rightmost if ``side=='right'``) position + of given label. + + Parameters + ---------- + label : object + side : {'left', 'right'} + + """ + if side not in ('left', 'right'): + raise ValueError( + "Invalid value for side kwarg," + " must be either 'left' or 'right': %s" % (side,)) + + original_label = label + # For datetime indices label may be a string that has to be converted + # to datetime boundary according to its resolution. + label = self._maybe_cast_slice_bound(label, side) + + try: + slc = self.get_loc(label) + except KeyError: + if self.is_monotonic_increasing: + return self.searchsorted(label, side=side) + elif self.is_monotonic_decreasing: + # np.searchsorted expects ascending sort order, have to reverse + # everything for it to work (element ordering, search side and + # resulting value). + pos = self[::-1].searchsorted( + label, side='right' if side == 'left' else 'right') + return len(self) - pos + + # In all other cases, just re-raise the KeyError + raise + + if isinstance(slc, np.ndarray): + # get_loc may return a boolean array or an array of indices, which + # is OK as long as they are representable by a slice. + if com.is_bool_dtype(slc): + slc = lib.maybe_booleans_to_slice(slc.view('u1')) + else: + slc = lib.maybe_indices_to_slice(slc.astype('i8')) + if isinstance(slc, np.ndarray): + raise KeyError( + "Cannot get %s slice bound for non-unique label:" + " %r" % (side, original_label)) + + if isinstance(slc, slice): + if side == 'left': + return slc.start + else: + return slc.stop + else: + if side == 'right': + return slc + 1 + else: + return slc + + def slice_locs(self, start=None, end=None, step=None): + """ + Compute slice locations for input labels. Parameters ---------- @@ -1986,51 +2062,51 @@ def slice_locs(self, start=None, end=None): Returns ------- - (start, end) : (int, int) + start, end : int - Notes - ----- - This function assumes that the data is sorted, so use at your own peril """ + inc = (step is None or step >= 0) - is_unique = self.is_unique - - def _get_slice(starting_value, offset, search_side, slice_property, - search_value): - if search_value is None: - return starting_value + if not inc: + # If it's a reverse slice, temporarily swap bounds. + start, end = end, start - try: - slc = self.get_loc(search_value) - - if not is_unique: - - # get_loc will return a boolean array for non_uniques - # if we are not monotonic - if isinstance(slc, (np.ndarray, Index)): - raise KeyError("cannot peform a slice operation " - "on a non-unique non-monotonic index") - - if isinstance(slc, slice): - slc = getattr(slc, slice_property) - else: - slc += offset + start_slice = None + if start is not None: + start_slice = self.get_slice_bound(start, 'left') + if start_slice is None: + start_slice = 0 - except KeyError: - if self.is_monotonic_increasing: - slc = self.searchsorted(search_value, side=search_side) - elif self.is_monotonic_decreasing: - search_side = 'right' if search_side == 'left' else 'left' - slc = len(self) - self[::-1].searchsorted(search_value, - side=search_side) - else: - raise - return slc + end_slice = None + if end is not None: + end_slice = self.get_slice_bound(end, 'right') + if end_slice is None: + end_slice = len(self) - start_slice = _get_slice(0, offset=0, search_side='left', - slice_property='start', search_value=start) - end_slice = _get_slice(len(self), offset=1, search_side='right', - slice_property='stop', search_value=end) + if not inc: + # Bounds at this moment are swapped, swap them back and shift by 1. + # + # slice_locs('B', 'A', step=-1): s='B', e='A' + # + # s='A' e='B' + # AFTER SWAP: | | + # v ------------------> V + # ----------------------------------- + # | | |A|A|A|A| | | | | |B|B| | | | | + # ----------------------------------- + # ^ <------------------ ^ + # SHOULD BE: | | + # end=s-1 start=e-1 + # + end_slice, start_slice = start_slice - 1, end_slice - 1 + + # i == -1 triggers ``len(self) + i`` selection that points to the + # last element, not before-the-first one, subtracting len(self) + # compensates that. + if end_slice == -1: + end_slice -= len(self) + if start_slice == -1: + start_slice -= len(self) return start_slice, end_slice @@ -3887,7 +3963,12 @@ def _tuple_index(self): """ return Index(self.values) - def slice_locs(self, start=None, end=None, strict=False): + def get_slice_bound(self, label, side): + if not isinstance(label, tuple): + label = label, + return self._partial_tup_index(label, side=side) + + def slice_locs(self, start=None, end=None, step=None): """ For an ordered MultiIndex, compute the slice locations for input labels. They can be tuples representing partial levels, e.g. for a @@ -3900,7 +3981,8 @@ def slice_locs(self, start=None, end=None, strict=False): If None, defaults to the beginning end : label or tuple If None, defaults to the end - strict : boolean, + step : int or None + Slice step Returns ------- @@ -3910,21 +3992,9 @@ def slice_locs(self, start=None, end=None, strict=False): ----- This function assumes that the data is sorted by the first level """ - if start is None: - start_slice = 0 - else: - if not isinstance(start, tuple): - start = start, - start_slice = self._partial_tup_index(start, side='left') - - if end is None: - end_slice = len(self) - else: - if not isinstance(end, tuple): - end = end, - end_slice = self._partial_tup_index(end, side='right') - - return start_slice, end_slice + # This function adds nothing to its parent implementation (the magic + # happens in get_slice_bound method), but it adds meaningful doc. + return super(MultiIndex, self).slice_locs(start, end, step) def _partial_tup_index(self, tup, side='left'): if len(tup) > self.lexsort_depth: diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index cca8324b42b93..b7a18da3924c8 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -910,8 +910,34 @@ def test_slice_locs_na(self): self.assertEqual(idx.slice_locs(1), (1, 3)) self.assertEqual(idx.slice_locs(np.nan), (0, 3)) - idx = Index([np.nan, np.nan, 1, 2]) - self.assertRaises(KeyError, idx.slice_locs, np.nan) + idx = Index([0, np.nan, np.nan, 1, 2]) + self.assertEqual(idx.slice_locs(np.nan), (1, 5)) + + def test_slice_locs_negative_step(self): + idx = Index(list('bcdxy')) + + SLC = pd.IndexSlice + + def check_slice(in_slice, expected): + s_start, s_stop = idx.slice_locs(in_slice.start, in_slice.stop, + in_slice.step) + result = idx[s_start:s_stop:in_slice.step] + expected = pd.Index(list(expected)) + self.assertTrue(result.equals(expected)) + + for in_slice, expected in [ + (SLC[::-1], 'yxdcb'), (SLC['b':'y':-1], ''), + (SLC['b'::-1], 'b'), (SLC[:'b':-1], 'yxdcb'), + (SLC[:'y':-1], 'y'), (SLC['y'::-1], 'yxdcb'), + (SLC['y'::-4], 'yb'), + # absent labels + (SLC[:'a':-1], 'yxdcb'), (SLC[:'a':-2], 'ydb'), + (SLC['z'::-1], 'yxdcb'), (SLC['z'::-3], 'yc'), + (SLC['m'::-1], 'dcb'), (SLC[:'m':-1], 'yx'), + (SLC['a':'a':-1], ''), (SLC['z':'z':-1], ''), + (SLC['m':'m':-1], '') + ]: + check_slice(in_slice, expected) def test_drop(self): n = len(self.strIndex) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 66307d58b2f27..76be2e64de8d0 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -4141,6 +4141,64 @@ def run_tests(df, rhs, right): run_tests(df, rhs, right) + def test_str_label_slicing_with_negative_step(self): + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + assert_series_equal(s.loc[l_slc], s.iloc[i_slc]) + + if not idx.is_integer: + # For integer indices, ix and plain getitem are position-based. + assert_series_equal(s[l_slc], s.iloc[i_slc]) + assert_series_equal(s.ix[l_slc], s.iloc[i_slc]) + + for idx in [_mklbl('A', 20), np.arange(20) + 100, + np.linspace(100, 150, 20)]: + idx = Index(idx) + s = Series(np.arange(20), index=idx) + assert_slices_equivalent(SLC[idx[9]::-1], SLC[9::-1]) + assert_slices_equivalent(SLC[:idx[9]:-1], SLC[:8:-1]) + assert_slices_equivalent(SLC[idx[13]:idx[9]:-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC[idx[9]:idx[13]:-1], SLC[:0]) + + def test_multiindex_label_slicing_with_negative_step(self): + s = Series(np.arange(20), + MultiIndex.from_product([list('abcde'), np.arange(4)])) + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + assert_series_equal(s.loc[l_slc], s.iloc[i_slc]) + assert_series_equal(s[l_slc], s.iloc[i_slc]) + assert_series_equal(s.ix[l_slc], s.iloc[i_slc]) + + assert_slices_equivalent(SLC[::-1], SLC[::-1]) + + assert_slices_equivalent(SLC['d'::-1], SLC[15::-1]) + assert_slices_equivalent(SLC[('d',)::-1], SLC[15::-1]) + + assert_slices_equivalent(SLC[:'d':-1], SLC[:11:-1]) + assert_slices_equivalent(SLC[:('d',):-1], SLC[:11:-1]) + + assert_slices_equivalent(SLC['d':'b':-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC[('d',):'b':-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC['d':('b',):-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC[('d',):('b',):-1], SLC[15:3:-1]) + assert_slices_equivalent(SLC['b':'d':-1], SLC[:0]) + + assert_slices_equivalent(SLC[('c', 2)::-1], SLC[10::-1]) + assert_slices_equivalent(SLC[:('c', 2):-1], SLC[:9:-1]) + assert_slices_equivalent(SLC[('e', 0):('c', 2):-1], SLC[16:9:-1]) + + def test_slice_with_zero_step_raises(self): + s = Series(np.arange(20), index=_mklbl('A', 20)) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: s[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: s.loc[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: s.ix[::0]) + + class TestSeriesNoneCoercion(tm.TestCase): EXPECTED_RESULTS = [ # For numeric series, we should coerce to NaN. diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 0a446919e95d2..d47544149c381 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -114,52 +114,6 @@ def take(self, indices, axis=0): return self[maybe_slice] return super(DatetimeIndexOpsMixin, self).take(indices, axis) - def slice_locs(self, start=None, end=None): - """ - Index.slice_locs, customized to handle partial ISO-8601 string slicing - """ - if isinstance(start, compat.string_types) or isinstance(end, compat.string_types): - - if self.is_monotonic: - try: - if start: - start_loc = self._get_string_slice(start).start - else: - start_loc = 0 - - if end: - end_loc = self._get_string_slice(end).stop - else: - end_loc = len(self) - - return start_loc, end_loc - except KeyError: - pass - - else: - # can't use a slice indexer because we are not sorted! - # so create an indexer directly - try: - if start: - start_loc = self._get_string_slice(start, - use_rhs=False) - else: - start_loc = np.arange(len(self)) - - if end: - end_loc = self._get_string_slice(end, use_lhs=False) - else: - end_loc = np.arange(len(self)) - - return start_loc, end_loc - except KeyError: - pass - - if isinstance(start, time) or isinstance(end, time): - raise KeyError('Cannot use slice_locs with time slice keys') - - return Index.slice_locs(self, start, end) - def get_duplicates(self): values = Index.get_duplicates(self) return self._simple_new(values) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index bf99de902188f..202e30cc2eb5e 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1092,51 +1092,83 @@ def intersection(self, other): left_chunk = left.values[lslice] return self._shallow_copy(left_chunk) - def _partial_date_slice(self, reso, parsed, use_lhs=True, use_rhs=True): + def _parsed_string_to_bounds(self, reso, parsed): + """ + Calculate datetime bounds for parsed time string and its resolution. - is_monotonic = self.is_monotonic + Parameters + ---------- + reso : Resolution + Resolution provided by parsed string. + parsed : datetime + Datetime from parsed string. + Returns + ------- + lower, upper: pd.Timestamp + + """ + is_monotonic = self.is_monotonic if reso == 'year': - t1 = Timestamp(datetime(parsed.year, 1, 1), tz=self.tz) - t2 = Timestamp(datetime(parsed.year, 12, 31, 23, 59, 59, 999999), tz=self.tz) + return (Timestamp(datetime(parsed.year, 1, 1), tz=self.tz), + Timestamp(datetime(parsed.year, 12, 31, 23, 59, 59, 999999), tz=self.tz)) elif reso == 'month': d = tslib.monthrange(parsed.year, parsed.month)[1] - t1 = Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz) - t2 = Timestamp(datetime(parsed.year, parsed.month, d, 23, 59, 59, 999999), tz=self.tz) + return (Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz), + Timestamp(datetime(parsed.year, parsed.month, d, 23, 59, 59, 999999), tz=self.tz)) elif reso == 'quarter': qe = (((parsed.month - 1) + 2) % 12) + 1 # two months ahead d = tslib.monthrange(parsed.year, qe)[1] # at end of month - t1 = Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz) - t2 = Timestamp(datetime(parsed.year, qe, d, 23, 59, 59, 999999), tz=self.tz) - elif (reso == 'day' and (self._resolution < Resolution.RESO_DAY or not is_monotonic)): + return (Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz), + Timestamp(datetime(parsed.year, qe, d, 23, 59, 59, 999999), tz=self.tz)) + elif reso == 'day': st = datetime(parsed.year, parsed.month, parsed.day) - t1 = Timestamp(st, tz=self.tz) - t2 = st + offsets.Day() - t2 = Timestamp(Timestamp(t2, tz=self.tz).value - 1) - elif (reso == 'hour' and ( - self._resolution < Resolution.RESO_HR or not is_monotonic)): + return (Timestamp(st, tz=self.tz), + Timestamp(Timestamp(st + offsets.Day(), tz=self.tz).value - 1)) + elif reso == 'hour': st = datetime(parsed.year, parsed.month, parsed.day, hour=parsed.hour) - t1 = Timestamp(st, tz=self.tz) - t2 = Timestamp(Timestamp(st + offsets.Hour(), - tz=self.tz).value - 1) - elif (reso == 'minute' and ( - self._resolution < Resolution.RESO_MIN or not is_monotonic)): + return (Timestamp(st, tz=self.tz), + Timestamp(Timestamp(st + offsets.Hour(), + tz=self.tz).value - 1)) + elif reso == 'minute': st = datetime(parsed.year, parsed.month, parsed.day, hour=parsed.hour, minute=parsed.minute) - t1 = Timestamp(st, tz=self.tz) - t2 = Timestamp(Timestamp(st + offsets.Minute(), - tz=self.tz).value - 1) - elif (reso == 'second' and ( - self._resolution == Resolution.RESO_SEC or not is_monotonic)): + return (Timestamp(st, tz=self.tz), + Timestamp(Timestamp(st + offsets.Minute(), + tz=self.tz).value - 1)) + elif reso == 'second': st = datetime(parsed.year, parsed.month, parsed.day, hour=parsed.hour, minute=parsed.minute, second=parsed.second) - t1 = Timestamp(st, tz=self.tz) - t2 = Timestamp(Timestamp(st + offsets.Second(), - tz=self.tz).value - 1) + return (Timestamp(st, tz=self.tz), + Timestamp(Timestamp(st + offsets.Second(), + tz=self.tz).value - 1)) + elif reso == 'microsecond': + st = datetime(parsed.year, parsed.month, parsed.day, + parsed.hour, parsed.minute, parsed.second, + parsed.microsecond) + return (Timestamp(st, tz=self.tz), Timestamp(st, tz=self.tz)) else: raise KeyError + def _partial_date_slice(self, reso, parsed, use_lhs=True, use_rhs=True): + is_monotonic = self.is_monotonic + if ((reso in ['day', 'hour', 'minute'] and + not (self._resolution < Resolution.get_reso(reso) or + not is_monotonic)) or + (reso == 'second' and + not (self._resolution <= Resolution.RESO_SEC or + not is_monotonic))): + # These resolution/monotonicity validations came from GH3931, + # GH3452 and GH2369. + raise KeyError + + if reso == 'microsecond': + # _partial_date_slice doesn't allow microsecond resolution, but + # _parsed_string_to_bounds allows it. + raise KeyError + + t1, t2 = self._parsed_string_to_bounds(reso, parsed) stamps = self.asi8 if is_monotonic: @@ -1235,6 +1267,34 @@ def get_loc(self, key): except (KeyError, ValueError): raise KeyError(key) + def _maybe_cast_slice_bound(self, label, side): + """ + If label is a string, cast it to datetime according to resolution. + + Parameters + ---------- + label : object + side : {'left', 'right'} + + Notes + ----- + Value of `side` parameter should be validated in caller. + + """ + if isinstance(label, float): + raise TypeError('Cannot index datetime64 with float keys') + if isinstance(label, time): + raise KeyError('Cannot index datetime64 with time keys') + + if isinstance(label, compat.string_types): + freq = getattr(self, 'freqstr', + getattr(self, 'inferred_freq', None)) + _, parsed, reso = parse_time_string(label, freq) + bounds = self._parsed_string_to_bounds(reso, parsed) + return bounds[0 if side == 'left' else 1] + else: + return label + def _get_string_slice(self, key, use_lhs=True, use_rhs=True): freq = getattr(self, 'freqstr', getattr(self, 'inferred_freq', None)) @@ -1245,8 +1305,21 @@ def _get_string_slice(self, key, use_lhs=True, use_rhs=True): def slice_indexer(self, start=None, end=None, step=None): """ - Index.slice_indexer, customized to handle time slicing + Return indexer for specified label slice. + Index.slice_indexer, customized to handle time slicing. + + In addition to functionality provided by Index.slice_indexer, does the + following: + + - if both `start` and `end` are instances of `datetime.time`, it + invokes `indexer_between_time` + - if `start` and `end` are both either string or None perform + value-based selection in non-monotonic cases. + """ + # For historical reasons DatetimeIndex supports slices between two + # instances of datetime.time as if it were applying a slice mask to + # an array of (self.hour, self.minute, self.seconds, self.microsecond). if isinstance(start, time) and isinstance(end, time): if step is not None and step != 1: raise ValueError('Must have step size of 1 with time slices') @@ -1255,10 +1328,30 @@ def slice_indexer(self, start=None, end=None, step=None): if isinstance(start, time) or isinstance(end, time): raise KeyError('Cannot mix time and non-time slice keys') - if isinstance(start, float) or isinstance(end, float): - raise TypeError('Cannot index datetime64 with float keys') - - return Index.slice_indexer(self, start, end, step) + try: + return Index.slice_indexer(self, start, end, step) + except KeyError: + # For historical reasons DatetimeIndex by default supports + # value-based partial (aka string) slices on non-monotonic arrays, + # let's try that. + if ((start is None or isinstance(start, compat.string_types)) and + (end is None or isinstance(end, compat.string_types))): + mask = True + if start is not None: + start_casted = self._maybe_cast_slice_bound(start, 'left') + mask = start_casted <= self + + if end is not None: + end_casted = self._maybe_cast_slice_bound(end, 'right') + mask = (self <= end_casted) & mask + + indexer = mask.nonzero()[0][::step] + if len(indexer) == len(self): + return slice(None) + else: + return indexer + else: + raise def __getitem__(self, key): getitem = self._data.__getitem__ diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 0b4ca5014e76b..fbea7a3e1af67 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -783,7 +783,11 @@ def astype(self, dtype): raise ValueError('Cannot cast PeriodIndex to dtype %s' % dtype) def searchsorted(self, key, side='left'): - if isinstance(key, compat.string_types): + if isinstance(key, Period): + if key.freq != self.freq: + raise ValueError("Different period frequency: %s" % key.freq) + key = key.ordinal + elif isinstance(key, compat.string_types): key = Period(key, freq=self.freq).ordinal return self.values.searchsorted(key, side=side) @@ -982,6 +986,9 @@ def get_loc(self, key): try: return self._engine.get_loc(key) except KeyError: + if com.is_integer(key): + raise + try: asdt, parsed, reso = parse_time_string(key, self.freq) key = asdt @@ -994,47 +1001,38 @@ def get_loc(self, key): except KeyError: raise KeyError(key) - def slice_locs(self, start=None, end=None): - """ - Index.slice_locs, customized to handle partial ISO-8601 string slicing + def _maybe_cast_slice_bound(self, label, side): """ - if isinstance(start, compat.string_types) or isinstance(end, compat.string_types): - try: - if start: - start_loc = self._get_string_slice(start).start - else: - start_loc = 0 - - if end: - end_loc = self._get_string_slice(end).stop - else: - end_loc = len(self) - - return start_loc, end_loc - except KeyError: - pass - - if isinstance(start, datetime) and isinstance(end, datetime): - ordinals = self.values - t1 = Period(start, freq=self.freq) - t2 = Period(end, freq=self.freq) + If label is a string or a datetime, cast it to Period.ordinal according to + resolution. - left = ordinals.searchsorted(t1.ordinal, side='left') - right = ordinals.searchsorted(t2.ordinal, side='right') - return left, right + Parameters + ---------- + label : object + side : {'left', 'right'} - return Int64Index.slice_locs(self, start, end) + Returns + ------- + bound : Period or object - def _get_string_slice(self, key): - if not self.is_monotonic: - raise ValueError('Partial indexing only valid for ' - 'ordered time series') + Notes + ----- + Value of `side` parameter should be validated in caller. - key, parsed, reso = parse_time_string(key, self.freq) + """ + if isinstance(label, datetime): + return Period(label, freq=self.freq) + elif isinstance(label, compat.string_types): + try: + _, parsed, reso = parse_time_string(label, self.freq) + bounds = self._parsed_string_to_bounds(reso, parsed) + return bounds[0 if side == 'left' else 1] + except Exception: + raise KeyError(label) - grp = frequencies._infer_period_group(reso) - freqn = frequencies._period_group(self.freq) + return label + def _parsed_string_to_bounds(self, reso, parsed): if reso == 'year': t1 = Period(year=parsed.year, freq='A') elif reso == 'month': @@ -1042,30 +1040,39 @@ def _get_string_slice(self, key): elif reso == 'quarter': q = (parsed.month - 1) // 3 + 1 t1 = Period(year=parsed.year, quarter=q, freq='Q-DEC') - elif reso == 'day' and grp < freqn: + elif reso == 'day': t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day, freq='D') - elif reso == 'hour' and grp < freqn: + elif reso == 'hour': t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day, hour=parsed.hour, freq='H') - elif reso == 'minute' and grp < freqn: + elif reso == 'minute': t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day, hour=parsed.hour, minute=parsed.minute, freq='T') - elif reso == 'second' and grp < freqn: + elif reso == 'second': t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day, hour=parsed.hour, minute=parsed.minute, second=parsed.second, freq='S') else: raise KeyError(key) + return (t1.asfreq(self.freq, how='start'), + t1.asfreq(self.freq, how='end')) + + def _get_string_slice(self, key): + if not self.is_monotonic: + raise ValueError('Partial indexing only valid for ' + 'ordered time series') - ordinals = self.values + key, parsed, reso = parse_time_string(key, self.freq) - t2 = t1.asfreq(self.freq, how='end') - t1 = t1.asfreq(self.freq, how='start') + grp = frequencies._infer_period_group(reso) + freqn = frequencies._period_group(self.freq) + if reso in ['day', 'hour', 'minute', 'second'] and not grp < freqn: + raise KeyError(key) - left = ordinals.searchsorted(t1.ordinal, side='left') - right = ordinals.searchsorted(t2.ordinal, side='right') - return slice(left, right) + t1, t2 = self._parsed_string_to_bounds(reso, parsed) + return slice(self.searchsorted(t1.ordinal, side='left'), + self.searchsorted(t2.ordinal, side='right')) def join(self, other, how='left', level=None, return_indexers=False): """ diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 0d99cd16d8c99..7fb897aecc809 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -76,6 +76,7 @@ def wrapper(self, other): return wrapper + class TimedeltaIndex(DatetimeIndexOpsMixin, Int64Index): """ Immutable ndarray of timedelta64 data, represented internally as int64, and @@ -705,6 +706,31 @@ def get_loc(self, key): except (KeyError, ValueError): raise KeyError(key) + def _maybe_cast_slice_bound(self, label, side): + """ + If label is a string, cast it to timedelta according to resolution. + + + Parameters + ---------- + label : object + side : {'left', 'right'} + + Returns + ------- + bound : Timedelta or object + + """ + if isinstance(label, compat.string_types): + parsed = _coerce_scalar_to_timedelta_type(label, box=True) + lbound = parsed.round(parsed.resolution) + if side == 'left': + return lbound + else: + return (lbound + _resolution_map[parsed.resolution]() - + Timedelta(1, 'ns')) + return label + def _get_string_slice(self, key, use_lhs=True, use_rhs=True): freq = getattr(self, 'freqstr', getattr(self, 'inferred_freq', None)) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index e046d687435e7..1fd2d7b8fa8e5 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -1352,7 +1352,9 @@ def test_getitem_partial(self): assert_series_equal(exp, result) ts = ts[10:].append(ts[10:]) - self.assertRaises(ValueError, ts.__getitem__, slice('2008', '2009')) + self.assertRaisesRegexp( + KeyError, "left slice bound for non-unique label: '2008'", + ts.__getitem__, slice('2008', '2009')) def test_getitem_datetime(self): rng = period_range(start='2012-01-01', periods=10, freq='W-MON') @@ -1364,6 +1366,39 @@ def test_getitem_datetime(self): rs = ts[dt1:dt4] assert_series_equal(rs, ts) + def test_slice_with_negative_step(self): + ts = Series(np.arange(20), + period_range('2014-01', periods=20, freq='M')) + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + assert_series_equal(ts[l_slc], ts.iloc[i_slc]) + assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) + assert_series_equal(ts.ix[l_slc], ts.iloc[i_slc]) + + assert_slices_equivalent(SLC[Period('2014-10')::-1], SLC[9::-1]) + assert_slices_equivalent(SLC['2014-10'::-1], SLC[9::-1]) + + assert_slices_equivalent(SLC[:Period('2014-10'):-1], SLC[:8:-1]) + assert_slices_equivalent(SLC[:'2014-10':-1], SLC[:8:-1]) + + assert_slices_equivalent(SLC['2015-02':'2014-10':-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC[Period('2015-02'):Period('2014-10'):-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC['2015-02':Period('2014-10'):-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC[Period('2015-02'):'2014-10':-1], SLC[13:8:-1]) + + assert_slices_equivalent(SLC['2014-10':'2015-02':-1], SLC[:0]) + + def test_slice_with_zero_step_raises(self): + ts = Series(np.arange(20), + period_range('2014-01', periods=20, freq='M')) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.loc[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.ix[::0]) + def test_sub(self): rng = period_range('2007-01', periods=50) @@ -2464,6 +2499,13 @@ def test_combine_first(self): expected = pd.Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64) tm.assert_series_equal(result, expected) + def test_searchsorted(self): + pidx = pd.period_range('2014-01-01', periods=10, freq='D') + self.assertEqual( + pidx.searchsorted(pd.Period('2014-01-01', freq='D')), 0) + self.assertRaisesRegexp( + ValueError, 'Different period frequency: H', + lambda: pidx.searchsorted(pd.Period('2014-01-01', freq='H'))) def _permute(obj): return obj.take(np.random.permutation(len(obj))) diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index c8dd5573370d9..9ad2a090ee0cf 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -1232,6 +1232,40 @@ def test_partial_slice_high_reso(self): result = s['1 days, 10:11:12.001001'] self.assertEqual(result, s.irow(1001)) + def test_slice_with_negative_step(self): + ts = Series(np.arange(20), + timedelta_range('0', periods=20, freq='H')) + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + assert_series_equal(ts[l_slc], ts.iloc[i_slc]) + assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) + assert_series_equal(ts.ix[l_slc], ts.iloc[i_slc]) + + assert_slices_equivalent(SLC[Timedelta(hours=7)::-1], SLC[7::-1]) + assert_slices_equivalent(SLC['7 hours'::-1], SLC[7::-1]) + + assert_slices_equivalent(SLC[:Timedelta(hours=7):-1], SLC[:6:-1]) + assert_slices_equivalent(SLC[:'7 hours':-1], SLC[:6:-1]) + + assert_slices_equivalent(SLC['15 hours':'7 hours':-1], SLC[15:6:-1]) + assert_slices_equivalent(SLC[Timedelta(hours=15):Timedelta(hours=7):-1], SLC[15:6:-1]) + assert_slices_equivalent(SLC['15 hours':Timedelta(hours=7):-1], SLC[15:6:-1]) + assert_slices_equivalent(SLC[Timedelta(hours=15):'7 hours':-1], SLC[15:6:-1]) + + assert_slices_equivalent(SLC['7 hours':'15 hours':-1], SLC[:0]) + + def test_slice_with_zero_step_raises(self): + ts = Series(np.arange(20), + timedelta_range('0', periods=20, freq='H')) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.loc[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.ix[::0]) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index e6b4bf23e806f..436f9f3b9c9b3 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -198,7 +198,6 @@ def test_indexing_over_size_cutoff(self): _index._SIZE_CUTOFF = old_cutoff def test_indexing_unordered(self): - # GH 2437 rng = date_range(start='2011-01-01', end='2011-01-15') ts = Series(randn(len(rng)), index=rng) @@ -2767,6 +2766,41 @@ def test_factorize(self): self.assertTrue(idx.equals(idx3)) + def test_slice_with_negative_step(self): + ts = Series(np.arange(20), + date_range('2014-01-01', periods=20, freq='MS')) + SLC = pd.IndexSlice + + def assert_slices_equivalent(l_slc, i_slc): + assert_series_equal(ts[l_slc], ts.iloc[i_slc]) + assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc]) + assert_series_equal(ts.ix[l_slc], ts.iloc[i_slc]) + + assert_slices_equivalent(SLC[Timestamp('2014-10-01')::-1], SLC[9::-1]) + assert_slices_equivalent(SLC['2014-10-01'::-1], SLC[9::-1]) + + assert_slices_equivalent(SLC[:Timestamp('2014-10-01'):-1], SLC[:8:-1]) + assert_slices_equivalent(SLC[:'2014-10-01':-1], SLC[:8:-1]) + + assert_slices_equivalent(SLC['2015-02-01':'2014-10-01':-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC[Timestamp('2015-02-01'):Timestamp('2014-10-01'):-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC['2015-02-01':Timestamp('2014-10-01'):-1], SLC[13:8:-1]) + assert_slices_equivalent(SLC[Timestamp('2015-02-01'):'2014-10-01':-1], SLC[13:8:-1]) + + assert_slices_equivalent(SLC['2014-10-01':'2015-02-01':-1], SLC[:0]) + + def test_slice_with_zero_step_raises(self): + ts = Series(np.arange(20), + date_range('2014-01-01', periods=20, freq='MS')) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.loc[::0]) + self.assertRaisesRegexp(ValueError, 'slice step cannot be zero', + lambda: ts.ix[::0]) + + + class TestDatetime64(tm.TestCase): """ Also test support for datetime64[ns] in Series / DataFrame @@ -3745,6 +3779,22 @@ def test_partial_slice_minutely(self): self.assertEqual(s[Timestamp('2005-1-1 23:59:00')], s.ix[0]) self.assertRaises(Exception, s.__getitem__, '2004-12-31 00:00:00') + def test_partial_slice_second_precision(self): + rng = DatetimeIndex(start=datetime(2005, 1, 1, 0, 0, 59, + microsecond=999990), + periods=20, freq='US') + s = Series(np.arange(20), rng) + + assert_series_equal(s['2005-1-1 00:00'], s.iloc[:10]) + assert_series_equal(s['2005-1-1 00:00:59'], s.iloc[:10]) + + assert_series_equal(s['2005-1-1 00:01'], s.iloc[10:]) + assert_series_equal(s['2005-1-1 00:01:00'], s.iloc[10:]) + + self.assertEqual(s[Timestamp('2005-1-1 00:00:59.999990')], s.iloc[0]) + self.assertRaisesRegexp(KeyError, '2005-1-1 00:00:00', + lambda: s['2005-1-1 00:00:00']) + def test_partial_slicing_with_multiindex(self): # GH 4758 @@ -3955,6 +4005,24 @@ def test_date_range_fy5252(self): self.assertEqual(dr[0], Timestamp('2013-01-31')) self.assertEqual(dr[1], Timestamp('2014-01-30')) + def test_partial_slice_doesnt_require_monotonicity(self): + # For historical reasons. + s = pd.Series(np.arange(10), + pd.date_range('2014-01-01', periods=10)) + + nonmonotonic = s[[3, 5, 4]] + expected = nonmonotonic.iloc[:0] + timestamp = pd.Timestamp('2014-01-10') + + assert_series_equal(nonmonotonic['2014-01-10':], expected) + self.assertRaisesRegexp(KeyError, "Timestamp\('2014-01-10 00:00:00'\)", + lambda: nonmonotonic[timestamp:]) + + assert_series_equal(nonmonotonic.ix['2014-01-10':], expected) + self.assertRaisesRegexp(KeyError, "Timestamp\('2014-01-10 00:00:00'\)", + lambda: nonmonotonic.ix[timestamp:]) + + class TimeConversionFormats(tm.TestCase): def test_to_datetime_format(self): values = ['1/1/2000', '1/2/2000', '1/3/2000']
This should fix #8716. Some of this refactoring may be useful for #8613, so I'd like someone to look through this. cc'ing @shoyer, @jreback and @jorisvandenbossche . TODO: - add tests for label slicing with negative step (ix, loc) - [x] int64index - [x] float64index - [x] objectindex - [x] fix datetime/period indices - add tests for label slicing with negative step (datetime, string, native pandas scalar) x (ix, loc) - [x] datetimeindex - [x] periodindex - [x] timedeltaindex - [x] clean up old partial string code? - [x] benchmark
https://api.github.com/repos/pandas-dev/pandas/pulls/8753
2014-11-08T07:14:47Z
2014-11-19T22:05:10Z
2014-11-19T22:05:09Z
2014-11-19T22:15:58Z
Update tokenizer to fix #8679 #8661
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index d6d36fd8d14ba..1e84762b60caa 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -74,6 +74,7 @@ Enhancements Performance ~~~~~~~~~~~ +- Reduce memory usage when skiprows is an integer in read_csv (:issue:`8681`) .. _whatsnew_0152.experimental: @@ -155,3 +156,4 @@ Bug Fixes of the level names are numbers (:issue:`8584`). - Bug in ``MultiIndex`` where ``__contains__`` returns wrong result if index is not lexically sorted or unique (:issue:`7724`) +- BUG CSV: fix problem with trailing whitespace in skipped rows, (:issue:`8679`), (:issue:`8661`) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 228dad984bb3c..59647b4c781e5 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -3048,6 +3048,29 @@ def test_comment_skiprows(self): df = self.read_csv(StringIO(data), comment='#', skiprows=4) tm.assert_almost_equal(df.values, expected) + def test_trailing_spaces(self): + data = """skip +random line with trailing spaces +skip +1,2,3 +1,2.,4. +random line with trailing tabs\t\t\t + +5.,NaN,10.0 +""" + expected = pd.DataFrame([[1., 2., 4.], + [5., np.nan, 10.]]) + # this should ignore six lines including lines with trailing + # whitespace and blank lines. issues 8661, 8679 + df = self.read_csv(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0,1,2,3,5,6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + df = self.read_table(StringIO(data.replace(',', ' ')), + header=None, delim_whitespace=True, + skiprows=[0,1,2,3,5,6], skip_blank_lines=True) + tm.assert_frame_equal(df, expected) + def test_comment_header(self): data = """# empty # second empty line diff --git a/pandas/parser.pyx b/pandas/parser.pyx index afaa5219ab0cd..0409ee56f22bb 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -86,6 +86,7 @@ cdef extern from "parser/tokenizer.h": EAT_COMMENT EAT_LINE_COMMENT WHITESPACE_LINE + SKIP_LINE FINISHED enum: ERROR_OVERFLOW @@ -158,6 +159,7 @@ cdef extern from "parser/tokenizer.h": int header_end # header row end void *skipset + int64_t skip_first_N_rows int skip_footer double (*converter)(const char *, char **, char, char, char, int) @@ -181,6 +183,8 @@ cdef extern from "parser/tokenizer.h": void parser_free(parser_t *self) nogil int parser_add_skiprow(parser_t *self, int64_t row) + int parser_set_skipfirstnrows(parser_t *self, int64_t nrows) + void parser_set_default_options(parser_t *self) int parser_consume_rows(parser_t *self, size_t nrows) @@ -524,10 +528,10 @@ cdef class TextReader: cdef _make_skiprow_set(self): if isinstance(self.skiprows, (int, np.integer)): - self.skiprows = range(self.skiprows) - - for i in self.skiprows: - parser_add_skiprow(self.parser, i) + parser_set_skipfirstnrows(self.parser, self.skiprows) + else: + for i in self.skiprows: + parser_add_skiprow(self.parser, i) cdef _setup_parser_source(self, source): cdef: diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 9a7303b6874db..fc96cc5429775 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -156,6 +156,7 @@ void parser_set_default_options(parser_t *self) { self->thousands = '\0'; self->skipset = NULL; + self-> skip_first_N_rows = -1; self->skip_footer = 0; } @@ -444,21 +445,17 @@ static int end_line(parser_t *self) { } } - if (self->skipset != NULL) { - k = kh_get_int64((kh_int64_t*) self->skipset, self->file_lines); - - if (k != ((kh_int64_t*)self->skipset)->n_buckets) { - TRACE(("Skipping row %d\n", self->file_lines)); - // increment file line count - self->file_lines++; - - // skip the tokens from this bad line - self->line_start[self->lines] += fields; + if (self->state == SKIP_LINE) { + TRACE(("Skipping row %d\n", self->file_lines)); + // increment file line count + self->file_lines++; + + // skip the tokens from this bad line + self->line_start[self->lines] += fields; - // reset field count - self->line_fields[self->lines] = 0; - return 0; - } + // reset field count + self->line_fields[self->lines] = 0; + return 0; } /* printf("Line: %d, Fields: %d, Ex-fields: %d\n", self->lines, fields, ex_fields); */ @@ -556,6 +553,15 @@ int parser_add_skiprow(parser_t *self, int64_t row) { return 0; } +int parser_set_skipfirstnrows(parser_t *self, int64_t nrows) { + // self->file_lines is zero based so subtract 1 from nrows + if (nrows > 0) { + self->skip_first_N_rows = nrows - 1; + } + + return 0; +} + static int parser_buffer_bytes(parser_t *self, size_t nbytes) { int status; size_t bytes_read; @@ -656,6 +662,15 @@ typedef int (*parser_op)(parser_t *self, size_t line_limit); TRACE(("datapos: %d, datalen: %d\n", self->datapos, self->datalen)); +int skip_this_line(parser_t *self, int64_t rownum) { + if (self->skipset != NULL) { + return ( kh_get_int64((kh_int64_t*) self->skipset, self->file_lines) != + ((kh_int64_t*)self->skipset)->n_buckets ); + } + else { + return ( rownum <= self->skip_first_N_rows ); + } +} int tokenize_delimited(parser_t *self, size_t line_limit) { @@ -688,10 +703,25 @@ int tokenize_delimited(parser_t *self, size_t line_limit) switch(self->state) { + case SKIP_LINE: +// TRACE(("tokenize_delimited SKIP_LINE %c, state %d\n", c, self->state)); + if (c == '\n') { + END_LINE(); + } + break; + case START_RECORD: // start of record - - if (c == '\n') { + if (skip_this_line(self, self->file_lines)) { + if (c == '\n') { + END_LINE() + } + else { + self->state = SKIP_LINE; + } + break; + } + else if (c == '\n') { // \n\r possible? if (self->skip_empty_lines) { @@ -1006,9 +1036,26 @@ int tokenize_delim_customterm(parser_t *self, size_t line_limit) self->state)); switch(self->state) { + + case SKIP_LINE: +// TRACE(("tokenize_delim_customterm SKIP_LINE %c, state %d\n", c, self->state)); + if (c == self->lineterminator) { + END_LINE(); + } + break; + case START_RECORD: // start of record - if (c == self->lineterminator) { + if (skip_this_line(self, self->file_lines)) { + if (c == self->lineterminator) { + END_LINE() + } + else { + self->state = SKIP_LINE; + } + break; + } + else if (c == self->lineterminator) { // \n\r possible? if (self->skip_empty_lines) { @@ -1252,6 +1299,14 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) self->state)); switch(self->state) { + + case SKIP_LINE: +// TRACE(("tokenize_whitespace SKIP_LINE %c, state %d\n", c, self->state)); + if (c == '\n') { + END_LINE(); + } + break; + case WHITESPACE_LINE: if (c == '\n') { self->file_lines++; @@ -1283,9 +1338,17 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) case START_RECORD: // start of record - if (c == '\n') { - // \n\r possible? + if (skip_this_line(self, self->file_lines)) { + if (c == '\n') { + END_LINE() + } + else { + self->state = SKIP_LINE; + } + break; + } else if (c == '\n') { if (self->skip_empty_lines) + // \n\r possible? { self->file_lines++; } diff --git a/pandas/src/parser/tokenizer.h b/pandas/src/parser/tokenizer.h index 0947315fbe6b7..07f4153038dd8 100644 --- a/pandas/src/parser/tokenizer.h +++ b/pandas/src/parser/tokenizer.h @@ -127,6 +127,7 @@ typedef enum { EAT_COMMENT, EAT_LINE_COMMENT, WHITESPACE_LINE, + SKIP_LINE, FINISHED } ParserState; @@ -203,6 +204,7 @@ typedef struct parser_t { int header_end; // header row end void *skipset; + int64_t skip_first_N_rows; int skip_footer; double (*converter)(const char *, char **, char, char, char, int); @@ -240,6 +242,8 @@ int parser_trim_buffers(parser_t *self); int parser_add_skiprow(parser_t *self, int64_t row); +int parser_set_skipfirstnrows(parser_t *self, int64_t nrows); + void parser_free(parser_t *self); void parser_set_default_options(parser_t *self); diff --git a/vb_suite/io_bench.py b/vb_suite/io_bench.py index 0b9f68f0e6ed5..a70c543ca59eb 100644 --- a/vb_suite/io_bench.py +++ b/vb_suite/io_bench.py @@ -21,6 +21,22 @@ read_csv_standard = Benchmark("read_csv('__test__.csv')", setup1, start_date=datetime(2011, 9, 15)) +#---------------------------------- +# skiprows + +setup1 = common_setup + """ +index = tm.makeStringIndex(20000) +df = DataFrame({'float1' : randn(20000), + 'float2' : randn(20000), + 'string1' : ['foo'] * 20000, + 'bool1' : [True] * 20000, + 'int1' : np.random.randint(0, 200000, size=20000)}, + index=index) +df.to_csv('__test__.csv') +""" + +read_csv_skiprows = Benchmark("read_csv('__test__.csv', skiprows=10000)", setup1, + start_date=datetime(2011, 9, 15)) #---------------------------------------------------------------------- # write_csv
Update tokenizer's handling of skipped lines. Fixes a problem with read_csv(delim_whitespace=True) and read_table(engine='c') when lines being skipped have trailing whitespace. closes #8679 closes #8681
https://api.github.com/repos/pandas-dev/pandas/pulls/8752
2014-11-07T22:53:11Z
2014-11-27T02:52:38Z
2014-11-27T02:52:38Z
2015-02-01T03:19:50Z
pd.show_versions()
diff --git a/doc/README.rst b/doc/README.rst index 660a3b7232891..4b5b0d8818e2d 100644 --- a/doc/README.rst +++ b/doc/README.rst @@ -88,7 +88,7 @@ Furthermore, it is recommended to have all `optional dependencies installed. This is not needed, but be aware that you will see some error messages. Because all the code in the documentation is executed during the doc build, the examples using this optional dependencies will generate errors. -Run ``pd.show_version()`` to get an overview of the installed version of all +Run ``pd.show_versions()`` to get an overview of the installed version of all dependencies. .. warning::
Shouldn't this be `pd.show_versions()`? Maybe it's a typo.
https://api.github.com/repos/pandas-dev/pandas/pulls/8746
2014-11-06T11:54:04Z
2014-11-06T12:09:38Z
2014-11-06T12:09:38Z
2014-11-06T12:09:38Z
BUG: DataReaders return missing data as NaN rather than warn.
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index aca3e651c7ddf..f0767b9d66c24 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -300,3 +300,5 @@ Bug Fixes - Bug in ``date_range`` where partially-specified dates would incorporate current date (:issue:`6961`) - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue:`8702`) + +- Bug where ``DataReader``'s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (:issue:`8494`) \ No newline at end of file diff --git a/pandas/io/data.py b/pandas/io/data.py index 6cad478ee841b..8e7474b6e5d1d 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -317,6 +317,7 @@ def get_components_yahoo(idx_sym): def _dl_mult_symbols(symbols, start, end, chunksize, retry_count, pause, method): stocks = {} + failed = [] for sym_group in _in_chunks(symbols, chunksize): for sym in sym_group: try: @@ -324,9 +325,14 @@ def _dl_mult_symbols(symbols, start, end, chunksize, retry_count, pause, except IOError: warnings.warn('Failed to read symbol: {0!r}, replacing with ' 'NaN.'.format(sym), SymbolWarning) - stocks[sym] = np.nan + failed.append(sym) try: + if len(stocks) > 0 and len(failed) > 0: + df_na = stocks.values()[0].copy() + df_na[:] = np.nan + for sym in failed: + stocks[sym] = df_na return Panel(stocks).swapaxes('items', 'minor') except AttributeError: # cannot construct a panel with just 1D nans indicating no data diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index f872c15446f08..549a60a85e25e 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -94,6 +94,12 @@ def test_get_multi1(self): else: self.assertRaises(AttributeError, lambda: pan.Close) + @network + def test_get_multi_invalid(self): + sl = ['AAPL', 'AMZN', 'INVALID'] + pan = web.get_data_google(sl, '2012') + self.assertIn('INVALID', pan.minor_axis) + @network def test_get_multi2(self): with warnings.catch_warnings(record=True) as w:
Fixes #8433
https://api.github.com/repos/pandas-dev/pandas/pulls/8743
2014-11-06T06:15:51Z
2014-11-06T11:26:17Z
2014-11-06T11:26:17Z
2014-11-10T01:18:27Z
BUG: Allow non-float values in get_yahoo_quotes.
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index aeef6d6b1c1f1..be1790103bb35 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -301,4 +301,5 @@ Bug Fixes - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue:`8702`) -- Bug where ``DataReader``'s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (:issue:`8494`) \ No newline at end of file +- Bug where ``DataReader``'s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (:issue:`8494`) +- Bug in ``get_quote_yahoo`` that wouldn't allow non-float return values (:issue:`5229`). \ No newline at end of file diff --git a/pandas/io/data.py b/pandas/io/data.py index 490d8998a3148..982755a854e36 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -143,7 +143,7 @@ def get_quote_yahoo(symbols): try: v = float(field) except ValueError: - v = np.nan + v = field data[header[i]].append(v) idx = data.pop('symbol') diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index 0047bc855e446..dbc439e6c872e 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -10,7 +10,7 @@ import pandas as pd from pandas import DataFrame, Timestamp from pandas.io import data as web -from pandas.io.data import DataReader, SymbolWarning, RemoteDataError +from pandas.io.data import DataReader, SymbolWarning, RemoteDataError, _yahoo_codes from pandas.util.testing import (assert_series_equal, assert_produces_warning, network, assert_frame_equal) import pandas.util.testing as tm @@ -151,6 +151,12 @@ def test_get_quote_series(self): def test_get_quote_string(self): df = web.get_quote_yahoo('GOOG') + @network + def test_get_quote_string(self): + _yahoo_codes.update({'MarketCap': 'j1'}) + df = web.get_quote_yahoo('GOOG') + self.assertFalse(pd.isnull(df['MarketCap'][0])) + @network def test_get_quote_stringlist(self): df = web.get_quote_yahoo(['GOOG', 'AAPL', 'GOOG'])
Fixes issue #5229
https://api.github.com/repos/pandas-dev/pandas/pulls/8742
2014-11-06T04:41:02Z
2014-11-07T11:47:17Z
2014-11-07T11:47:17Z
2014-11-10T01:18:28Z
BUG: Fix io.data.Options quote time for DST.
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index f0767b9d66c24..aeef6d6b1c1f1 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -170,7 +170,7 @@ API changes - added Index properties `is_monotonic_increasing` and `is_monotonic_decreasing` (:issue:`8680`). -.. note:: io.data.Options has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`) +.. note:: io.data.Options has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`), (:issue:`8741`) As a result of a change in Yahoo's option page layout, when an expiry date is given, ``Options`` methods now return data for a single expiry date. Previously, methods returned all diff --git a/pandas/io/data.py b/pandas/io/data.py index 8e7474b6e5d1d..490d8998a3148 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -698,10 +698,13 @@ def _get_underlying_price(self, url): #Gets the time of the quote, note this is actually the time of the underlying price. try: quote_time_text = root.xpath('.//*[@class="time_rtq Fz-m"]')[0].getchildren()[1].getchildren()[0].text - quote_time = dt.datetime.strptime(quote_time_text, "%I:%M%p EDT") + ##TODO: Enable timezone matching when strptime can match EST with %Z + quote_time_text = quote_time_text.split(' ')[0] + quote_time = dt.datetime.strptime(quote_time_text, "%I:%M%p") + quote_time = quote_time.replace(year=CUR_YEAR, month=CUR_MONTH, day=CUR_DAY) except ValueError: - raise RemoteDataError('Unable to determine time of quote for page %s' % url) + quote_time = np.nan return underlying_price, quote_time diff --git a/pandas/io/tests/data/yahoo_options2.html b/pandas/io/tests/data/yahoo_options2.html index 431e6c5200034..bae9c193e03e1 100644 --- a/pandas/io/tests/data/yahoo_options2.html +++ b/pandas/io/tests/data/yahoo_options2.html @@ -2,7 +2,7 @@ <html> <head> <!-- customizable : anything you expected. --> - <title>AAPL Options | Yahoo! Inc. Stock - Yahoo! Finance</title> + <title>AAPL Option Chain | Yahoo! Inc. Stock - Yahoo! Finance</title> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" /> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> @@ -10,7 +10,7 @@ - <link rel="stylesheet" type="text/css" href="https://s.yimg.com/zz/combo?/os/mit/td/stencil-0.1.306/stencil-css/stencil-css-min.css&/os/mit/td/finance-td-app-mobile-web-2.0.296/css.master/css.master-min.css"/><link rel="stylesheet" type="text/css" href="https://s.yimg.com/os/mit/media/m/quotes/quotes-search-gs-smartphone-min-1680382.css"/> + <link rel="stylesheet" type="text/css" href="https://s.yimg.com/zz/combo?/os/mit/td/stencil-0.1.306/stencil-css/stencil-css-min.css&/os/mit/td/finance-td-app-mobile-web-2.0.305/css.master/css.master-min.css"/><link rel="stylesheet" type="text/css" href="https://s.yimg.com/os/mit/media/m/quotes/quotes-search-gs-smartphone-min-1680382.css"/> <script>(function(html){var c = html.className;c += " JsEnabled";c = c.replace("NoJs","");html.className = 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data-crumb="ZW4AS4U53s3"><!-- empty for ie --></div><div id="yUnivHead" class="yucs-hide"><!-- empty --></div><div id="yhelp_container" class="yui3-skin-sam"></div></div><!-- alert --><!-- /alert --> </div> @@ -334,8 +334,6 @@ <li><a href="/blogs/author/jeff-macke/" title="" class="">Jeff Macke</a></li> - <li><a href="/blogs/author/lauren-lyster/" title="" class="">Lauren Lyster</a></li> - <li><a href="/blogs/author/aaron-pressman/" title="" class="">Aaron Pressman</a></li> <li><a href="/blogs/author/rick-newman/" title="" class="">Rick Newman</a></li> @@ -450,7 +448,7 @@ <div data-region="td-applet-mw-quote-search"> -<div id="applet_5264156462306630" class="App_v2 js-applet" data-applet-guid="5264156462306630" data-applet-type="td-applet-mw-quote-search"> +<div id="applet_7416600208709417" class="App_v2 js-applet" data-applet-guid="7416600208709417" data-applet-type="td-applet-mw-quote-search"> @@ -580,7 +578,7 @@ <h2 class="yfi_signpost">Search for share prices</h2> </form> </div> <div class="ft"><a href="http://finance.search.yahoo.com?fr=fin-v1" data-rapid_p="4">Finance Search</a> - <p><span id="yfs_market_time">Mon, Oct 27 2014, 10:14am EDT - U.S. Markets close in 5 hrs 46 mins</span></p></div> + <p><span id="yfs_market_time">Wed, Nov 05 2014, 11:00pm EST - U.S. Markets closed</span></p></div> </div> @@ -609,10 +607,10 @@ <h2 class="yfi_signpost">Search for share prices</h2> <span id="yfs_pp0_^dji"> - <img width="10" height="14" border="0" alt="Down" class="neg_arrow" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" style="margin-right:-2px;"> + <img width="10" height="14" border="0" alt="Up" class="pos_arrow" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" style="margin-right:-2px;"> - <b class="yfi-price-change-down">0.24%</b> + <b class="yfi-price-change-up">0.58%</b> </span> @@ -626,7 +624,7 @@ <h2 class="yfi_signpost">Search for share prices</h2> <img width="10" height="14" border="0" alt="Down" class="neg_arrow" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" style="margin-right:-2px;"> - <b class="yfi-price-change-down">0.52%</b> + <b class="yfi-price-change-down">0.06%</b> @@ -753,13 +751,13 @@ <h3>Financials</h3> <div id="yom-ad-FB2-1"><div id="yom-ad-FB2-1-iframe"></div></div><!--ESI Ads for FB2-1 --> </div> <div class='yom-ad D-ib W-25'> - <div id="yom-ad-FB2-2"><div id="yom-ad-FB2-2-iframe"><script>var FB2_2_noadPos = document.getElementById("yom-ad-FB2-2"); if (FB2_2_noadPos) {FB2_2_noadPos.style.display="none";}</script></div></div><!--ESI Ads for FB2-2 --> + <div id="yom-ad-FB2-2"><div id="yom-ad-FB2-2-iframe"></div></div><!--ESI Ads for FB2-2 --> </div> <div class='yom-ad D-ib W-25'> <div id="yom-ad-FB2-3"><div id="yom-ad-FB2-3-iframe"></div></div><!--ESI Ads for FB2-3 --> </div> <div class='yom-ad D-ib W-25'> - <div id="yom-ad-FB2-4"><div id="yom-ad-FB2-4-iframe"></div></div><!--ESI Ads for FB2-4 --> + <div id="yom-ad-FB2-4"><div id="yom-ad-FB2-4-iframe"><script>var FB2_4_noadPos = document.getElementById("yom-ad-FB2-4"); if (FB2_4_noadPos) {FB2_4_noadPos.style.display="none";}</script></div></div><!--ESI Ads for FB2-4 --> </div> </div> @@ -838,7 +836,7 @@ <h3>Financials</h3> max-width: 50px; _width: 50px; }</style> -<div id="applet_5264156473588491" class="App_v2 js-applet" data-applet-guid="5264156473588491" data-applet-type="td-applet-mw-quote-details"> +<div id="applet_7416600209955624" class="App_v2 js-applet" data-applet-guid="7416600209955624" data-applet-type="td-applet-mw-quote-details"> @@ -904,19 +902,22 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="yfi_rt_quote_summary_rt_top sigfig_promo_1"> <div> <span class="time_rtq_ticker Fz-30 Fw-b"> - <span id="yfs_l84_AAPL" data-sq="AAPL:value">104.8999</span> + <span id="yfs_l84_AAPL" data-sq="AAPL:value">108.86</span> </span> - <span class="down_r time_rtq_content Fz-2xl Fw-b"><span id="yfs_c63_AAPL"><img width="10" height="14" border="0" style="margin-right:-2px;" src="https://s.yimg.com/lq/i/us/fi/03rd/down_r.gif" alt="Down"> <span class="yfi-price-change-red" data-sq="AAPL:chg">-0.3201</span></span><span id="yfs_p43_AAPL">(<span class="yfi-price-change-red" data-sq="AAPL:pctChg">0.30%</span>)</span> </span> + <span class="up_g time_rtq_content Fz-2xl Fw-b"><span id="yfs_c63_AAPL"><img width="10" height="14" border="0" style="margin-right:-2px;" src="https://s.yimg.com/lq/i/us/fi/03rd/up_g.gif" alt="Up"> <span class="yfi-price-change-green" data-sq="AAPL:chg">+0.26</span></span><span id="yfs_p43_AAPL">(<span class="yfi-price-change-green" data-sq="AAPL:pctChg">0.24%</span>)</span> </span> - <span class="time_rtq Fz-m"><span class="rtq_exch">NasdaqGS - </span><span id="yfs_t53_AAPL">As of <span data-sq="AAPL:lstTrdTime">10:14AM EDT</span></span></span> + <span class="time_rtq Fz-m"><span class="rtq_exch">NasdaqGS - </span><span id="yfs_t53_AAPL">As of <span data-sq="AAPL:lstTrdTime">4:00PM EST</span></span></span> </div> <div><span class="rtq_separator">|</span> + After Hours: + <span class="yfs_rtq_quote"><span id="yfs_l86_AAPL" data-sq="AAPL:ahValue">108.86</span></span> <span class=""><span id="yfs_c85_AAPL"><img width="10" height="14" style="margin-right:-2px;" border="0" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="" alt="" data-sq="AAPL:ahChg"> 0.00</span> (<span id="yfs_c86_AAPL" data-sq="AAPL:ahPctChg">0.00%</span>)</span><span class="time_rtq"> <span id="yfs_t54_AAPL" data-sq="AAPL:ahLstTrdTime">7:59PM EST</span></span> + </div> </div> @@ -925,7 +926,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> bottom:45px !important; } </style> - <div class="app_promo " > + <div class="app_promo after_hours " > <a href="https://mobile.yahoo.com/finance/?src=gta" title="Get the App" target="_blank" ></a> </div> @@ -1014,7 +1015,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> } .toggle li a { - padding: 7px; + padding: 7px; display: block; } @@ -1242,13 +1243,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> box-sizing: border-box; } #quote-table caption .callStraddles { - width:50%; - text-align:center; + width:50%; + text-align:center; float:left; } #quote-table caption .putStraddles { - width:50%; - text-align:center; + width:50%; + text-align:center; float:right; } #quote-table .in-the-money.even { @@ -1571,9 +1572,14 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> width: 1070px; } -#yfi_charts.desktop #yfi_doc { +#yfi_charts.desktop #yfi_doc, #yfi_charts.tablet #yfi_doc { width: 1440px; } + +#yfi_charts.tablet #yfi_investing_content { + width: 1070px; +} + #sky { float: right; margin-left: 30px; @@ -1581,7 +1587,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> width: 170px; } </style> -<div id="applet_5264156462795343" class="App_v2 js-applet" data-applet-guid="5264156462795343" data-applet-type="td-applet-options-table"> +<div id="applet_7416600209231742" class="App_v2 js-applet" data-applet-guid="7416600209231742" data-applet-type="td-applet-options-table"> @@ -1594,12 +1600,9 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div id="quote-table"> <div id="options_menu" class="Grid-U options_menu"> - <form class="Grid-U SelectBox"> - <div class="SelectBox-Pick"><b class='SelectBox-Text '>October 31, 2014</b><i class='Icon Va-m'>&#xe002;</i></div> - <select class='Start-0' data-plugin="selectbox"> - - - <option data-selectbox-link="/q/op?s=AAPL&date=1414713600" value="1414713600" >October 31, 2014</option> + <form class="Grid-U SelectBox Disabled"> + <div class="SelectBox-Pick"><b class='SelectBox-Text '>November 7, 2014</b><i class='Icon Va-m'>&#xe002;</i></div> + <select class='Start-0' disabled data-plugin="selectbox"> <option data-selectbox-link="/q/op?s=AAPL&date=1415318400" value="1415318400" >November 7, 2014</option> @@ -1617,6 +1620,9 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <option data-selectbox-link="/q/op?s=AAPL&date=1417737600" value="1417737600" >December 5, 2014</option> + <option data-selectbox-link="/q/op?s=AAPL&date=1418342400" value="1418342400" >December 12, 2014</option> + + <option data-selectbox-link="/q/op?s=AAPL&date=1419033600" value="1419033600" >December 20, 2014</option> @@ -1641,17 +1647,6 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </form> - <div class="Grid-U options-menu-item size-toggle-menu"> - <ul class="toggle size-toggle"> - <li data-size="REGULAR" class="size-toggle-option toggle-regular active Cur-p"> - <a href="/q/op?s=AAPL&date=1414713600">Regular</a> - </li> - <li data-size="MINI" class="size-toggle-option toggle-mini Cur-p"> - <a href="/q/op?s=AAPL&size=mini&date=1414713600">Mini</a> - </li> - </ul> - </div> - <div class="Grid-U options-menu-item symbol_lookup"> <div class="Cf"> @@ -1665,10 +1660,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="Grid-U option_view options-menu-item"> <ul class="toggle toggle-view-mode"> <li class="toggle-list active"> - <a href="/q/op?s=AAPL&date=1414713600">List</a> + <a href="/q/op?s=AAPL&date=1415318400">List</a> </li> <li class="toggle-straddle "> - <a href="/q/op?s=AAPL&straddle=true&date=1414713600">Straddle</a> + <a href="/q/op?s=AAPL&straddle=true&date=1415318400">Straddle</a> </li> </ul> @@ -1872,39 +1867,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=60.00">60.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00075000">AAPL141031C00075000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00060000">AAPL141107C00060000</a></div> </td> <td> - <div class="option_entry Fz-m" >26.90</div> + <div class="option_entry Fz-m" >48.85</div> </td> <td> - <div class="option_entry Fz-m" >29.35</div> + <div class="option_entry Fz-m" >48.65</div> </td> <td> - <div class="option_entry Fz-m" >30.45</div> + <div class="option_entry Fz-m" >48.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-0.95</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-1.91%</div> </td> <td> - <strong data-sq=":volume" data-raw="2">2</strong> + <strong data-sq=":volume" data-raw="60">60</strong> </td> <td> - <div class="option_entry Fz-m" >2</div> + <div class="option_entry Fz-m" >61</div> </td> <td> - <div class="option_entry Fz-m" >50.00%</div> + <div class="option_entry Fz-m" >323.44%</div> </td> </tr> @@ -1914,19 +1909,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00080000">AAPL141031C00080000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00075000">AAPL141107C00075000</a></div> </td> <td> - <div class="option_entry Fz-m" >25.03</div> + <div class="option_entry Fz-m" >30.05</div> </td> <td> - <div class="option_entry Fz-m" >24.00</div> + <div class="option_entry Fz-m" >33.65</div> </td> <td> - <div class="option_entry Fz-m" >25.45</div> + <div class="option_entry Fz-m" >34.00</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -1940,13 +1935,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="191">191</strong> + <strong data-sq=":volume" data-raw="1">1</strong> </td> <td> - <div class="option_entry Fz-m" >250</div> + <div class="option_entry Fz-m" >1</div> </td> <td> - <div class="option_entry Fz-m" >158.98%</div> + <div class="option_entry Fz-m" >250.78%</div> </td> </tr> @@ -1956,39 +1951,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00085000">AAPL141031C00085000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00080000">AAPL141107C00080000</a></div> </td> <td> - <div class="option_entry Fz-m" >20.20</div> + <div class="option_entry Fz-m" >28.76</div> </td> <td> - <div class="option_entry Fz-m" >19.60</div> + <div class="option_entry Fz-m" >28.65</div> </td> <td> - <div class="option_entry Fz-m" >20.55</div> + <div class="option_entry Fz-m" >28.90</div> </td> <td> - <div class="option_entry Fz-m" >0.21</div> + <div class="option_entry Fz-m" >0.66</div> </td> <td> - <div class="option_entry Fz-m option-change-pos">+1.05%</div> + <div class="option_entry Fz-m option-change-pos">+2.35%</div> </td> <td> - <strong data-sq=":volume" data-raw="5">5</strong> + <strong data-sq=":volume" data-raw="16">16</strong> </td> <td> - <div class="option_entry Fz-m" >729</div> + <div class="option_entry Fz-m" >8</div> </td> <td> - <div class="option_entry Fz-m" >101.76%</div> + <div class="option_entry Fz-m" >178.13%</div> </td> </tr> @@ -1998,39 +1993,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=86.00">86.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00086000">AAPL141031C00086000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00085000">AAPL141107C00085000</a></div> </td> <td> - <div class="option_entry Fz-m" >19.00</div> + <div class="option_entry Fz-m" >23.80</div> </td> <td> - <div class="option_entry Fz-m" >18.20</div> + <div class="option_entry Fz-m" >23.65</div> </td> <td> - <div class="option_entry Fz-m" >19.35</div> + <div class="option_entry Fz-m" >23.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-0.07</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-0.29%</div> </td> <td> - <strong data-sq=":volume" data-raw="3">3</strong> + <strong data-sq=":volume" data-raw="600">600</strong> </td> <td> - <div class="option_entry Fz-m" >13</div> + <div class="option_entry Fz-m" >297</div> </td> <td> - <div class="option_entry Fz-m" >118.56%</div> + <div class="option_entry Fz-m" >146.09%</div> </td> </tr> @@ -2040,39 +2035,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=87.00">87.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00087000">AAPL141031C00087000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00088000">AAPL141107C00088000</a></div> </td> <td> - <div class="option_entry Fz-m" >18.20</div> + <div class="option_entry Fz-m" >20.85</div> </td> <td> - <div class="option_entry Fz-m" >18.15</div> + <div class="option_entry Fz-m" >20.70</div> </td> <td> - <div class="option_entry Fz-m" >18.30</div> + <div class="option_entry Fz-m" >20.90</div> </td> <td> - <div class="option_entry Fz-m" >0.30</div> + <div class="option_entry Fz-m" >-0.45</div> </td> <td> - <div class="option_entry Fz-m option-change-pos">+1.68%</div> + <div class="option_entry Fz-m option-change-neg">-2.11%</div> </td> <td> - <strong data-sq=":volume" data-raw="21">21</strong> + <strong data-sq=":volume" data-raw="90">90</strong> </td> <td> - <div class="option_entry Fz-m" >161</div> + <div class="option_entry Fz-m" >90</div> </td> <td> - <div class="option_entry Fz-m" >104.88%</div> + <div class="option_entry Fz-m" >128.13%</div> </td> </tr> @@ -2082,39 +2077,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00088000">AAPL141031C00088000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00089000">AAPL141107C00089000</a></div> </td> <td> - <div class="option_entry Fz-m" >17.00</div> + <div class="option_entry Fz-m" >19.80</div> </td> <td> - <div class="option_entry Fz-m" >16.20</div> + <div class="option_entry Fz-m" >19.70</div> </td> <td> - <div class="option_entry Fz-m" >17.35</div> + <div class="option_entry Fz-m" >19.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >1.51</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+8.26%</div> </td> <td> - <strong data-sq=":volume" data-raw="19">19</strong> + <strong data-sq=":volume" data-raw="290">290</strong> </td> <td> - <div class="option_entry Fz-m" >148</div> + <div class="option_entry Fz-m" >135</div> </td> <td> - <div class="option_entry Fz-m" >107.72%</div> + <div class="option_entry Fz-m" >121.88%</div> </td> </tr> @@ -2124,39 +2119,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00089000">AAPL141031C00089000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00090000">AAPL141107C00090000</a></div> </td> <td> - <div class="option_entry Fz-m" >13.95</div> + <div class="option_entry Fz-m" >18.85</div> </td> <td> - <div class="option_entry Fz-m" >15.20</div> + <div class="option_entry Fz-m" >18.70</div> </td> <td> - <div class="option_entry Fz-m" >16.35</div> + <div class="option_entry Fz-m" >18.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.20</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+1.07%</div> </td> <td> - <strong data-sq=":volume" data-raw="2">2</strong> + <strong data-sq=":volume" data-raw="480">480</strong> </td> <td> - <div class="option_entry Fz-m" >65</div> + <div class="option_entry Fz-m" >227</div> </td> <td> - <div class="option_entry Fz-m" >102.34%</div> + <div class="option_entry Fz-m" >116.41%</div> </td> </tr> @@ -2166,39 +2161,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00090000">AAPL141031C00090000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00091000">AAPL141107C00091000</a></div> </td> <td> - <div class="option_entry Fz-m" >15.20</div> + <div class="option_entry Fz-m" >17.76</div> </td> <td> - <div class="option_entry Fz-m" >14.40</div> + <div class="option_entry Fz-m" >17.65</div> </td> <td> - <div class="option_entry Fz-m" >15.60</div> + <div class="option_entry Fz-m" >17.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >1.26</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+7.64%</div> </td> <td> - <strong data-sq=":volume" data-raw="168">168</strong> + <strong data-sq=":volume" data-raw="43">43</strong> </td> <td> - <div class="option_entry Fz-m" >687</div> + <div class="option_entry Fz-m" >43</div> </td> <td> - <div class="option_entry Fz-m" >70.70%</div> + <div class="option_entry Fz-m" >110.16%</div> </td> </tr> @@ -2208,39 +2203,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00091000">AAPL141031C00091000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00092000">AAPL141107C00092000</a></div> </td> <td> - <div class="option_entry Fz-m" >14.00</div> + <div class="option_entry Fz-m" >16.90</div> </td> <td> - <div class="option_entry Fz-m" >13.20</div> + <div class="option_entry Fz-m" >16.65</div> </td> <td> - <div class="option_entry Fz-m" >14.35</div> + <div class="option_entry Fz-m" >16.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.84</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+5.23%</div> </td> <td> - <strong data-sq=":volume" data-raw="3">3</strong> + <strong data-sq=":volume" data-raw="240">240</strong> </td> <td> - <div class="option_entry Fz-m" >222</div> + <div class="option_entry Fz-m" >142</div> </td> <td> - <div class="option_entry Fz-m" >91.60%</div> + <div class="option_entry Fz-m" >104.30%</div> </td> </tr> @@ -2250,39 +2245,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00092000">AAPL141031C00092000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00093000">AAPL141107C00093000</a></div> </td> <td> - <div class="option_entry Fz-m" >13.02</div> + <div class="option_entry Fz-m" >15.56</div> </td> <td> - <div class="option_entry Fz-m" >12.20</div> + <div class="option_entry Fz-m" >15.65</div> </td> <td> - <div class="option_entry Fz-m" >13.35</div> + <div class="option_entry Fz-m" >15.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-1.09</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-6.55%</div> </td> <td> - <strong data-sq=":volume" data-raw="2">2</strong> + <strong data-sq=":volume" data-raw="121">121</strong> </td> <td> - <div class="option_entry Fz-m" >237</div> + <div class="option_entry Fz-m" >96</div> </td> <td> - <div class="option_entry Fz-m" >86.13%</div> + <div class="option_entry Fz-m" >98.44%</div> </td> </tr> @@ -2292,39 +2287,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00093000">AAPL141031C00093000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00094000">AAPL141107C00094000</a></div> </td> <td> - <div class="option_entry Fz-m" >12.00</div> + <div class="option_entry Fz-m" >14.87</div> </td> <td> - <div class="option_entry Fz-m" >11.35</div> + <div class="option_entry Fz-m" >14.65</div> </td> <td> - <div class="option_entry Fz-m" >12.60</div> + <div class="option_entry Fz-m" >14.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >1.88</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+14.47%</div> </td> <td> - <strong data-sq=":volume" data-raw="36">36</strong> + <strong data-sq=":volume" data-raw="981">981</strong> </td> <td> - <div class="option_entry Fz-m" >293</div> + <div class="option_entry Fz-m" >510</div> </td> <td> - <div class="option_entry Fz-m" >54.88%</div> + <div class="option_entry Fz-m" >92.58%</div> </td> </tr> @@ -2334,39 +2329,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00094000">AAPL141031C00094000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00095000">AAPL141107C00095000</a></div> </td> <td> - <div class="option_entry Fz-m" >11.04</div> + <div class="option_entry Fz-m" >13.80</div> </td> <td> - <div class="option_entry Fz-m" >10.60</div> + <div class="option_entry Fz-m" >13.65</div> </td> <td> - <div class="option_entry Fz-m" >11.20</div> + <div class="option_entry Fz-m" >13.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.07</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+0.51%</div> </td> <td> - <strong data-sq=":volume" data-raw="109">109</strong> + <strong data-sq=":volume" data-raw="4116">4116</strong> </td> <td> - <div class="option_entry Fz-m" >678</div> + <div class="option_entry Fz-m" >1526</div> </td> <td> - <div class="option_entry Fz-m" >67.77%</div> + <div class="option_entry Fz-m" >86.72%</div> </td> </tr> @@ -2376,39 +2371,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00095000">AAPL141031C00095000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00096000">AAPL141107C00096000</a></div> </td> <td> - <div class="option_entry Fz-m" >10.25</div> + <div class="option_entry Fz-m" >12.95</div> </td> <td> - <div class="option_entry Fz-m" >9.80</div> + <div class="option_entry Fz-m" >12.65</div> </td> <td> - <div class="option_entry Fz-m" >10.05</div> + <div class="option_entry Fz-m" >12.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.20</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+1.57%</div> </td> <td> - <strong data-sq=":volume" data-raw="338">338</strong> + <strong data-sq=":volume" data-raw="891">891</strong> </td> <td> - <div class="option_entry Fz-m" >6269</div> + <div class="option_entry Fz-m" >413</div> </td> <td> - <div class="option_entry Fz-m" >53.42%</div> + <div class="option_entry Fz-m" >81.25%</div> </td> </tr> @@ -2418,39 +2413,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00096000">AAPL141031C00096000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00097000">AAPL141107C00097000</a></div> </td> <td> - <div class="option_entry Fz-m" >9.30</div> + <div class="option_entry Fz-m" >11.75</div> </td> <td> - <div class="option_entry Fz-m" >9.25</div> + <div class="option_entry Fz-m" >11.65</div> </td> <td> - <div class="option_entry Fz-m" >9.40</div> + <div class="option_entry Fz-m" >11.90</div> </td> <td> - <div class="option_entry Fz-m" >0.10</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m option-change-pos">+1.09%</div> + <div class="option_entry Fz-m option-change-pos">+0.17%</div> </td> <td> - <strong data-sq=":volume" data-raw="1">1</strong> + <strong data-sq=":volume" data-raw="1423">1423</strong> </td> <td> - <div class="option_entry Fz-m" >1512</div> + <div class="option_entry Fz-m" >719</div> </td> <td> - <div class="option_entry Fz-m" >63.53%</div> + <div class="option_entry Fz-m" >75.00%</div> </td> </tr> @@ -2460,39 +2455,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00097000">AAPL141031C00097000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00098000">AAPL141107C00098000</a></div> </td> <td> - <div class="option_entry Fz-m" >8.31</div> + <div class="option_entry Fz-m" >10.70</div> </td> <td> - <div class="option_entry Fz-m" >7.80</div> + <div class="option_entry Fz-m" >10.65</div> </td> <td> - <div class="option_entry Fz-m" >8.05</div> + <div class="option_entry Fz-m" >10.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-0.03</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-0.28%</div> </td> <td> - <strong data-sq=":volume" data-raw="280">280</strong> + <strong data-sq=":volume" data-raw="2075">2075</strong> </td> <td> - <div class="option_entry Fz-m" >2129</div> + <div class="option_entry Fz-m" >1130</div> </td> <td> - <div class="option_entry Fz-m" >44.34%</div> + <div class="option_entry Fz-m" >69.53%</div> </td> </tr> @@ -2502,39 +2497,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00098000">AAPL141031C00098000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00099000">AAPL141107C00099000</a></div> </td> <td> - <div class="option_entry Fz-m" >7.33</div> + <div class="option_entry Fz-m" >9.75</div> </td> <td> - <div class="option_entry Fz-m" >7.25</div> + <div class="option_entry Fz-m" >9.70</div> </td> <td> - <div class="option_entry Fz-m" >7.40</div> + <div class="option_entry Fz-m" >9.90</div> </td> <td> - <div class="option_entry Fz-m" >0.17</div> + <div class="option_entry Fz-m" >0.10</div> </td> <td> - <div class="option_entry Fz-m option-change-pos">+2.37%</div> + <div class="option_entry Fz-m option-change-pos">+1.04%</div> </td> <td> - <strong data-sq=":volume" data-raw="31">31</strong> + <strong data-sq=":volume" data-raw="4252">4252</strong> </td> <td> - <div class="option_entry Fz-m" >3441</div> + <div class="option_entry Fz-m" >3893</div> </td> <td> - <div class="option_entry Fz-m" >52.64%</div> + <div class="option_entry Fz-m" >63.67%</div> </td> </tr> @@ -2544,39 +2539,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00099000">AAPL141031C00099000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00100000">AAPL141107C00100000</a></div> </td> <td> - <div class="option_entry Fz-m" >6.24</div> + <div class="option_entry Fz-m" >8.84</div> </td> <td> - <div class="option_entry Fz-m" >6.05</div> + <div class="option_entry Fz-m" >8.75</div> </td> <td> - <div class="option_entry Fz-m" >6.25</div> + <div class="option_entry Fz-m" >8.90</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >-0.04</div> </td> <td> - <div class="option_entry Fz-m option-change-pos">+0.65%</div> + <div class="option_entry Fz-m option-change-neg">-0.45%</div> </td> <td> - <strong data-sq=":volume" data-raw="41">41</strong> + <strong data-sq=":volume" data-raw="22067">22067</strong> </td> <td> - <div class="option_entry Fz-m" >7373</div> + <div class="option_entry Fz-m" >7752</div> </td> <td> - <div class="option_entry Fz-m" >44.24%</div> + <div class="option_entry Fz-m" >57.81%</div> </td> </tr> @@ -2586,39 +2581,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00100000">AAPL141031C00100000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00101000">AAPL141107C00101000</a></div> </td> <td> - <div class="option_entry Fz-m" >5.34</div> + <div class="option_entry Fz-m" >7.80</div> </td> <td> - <div class="option_entry Fz-m" >5.25</div> + <div class="option_entry Fz-m" >7.75</div> </td> <td> - <div class="option_entry Fz-m" >5.45</div> + <div class="option_entry Fz-m" >7.90</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-0.19%</div> + <div class="option_entry Fz-m option-change-pos">+0.52%</div> </td> <td> - <strong data-sq=":volume" data-raw="48">48</strong> + <strong data-sq=":volume" data-raw="6048">6048</strong> </td> <td> - <div class="option_entry Fz-m" >12778</div> + <div class="option_entry Fz-m" >1795</div> </td> <td> - <div class="option_entry Fz-m" >45.51%</div> + <div class="option_entry Fz-m" >51.95%</div> </td> </tr> @@ -2628,39 +2623,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00101000">AAPL141031C00101000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00102000">AAPL141107C00102000</a></div> </td> <td> - <div class="option_entry Fz-m" >4.40</div> + <div class="option_entry Fz-m" >6.90</div> </td> <td> - <div class="option_entry Fz-m" >4.30</div> + <div class="option_entry Fz-m" >6.75</div> </td> <td> - <div class="option_entry Fz-m" >4.50</div> + <div class="option_entry Fz-m" >6.90</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.10</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+1.47%</div> </td> <td> - <strong data-sq=":volume" data-raw="125">125</strong> + <strong data-sq=":volume" data-raw="3488">3488</strong> </td> <td> - <div class="option_entry Fz-m" >10047</div> + <div class="option_entry Fz-m" >2828</div> </td> <td> - <div class="option_entry Fz-m" >40.87%</div> + <div class="option_entry Fz-m" >46.09%</div> </td> </tr> @@ -2670,39 +2665,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00102000">AAPL141031C00102000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00103000">AAPL141107C00103000</a></div> </td> <td> - <div class="option_entry Fz-m" >3.40</div> + <div class="option_entry Fz-m" >5.85</div> </td> <td> - <div class="option_entry Fz-m" >3.40</div> + <div class="option_entry Fz-m" >5.75</div> </td> <td> - <div class="option_entry Fz-m" >3.55</div> + <div class="option_entry Fz-m" >5.90</div> </td> <td> - <div class="option_entry Fz-m" >-0.10</div> + <div class="option_entry Fz-m" >0.15</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-2.86%</div> + <div class="option_entry Fz-m option-change-pos">+2.63%</div> </td> <td> - <strong data-sq=":volume" data-raw="1344">1344</strong> + <strong data-sq=":volume" data-raw="7725">7725</strong> </td> <td> - <div class="option_entry Fz-m" >11868</div> + <div class="option_entry Fz-m" >3505</div> </td> <td> - <div class="option_entry Fz-m" >35.74%</div> + <div class="option_entry Fz-m" >40.43%</div> </td> </tr> @@ -2712,39 +2707,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00103000">AAPL141031C00103000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00104000">AAPL141107C00104000</a></div> </td> <td> - <div class="option_entry Fz-m" >2.59</div> + <div class="option_entry Fz-m" >4.80</div> </td> <td> - <div class="option_entry Fz-m" >2.56</div> + <div class="option_entry Fz-m" >4.75</div> </td> <td> - <div class="option_entry Fz-m" >2.59</div> + <div class="option_entry Fz-m" >4.90</div> </td> <td> - <div class="option_entry Fz-m" >-0.06</div> + <div class="option_entry Fz-m" >0.20</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-2.26%</div> + <div class="option_entry Fz-m option-change-pos">+4.35%</div> </td> <td> - <strong data-sq=":volume" data-raw="932">932</strong> + <strong data-sq=":volume" data-raw="6271">6271</strong> </td> <td> - <div class="option_entry Fz-m" >12198</div> + <div class="option_entry Fz-m" >3082</div> </td> <td> - <div class="option_entry Fz-m" >29.79%</div> + <div class="option_entry Fz-m" >34.38%</div> </td> </tr> @@ -2754,165 +2749,165 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00104000">AAPL141031C00104000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00105000">AAPL141107C00105000</a></div> </td> <td> - <div class="option_entry Fz-m" >1.83</div> + <div class="option_entry Fz-m" >3.80</div> </td> <td> - <div class="option_entry Fz-m" >1.78</div> + <div class="option_entry Fz-m" >3.75</div> </td> <td> - <div class="option_entry Fz-m" >1.83</div> + <div class="option_entry Fz-m" >3.90</div> </td> <td> - <div class="option_entry Fz-m" >-0.06</div> + <div class="option_entry Fz-m" >0.20</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-3.17%</div> + <div class="option_entry Fz-m option-change-pos">+5.56%</div> </td> <td> - <strong data-sq=":volume" data-raw="1292">1292</strong> + <strong data-sq=":volume" data-raw="16703">16703</strong> </td> <td> - <div class="option_entry Fz-m" >13661</div> + <div class="option_entry Fz-m" >6176</div> </td> <td> - <div class="option_entry Fz-m" >27.30%</div> + <div class="option_entry Fz-m" >28.52%</div> </td> </tr> - <tr data-row="22" data-row-quote="_" class=" + <tr data-row="22" data-row-quote="_" class="in-the-money even "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00105000">AAPL141031C00105000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00106000">AAPL141107C00106000</a></div> </td> <td> - <div class="option_entry Fz-m" >1.18</div> + <div class="option_entry Fz-m" >2.78</div> </td> <td> - <div class="option_entry Fz-m" >1.18</div> + <div class="option_entry Fz-m" >2.78</div> </td> <td> - <div class="option_entry Fz-m" >1.19</div> + <div class="option_entry Fz-m" >2.87</div> </td> <td> - <div class="option_entry Fz-m" >-0.07</div> + <div class="option_entry Fz-m" >0.16</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-5.60%</div> + <div class="option_entry Fz-m option-change-pos">+6.11%</div> </td> <td> - <strong data-sq=":volume" data-raw="2104">2104</strong> + <strong data-sq=":volume" data-raw="21589">21589</strong> </td> <td> - <div class="option_entry Fz-m" >25795</div> + <div class="option_entry Fz-m" >9798</div> </td> <td> - <div class="option_entry Fz-m" >25.29%</div> + <div class="option_entry Fz-m" >17.19%</div> </td> </tr> - <tr data-row="23" data-row-quote="_" class=" + <tr data-row="23" data-row-quote="_" class="in-the-money odd "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00106000">AAPL141031C00106000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00107000">AAPL141107C00107000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.67</div> + <div class="option_entry Fz-m" >1.83</div> </td> <td> - <div class="option_entry Fz-m" >0.67</div> + <div class="option_entry Fz-m" >1.78</div> </td> <td> - <div class="option_entry Fz-m" >0.70</div> + <div class="option_entry Fz-m" >1.86</div> </td> <td> - <div class="option_entry Fz-m" >-0.05</div> + <div class="option_entry Fz-m" >0.07</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-6.94%</div> + <div class="option_entry Fz-m option-change-pos">+3.98%</div> </td> <td> - <strong data-sq=":volume" data-raw="950">950</strong> + <strong data-sq=":volume" data-raw="22126">22126</strong> </td> <td> - <div class="option_entry Fz-m" >16610</div> + <div class="option_entry Fz-m" >13365</div> </td> <td> - <div class="option_entry Fz-m" >23.73%</div> + <div class="option_entry Fz-m" >6.25%</div> </td> </tr> - <tr data-row="24" data-row-quote="_" class=" + <tr data-row="24" data-row-quote="_" class="in-the-money even "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00107000">AAPL141031C00107000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00108000">AAPL141107C00108000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.36</div> + <div class="option_entry Fz-m" >0.86</div> </td> <td> - <div class="option_entry Fz-m" >0.36</div> + <div class="option_entry Fz-m" >0.87</div> </td> <td> - <div class="option_entry Fz-m" >0.37</div> + <div class="option_entry Fz-m" >0.90</div> </td> <td> - <div class="option_entry Fz-m" >-0.05</div> + <div class="option_entry Fz-m" >-0.12</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-12.20%</div> + <div class="option_entry Fz-m option-change-neg">-12.24%</div> </td> <td> - <strong data-sq=":volume" data-raw="7129">7129</strong> + <strong data-sq=":volume" data-raw="15256">15256</strong> </td> <td> - <div class="option_entry Fz-m" >15855</div> + <div class="option_entry Fz-m" >14521</div> </td> <td> - <div class="option_entry Fz-m" >22.66%</div> + <div class="option_entry Fz-m" >8.89%</div> </td> </tr> @@ -2922,39 +2917,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00108000">AAPL141031C00108000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00109000">AAPL141107C00109000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.19</div> + <div class="option_entry Fz-m" >0.37</div> </td> <td> - <div class="option_entry Fz-m" >0.18</div> + <div class="option_entry Fz-m" >0.36</div> </td> <td> - <div class="option_entry Fz-m" >0.20</div> + <div class="option_entry Fz-m" >0.38</div> </td> <td> - <div class="option_entry Fz-m" >-0.04</div> + <div class="option_entry Fz-m" >-0.11</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-17.39%</div> + <div class="option_entry Fz-m option-change-neg">-22.92%</div> </td> <td> - <strong data-sq=":volume" data-raw="2455">2455</strong> + <strong data-sq=":volume" data-raw="30797">30797</strong> </td> <td> - <div class="option_entry Fz-m" >8253</div> + <div class="option_entry Fz-m" >25097</div> </td> <td> - <div class="option_entry Fz-m" >22.85%</div> + <div class="option_entry Fz-m" >13.87%</div> </td> </tr> @@ -2964,39 +2959,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00109000">AAPL141031C00109000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00110000">AAPL141107C00110000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.10</div> + <div class="option_entry Fz-m" >0.14</div> </td> <td> - <div class="option_entry Fz-m" >0.09</div> + <div class="option_entry Fz-m" >0.14</div> </td> <td> - <div class="option_entry Fz-m" >0.10</div> + <div class="option_entry Fz-m" >0.15</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >-0.07</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-9.09%</div> + <div class="option_entry Fz-m option-change-neg">-33.33%</div> </td> <td> - <strong data-sq=":volume" data-raw="382">382</strong> + <strong data-sq=":volume" data-raw="27366">27366</strong> </td> <td> - <div class="option_entry Fz-m" >3328</div> + <div class="option_entry Fz-m" >44444</div> </td> <td> - <div class="option_entry Fz-m" >23.05%</div> + <div class="option_entry Fz-m" >16.70%</div> </td> </tr> @@ -3006,39 +3001,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00110000">AAPL141031C00110000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00111000">AAPL141107C00111000</a></div> </td> <td> <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >0.05</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >0.06</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >-0.04</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-28.57%</div> + <div class="option_entry Fz-m option-change-neg">-44.44%</div> </td> <td> - <strong data-sq=":volume" data-raw="803">803</strong> + <strong data-sq=":volume" data-raw="10243">10243</strong> </td> <td> - <div class="option_entry Fz-m" >9086</div> + <div class="option_entry Fz-m" >17981</div> </td> <td> - <div class="option_entry Fz-m" >24.22%</div> + <div class="option_entry Fz-m" >18.36%</div> </td> </tr> @@ -3048,39 +3043,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=112.00">112.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00111000">AAPL141031C00111000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00112000">AAPL141107C00112000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.03</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.03</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >-0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-40.00%</div> + <div class="option_entry Fz-m option-change-neg">-33.33%</div> </td> <td> - <strong data-sq=":volume" data-raw="73">73</strong> + <strong data-sq=":volume" data-raw="2280">2280</strong> </td> <td> - <div class="option_entry Fz-m" >1275</div> + <div class="option_entry Fz-m" >13500</div> </td> <td> - <div class="option_entry Fz-m" >25.98%</div> + <div class="option_entry Fz-m" >22.07%</div> </td> </tr> @@ -3090,19 +3085,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=112.00">112.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=113.00">113.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00112000">AAPL141031C00112000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00113000">AAPL141107C00113000</a></div> </td> <td> <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3116,13 +3111,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="187">187</strong> + <strong data-sq=":volume" data-raw="582">582</strong> </td> <td> - <div class="option_entry Fz-m" >775</div> + <div class="option_entry Fz-m" >9033</div> </td> <td> - <div class="option_entry Fz-m" >29.30%</div> + <div class="option_entry Fz-m" >25.78%</div> </td> </tr> @@ -3132,39 +3127,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=113.00">113.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=114.00">114.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00113000">AAPL141031C00113000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00114000">AAPL141107C00114000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-0.01</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-50.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="33">33</strong> + <strong data-sq=":volume" data-raw="496">496</strong> </td> <td> - <div class="option_entry Fz-m" >1198</div> + <div class="option_entry Fz-m" >5402</div> </td> <td> - <div class="option_entry Fz-m" >32.42%</div> + <div class="option_entry Fz-m" >30.86%</div> </td> </tr> @@ -3174,19 +3169,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=114.00">114.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00114000">AAPL141031C00114000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00115000">AAPL141107C00115000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3200,13 +3195,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="170">170</strong> + <strong data-sq=":volume" data-raw="1965">1965</strong> </td> <td> - <div class="option_entry Fz-m" >931</div> + <div class="option_entry Fz-m" >4971</div> </td> <td> - <div class="option_entry Fz-m" >35.74%</div> + <div class="option_entry Fz-m" >35.55%</div> </td> </tr> @@ -3216,10 +3211,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=116.00">116.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00115000">AAPL141031C00115000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00116000">AAPL141107C00116000</a></div> </td> <td> <div class="option_entry Fz-m" >0.01</div> @@ -3228,7 +3223,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3242,13 +3237,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="8">8</strong> + <strong data-sq=":volume" data-raw="71">71</strong> </td> <td> - <div class="option_entry Fz-m" >550</div> + <div class="option_entry Fz-m" >2303</div> </td> <td> - <div class="option_entry Fz-m" >35.16%</div> + <div class="option_entry Fz-m" >36.72%</div> </td> </tr> @@ -3258,19 +3253,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=116.00">116.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=117.00">117.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00116000">AAPL141031C00116000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00117000">AAPL141107C00117000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3284,13 +3279,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="43">43</strong> + <strong data-sq=":volume" data-raw="2">2</strong> </td> <td> - <div class="option_entry Fz-m" >86</div> + <div class="option_entry Fz-m" >899</div> </td> <td> - <div class="option_entry Fz-m" >37.89%</div> + <div class="option_entry Fz-m" >40.63%</div> </td> </tr> @@ -3303,16 +3298,16 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=118.00">118.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00118000">AAPL141031C00118000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00118000">AAPL141107C00118000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.05</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3326,13 +3321,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="49">49</strong> + <strong data-sq=":volume" data-raw="30">30</strong> </td> <td> - <div class="option_entry Fz-m" >49</div> + <div class="option_entry Fz-m" >38</div> </td> <td> - <div class="option_entry Fz-m" >47.66%</div> + <div class="option_entry Fz-m" >45.31%</div> </td> </tr> @@ -3345,16 +3340,16 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=119.00">119.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00119000">AAPL141031C00119000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00119000">AAPL141107C00119000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.09</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3368,13 +3363,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="5">5</strong> + <strong data-sq=":volume" data-raw="935">935</strong> </td> <td> - <div class="option_entry Fz-m" >22</div> + <div class="option_entry Fz-m" >969</div> </td> <td> - <div class="option_entry Fz-m" >46.09%</div> + <div class="option_entry Fz-m" >49.22%</div> </td> </tr> @@ -3387,16 +3382,16 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=120.00">120.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00120000">AAPL141031C00120000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00120000">AAPL141107C00120000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3410,13 +3405,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="43">43</strong> + <strong data-sq=":volume" data-raw="3800">3800</strong> </td> <td> - <div class="option_entry Fz-m" >69</div> + <div class="option_entry Fz-m" >558</div> </td> <td> - <div class="option_entry Fz-m" >48.83%</div> + <div class="option_entry Fz-m" >50.00%</div> </td> </tr> @@ -3426,10 +3421,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=121.00">121.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=123.00">123.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00121000">AAPL141031C00121000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00123000">AAPL141107C00123000</a></div> </td> <td> <div class="option_entry Fz-m" >0.01</div> @@ -3438,7 +3433,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3452,13 +3447,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="0">0</strong> + <strong data-sq=":volume" data-raw="176">176</strong> </td> <td> - <div class="option_entry Fz-m" >10</div> + <div class="option_entry Fz-m" >176</div> </td> <td> - <div class="option_entry Fz-m" >51.56%</div> + <div class="option_entry Fz-m" >59.38%</div> </td> </tr> @@ -3468,19 +3463,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=122.00">122.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=130.00">130.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00122000">AAPL141031C00122000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107C00130000">AAPL141107C00130000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3494,13 +3489,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="1">1</strong> + <strong data-sq=":volume" data-raw="10">10</strong> </td> <td> - <div class="option_entry Fz-m" >3</div> + <div class="option_entry Fz-m" >499</div> </td> <td> - <div class="option_entry Fz-m" >50.00%</div> + <div class="option_entry Fz-m" >84.38%</div> </td> </tr> @@ -3709,10 +3704,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=70.00">70.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00075000">AAPL141031P00075000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00070000">AAPL141107P00070000</a></div> </td> <td> <div class="option_entry Fz-m" >0.01</div> @@ -3735,13 +3730,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="3">3</strong> + <strong data-sq=":volume" data-raw="10">10</strong> </td> <td> - <div class="option_entry Fz-m" >365</div> + <div class="option_entry Fz-m" >295</div> </td> <td> - <div class="option_entry Fz-m" >96.88%</div> + <div class="option_entry Fz-m" >196.88%</div> </td> </tr> @@ -3751,10 +3746,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00080000">AAPL141031P00080000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00075000">AAPL141107P00075000</a></div> </td> <td> <div class="option_entry Fz-m" >0.01</div> @@ -3777,13 +3772,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="500">500</strong> + <strong data-sq=":volume" data-raw="525">525</strong> </td> <td> - <div class="option_entry Fz-m" >973</div> + <div class="option_entry Fz-m" >2086</div> </td> <td> - <div class="option_entry Fz-m" >85.94%</div> + <div class="option_entry Fz-m" >181.25%</div> </td> </tr> @@ -3793,10 +3788,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00085000">AAPL141031P00085000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00080000">AAPL141107P00080000</a></div> </td> <td> <div class="option_entry Fz-m" >0.01</div> @@ -3805,7 +3800,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="option_entry Fz-m" >0.00</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3819,13 +3814,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="50">50</strong> + <strong data-sq=":volume" data-raw="3672">3672</strong> </td> <td> - <div class="option_entry Fz-m" >1303</div> + <div class="option_entry Fz-m" >7955</div> </td> <td> - <div class="option_entry Fz-m" >68.75%</div> + <div class="option_entry Fz-m" >143.75%</div> </td> </tr> @@ -3835,16 +3830,16 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=86.00">86.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00086000">AAPL141031P00086000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00085000">AAPL141107P00085000</a></div> </td> <td> <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.02</div> @@ -3861,13 +3856,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="3">3</strong> + <strong data-sq=":volume" data-raw="2120">2120</strong> </td> <td> - <div class="option_entry Fz-m" >655</div> + <div class="option_entry Fz-m" >1425</div> </td> <td> - <div class="option_entry Fz-m" >64.84%</div> + <div class="option_entry Fz-m" >129.69%</div> </td> </tr> @@ -3877,13 +3872,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=87.00">87.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00087000">AAPL141031P00087000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00088000">AAPL141107P00088000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -3903,13 +3898,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="39">39</strong> + <strong data-sq=":volume" data-raw="301">301</strong> </td> <td> - <div class="option_entry Fz-m" >808</div> + <div class="option_entry Fz-m" >1072</div> </td> <td> - <div class="option_entry Fz-m" >60.94%</div> + <div class="option_entry Fz-m" >109.38%</div> </td> </tr> @@ -3919,39 +3914,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00088000">AAPL141031P00088000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00089000">AAPL141107P00089000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.01</div> + <div class="option_entry Fz-m" >0.00</div> </td> <td> + <div class="option_entry Fz-m">0.00%</div> - <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="30">30</strong> + <strong data-sq=":volume" data-raw="211">211</strong> </td> <td> - <div class="option_entry Fz-m" >1580</div> + <div class="option_entry Fz-m" >760</div> </td> <td> - <div class="option_entry Fz-m" >57.81%</div> + <div class="option_entry Fz-m" >107.81%</div> </td> </tr> @@ -3961,10 +3956,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00089000">AAPL141031P00089000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00090000">AAPL141107P00090000</a></div> </td> <td> <div class="option_entry Fz-m" >0.02</div> @@ -3973,27 +3968,27 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="350">350</strong> + <strong data-sq=":volume" data-raw="4870">4870</strong> </td> <td> - <div class="option_entry Fz-m" >794</div> + <div class="option_entry Fz-m" >3693</div> </td> <td> - <div class="option_entry Fz-m" >60.94%</div> + <div class="option_entry Fz-m" >103.13%</div> </td> </tr> @@ -4003,10 +3998,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00090000">AAPL141031P00090000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00091000">AAPL141107P00091000</a></div> </td> <td> <div class="option_entry Fz-m" >0.02</div> @@ -4018,24 +4013,24 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="162">162</strong> + <strong data-sq=":volume" data-raw="333">333</strong> </td> <td> - <div class="option_entry Fz-m" >7457</div> + <div class="option_entry Fz-m" >1196</div> </td> <td> - <div class="option_entry Fz-m" >53.91%</div> + <div class="option_entry Fz-m" >96.88%</div> </td> </tr> @@ -4045,10 +4040,10 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00091000">AAPL141031P00091000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00092000">AAPL141107P00092000</a></div> </td> <td> <div class="option_entry Fz-m" >0.02</div> @@ -4057,27 +4052,27 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="109">109</strong> + <strong data-sq=":volume" data-raw="4392">4392</strong> </td> <td> - <div class="option_entry Fz-m" >2469</div> + <div class="option_entry Fz-m" >1294</div> </td> <td> - <div class="option_entry Fz-m" >53.91%</div> + <div class="option_entry Fz-m" >92.19%</div> </td> </tr> @@ -4087,39 +4082,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00092000">AAPL141031P00092000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00093000">AAPL141107P00093000</a></div> </td> <td> <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-33.33%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="702">702</strong> + <strong data-sq=":volume" data-raw="3780">3780</strong> </td> <td> - <div class="option_entry Fz-m" >21633</div> + <div class="option_entry Fz-m" >778</div> </td> <td> - <div class="option_entry Fz-m" >50.00%</div> + <div class="option_entry Fz-m" >89.84%</div> </td> </tr> @@ -4129,39 +4124,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00093000">AAPL141031P00093000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00094000">AAPL141107P00094000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.03</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.03</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-25.00%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="1150">1150</strong> + <strong data-sq=":volume" data-raw="1463">1463</strong> </td> <td> - <div class="option_entry Fz-m" >21876</div> + <div class="option_entry Fz-m" >5960</div> </td> <td> - <div class="option_entry Fz-m" >49.61%</div> + <div class="option_entry Fz-m" >86.72%</div> </td> </tr> @@ -4171,39 +4166,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00094000">AAPL141031P00094000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00095000">AAPL141107P00095000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.03</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-40.00%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="30">30</strong> + <strong data-sq=":volume" data-raw="2098">2098</strong> </td> <td> - <div class="option_entry Fz-m" >23436</div> + <div class="option_entry Fz-m" >11469</div> </td> <td> - <div class="option_entry Fz-m" >45.70%</div> + <div class="option_entry Fz-m" >81.25%</div> </td> </tr> @@ -4213,19 +4208,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00095000">AAPL141031P00095000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00096000">AAPL141107P00096000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >0.03</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.05</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -4239,13 +4234,13 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="178">178</strong> + <strong data-sq=":volume" data-raw="4163">4163</strong> </td> <td> - <div class="option_entry Fz-m" >30234</div> + <div class="option_entry Fz-m" >3496</div> </td> <td> - <div class="option_entry Fz-m" >43.56%</div> + <div class="option_entry Fz-m" >75.78%</div> </td> </tr> @@ -4255,39 +4250,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00096000">AAPL141031P00096000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00097000">AAPL141107P00097000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >0.04</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.06</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-20.00%</div> + <div class="option_entry Fz-m option-change-pos">+50.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="5">5</strong> + <strong data-sq=":volume" data-raw="4364">4364</strong> </td> <td> - <div class="option_entry Fz-m" >13213</div> + <div class="option_entry Fz-m" >1848</div> </td> <td> - <div class="option_entry Fz-m" >40.82%</div> + <div class="option_entry Fz-m" >71.88%</div> </td> </tr> @@ -4297,39 +4292,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00097000">AAPL141031P00097000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00098000">AAPL141107P00098000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.05</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.05</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.06</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >0.00</div> </td> <td> + <div class="option_entry Fz-m">0.00%</div> - <div class="option_entry Fz-m option-change-neg">-16.67%</div> </td> <td> - <strong data-sq=":volume" data-raw="150">150</strong> + <strong data-sq=":volume" data-raw="1960">1960</strong> </td> <td> - <div class="option_entry Fz-m" >3145</div> + <div class="option_entry Fz-m" >6036</div> </td> <td> - <div class="option_entry Fz-m" >36.91%</div> + <div class="option_entry Fz-m" >66.41%</div> </td> </tr> @@ -4339,39 +4334,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00098000">AAPL141031P00098000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00099000">AAPL141107P00099000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.07</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >0.06</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m" >0.07</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >-0.01</div> + <div class="option_entry Fz-m" >0.02</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-12.50%</div> + <div class="option_entry Fz-m option-change-pos">+100.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="29">29</strong> + <strong data-sq=":volume" data-raw="852">852</strong> </td> <td> - <div class="option_entry Fz-m" >5478</div> + <div class="option_entry Fz-m" >5683</div> </td> <td> - <div class="option_entry Fz-m" >33.79%</div> + <div class="option_entry Fz-m" >60.94%</div> </td> </tr> @@ -4381,39 +4376,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00099000">AAPL141031P00099000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00100000">AAPL141107P00100000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.08</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >0.08</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >0.09</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-20.00%</div> + <div class="option_entry Fz-m option-change-pos">+50.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="182">182</strong> + <strong data-sq=":volume" data-raw="2204">2204</strong> </td> <td> - <div class="option_entry Fz-m" >4769</div> + <div class="option_entry Fz-m" >4774</div> </td> <td> - <div class="option_entry Fz-m" >31.25%</div> + <div class="option_entry Fz-m" >57.81%</div> </td> </tr> @@ -4423,39 +4418,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00100000">AAPL141031P00100000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00101000">AAPL141107P00101000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.11</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >0.10</div> + <div class="option_entry Fz-m" >0.03</div> </td> <td> - <div class="option_entry Fz-m" >0.11</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-15.38%</div> + <div class="option_entry Fz-m option-change-pos">+33.33%</div> </td> <td> - <strong data-sq=":volume" data-raw="1294">1294</strong> + <strong data-sq=":volume" data-raw="3596">3596</strong> </td> <td> - <div class="option_entry Fz-m" >13038</div> + <div class="option_entry Fz-m" >2621</div> </td> <td> - <div class="option_entry Fz-m" >28.13%</div> + <div class="option_entry Fz-m" >51.95%</div> </td> </tr> @@ -4465,39 +4460,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00101000">AAPL141031P00101000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00102000">AAPL141107P00102000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.14</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >0.14</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >0.15</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >-0.03</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-17.65%</div> + <div class="option_entry Fz-m option-change-pos">+25.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="219">219</strong> + <strong data-sq=":volume" data-raw="2445">2445</strong> </td> <td> - <div class="option_entry Fz-m" >9356</div> + <div class="option_entry Fz-m" >7791</div> </td> <td> - <div class="option_entry Fz-m" >25.54%</div> + <div class="option_entry Fz-m" >48.05%</div> </td> </tr> @@ -4507,39 +4502,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00102000">AAPL141031P00102000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00103000">AAPL141107P00103000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.21</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >0.21</div> + <div class="option_entry Fz-m" >0.04</div> </td> <td> - <div class="option_entry Fz-m" >0.22</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >-0.03</div> + <div class="option_entry Fz-m" >0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-12.50%</div> + <div class="option_entry Fz-m option-change-pos">+25.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="1614">1614</strong> + <strong data-sq=":volume" data-raw="8386">8386</strong> </td> <td> - <div class="option_entry Fz-m" >10835</div> + <div class="option_entry Fz-m" >7247</div> </td> <td> - <div class="option_entry Fz-m" >23.19%</div> + <div class="option_entry Fz-m" >42.19%</div> </td> </tr> @@ -4549,39 +4544,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00103000">AAPL141031P00103000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00104000">AAPL141107P00104000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.35</div> + <div class="option_entry Fz-m" >0.06</div> </td> <td> - <div class="option_entry Fz-m" >0.34</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m" >0.35</div> + <div class="option_entry Fz-m" >0.06</div> </td> <td> - <div class="option_entry Fz-m" >-0.03</div> + <div class="option_entry Fz-m" >0.00</div> </td> <td> + <div class="option_entry Fz-m">0.00%</div> - <div class="option_entry Fz-m option-change-neg">-7.89%</div> </td> <td> - <strong data-sq=":volume" data-raw="959">959</strong> + <strong data-sq=":volume" data-raw="2939">2939</strong> </td> <td> - <div class="option_entry Fz-m" >11228</div> + <div class="option_entry Fz-m" >12639</div> </td> <td> - <div class="option_entry Fz-m" >21.29%</div> + <div class="option_entry Fz-m" >37.31%</div> </td> </tr> @@ -4591,165 +4586,165 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00104000">AAPL141031P00104000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00105000">AAPL141107P00105000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.58</div> + <div class="option_entry Fz-m" >0.08</div> </td> <td> - <div class="option_entry Fz-m" >0.58</div> + <div class="option_entry Fz-m" >0.07</div> </td> <td> - <div class="option_entry Fz-m" >0.60</div> + <div class="option_entry Fz-m" >0.08</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >-0.01</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-3.33%</div> + <div class="option_entry Fz-m option-change-neg">-11.11%</div> </td> <td> - <strong data-sq=":volume" data-raw="1424">1424</strong> + <strong data-sq=":volume" data-raw="2407">2407</strong> </td> <td> - <div class="option_entry Fz-m" >9823</div> + <div class="option_entry Fz-m" >14842</div> </td> <td> - <div class="option_entry Fz-m" >20.22%</div> + <div class="option_entry Fz-m" >33.01%</div> </td> </tr> - <tr data-row="22" data-row-quote="_" class="in-the-money + <tr data-row="22" data-row-quote="_" class=" even "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00105000">AAPL141031P00105000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00106000">AAPL141107P00106000</a></div> </td> <td> - <div class="option_entry Fz-m" >0.94</div> + <div class="option_entry Fz-m" >0.10</div> </td> <td> - <div class="option_entry Fz-m" >0.93</div> + <div class="option_entry Fz-m" >0.11</div> </td> <td> - <div class="option_entry Fz-m" >0.96</div> + <div class="option_entry Fz-m" >0.12</div> </td> <td> - <div class="option_entry Fz-m" >-0.05</div> + <div class="option_entry Fz-m" >-0.08</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-5.05%</div> + <div class="option_entry Fz-m option-change-neg">-44.44%</div> </td> <td> - <strong data-sq=":volume" data-raw="2934">2934</strong> + <strong data-sq=":volume" data-raw="8659">8659</strong> </td> <td> - <div class="option_entry Fz-m" >7424</div> + <div class="option_entry Fz-m" >13528</div> </td> <td> - <div class="option_entry Fz-m" >18.56%</div> + <div class="option_entry Fz-m" >29.10%</div> </td> </tr> - <tr data-row="23" data-row-quote="_" class="in-the-money + <tr data-row="23" data-row-quote="_" class=" odd "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00106000">AAPL141031P00106000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00107000">AAPL141107P00107000</a></div> </td> <td> - <div class="option_entry Fz-m" >1.49</div> + <div class="option_entry Fz-m" >0.22</div> </td> <td> - <div class="option_entry Fz-m" >1.45</div> + <div class="option_entry Fz-m" >0.21</div> </td> <td> - <div class="option_entry Fz-m" >1.50</div> + <div class="option_entry Fz-m" >0.22</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >-0.14</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-1.32%</div> + <div class="option_entry Fz-m option-change-neg">-38.89%</div> </td> <td> - <strong data-sq=":volume" data-raw="384">384</strong> + <strong data-sq=":volume" data-raw="5825">5825</strong> </td> <td> - <div class="option_entry Fz-m" >1441</div> + <div class="option_entry Fz-m" >17069</div> </td> <td> - <div class="option_entry Fz-m" >16.99%</div> + <div class="option_entry Fz-m" >26.47%</div> </td> </tr> - <tr data-row="24" data-row-quote="_" class="in-the-money + <tr data-row="24" data-row-quote="_" class=" even "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00107000">AAPL141031P00107000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00108000">AAPL141107P00108000</a></div> </td> <td> - <div class="option_entry Fz-m" >2.15</div> + <div class="option_entry Fz-m" >0.50</div> </td> <td> - <div class="option_entry Fz-m" >2.13</div> + <div class="option_entry Fz-m" >0.48</div> </td> <td> - <div class="option_entry Fz-m" >2.15</div> + <div class="option_entry Fz-m" >0.50</div> </td> <td> - <div class="option_entry Fz-m" >-0.03</div> + <div class="option_entry Fz-m" >-0.22</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-1.38%</div> + <div class="option_entry Fz-m option-change-neg">-30.56%</div> </td> <td> - <strong data-sq=":volume" data-raw="104">104</strong> + <strong data-sq=":volume" data-raw="12554">12554</strong> </td> <td> - <div class="option_entry Fz-m" >573</div> + <div class="option_entry Fz-m" >12851</div> </td> <td> - <div class="option_entry Fz-m" >11.82%</div> + <div class="option_entry Fz-m" >26.95%</div> </td> </tr> @@ -4759,39 +4754,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00108000">AAPL141031P00108000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00109000">AAPL141107P00109000</a></div> </td> <td> - <div class="option_entry Fz-m" >2.99</div> + <div class="option_entry Fz-m" >1.01</div> </td> <td> - <div class="option_entry Fz-m" >2.88</div> + <div class="option_entry Fz-m" >0.96</div> </td> <td> - <div class="option_entry Fz-m" >3.05</div> + <div class="option_entry Fz-m" >1.02</div> </td> <td> - <div class="option_entry Fz-m" >-0.04</div> + <div class="option_entry Fz-m" >-0.27</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-1.32%</div> + <div class="option_entry Fz-m option-change-neg">-21.09%</div> </td> <td> - <strong data-sq=":volume" data-raw="5">5</strong> + <strong data-sq=":volume" data-raw="9877">9877</strong> </td> <td> - <div class="option_entry Fz-m" >165</div> + <div class="option_entry Fz-m" >9295</div> </td> <td> - <div class="option_entry Fz-m" >0.00%</div> + <div class="option_entry Fz-m" >29.49%</div> </td> </tr> @@ -4801,39 +4796,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00109000">AAPL141031P00109000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00110000">AAPL141107P00110000</a></div> </td> <td> - <div class="option_entry Fz-m" >3.88</div> + <div class="option_entry Fz-m" >1.76</div> </td> <td> - <div class="option_entry Fz-m" >3.80</div> + <div class="option_entry Fz-m" >1.74</div> </td> <td> - <div class="option_entry Fz-m" >3.95</div> + <div class="option_entry Fz-m" >1.89</div> </td> <td> - <div class="option_entry Fz-m" >-0.02</div> + <div class="option_entry Fz-m" >-0.24</div> </td> <td> - <div class="option_entry Fz-m option-change-neg">-0.51%</div> + <div class="option_entry Fz-m option-change-neg">-12.00%</div> </td> <td> - <strong data-sq=":volume" data-raw="10">10</strong> + <strong data-sq=":volume" data-raw="2722">2722</strong> </td> <td> - <div class="option_entry Fz-m" >115</div> + <div class="option_entry Fz-m" >7129</div> </td> <td> - <div class="option_entry Fz-m" >0.00%</div> + <div class="option_entry Fz-m" >38.28%</div> </td> </tr> @@ -4843,39 +4838,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00110000">AAPL141031P00110000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00111000">AAPL141107P00111000</a></div> </td> <td> - <div class="option_entry Fz-m" >4.95</div> + <div class="option_entry Fz-m" >2.71</div> </td> <td> - <div class="option_entry Fz-m" >4.95</div> + <div class="option_entry Fz-m" >2.64</div> </td> <td> - <div class="option_entry Fz-m" >5.20</div> + <div class="option_entry Fz-m" >2.75</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-0.05</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-1.81%</div> </td> <td> - <strong data-sq=":volume" data-raw="275">275</strong> + <strong data-sq=":volume" data-raw="4925">4925</strong> </td> <td> - <div class="option_entry Fz-m" >302</div> + <div class="option_entry Fz-m" >930</div> </td> <td> - <div class="option_entry Fz-m" >27.05%</div> + <div class="option_entry Fz-m" >44.14%</div> </td> </tr> @@ -4885,39 +4880,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=112.00">112.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00111000">AAPL141031P00111000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00112000">AAPL141107P00112000</a></div> </td> <td> - <div class="option_entry Fz-m" >6.05</div> + <div class="option_entry Fz-m" >3.65</div> </td> <td> - <div class="option_entry Fz-m" >5.95</div> + <div class="option_entry Fz-m" >3.60</div> </td> <td> - <div class="option_entry Fz-m" >6.20</div> + <div class="option_entry Fz-m" >3.80</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.05</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+1.39%</div> </td> <td> - <strong data-sq=":volume" data-raw="2">2</strong> + <strong data-sq=":volume" data-raw="247">247</strong> </td> <td> - <div class="option_entry Fz-m" >7</div> + <div class="option_entry Fz-m" >328</div> </td> <td> - <div class="option_entry Fz-m" >30.96%</div> + <div class="option_entry Fz-m" >51.66%</div> </td> </tr> @@ -4927,39 +4922,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=113.00">113.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00115000">AAPL141031P00115000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00113000">AAPL141107P00113000</a></div> </td> <td> - <div class="option_entry Fz-m" >10.10</div> + <div class="option_entry Fz-m" >4.70</div> </td> <td> - <div class="option_entry Fz-m" >9.85</div> + <div class="option_entry Fz-m" >4.55</div> </td> <td> - <div class="option_entry Fz-m" >10.40</div> + <div class="option_entry Fz-m" >4.80</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-0.60</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-11.32%</div> </td> <td> - <strong data-sq=":volume" data-raw="0">0</strong> + <strong data-sq=":volume" data-raw="14">14</strong> </td> <td> - <div class="option_entry Fz-m" >52</div> + <div class="option_entry Fz-m" >354</div> </td> <td> - <div class="option_entry Fz-m" >57.91%</div> + <div class="option_entry Fz-m" >59.28%</div> </td> </tr> @@ -4969,39 +4964,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=116.00">116.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=114.00">114.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00116000">AAPL141031P00116000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00114000">AAPL141107P00114000</a></div> </td> <td> - <div class="option_entry Fz-m" >16.24</div> + <div class="option_entry Fz-m" >5.65</div> </td> <td> - <div class="option_entry Fz-m" >10.85</div> + <div class="option_entry Fz-m" >5.60</div> </td> <td> - <div class="option_entry Fz-m" >11.45</div> + <div class="option_entry Fz-m" >5.85</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.20</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+3.67%</div> </td> <td> - <strong data-sq=":volume" data-raw="2">2</strong> + <strong data-sq=":volume" data-raw="16">16</strong> </td> <td> - <div class="option_entry Fz-m" >38</div> + <div class="option_entry Fz-m" >71</div> </td> <td> - <div class="option_entry Fz-m" >64.36%</div> + <div class="option_entry Fz-m" >69.82%</div> </td> </tr> @@ -5011,39 +5006,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=117.00">117.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00117000">AAPL141031P00117000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00115000">AAPL141107P00115000</a></div> </td> <td> - <div class="option_entry Fz-m" >15.35</div> + <div class="option_entry Fz-m" >6.61</div> </td> <td> - <div class="option_entry Fz-m" >11.70</div> + <div class="option_entry Fz-m" >6.55</div> </td> <td> - <div class="option_entry Fz-m" >12.55</div> + <div class="option_entry Fz-m" >6.80</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >0.06</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-pos">+0.92%</div> </td> <td> - <strong data-sq=":volume" data-raw="2">2</strong> + <strong data-sq=":volume" data-raw="6">6</strong> </td> <td> - <div class="option_entry Fz-m" >0</div> + <div class="option_entry Fz-m" >51</div> </td> <td> - <div class="option_entry Fz-m" >72.95%</div> + <div class="option_entry Fz-m" >75.29%</div> </td> </tr> @@ -5053,39 +5048,39 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=118.00">118.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=117.00">117.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00118000">AAPL141031P00118000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00117000">AAPL141107P00117000</a></div> </td> <td> - <div class="option_entry Fz-m" >17.65</div> + <div class="option_entry Fz-m" >8.55</div> </td> <td> - <div class="option_entry Fz-m" >12.70</div> + <div class="option_entry Fz-m" >8.55</div> </td> <td> - <div class="option_entry Fz-m" >13.55</div> + <div class="option_entry Fz-m" >8.80</div> </td> <td> - <div class="option_entry Fz-m" >0.00</div> + <div class="option_entry Fz-m" >-2.20</div> </td> <td> - <div class="option_entry Fz-m">0.00%</div> + <div class="option_entry Fz-m option-change-neg">-20.47%</div> </td> <td> - <strong data-sq=":volume" data-raw="1">1</strong> + <strong data-sq=":volume" data-raw="5">5</strong> </td> <td> <div class="option_entry Fz-m" >1</div> </td> <td> - <div class="option_entry Fz-m" >76.95%</div> + <div class="option_entry Fz-m" >90.04%</div> </td> </tr> @@ -5095,19 +5090,19 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> "> <td> - <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=120.00">120.00</a></strong> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=119.00">119.00</a></strong> </td> <td> - <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00120000">AAPL141031P00120000</a></div> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00119000">AAPL141107P00119000</a></div> </td> <td> - <div class="option_entry Fz-m" >19.80</div> + <div class="option_entry Fz-m" >14.35</div> </td> <td> - <div class="option_entry Fz-m" >14.70</div> + <div class="option_entry Fz-m" >10.55</div> </td> <td> - <div class="option_entry Fz-m" >15.55</div> + <div class="option_entry Fz-m" >10.80</div> </td> <td> <div class="option_entry Fz-m" >0.00</div> @@ -5121,13 +5116,97 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> </td> <td> - <strong data-sq=":volume" data-raw="0">0</strong> + <strong data-sq=":volume" data-raw="1">1</strong> </td> <td> - <div class="option_entry Fz-m" >0</div> + <div class="option_entry Fz-m" >1</div> </td> <td> - <div class="option_entry Fz-m" >50.00%</div> + <div class="option_entry Fz-m" >103.91%</div> + </td> + </tr> + + <tr data-row="34" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=120.00">120.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00120000">AAPL141107P00120000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >11.55</div> + </td> + <td> + <div class="option_entry Fz-m" >11.55</div> + </td> + <td> + <div class="option_entry Fz-m" >11.80</div> + </td> + <td> + <div class="option_entry Fz-m" >0.65</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+5.96%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="3800">3800</strong> + </td> + <td> + <div class="option_entry Fz-m" >152</div> + </td> + <td> + <div class="option_entry Fz-m" >110.55%</div> + </td> + </tr> + + <tr data-row="35" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=122.00">122.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141107P00122000">AAPL141107P00122000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >13.66</div> + </td> + <td> + <div class="option_entry Fz-m" >13.55</div> + </td> + <td> + <div class="option_entry Fz-m" >13.80</div> + </td> + <td> + <div class="option_entry Fz-m" >-9.59</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-41.25%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="7500">7500</strong> + </td> + <td> + <div class="option_entry Fz-m" >2</div> + </td> + <td> + <div class="option_entry Fz-m" >123.44%</div> </td> </tr> @@ -5247,9 +5326,9 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> data.uh_markup = header.uh_markup; //TODO - localize these strings if (path && path.indexOf('op') > -1) { - res.locals.page_title = parseSymbol(req.query.s) + " Options | Yahoo! Inc. Stock - Yahoo! Finance"; + res.locals.page_title = parseSymbol(req.query.s) + " Option Chain | Yahoo! Inc. Stock - Yahoo! Finance"; } else if (path && ((path.indexOf('echarts') > -1) || (path.indexOf('q') > -1))) { - res.locals.page_title = parseSymbol(req.query.s) + " Interactive Chart | Yahoo! Inc. Stock - Yahoo! Finance"; + res.locals.page_title = parseSymbol(req.query.s) + " Interactive Stock Chart | Yahoo! Inc. Stock - Yahoo! Finance"; } else { res.locals.page_title = config.title; } @@ -5344,7 +5423,7 @@ <h2 class="symbol-name">Apple Inc. (AAPL)</h2> } callback(); }); -}],"after":[]}},"context":{"authed":"0","ynet":"0","ssl":"1","spdy":"0","bucket":"","colo":"gq1","device":"desktop","environment":"prod","lang":"en-US","partner":"none","site":"finance","region":"US","intl":"us","tz":"America\u002FLos_Angeles","edgepipeEnabled":false,"urlPath":"\u002Fq\u002Fop"},"intl":{"locales":"en-US"},"user":{"crumb":"7UQ1gVX3rPU"}}; +}],"after":[]}},"context":{"authed":"0","ynet":"0","ssl":"1","spdy":"0","bucket":"","colo":"gq1","device":"desktop","environment":"prod","lang":"en-US","partner":"none","site":"finance","region":"US","intl":"us","tz":"America\u002FLos_Angeles","edgepipeEnabled":false,"urlPath":"\u002Fq\u002Fop"},"intl":{"locales":"en-US"},"user":{"crumb":"ly1MJzURQo0"}}; root.YUI_config = {"version":"3.17.2","base":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?yui:3.17.2\u002F","comboBase":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?","comboSep":"&","root":"yui:3.17.2\u002F","filter":"min","logLevel":"error","combine":true,"patches":[function patchLangBundlesRequires(Y, loader) { var getRequires = loader.getRequires; loader.getRequires = function (mod) { @@ -5395,16 +5474,16 @@ <h2 class="symbol-name">Apple Inc. 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Updated the second test html so that it uses a DST sample. Changed failure to get quote time from raising RemoteDataError to setting to np.nan @jorisvandenbossche This fixes the doc error you noted on #8631
https://api.github.com/repos/pandas-dev/pandas/pulls/8741
2014-11-06T04:11:36Z
2014-11-07T00:43:15Z
2014-11-07T00:43:15Z
2014-11-10T01:18:27Z
COMPAT: Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 16c8bf12b99c0..e4fc085d4b16a 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -212,7 +212,7 @@ Bug Fixes - Bug in duplicated/drop_duplicates with a Categorical (:issue:`8623`) - Bug in ``Categorical`` reflected comparison operator raising if the first argument was a numpy array scalar (e.g. np.int64) (:issue:`8658`) - Bug in Panel indexing with a list-like (:issue:`8710`) - +- Compat issue is ``DataFrame.dtypes`` when ``options.mode.use_inf_as_null`` is True (:issue:`8722`) diff --git a/pandas/lib.pyx b/pandas/lib.pyx index a1832728a86e1..82408cd460fcd 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -289,8 +289,8 @@ def isnullobj(ndarray[object] arr): n = len(arr) result = np.zeros(n, dtype=np.uint8) for i from 0 <= i < n: - arobj = arr[i] - result[i] = arobj is NaT or _checknull(arobj) + val = arr[i] + result[i] = val is NaT or _checknull(val) return result.view(np.bool_) @cython.wraparound(False) @@ -303,10 +303,10 @@ def isnullobj_old(ndarray[object] arr): n = len(arr) result = np.zeros(n, dtype=np.uint8) for i from 0 <= i < n: - result[i] = util._checknull_old(arr[i]) + val = arr[i] + result[i] = val is NaT or util._checknull_old(val) return result.view(np.bool_) - @cython.wraparound(False) @cython.boundscheck(False) def isnullobj2d(ndarray[object, ndim=2] arr): @@ -323,20 +323,6 @@ def isnullobj2d(ndarray[object, ndim=2] arr): result[i, j] = 1 return result.view(np.bool_) -@cython.wraparound(False) -@cython.boundscheck(False) -def isnullobj_old(ndarray[object] arr): - cdef Py_ssize_t i, n - cdef object val - cdef ndarray[uint8_t] result - - n = len(arr) - result = np.zeros(n, dtype=np.uint8) - for i from 0 <= i < n: - result[i] = util._checknull_old(arr[i]) - return result.view(np.bool_) - - @cython.wraparound(False) @cython.boundscheck(False) def isnullobj2d_old(ndarray[object, ndim=2] arr): diff --git a/pandas/src/util.pxd b/pandas/src/util.pxd index 70c599861d715..84b331f1e8e6f 100644 --- a/pandas/src/util.pxd +++ b/pandas/src/util.pxd @@ -76,7 +76,7 @@ cdef inline bint _checknull_old(object val): cdef double INF = <double> np.inf cdef double NEGINF = -INF try: - return val is None or val != val or val == INF or val == NEGINF + return val is None or (cpython.PyFloat_Check(val) and (val != val or val == INF or val == NEGINF)) except ValueError: return False diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index d46a219f3b1eb..4b17640adf3bd 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -6783,6 +6783,12 @@ def test_dtypes(self): index=result.index) assert_series_equal(result, expected) + # compat, GH 8722 + with option_context('use_inf_as_null',True): + df = DataFrame([[1]]) + result = df.dtypes + assert_series_equal(result,Series({0:np.dtype('int64')})) + def test_convert_objects(self): oops = self.mixed_frame.T.T diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index 1b7b6c5c5ee4e..2873cd81d4744 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -5,7 +5,7 @@ import pandas as pd from pandas.lib import isscalar, item_from_zerodim import pandas.util.testing as tm - +from pandas.compat import u class TestIsscalar(tm.TestCase): def test_isscalar_builtin_scalars(self): @@ -16,7 +16,7 @@ def test_isscalar_builtin_scalars(self): self.assertTrue(isscalar(np.nan)) self.assertTrue(isscalar('foobar')) self.assertTrue(isscalar(b'foobar')) - self.assertTrue(isscalar(u'foobar')) + self.assertTrue(isscalar(u('efoobar'))) self.assertTrue(isscalar(datetime(2014, 1, 1))) self.assertTrue(isscalar(date(2014, 1, 1))) self.assertTrue(isscalar(time(12, 0))) @@ -38,7 +38,7 @@ def test_isscalar_numpy_array_scalars(self): self.assertTrue(isscalar(np.int32(1))) self.assertTrue(isscalar(np.object_('foobar'))) self.assertTrue(isscalar(np.str_('foobar'))) - self.assertTrue(isscalar(np.unicode_(u'foobar'))) + self.assertTrue(isscalar(np.unicode_(u('foobar')))) self.assertTrue(isscalar(np.bytes_(b'foobar'))) self.assertTrue(isscalar(np.datetime64('2014-01-01'))) self.assertTrue(isscalar(np.timedelta64(1, 'h')))
closes #8722
https://api.github.com/repos/pandas-dev/pandas/pulls/8726
2014-11-03T23:21:37Z
2014-11-04T01:04:30Z
2014-11-04T01:04:30Z
2014-11-04T01:04:30Z
DOC: Select row with maximum value from each group
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index edff461d7989d..8378873db9a65 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -588,6 +588,19 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to df['beyer_shifted'] = df.groupby(level=0)['beyer'].shift(1) df +`Select row with maximum value from each group +<http://stackoverflow.com/q/26701849/190597>`__ + +.. ipython:: python + + df = pd.DataFrame({'host':['other','other','that','this','this'], + 'service':['mail','web','mail','mail','web'], + 'no':[1, 2, 1, 2, 1]}).set_index(['host', 'service']) + mask = df.groupby(level=0).agg('idxmax') + df_count = df.loc[mask['no']].reset_index() + df_count + + Expanding Data ************** @@ -1202,4 +1215,4 @@ of the data values: {'height': [60, 70], 'weight': [100, 140, 180], 'sex': ['Male', 'Female']}) - df \ No newline at end of file + df
Re: http://stackoverflow.com/q/26701849/190597 Included is a cookbook recipe for finding maximal rows per group.
https://api.github.com/repos/pandas-dev/pandas/pulls/8724
2014-11-03T19:16:17Z
2014-11-03T23:20:50Z
2014-11-03T23:20:50Z
2014-11-04T01:15:48Z
ERR: leftover from renaming outtype to orient in to_dict
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index a734baf28464b..4ce9cc5804264 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -708,8 +708,8 @@ def to_dict(self, orient='dict'): elif orient.lower().startswith('r'): return [dict((k, v) for k, v in zip(self.columns, row)) for row in self.values] - else: # pragma: no cover - raise ValueError("outtype %s not understood" % outtype) + else: + raise ValueError("orient '%s' not understood" % orient) def to_gbq(self, destination_table, project_id=None, chunksize=10000, verbose=True, reauth=False): diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index d46a219f3b1eb..e8ba97ca4f346 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4158,6 +4158,10 @@ def test_to_dict(self): tm.assert_almost_equal(recons_data, expected_records) + def test_to_dict_invalid_orient(self): + df = DataFrame({'A':[0, 1]}) + self.assertRaises(ValueError, df.to_dict, orient='invalid') + def test_to_records_dt64(self): df = DataFrame([["one", "two", "three"], ["four", "five", "six"]],
https://api.github.com/repos/pandas-dev/pandas/pulls/8721
2014-11-03T15:19:36Z
2014-11-04T09:35:27Z
2014-11-04T09:35:27Z
2014-11-04T09:35:42Z
BUG: Bug in Panel indexing with a list-like (GH8710)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 343c6451d15ac..a0aa1ca2716fc 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -188,7 +188,7 @@ Bug Fixes - Bug in groupby-transform with a Categorical (:issue:`8623`) - Bug in duplicated/drop_duplicates with a Categorical (:issue:`8623`) - Bug in ``Categorical`` reflected comparison operator raising if the first argument was a numpy array scalar (e.g. np.int64) (:issue:`8658`) - +- Bug in Panel indexing with a list-like (:issue:`8710`) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 72f9c5bd00cb7..c35eb3f88bc4a 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -29,7 +29,7 @@ import pandas.core.ops as ops import pandas.core.nanops as nanops import pandas.computation.expressions as expressions - +from pandas import lib _shared_doc_kwargs = dict( axes='items, major_axis, minor_axis', @@ -253,7 +253,9 @@ def from_dict(cls, data, intersect=False, orient='items', dtype=None): def __getitem__(self, key): if isinstance(self._info_axis, MultiIndex): return self._getitem_multilevel(key) - return super(Panel, self).__getitem__(key) + if lib.isscalar(key): + return super(Panel, self).__getitem__(key) + return self.ix[key] def _getitem_multilevel(self, key): info = self._info_axis diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 7bc5ca0bfb2e7..32b27e139cc21 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -2325,6 +2325,30 @@ def test_panel_getitem(self): test1 = panel.ix[:, "2002"] tm.assert_panel_equal(test1,test2) + # GH8710 + # multi-element getting with a list + panel = tm.makePanel() + + expected = panel.iloc[[0,1]] + + result = panel.loc[['ItemA','ItemB']] + tm.assert_panel_equal(result,expected) + + result = panel.loc[['ItemA','ItemB'],:,:] + tm.assert_panel_equal(result,expected) + + result = panel[['ItemA','ItemB']] + tm.assert_panel_equal(result,expected) + + result = panel.loc['ItemA':'ItemB'] + tm.assert_panel_equal(result,expected) + + result = panel.ix['ItemA':'ItemB'] + tm.assert_panel_equal(result,expected) + + result = panel.ix[['ItemA','ItemB']] + tm.assert_panel_equal(result,expected) + def test_panel_setitem(self): # GH 7763
closes #8710
https://api.github.com/repos/pandas-dev/pandas/pulls/8715
2014-11-02T19:30:43Z
2014-11-02T21:28:33Z
2014-11-02T21:28:33Z
2014-11-02T21:28:33Z
BUG: concat of series of dtype category converting to object dtype (GH8641)
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt index 66b839ed01a29..8ea79089f95e3 100644 --- a/doc/source/whatsnew/v0.15.2.txt +++ b/doc/source/whatsnew/v0.15.2.txt @@ -20,6 +20,7 @@ users upgrade to this version. API changes ~~~~~~~~~~~ +- Bug in concat of Series with ``category`` dtype which were coercing to ``object``. (:issue:`8641`) .. _whatsnew_0152.enhancements: diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index dd23897a3f7e9..414c4a8315e6d 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -15,7 +15,12 @@ import pandas.core.common as com from pandas.util.decorators import cache_readonly -from pandas.core.common import isnull +from pandas.core.common import (CategoricalDtype, ABCSeries, isnull, notnull, + is_categorical_dtype, is_integer_dtype, is_object_dtype, + _possibly_infer_to_datetimelike, get_dtype_kinds, + is_list_like, _is_sequence, + _ensure_platform_int, _ensure_object, _ensure_int64, + _coerce_indexer_dtype, _values_from_object, take_1d) from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option from pandas.core import format as fmt @@ -69,11 +74,11 @@ def f(self, other): def _is_categorical(array): """ return if we are a categorical possibility """ - return isinstance(array, Categorical) or isinstance(array.dtype, com.CategoricalDtype) + return isinstance(array, Categorical) or isinstance(array.dtype, CategoricalDtype) def _maybe_to_categorical(array): """ coerce to a categorical if a series is given """ - if isinstance(array, com.ABCSeries): + if isinstance(array, ABCSeries): return array.values return array @@ -175,7 +180,7 @@ class Categorical(PandasObject): >>> a.min() 'c' """ - dtype = com.CategoricalDtype() + dtype = CategoricalDtype() """The dtype (always "category")""" ordered = None @@ -203,7 +208,7 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa if fastpath: # fast path - self._codes = com._coerce_indexer_dtype(values, categories) + self._codes = _coerce_indexer_dtype(values, categories) self.name = name self.categories = categories self.ordered = ordered @@ -223,11 +228,11 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa "use only 'categories'") # sanitize input - if com.is_categorical_dtype(values): + if is_categorical_dtype(values): # we are either a Series or a Categorical cat = values - if isinstance(values, com.ABCSeries): + if isinstance(values, ABCSeries): cat = values.values if categories is None: categories = cat.categories @@ -244,7 +249,7 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa # which is fine, but since factorize does this correctly no need here # this is an issue because _sanitize_array also coerces np.nan to a string # under certain versions of numpy as well - values = com._possibly_infer_to_datetimelike(values, convert_dates=True) + values = _possibly_infer_to_datetimelike(values, convert_dates=True) if not isinstance(values, np.ndarray): values = _convert_to_list_like(values) from pandas.core.series import _sanitize_array @@ -286,11 +291,11 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa codes = _get_codes_for_values(values, categories) # TODO: check for old style usage. These warnings should be removes after 0.18/ in 2016 - if com.is_integer_dtype(values) and not com.is_integer_dtype(categories): + if is_integer_dtype(values) and not is_integer_dtype(categories): warn("Values and categories have different dtypes. Did you mean to use\n" "'Categorical.from_codes(codes, categories)'?", RuntimeWarning) - if com.is_integer_dtype(values) and (codes == -1).all(): + if is_integer_dtype(values) and (codes == -1).all(): warn("None of the categories were found in values. Did you mean to use\n" "'Categorical.from_codes(codes, categories)'?", RuntimeWarning) @@ -302,7 +307,7 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa self.ordered = False if ordered is None else ordered self.categories = categories self.name = name - self._codes = com._coerce_indexer_dtype(codes, categories) + self._codes = _coerce_indexer_dtype(codes, categories) def copy(self): """ Copy constructor. """ @@ -409,7 +414,7 @@ def _validate_categories(cls, categories): # on categories with NaNs, int values would be converted to float. # Use "object" dtype to prevent this. if isnull(categories).any(): - without_na = np.array([x for x in categories if com.notnull(x)]) + without_na = np.array([x for x in categories if notnull(x)]) with_na = np.array(categories) if with_na.dtype != without_na.dtype: dtype = "object" @@ -617,7 +622,7 @@ def add_categories(self, new_categories, inplace=False): remove_unused_categories set_categories """ - if not com.is_list_like(new_categories): + if not is_list_like(new_categories): new_categories = [new_categories] already_included = set(new_categories) & set(self._categories) if len(already_included) != 0: @@ -627,7 +632,7 @@ def add_categories(self, new_categories, inplace=False): new_categories = self._validate_categories(new_categories) cat = self if inplace else self.copy() cat._categories = new_categories - cat._codes = com._coerce_indexer_dtype(cat._codes, new_categories) + cat._codes = _coerce_indexer_dtype(cat._codes, new_categories) if not inplace: return cat @@ -662,7 +667,7 @@ def remove_categories(self, removals, inplace=False): remove_unused_categories set_categories """ - if not com.is_list_like(removals): + if not is_list_like(removals): removals = [removals] removals = set(list(removals)) not_included = removals - set(self._categories) @@ -696,7 +701,7 @@ def remove_unused_categories(self, inplace=False): """ cat = self if inplace else self.copy() _used = sorted(np.unique(cat._codes)) - new_categories = cat.categories.take(com._ensure_platform_int(_used)) + new_categories = cat.categories.take(_ensure_platform_int(_used)) new_categories = _ensure_index(new_categories) cat._codes = _get_codes_for_values(cat.__array__(), new_categories) cat._categories = new_categories @@ -734,7 +739,7 @@ def __array__(self, dtype=None): A numpy array of either the specified dtype or, if dtype==None (default), the same dtype as categorical.categories.dtype """ - ret = com.take_1d(self.categories.values, self._codes) + ret = take_1d(self.categories.values, self._codes) if dtype and dtype != self.categories.dtype: return np.asarray(ret, dtype) return ret @@ -822,8 +827,8 @@ def get_values(self): # if we are a period index, return a string repr if isinstance(self.categories, PeriodIndex): - return com.take_1d(np.array(self.categories.to_native_types(), dtype=object), - self._codes) + return take_1d(np.array(self.categories.to_native_types(), dtype=object), + self._codes) return np.array(self) @@ -1010,7 +1015,7 @@ def fillna(self, fill_value=None, method=None, limit=None, **kwargs): else: - if not com.isnull(fill_value) and fill_value not in self.categories: + if not isnull(fill_value) and fill_value not in self.categories: raise ValueError("fill value must be in categories") mask = values==-1 @@ -1031,7 +1036,7 @@ def take_nd(self, indexer, allow_fill=True, fill_value=None): # but is passed thru internally assert isnull(fill_value) - codes = com.take_1d(self._codes, indexer, allow_fill=True, fill_value=-1) + codes = take_1d(self._codes, indexer, allow_fill=True, fill_value=-1) result = Categorical(codes, categories=self.categories, ordered=self.ordered, name=self.name, fastpath=True) return result @@ -1178,7 +1183,7 @@ def __setitem__(self, key, value): raise ValueError("Cannot set a Categorical with another, without identical " "categories") - rvalue = value if com.is_list_like(value) else [value] + rvalue = value if is_list_like(value) else [value] to_add = Index(rvalue).difference(self.categories) # no assignments of values not in categories, but it's always ok to set something to np.nan if len(to_add) and not isnull(to_add).all(): @@ -1221,7 +1226,7 @@ def __setitem__(self, key, value): # float categories do currently return -1 for np.nan, even if np.nan is included in the # index -> "repair" this here if isnull(rvalue).any() and isnull(self.categories).any(): - nan_pos = np.where(com.isnull(self.categories))[0] + nan_pos = np.where(isnull(self.categories))[0] lindexer[lindexer == -1] = nan_pos key = self._maybe_coerce_indexer(key) @@ -1304,7 +1309,7 @@ def mode(self): import pandas.hashtable as htable good = self._codes != -1 - result = Categorical(sorted(htable.mode_int64(com._ensure_int64(self._codes[good]))), + result = Categorical(sorted(htable.mode_int64(_ensure_int64(self._codes[good]))), categories=self.categories,ordered=self.ordered, name=self.name, fastpath=True) return result @@ -1373,9 +1378,9 @@ def describe(self): categories = np.arange(0,len(self.categories)+1 ,dtype=object) categories[:-1] = self.categories categories[-1] = np.nan - result.index = categories.take(com._ensure_platform_int(result.index)) + result.index = categories.take(_ensure_platform_int(result.index)) else: - result.index = self.categories.take(com._ensure_platform_int(result.index)) + result.index = self.categories.take(_ensure_platform_int(result.index)) result = result.reindex(self.categories) result.index.name = 'categories' @@ -1447,23 +1452,72 @@ def _get_codes_for_values(values, categories): from pandas.core.algorithms import _get_data_algo, _hashtables if values.dtype != categories.dtype: - values = com._ensure_object(values) - categories = com._ensure_object(categories) + values = _ensure_object(values) + categories = _ensure_object(categories) (hash_klass, vec_klass), vals = _get_data_algo(values, _hashtables) t = hash_klass(len(categories)) - t.map_locations(com._values_from_object(categories)) - return com._coerce_indexer_dtype(t.lookup(values), categories) + t.map_locations(_values_from_object(categories)) + return _coerce_indexer_dtype(t.lookup(values), categories) def _convert_to_list_like(list_like): if hasattr(list_like, "dtype"): return list_like if isinstance(list_like, list): return list_like - if (com._is_sequence(list_like) or isinstance(list_like, tuple) - or isinstance(list_like, types.GeneratorType)): + if (_is_sequence(list_like) or isinstance(list_like, tuple) + or isinstance(list_like, types.GeneratorType)): return list(list_like) elif np.isscalar(list_like): return [list_like] else: # is this reached? return [list_like] + +def _concat_compat(to_concat, axis=0): + """ + provide concatenation of an object/categorical array of arrays each of which is a single dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + + Returns + ------- + a single array, preserving the combined dtypes + """ + + def convert_categorical(x): + # coerce to object dtype + if is_categorical_dtype(x.dtype): + return x.get_values() + return x.ravel() + + typs = get_dtype_kinds(to_concat) + if not len(typs-set(['object','category'])): + + # we only can deal with object & category types + pass + + else: + + # convert to object type and perform a regular concat + from pandas.core.common import _concat_compat + return _concat_compat([ np.array(x,copy=False).astype('object') for x in to_concat ],axis=axis) + + # we could have object blocks and categorical's here + # if we only have a single cateogoricals then combine everything + # else its a non-compat categorical + categoricals = [ x for x in to_concat if is_categorical_dtype(x.dtype) ] + objects = [ x for x in to_concat if is_object_dtype(x.dtype) ] + + # validate the categories + categories = None + for x in categoricals: + if categories is None: + categories = x.categories + if not categories.equals(x.categories): + raise ValueError("incompatible categories in categorical concat") + + # concat them + return Categorical(np.concatenate([ convert_categorical(x) for x in to_concat ],axis=axis), categories=categories) diff --git a/pandas/core/common.py b/pandas/core/common.py index f5de6c7da8914..759f5f1dfaf7a 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2768,9 +2768,62 @@ def _check_as_is(x): self.queue.truncate(0) +def get_dtype_kinds(l): + """ + Parameters + ---------- + l : list of arrays + + Returns + ------- + a set of kinds that exist in this list of arrays + """ + + typs = set() + for arr in l: + + dtype = arr.dtype + if is_categorical_dtype(dtype): + typ = 'category' + elif isinstance(arr, ABCSparseArray): + typ = 'sparse' + elif is_datetime64_dtype(dtype): + typ = 'datetime' + elif is_timedelta64_dtype(dtype): + typ = 'timedelta' + elif is_object_dtype(dtype): + typ = 'object' + elif is_bool_dtype(dtype): + typ = 'bool' + else: + typ = dtype.kind + typs.add(typ) + return typs + def _concat_compat(to_concat, axis=0): + """ + provide concatenation of an array of arrays each of which is a single + 'normalized' dtypes (in that for example, if its object, then it is a non-datetimelike + provde a combined dtype for the resulting array the preserves the overall dtype if possible) + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + + Returns + ------- + a single array, preserving the combined dtypes + """ + # filter empty arrays - nonempty = [x for x in to_concat if x.shape[axis] > 0] + # 1-d dtypes always are included here + def is_nonempty(x): + try: + return x.shape[axis] > 0 + except Exception: + return True + nonempty = [x for x in to_concat if is_nonempty(x)] # If all arrays are empty, there's nothing to convert, just short-cut to # the concatenation, #3121. @@ -2778,38 +2831,37 @@ def _concat_compat(to_concat, axis=0): # Creating an empty array directly is tempting, but the winnings would be # marginal given that it would still require shape & dtype calculation and # np.concatenate which has them both implemented is compiled. - if nonempty: - - is_datetime64 = [x.dtype == _NS_DTYPE for x in nonempty] - is_timedelta64 = [x.dtype == _TD_DTYPE for x in nonempty] - - if all(is_datetime64): - new_values = np.concatenate([x.view(np.int64) for x in nonempty], - axis=axis) - return new_values.view(_NS_DTYPE) - elif all(is_timedelta64): - new_values = np.concatenate([x.view(np.int64) for x in nonempty], - axis=axis) - return new_values.view(_TD_DTYPE) - elif any(is_datetime64) or any(is_timedelta64): - to_concat = [_to_pydatetime(x) for x in nonempty] - - return np.concatenate(to_concat, axis=axis) - - -def _to_pydatetime(x): - # coerce to an object dtyped - - if x.dtype == _NS_DTYPE: - shape = x.shape - x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) - x = x.reshape(shape) - elif x.dtype == _TD_DTYPE: - shape = x.shape - x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) - x = x.reshape(shape) - - return x + + typs = get_dtype_kinds(to_concat) + + # these are mandated to handle empties as well + if 'datetime' in typs or 'timedelta' in typs: + from pandas.tseries.common import _concat_compat + return _concat_compat(to_concat, axis=axis) + + elif 'sparse' in typs: + from pandas.sparse.array import _concat_compat + return _concat_compat(to_concat, axis=axis) + + elif 'category' in typs: + from pandas.core.categorical import _concat_compat + return _concat_compat(to_concat, axis=axis) + + if not nonempty: + + # we have all empties, but may need to coerce the result dtype to object if we + # have non-numeric type operands (numpy would otherwise cast this to float) + typs = get_dtype_kinds(to_concat) + if len(typs) != 1: + + if not len(typs-set(['i','u','f'])) or not len(typs-set(['bool','i','u'])): + # let numpy coerce + pass + else: + # coerce to object + to_concat = [ x.astype('object') for x in to_concat ] + + return np.concatenate(to_concat,axis=axis) def _where_compat(mask, arr1, arr2): if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index bccc0e7b6be14..89178ba2d9dcc 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -271,14 +271,15 @@ def _construct_axes_from_arguments(self, args, kwargs, require_all=False): return axes, kwargs @classmethod - def _from_axes(cls, data, axes): + def _from_axes(cls, data, axes, **kwargs): # for construction from BlockManager if isinstance(data, BlockManager): - return cls(data) + return cls(data, **kwargs) else: if cls._AXIS_REVERSED: axes = axes[::-1] d = cls._construct_axes_dict_from(cls, axes, copy=False) + d.update(kwargs) return cls(data, **d) def _get_axis_number(self, axis): diff --git a/pandas/core/internals.py b/pandas/core/internals.py index bb81258efe4c5..7ab3e4d8d9482 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -493,18 +493,6 @@ def to_native_types(self, slicer=None, na_rep='', **kwargs): values[mask] = na_rep return values.tolist() - def _concat_blocks(self, blocks, values): - """ return the block concatenation """ - - # dispatch to a categorical to handle the concat - if self._holder is None: - - for b in blocks: - if b.is_categorical: - return b._concat_blocks(blocks,values) - - return self._holder(values[0]) - # block actions #### def copy(self, deep=True): values = self.values @@ -1759,34 +1747,6 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, ndim=self.ndim, placement=self.mgr_locs) - def _concat_blocks(self, blocks, values): - """ - validate that we can merge these blocks - - return the block concatenation - """ - - # we could have object blocks and categorical's here - # if we only have a single cateogoricals then combine everything - # else its a non-compat categorical - - categoricals = [ b for b in blocks if b.is_categorical ] - objects = [ b for b in blocks if not b.is_categorical and b.is_object ] - - # convert everything to object and call it a day - if len(objects) + len(categoricals) != len(blocks): - raise ValueError("try to combine non-object blocks and categoricals") - - # validate the categories - categories = None - for b in categoricals: - if categories is None: - categories = b.values.categories - if not categories.equals(b.values.categories): - raise ValueError("incompatible categories in categorical block merge") - - return self._holder(values[0], categories=categories) - def to_native_types(self, slicer=None, na_rep='', **kwargs): """ convert to our native types format, slicing if desired """ @@ -4102,22 +4062,15 @@ def get_empty_dtype_and_na(join_units): blk = join_units[0].block if blk is None: return np.float64, np.nan - else: - return blk.dtype, None has_none_blocks = False dtypes = [None] * len(join_units) - for i, unit in enumerate(join_units): if unit.block is None: has_none_blocks = True else: dtypes[i] = unit.dtype - if not has_none_blocks and len(set(dtypes)) == 1: - # Unanimous decision, nothing to upcast. - return dtypes[0], None - # dtypes = set() upcast_classes = set() null_upcast_classes = set() @@ -4127,7 +4080,9 @@ def get_empty_dtype_and_na(join_units): if com.is_categorical_dtype(dtype): upcast_cls = 'category' - elif issubclass(dtype.type, (np.object_, np.bool_)): + elif issubclass(dtype.type, np.bool_): + upcast_cls = 'bool' + elif issubclass(dtype.type, np.object_): upcast_cls = 'object' elif is_datetime64_dtype(dtype): upcast_cls = 'datetime' @@ -4150,6 +4105,11 @@ def get_empty_dtype_and_na(join_units): # create the result if 'object' in upcast_classes: return np.dtype(np.object_), np.nan + elif 'bool' in upcast_classes: + if has_none_blocks: + return np.dtype(np.object_), np.nan + else: + return np.dtype(np.bool_), None elif 'category' in upcast_classes: return com.CategoricalDtype(), np.nan elif 'float' in upcast_classes: @@ -4184,14 +4144,7 @@ def concatenate_join_units(join_units, concat_axis, copy): else: concat_values = com._concat_compat(to_concat, axis=concat_axis) - if any(unit.needs_block_conversion for unit in join_units): - - # need to ask the join unit block to convert to the underlying repr for us - blocks = [ unit.block for unit in join_units if unit.block is not None ] - return blocks[0]._concat_blocks(blocks, concat_values) - else: - return concat_values - + return concat_values def get_mgr_concatenation_plan(mgr, indexers): """ @@ -4231,6 +4184,7 @@ def get_mgr_concatenation_plan(mgr, indexers): plan = [] for blkno, placements in _get_blkno_placements(blknos, len(mgr.blocks), group=False): + assert placements.is_slice_like join_unit_indexers = indexers.copy() @@ -4442,6 +4396,14 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): missing_arr.fill(fill_value) return missing_arr + if not self.indexers: + if self.block.is_categorical: + # preserve the categoricals for validation in _concat_compat + return self.block.values + elif self.block.is_sparse: + # preserve the sparse array for validation in _concat_compat + return self.block.values + if self.block.is_bool: # External code requested filling/upcasting, bool values must # be upcasted to object to avoid being upcasted to numeric. @@ -4455,13 +4417,14 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): # If there's no indexing to be done, we want to signal outside # code that this array must be copied explicitly. This is done # by returning a view and checking `retval.base`. - return values.view() + values = values.view() + else: for ax, indexer in self.indexers.items(): values = com.take_nd(values, indexer, axis=ax, fill_value=fill_value) - return values + return values def _fast_count_smallints(arr): diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 38a5688ed96e8..b765fdb8d67be 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -529,3 +529,46 @@ def make_sparse(arr, kind='block', fill_value=nan): ops.add_special_arithmetic_methods(SparseArray, arith_method=_arith_method, use_numexpr=False) + + + +def _concat_compat(to_concat, axis=0): + """ + provide concatenation of an sparse/dense array of arrays each of which is a single dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + + Returns + ------- + a single array, preserving the combined dtypes + """ + + def convert_sparse(x, axis): + # coerce to native type + if isinstance(x, SparseArray): + x = x.get_values() + x = x.ravel() + if axis > 0: + x = np.atleast_2d(x) + return x + + typs = com.get_dtype_kinds(to_concat) + + # we have more than one type here, so densify and regular concat + to_concat = [ convert_sparse(x, axis) for x in to_concat ] + result = np.concatenate(to_concat,axis=axis) + + if not len(typs-set(['sparse','f','i'])): + + # we can remain sparse + result = SparseArray(result.ravel()) + + else: + + # coerce to object if needed + result = result.astype('object') + + return result diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index 105f661f08b10..9197a4fc22b9c 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -168,6 +168,9 @@ def test_construct_DataFrame_with_sp_series(self): assert_sp_series_equal(df['col'], self.bseries) + result = df.iloc[:,0] + assert_sp_series_equal(result, self.bseries) + # blocking expected = Series({'col': 'float64:sparse'}) result = df.ftypes @@ -909,8 +912,8 @@ def test_dtypes(self): def test_str(self): df = DataFrame(np.random.randn(10000, 4)) df.ix[:9998] = np.nan - sdf = df.to_sparse() + sdf = df.to_sparse() str(sdf) def test_array_interface(self): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 624c6cf9688d6..dc82abfb40e02 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -2246,6 +2246,23 @@ def f(): dfx['grade'].cat.categories self.assert_numpy_array_equal(df['grade'].cat.categories, dfx['grade'].cat.categories) + # GH 8641 + # series concat not preserving category dtype + s = Series(list('abc'),dtype='category') + s2 = Series(list('abd'),dtype='category') + + def f(): + pd.concat([s,s2]) + self.assertRaises(ValueError, f) + + result = pd.concat([s,s],ignore_index=True) + expected = Series(list('abcabc')).astype('category') + tm.assert_series_equal(result, expected) + + result = pd.concat([s,s]) + expected = Series(list('abcabc'),index=[0,1,2,0,1,2]).astype('category') + tm.assert_series_equal(result, expected) + def test_append(self): cat = pd.Categorical(["a","b"], categories=["a","b"]) vals = [1,2] diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 938d171506461..9ecdcd2b12d75 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -6198,13 +6198,93 @@ def test_numpy_unique(self): # it works! result = np.unique(self.ts) + def test_concat_empty_series_dtypes_roundtrips(self): + + # round-tripping with self & like self + dtypes = map(np.dtype,['float64','int8','uint8','bool','m8[ns]','M8[ns]']) + + for dtype in dtypes: + self.assertEqual(pd.concat([Series(dtype=dtype)]).dtype, dtype) + self.assertEqual(pd.concat([Series(dtype=dtype), + Series(dtype=dtype)]).dtype, dtype) + + def int_result_type(dtype, dtype2): + typs = set([dtype.kind,dtype2.kind]) + if not len(typs-set(['i','u','b'])) and (dtype.kind == 'i' or dtype2.kind == 'i'): + return 'i' + elif not len(typs-set(['u','b'])) and (dtype.kind == 'u' or dtype2.kind == 'u'): + return 'u' + return None + + def float_result_type(dtype, dtype2): + typs = set([dtype.kind,dtype2.kind]) + if not len(typs-set(['f','i','u'])) and (dtype.kind == 'f' or dtype2.kind == 'f'): + return 'f' + return None + + def get_result_type(dtype, dtype2): + result = float_result_type(dtype, dtype2) + if result is not None: + return result + result = int_result_type(dtype, dtype2) + if result is not None: + return result + return 'O' + + for dtype in dtypes: + for dtype2 in dtypes: + if dtype == dtype2: + continue + + expected = get_result_type(dtype, dtype2) + result = pd.concat([Series(dtype=dtype), + Series(dtype=dtype2)]).dtype + self.assertEqual(result.kind, expected) + def test_concat_empty_series_dtypes(self): - self.assertEqual(pd.concat([Series(dtype=np.float64)]).dtype, np.float64) - self.assertEqual(pd.concat([Series(dtype=np.int8)]).dtype, np.int8) - self.assertEqual(pd.concat([Series(dtype=np.bool_)]).dtype, np.bool_) + # bools self.assertEqual(pd.concat([Series(dtype=np.bool_), Series(dtype=np.int32)]).dtype, np.int32) + self.assertEqual(pd.concat([Series(dtype=np.bool_), + Series(dtype=np.float32)]).dtype, np.object_) + + # datetimelike + self.assertEqual(pd.concat([Series(dtype='m8[ns]'), + Series(dtype=np.bool)]).dtype, np.object_) + self.assertEqual(pd.concat([Series(dtype='m8[ns]'), + Series(dtype=np.int64)]).dtype, np.object_) + self.assertEqual(pd.concat([Series(dtype='M8[ns]'), + Series(dtype=np.bool)]).dtype, np.object_) + self.assertEqual(pd.concat([Series(dtype='M8[ns]'), + Series(dtype=np.int64)]).dtype, np.object_) + self.assertEqual(pd.concat([Series(dtype='M8[ns]'), + Series(dtype=np.bool_), + Series(dtype=np.int64)]).dtype, np.object_) + + # categorical + self.assertEqual(pd.concat([Series(dtype='category'), + Series(dtype='category')]).dtype, 'category') + self.assertEqual(pd.concat([Series(dtype='category'), + Series(dtype='float64')]).dtype, np.object_) + self.assertEqual(pd.concat([Series(dtype='category'), + Series(dtype='object')]).dtype, 'category') + + # sparse + result = pd.concat([Series(dtype='float64').to_sparse(), + Series(dtype='float64').to_sparse()]) + self.assertEqual(result.dtype,np.float64) + self.assertEqual(result.ftype,'float64:sparse') + + result = pd.concat([Series(dtype='float64').to_sparse(), + Series(dtype='float64')]) + self.assertEqual(result.dtype,np.float64) + self.assertEqual(result.ftype,'float64:sparse') + + result = pd.concat([Series(dtype='float64').to_sparse(), + Series(dtype='object')]) + self.assertEqual(result.dtype,np.object_) + self.assertEqual(result.ftype,'object:dense') def test_searchsorted_numeric_dtypes_scalar(self): s = Series([1, 2, 90, 1000, 3e9]) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 7a89c317a69c6..2f0920b6d4e98 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -854,11 +854,17 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None, self.new_axes = self._get_new_axes() def get_result(self): + + # series only if self._is_series: + + # stack blocks if self.axis == 0: - new_data = com._concat_compat([x.get_values() for x in self.objs]) + new_data = com._concat_compat([x.values for x in self.objs]) name = com._consensus_name_attr(self.objs) return Series(new_data, index=self.new_axes[0], name=name).__finalize__(self, method='concat') + + # combine as columns in a frame else: data = dict(zip(range(len(self.objs)), self.objs)) index, columns = self.new_axes @@ -866,6 +872,8 @@ def get_result(self): if columns is not None: tmpdf.columns = columns return tmpdf.__finalize__(self, method='concat') + + # combine block managers else: mgrs_indexers = [] for obj in self.objs: diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 8f375ca168edd..c942998d430f4 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -2056,6 +2056,7 @@ def test_panel4d_concat_mixed_type(self): tm.assert_panel4d_equal(result, expected) def test_concat_series(self): + ts = tm.makeTimeSeries() ts.name = 'foo' diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 227af42f07411..f12e0263bcf0c 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -5,6 +5,9 @@ from pandas.core import common as com from pandas import Series, DatetimeIndex, PeriodIndex, TimedeltaIndex from pandas import lib, tslib +from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike, + is_datetime_arraylike, is_integer_dtype, is_list_like, + get_dtype_kinds) def is_datetimelike(data): """ return a boolean if we can be successfully converted to a datetimelike """ @@ -42,9 +45,9 @@ def maybe_to_datetimelike(data, copy=False): elif issubclass(data.dtype.type, np.timedelta64): return TimedeltaProperties(TimedeltaIndex(data, copy=copy, freq='infer'), index) else: - if com.is_period_arraylike(data): + if is_period_arraylike(data): return PeriodProperties(PeriodIndex(data, copy=copy), index) - if com.is_datetime_arraylike(data): + if is_datetime_arraylike(data): return DatetimeProperties(DatetimeIndex(data, copy=copy, freq='infer'), index) raise TypeError("cannot convert an object of type {0} to a datetimelike index".format(type(data))) @@ -60,9 +63,9 @@ def _delegate_property_get(self, name): # maybe need to upcast (ints) if isinstance(result, np.ndarray): - if com.is_integer_dtype(result): + if is_integer_dtype(result): result = result.astype('int64') - elif not com.is_list_like(result): + elif not is_list_like(result): return result # return the result as a Series, which is by definition a copy @@ -162,3 +165,50 @@ class PeriodProperties(Properties): PeriodProperties._add_delegate_accessors(delegate=PeriodIndex, accessors=PeriodIndex._datetimelike_ops, typ='property') + +def _concat_compat(to_concat, axis=0): + """ + provide concatenation of an datetimelike array of arrays each of which is a single + M8[ns], or m8[ns] dtype + + Parameters + ---------- + to_concat : array of arrays + axis : axis to provide concatenation + + Returns + ------- + a single array, preserving the combined dtypes + """ + + def convert_to_pydatetime(x, axis): + # coerce to an object dtype + if x.dtype == _NS_DTYPE: + shape = x.shape + x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) + x = x.reshape(shape) + elif x.dtype == _TD_DTYPE: + shape = x.shape + x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) + x = x.reshape(shape) + return x + + typs = get_dtype_kinds(to_concat) + + # single dtype + if len(typs) == 1: + + if not len(typs-set(['datetime'])): + new_values = np.concatenate([x.view(np.int64) for x in to_concat], + axis=axis) + return new_values.view(_NS_DTYPE) + + elif not len(typs-set(['timedelta'])): + new_values = np.concatenate([x.view(np.int64) for x in to_concat], + axis=axis) + return new_values.view(_TD_DTYPE) + + # need to coerce to object + to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] + + return np.concatenate(to_concat,axis=axis)
closes #8641
https://api.github.com/repos/pandas-dev/pandas/pulls/8714
2014-11-02T18:43:39Z
2014-11-09T22:10:39Z
2014-11-09T22:10:39Z
2014-11-09T22:10:39Z
BUG: fix reverse comparison operations for Categorical
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index e96adc2bd9559..486ba9cbadd7f 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -186,6 +186,7 @@ Bug Fixes - Bug in selecting from a ``Categorical`` with ``.iloc`` (:issue:`8623`) - Bug in groupby-transform with a Categorical (:issue:`8623`) - Bug in duplicated/drop_duplicates with a Categorical (:issue:`8623`) +- Bug in ``Categorical`` reflected comparison operator raising if the first argument was a numpy array scalar (e.g. np.int64) (:issue:`8658`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 598b29bf77e47..150da65580223 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -42,7 +42,16 @@ def f(self, other): # In other series, the leads to False, so do that here too ret[na_mask] = False return ret - elif lib.isscalar(other): + + # Numpy-1.9 and earlier may convert a scalar to a zerodim array during + # comparison operation when second arg has higher priority, e.g. + # + # cat[0] < cat + # + # With cat[0], for example, being ``np.int64(1)`` by the time it gets + # into this function would become ``np.array(1)``. + other = lib.item_from_zerodim(other) + if lib.isscalar(other): if other in self.categories: i = self.categories.get_loc(other) return getattr(self._codes, op)(i) diff --git a/pandas/core/common.py b/pandas/core/common.py index 51464e1809e75..1c117e1cae7dd 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -84,7 +84,7 @@ def _check(cls, inst): ABCSparseArray = create_pandas_abc_type("ABCSparseArray", "_subtyp", ('sparse_array', 'sparse_series')) ABCCategorical = create_pandas_abc_type("ABCCategorical","_typ",("categorical")) - +ABCPeriod = create_pandas_abc_type("ABCPeriod", "_typ", ("period",)) class _ABCGeneric(type): diff --git a/pandas/lib.pyx b/pandas/lib.pyx index 88c458ce95226..221ffe24a713b 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -4,6 +4,7 @@ import numpy as np from numpy cimport * +np.import_array() cdef extern from "numpy/arrayobject.h": cdef enum NPY_TYPES: @@ -234,8 +235,54 @@ cpdef checknull_old(object val): else: return util._checknull(val) +# ABCPeriod cannot be imported right away from pandas.core.common. +ABCPeriod = None def isscalar(object val): - return np.isscalar(val) or val is None or PyDateTime_Check(val) or PyDelta_Check(val) + """ + Return True if given value is scalar. + + This includes: + - numpy array scalar (e.g. np.int64) + - Python builtin numerics + - Python builtin byte arrays and strings + - None + - instances of datetime.datetime + - instances of datetime.timedelta + - any type previously registered with :func:`register_scalar_type` function + + """ + global ABCPeriod + if ABCPeriod is None: + from pandas.core.common import ABCPeriod as _ABCPeriod + ABCPeriod = _ABCPeriod + + return (np.PyArray_IsAnyScalar(val) + # As of numpy-1.9, PyArray_IsAnyScalar misses bytearrays on Py3. + or PyBytes_Check(val) + or val is None + or PyDate_Check(val) + or PyDelta_Check(val) + or PyTime_Check(val) + or isinstance(val, ABCPeriod)) + + +def item_from_zerodim(object val): + """ + If the value is a zerodim array, return the item it contains. + + Examples + -------- + >>> item_from_zerodim(1) + 1 + >>> item_from_zerodim('foobar') + 'foobar' + >>> item_from_zerodim(np.array(1)) + 1 + >>> item_from_zerodim(np.array([1])) + array([1]) + + """ + return util.unbox_if_zerodim(val) @cython.wraparound(False) diff --git a/pandas/src/numpy_helper.h b/pandas/src/numpy_helper.h index 69b849de47fe7..8b79bbe79ff2f 100644 --- a/pandas/src/numpy_helper.h +++ b/pandas/src/numpy_helper.h @@ -167,6 +167,21 @@ void set_array_not_contiguous(PyArrayObject *ao) { } +// If arr is zerodim array, return a proper array scalar (e.g. np.int64). +// Otherwise, return arr as is. +PANDAS_INLINE PyObject* +unbox_if_zerodim(PyObject* arr) { + if (PyArray_IsZeroDim(arr)) { + PyObject *ret; + ret = PyArray_ToScalar(PyArray_DATA(arr), arr); + return ret; + } else { + Py_INCREF(arr); + return arr; + } +} + + // PANDAS_INLINE PyObject* // get_base_ndarray(PyObject* ap) { // // if (!ap || (NULL == ap)) { diff --git a/pandas/src/util.pxd b/pandas/src/util.pxd index cc1921e6367c5..eff1728c6921a 100644 --- a/pandas/src/util.pxd +++ b/pandas/src/util.pxd @@ -22,6 +22,7 @@ cdef extern from "numpy_helper.h": inline void transfer_object_column(char *dst, char *src, size_t stride, size_t length) object sarr_from_data(cnp.dtype, int length, void* data) + inline object unbox_if_zerodim(object arr) cdef inline object get_value_at(ndarray arr, object loc): cdef: @@ -64,7 +65,6 @@ cdef inline int is_contiguous(ndarray arr): cdef inline is_array(object o): return cnp.PyArray_Check(o) - cdef inline bint _checknull(object val): try: return val is None or (cpython.PyFloat_Check(val) and val != val) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 444eb87a399e5..4bc7084c93b6b 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -917,6 +917,12 @@ def test_datetime_categorical_comparison(self): self.assert_numpy_array_equal(dt_cat > dt_cat[0], [False, True, True]) self.assert_numpy_array_equal(dt_cat[0] < dt_cat, [False, True, True]) + def test_reflected_comparison_with_scalars(self): + # GH8658 + cat = pd.Categorical([1, 2, 3]) + self.assert_numpy_array_equal(cat > cat[0], [False, True, True]) + self.assert_numpy_array_equal(cat[0] < cat, [False, True, True]) + class TestCategoricalAsBlock(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py new file mode 100644 index 0000000000000..1b7b6c5c5ee4e --- /dev/null +++ b/pandas/tests/test_lib.py @@ -0,0 +1,72 @@ +from datetime import datetime, timedelta, date, time + +import numpy as np + +import pandas as pd +from pandas.lib import isscalar, item_from_zerodim +import pandas.util.testing as tm + + +class TestIsscalar(tm.TestCase): + def test_isscalar_builtin_scalars(self): + self.assertTrue(isscalar(None)) + self.assertTrue(isscalar(True)) + self.assertTrue(isscalar(False)) + self.assertTrue(isscalar(0.)) + self.assertTrue(isscalar(np.nan)) + self.assertTrue(isscalar('foobar')) + self.assertTrue(isscalar(b'foobar')) + self.assertTrue(isscalar(u'foobar')) + self.assertTrue(isscalar(datetime(2014, 1, 1))) + self.assertTrue(isscalar(date(2014, 1, 1))) + self.assertTrue(isscalar(time(12, 0))) + self.assertTrue(isscalar(timedelta(hours=1))) + self.assertTrue(isscalar(pd.NaT)) + + def test_isscalar_builtin_nonscalars(self): + self.assertFalse(isscalar({})) + self.assertFalse(isscalar([])) + self.assertFalse(isscalar([1])) + self.assertFalse(isscalar(())) + self.assertFalse(isscalar((1,))) + self.assertFalse(isscalar(slice(None))) + self.assertFalse(isscalar(Ellipsis)) + + def test_isscalar_numpy_array_scalars(self): + self.assertTrue(isscalar(np.int64(1))) + self.assertTrue(isscalar(np.float64(1.))) + self.assertTrue(isscalar(np.int32(1))) + self.assertTrue(isscalar(np.object_('foobar'))) + self.assertTrue(isscalar(np.str_('foobar'))) + self.assertTrue(isscalar(np.unicode_(u'foobar'))) + self.assertTrue(isscalar(np.bytes_(b'foobar'))) + self.assertTrue(isscalar(np.datetime64('2014-01-01'))) + self.assertTrue(isscalar(np.timedelta64(1, 'h'))) + + def test_isscalar_numpy_zerodim_arrays(self): + for zerodim in [np.array(1), + np.array('foobar'), + np.array(np.datetime64('2014-01-01')), + np.array(np.timedelta64(1, 'h'))]: + self.assertFalse(isscalar(zerodim)) + self.assertTrue(isscalar(item_from_zerodim(zerodim))) + + def test_isscalar_numpy_arrays(self): + self.assertFalse(isscalar(np.array([]))) + self.assertFalse(isscalar(np.array([[]]))) + self.assertFalse(isscalar(np.matrix('1; 2'))) + + def test_isscalar_pandas_scalars(self): + self.assertTrue(isscalar(pd.Timestamp('2014-01-01'))) + self.assertTrue(isscalar(pd.Timedelta(hours=1))) + self.assertTrue(isscalar(pd.Period('2014-01-01'))) + + def test_isscalar_pandas_containers(self): + self.assertFalse(isscalar(pd.Series())) + self.assertFalse(isscalar(pd.Series([1]))) + self.assertFalse(isscalar(pd.DataFrame())) + self.assertFalse(isscalar(pd.DataFrame([[1]]))) + self.assertFalse(isscalar(pd.Panel())) + self.assertFalse(isscalar(pd.Panel([[[1]]]))) + self.assertFalse(isscalar(pd.Index([]))) + self.assertFalse(isscalar(pd.Index([1]))) diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index cba449b9596e1..742d8651a4035 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -63,6 +63,7 @@ class Period(PandasObject): """ __slots__ = ['freq', 'ordinal'] _comparables = ['name','freqstr'] + _typ = 'period' @classmethod def _from_ordinal(cls, ordinal, freq): @@ -498,7 +499,6 @@ def strftime(self, fmt): base, mult = _gfc(self.freq) return tslib.period_format(self.ordinal, base, fmt) - def _get_ordinals(data, freq): f = lambda x: Period(x, freq=freq).ordinal if isinstance(data[0], Period):
This PR fixes #8658 and also adds `pd.lib.unbox_if_zerodim` that extracts values from zerodim arrays and adds detection of `pd.Period`, `datetime.date` and `datetime.time` in pd.lib.isscalar. I tried making `unbox_if_zerodim` usage more implicit but there's just too much to test when this is introduced (think every method that accepts a scalar must have another test to check it also accepts zerodim array). Definitely too much to add for a single PR, but overall zerodim support could be added later on class-by-class basis.
https://api.github.com/repos/pandas-dev/pandas/pulls/8706
2014-11-02T07:59:54Z
2014-11-02T19:15:27Z
2014-11-02T19:15:27Z
2014-11-02T21:37:50Z
ENH/BUG: support Categorical in to_panel reshaping (GH8704)
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 150da65580223..dd23897a3f7e9 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -13,6 +13,7 @@ from pandas.core.indexing import _is_null_slice from pandas.tseries.period import PeriodIndex import pandas.core.common as com +from pandas.util.decorators import cache_readonly from pandas.core.common import isnull from pandas.util.terminal import get_terminal_size @@ -174,9 +175,6 @@ class Categorical(PandasObject): >>> a.min() 'c' """ - ndim = 1 - """Number of dimensions (always 1!)""" - dtype = com.CategoricalDtype() """The dtype (always "category")""" @@ -256,6 +254,7 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa dtype = 'object' if isnull(values).any() else None values = _sanitize_array(values, None, dtype=dtype) + if categories is None: try: codes, categories = factorize(values, sort=True) @@ -270,6 +269,11 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa # give us one by specifying categories raise TypeError("'values' is not ordered, please explicitly specify the " "categories order by passing in a categories argument.") + except ValueError: + + ### FIXME #### + raise NotImplementedError("> 1 ndim Categorical are not supported at this time") + else: # there were two ways if categories are present # - the old one, where each value is a int pointer to the levels array -> not anymore @@ -305,8 +309,13 @@ def copy(self): return Categorical(values=self._codes.copy(),categories=self.categories, name=self.name, ordered=self.ordered, fastpath=True) + @cache_readonly + def ndim(self): + """Number of dimensions of the Categorical """ + return self._codes.ndim + @classmethod - def from_array(cls, data): + def from_array(cls, data, **kwargs): """ Make a Categorical type from a single array-like object. @@ -318,7 +327,7 @@ def from_array(cls, data): Can be an Index or array-like. The categories are assumed to be the unique values of `data`. """ - return Categorical(data) + return Categorical(data, **kwargs) @classmethod def from_codes(cls, codes, categories, ordered=False, name=None): diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 4350d5aba3846..a734baf28464b 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -241,15 +241,19 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, if isinstance(data, types.GeneratorType): data = list(data) if len(data) > 0: - if index is None and isinstance(data[0], Series): - index = _get_names_from_index(data) - if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1: arrays, columns = _to_arrays(data, columns, dtype=dtype) columns = _ensure_index(columns) + # set the index if index is None: - index = _default_index(len(data)) + if isinstance(data[0], Series): + index = _get_names_from_index(data) + elif isinstance(data[0], Categorical): + index = _default_index(len(data[0])) + else: + index = _default_index(len(data)) + mgr = _arrays_to_mgr(arrays, columns, index, columns, dtype=dtype) else: @@ -1053,7 +1057,6 @@ def to_panel(self): panel : Panel """ from pandas.core.panel import Panel - from pandas.core.reshape import block2d_to_blocknd # only support this kind for now if (not isinstance(self.index, MultiIndex) or # pragma: no cover @@ -1073,20 +1076,9 @@ def to_panel(self): selfsorted = self major_axis, minor_axis = selfsorted.index.levels - major_labels, minor_labels = selfsorted.index.labels - shape = len(major_axis), len(minor_axis) - new_blocks = [] - for block in selfsorted._data.blocks: - newb = block2d_to_blocknd( - values=block.values.T, - placement=block.mgr_locs, shape=shape, - labels=[major_labels, minor_labels], - ref_items=selfsorted.columns) - new_blocks.append(newb) - # preserve names, if any major_axis = major_axis.copy() major_axis.name = self.index.names[0] @@ -1094,8 +1086,14 @@ def to_panel(self): minor_axis = minor_axis.copy() minor_axis.name = self.index.names[1] + # create new axes new_axes = [selfsorted.columns, major_axis, minor_axis] - new_mgr = create_block_manager_from_blocks(new_blocks, new_axes) + + # create new manager + new_mgr = selfsorted._data.reshape_nd(axes=new_axes, + labels=[major_labels, minor_labels], + shape=shape, + ref_items=selfsorted.columns) return Panel(new_mgr) @@ -4808,6 +4806,10 @@ def _to_arrays(data, columns, coerce_float=False, dtype=None): return _list_of_series_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype) + elif isinstance(data[0], Categorical): + if columns is None: + columns = _default_index(len(data)) + return data, columns elif (isinstance(data, (np.ndarray, Series, Index)) and data.dtype.names is not None): diff --git a/pandas/core/internals.py b/pandas/core/internals.py index f3f88583b2445..bb81258efe4c5 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -11,7 +11,7 @@ from pandas.core.common import (_possibly_downcast_to_dtype, isnull, _NS_DTYPE, _TD_DTYPE, ABCSeries, is_list_like, ABCSparseSeries, _infer_dtype_from_scalar, - _is_null_datelike_scalar, + _is_null_datelike_scalar, _maybe_promote, is_timedelta64_dtype, is_datetime64_dtype, _possibly_infer_to_datetimelike, array_equivalent) from pandas.core.index import Index, MultiIndex, _ensure_index @@ -177,6 +177,24 @@ def _slice(self, slicer): """ return a slice of my values """ return self.values[slicer] + def reshape_nd(self, labels, shape, ref_items): + """ + Parameters + ---------- + labels : list of new axis labels + shape : new shape + ref_items : new ref_items + + return a new block that is transformed to a nd block + """ + + return _block2d_to_blocknd( + values=self.get_values().T, + placement=self.mgr_locs, + shape=shape, + labels=labels, + ref_items=ref_items) + def getitem_block(self, slicer, new_mgr_locs=None): """ Perform __getitem__-like, return result as block. @@ -2573,6 +2591,10 @@ def comp(s): bm._consolidate_inplace() return bm + def reshape_nd(self, axes, **kwargs): + """ a 2d-nd reshape operation on a BlockManager """ + return self.apply('reshape_nd', axes=axes, **kwargs) + def is_consolidated(self): """ Return True if more than one block with the same dtype @@ -3895,6 +3917,43 @@ def _concat_indexes(indexes): return indexes[0].append(indexes[1:]) +def _block2d_to_blocknd(values, placement, shape, labels, ref_items): + """ pivot to the labels shape """ + from pandas.core.internals import make_block + + panel_shape = (len(placement),) + shape + + # TODO: lexsort depth needs to be 2!! + + # Create observation selection vector using major and minor + # labels, for converting to panel format. + selector = _factor_indexer(shape[1:], labels) + mask = np.zeros(np.prod(shape), dtype=bool) + mask.put(selector, True) + + if mask.all(): + pvalues = np.empty(panel_shape, dtype=values.dtype) + else: + dtype, fill_value = _maybe_promote(values.dtype) + pvalues = np.empty(panel_shape, dtype=dtype) + pvalues.fill(fill_value) + + values = values + for i in range(len(placement)): + pvalues[i].flat[mask] = values[:, i] + + return make_block(pvalues, placement=placement) + + +def _factor_indexer(shape, labels): + """ + given a tuple of shape and a list of Categorical labels, return the + expanded label indexer + """ + mult = np.array(shape)[::-1].cumprod()[::-1] + return com._ensure_platform_int( + np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T) + def _get_blkno_placements(blknos, blk_count, group=True): """ diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index bb6f6f4d00cd8..5cbf392f246ed 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -59,7 +59,12 @@ class _Unstacker(object): """ def __init__(self, values, index, level=-1, value_columns=None): + + self.is_categorical = None if values.ndim == 1: + if isinstance(values, Categorical): + self.is_categorical = values + values = np.array(values) values = values[:, np.newaxis] self.values = values self.value_columns = value_columns @@ -175,6 +180,12 @@ def get_result(self): else: index = index.take(self.unique_groups) + # may need to coerce categoricals here + if self.is_categorical is not None: + values = [ Categorical.from_array(values[:,i], + categories=self.is_categorical.categories) + for i in range(values.shape[-1]) ] + return DataFrame(values, index=index, columns=columns) def get_new_values(self): @@ -1188,40 +1199,3 @@ def make_axis_dummies(frame, axis='minor', transform=None): values = values.take(labels, axis=0) return DataFrame(values, columns=items, index=frame.index) - - -def block2d_to_blocknd(values, placement, shape, labels, ref_items): - """ pivot to the labels shape """ - from pandas.core.internals import make_block - - panel_shape = (len(placement),) + shape - - # TODO: lexsort depth needs to be 2!! - - # Create observation selection vector using major and minor - # labels, for converting to panel format. - selector = factor_indexer(shape[1:], labels) - mask = np.zeros(np.prod(shape), dtype=bool) - mask.put(selector, True) - - if mask.all(): - pvalues = np.empty(panel_shape, dtype=values.dtype) - else: - dtype, fill_value = _maybe_promote(values.dtype) - pvalues = np.empty(panel_shape, dtype=dtype) - pvalues.fill(fill_value) - - values = values - for i in range(len(placement)): - pvalues[i].flat[mask] = values[:, i] - - return make_block(pvalues, placement=placement) - - -def factor_indexer(shape, labels): - """ given a tuple of shape and a list of Categorical labels, return the - expanded label indexer - """ - mult = np.array(shape)[::-1].cumprod()[::-1] - return com._ensure_platform_int( - np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index f1745fe8579bb..6f8a774356293 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -23,8 +23,7 @@ from pandas.core.algorithms import match, unique from pandas.core.categorical import Categorical from pandas.core.common import _asarray_tuplesafe -from pandas.core.internals import BlockManager, make_block -from pandas.core.reshape import block2d_to_blocknd, factor_indexer +from pandas.core.internals import BlockManager, make_block, _block2d_to_blocknd, _factor_indexer from pandas.core.index import _ensure_index from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type import pandas.core.common as com @@ -332,7 +331,7 @@ def read_hdf(path_or_buf, key, **kwargs): key, auto_close=auto_close, **kwargs) if isinstance(path_or_buf, string_types): - + try: exists = os.path.exists(path_or_buf) @@ -3537,7 +3536,7 @@ def read(self, where=None, columns=None, **kwargs): labels = [f.codes for f in factors] # compute the key - key = factor_indexer(N[1:], labels) + key = _factor_indexer(N[1:], labels) objs = [] if len(unique(key)) == len(key): @@ -3556,7 +3555,7 @@ def read(self, where=None, columns=None, **kwargs): take_labels = [l.take(sorter) for l in labels] items = Index(c.values) - block = block2d_to_blocknd( + block = _block2d_to_blocknd( values=sorted_values, placement=np.arange(len(items)), shape=tuple(N), labels=take_labels, ref_items=items) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 4bc7084c93b6b..624c6cf9688d6 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1121,18 +1121,45 @@ def test_construction_frame(self): expected = Series(list('abc'),dtype='category') tm.assert_series_equal(df[0],expected) - # these coerces back to object as its spread across columns - # ndim != 1 df = DataFrame([pd.Categorical(list('abc'))]) - expected = DataFrame([list('abc')]) + expected = DataFrame({ 0 : Series(list('abc'),dtype='category')}) + tm.assert_frame_equal(df,expected) + + df = DataFrame([pd.Categorical(list('abc')),pd.Categorical(list('abd'))]) + expected = DataFrame({ 0 : Series(list('abc'),dtype='category'), + 1 : Series(list('abd'),dtype='category')},columns=[0,1]) tm.assert_frame_equal(df,expected) # mixed df = DataFrame([pd.Categorical(list('abc')),list('def')]) - expected = DataFrame([list('abc'),list('def')]) + expected = DataFrame({ 0 : Series(list('abc'),dtype='category'), + 1 : list('def')},columns=[0,1]) tm.assert_frame_equal(df,expected) + # invalid (shape) + self.assertRaises(ValueError, lambda : DataFrame([pd.Categorical(list('abc')),pd.Categorical(list('abdefg'))])) + + # ndim > 1 + self.assertRaises(NotImplementedError, lambda : pd.Categorical(np.array([list('abcd')]))) + + def test_reshaping(self): + + p = tm.makePanel() + p['str'] = 'foo' + df = p.to_frame() + df['category'] = df['str'].astype('category') + result = df['category'].unstack() + + c = Categorical(['foo']*len(p.major_axis)) + expected = DataFrame({'A' : c.copy(), + 'B' : c.copy(), + 'C' : c.copy(), + 'D' : c.copy()}, + columns=Index(list('ABCD'),name='minor'), + index=p.major_axis.set_names('major')) + tm.assert_frame_equal(result, expected) + def test_reindex(self): index = pd.date_range('20000101', periods=3) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 14e4e32acae9f..01d086f57718c 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1501,6 +1501,18 @@ def test_to_frame_mixed(self): # Previously, this was mutating the underlying index and changing its name assert_frame_equal(wp['bool'], panel['bool'], check_names=False) + # GH 8704 + # with categorical + df = panel.to_frame() + df['category'] = df['str'].astype('category') + + # to_panel + # TODO: this converts back to object + p = df.to_panel() + expected = panel.copy() + expected['category'] = 'foo' + assert_panel_equal(p,expected) + def test_to_frame_multi_major(self): idx = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')])
CLN: move block2d_to_blocknd support code to core/internal.py TST/BUG: support Categorical reshaping via .unstack closes #8704
https://api.github.com/repos/pandas-dev/pandas/pulls/8705
2014-11-02T02:13:15Z
2014-11-02T21:51:43Z
2014-11-02T21:51:43Z
2014-11-02T21:51:43Z
ENH: provide a null_counts keyword to df.info() to force showing of null counts
diff --git a/doc/source/options.rst b/doc/source/options.rst index 5edd28e559bc1..79145dfbf2939 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -86,7 +86,7 @@ pandas namespace. To change an option, call ``set_option('option regex', new_va pd.set_option('mode.sim_interactive', True) pd.get_option('mode.sim_interactive') -**Note:** that the option 'mode.sim_interactive' is mostly used for debugging purposes. +**Note:** that the option 'mode.sim_interactive' is mostly used for debugging purposes. All options also have a default value, and you can use ``reset_option`` to do just that: @@ -213,7 +213,8 @@ will be given. ``display.max_info_rows``: ``df.info()`` will usually show null-counts for each column. For large frames this can be quite slow. ``max_info_rows`` and ``max_info_cols`` -limit this null check only to frames with smaller dimensions then specified. +limit this null check only to frames with smaller dimensions then specified. Note that you +can specify the option ``df.info(null_counts=True)`` to override on showing a particular frame. .. ipython:: python diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 7d01cb997b611..dee83f8bb75ea 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -159,6 +159,7 @@ Enhancements - Added support for 3-character ISO and non-standard country codes in :func:``io.wb.download()`` (:issue:`8482`) - :ref:`World Bank data requests <remote_data.wb>` now will warn/raise based on an ``errors`` argument, as well as a list of hard-coded country codes and the World Bank's JSON response. In prior versions, the error messages didn't look at the World Bank's JSON response. Problem-inducing input were simply dropped prior to the request. The issue was that many good countries were cropped in the hard-coded approach. All countries will work now, but some bad countries will raise exceptions because some edge cases break the entire response. (:issue:`8482`) - Added option to ``Series.str.split()`` to return a ``DataFrame`` rather than a ``Series`` (:issue:`8428`) +- Added option to ``df.info(null_counts=None|True|False)`` to override the default display options and force showing of the null-counts (:issue:`8701`) .. _whatsnew_0151.performance: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 29aad379c8424..4350d5aba3846 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1424,7 +1424,7 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, if buf is None: return formatter.buf.getvalue() - def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None): + def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None): """ Concise summary of a DataFrame. @@ -1444,6 +1444,12 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None): the `display.memory_usage` setting. True or False overrides the `display.memory_usage` setting. Memory usage is shown in human-readable units (base-2 representation). + null_counts : boolean, default None + Whether to show the non-null counts + If None, then only show if the frame is smaller than max_info_rows and max_info_columns. + If True, always show counts. + If False, never show counts. + """ from pandas.core.format import _put_lines @@ -1469,8 +1475,11 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None): max_rows = get_option('display.max_info_rows', len(self) + 1) - show_counts = ((len(self.columns) <= max_cols) and - (len(self) < max_rows)) + if null_counts is None: + show_counts = ((len(self.columns) <= max_cols) and + (len(self) < max_rows)) + else: + show_counts = null_counts exceeds_info_cols = len(self.columns) > max_cols def _verbose_repr(): diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 89d08d37e0a30..47f9762eb0fa3 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -117,6 +117,26 @@ def test_eng_float_formatter(self): repr(self.frame) self.reset_display_options() + def test_show_null_counts(self): + + df = DataFrame(1,columns=range(10),index=range(10)) + df.iloc[1,1] = np.nan + + def check(null_counts, result): + buf = StringIO() + r = df.info(buf=buf,null_counts=null_counts) + self.assertTrue(('non-null' in buf.getvalue()) is result) + + with option_context('display.max_info_rows',20,'display.max_info_columns',20): + check(None, True) + check(True, True) + check(False, False) + + with option_context('display.max_info_rows',5,'display.max_info_columns',5): + check(None, False) + check(True, False) + check(False, False) + def test_repr_tuples(self): buf = StringIO()
Allows run-time control of showing of null-counts with `df.info()` (similar to what we allow with for example memory_usage in that this will override the default options for that call) ``` In [2]: df = DataFrame(1,columns=range(10),index=range(10)) In [3]: df.iloc[1,1] = np.nan ``` Default for a small frame (currently) is to show the non-null counts ``` In [5]: df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 10 entries, 0 to 9 Data columns (total 10 columns): 0 10 non-null float64 1 9 non-null float64 2 10 non-null float64 3 10 non-null float64 4 10 non-null float64 5 10 non-null float64 6 10 non-null float64 7 10 non-null float64 8 10 non-null float64 9 10 non-null float64 dtypes: float64(10) memory usage: 880.0 bytes # allow it to be turned off In [6]: df.info(null_counts=False) <class 'pandas.core.frame.DataFrame'> Int64Index: 10 entries, 0 to 9 Data columns (total 10 columns): 0 float64 1 float64 2 float64 3 float64 4 float64 5 float64 6 float64 7 float64 8 float64 9 float64 dtypes: float64(10) memory usage: 880.0 bytes ``` When you have a big frame, right now you need to set max_info_rows to control this option ``` In [7]: pd.set_option('max_info_rows',5) In [8]: df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 10 entries, 0 to 9 Data columns (total 10 columns): 0 float64 1 float64 2 float64 3 float64 4 float64 5 float64 6 float64 7 float64 8 float64 9 float64 dtypes: float64(10) memory usage: 880.0 bytes # force it to show In [9]: df.info(null_counts=True) <class 'pandas.core.frame.DataFrame'> Int64Index: 10 entries, 0 to 9 Data columns (total 10 columns): 0 10 non-null float64 1 9 non-null float64 2 10 non-null float64 3 10 non-null float64 4 10 non-null float64 5 10 non-null float64 6 10 non-null float64 7 10 non-null float64 8 10 non-null float64 9 10 non-null float64 dtypes: float64(10) memory usage: 880.0 bytes ```
https://api.github.com/repos/pandas-dev/pandas/pulls/8701
2014-11-01T16:27:22Z
2014-11-02T18:57:21Z
2014-11-02T18:57:21Z
2014-11-02T18:57:21Z
BUG: various Categorical fixes (GH8623)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 2b7a75f29705e..e96adc2bd9559 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -183,7 +183,9 @@ Bug Fixes - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) - Bug in writing Categorical columns to an SQL database with ``to_sql`` (:issue:`8624`). - Bug in comparing ``Categorical`` of datetime raising when being compared to a scalar datetime (:issue:`8687`) - +- Bug in selecting from a ``Categorical`` with ``.iloc`` (:issue:`8623`) +- Bug in groupby-transform with a Categorical (:issue:`8623`) +- Bug in duplicated/drop_duplicates with a Categorical (:issue:`8623`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 524c485db218b..29aad379c8424 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2732,19 +2732,20 @@ def _m8_to_i8(x): return x.view(np.int64) return x + # if we are only duplicating on Categoricals this can be much faster if subset is None: - values = list(_m8_to_i8(self.values.T)) + values = list(_m8_to_i8(self.get_values().T)) else: if np.iterable(subset) and not isinstance(subset, compat.string_types): if isinstance(subset, tuple): if subset in self.columns: - values = [self[subset].values] + values = [self[subset].get_values()] else: - values = [_m8_to_i8(self[x].values) for x in subset] + values = [_m8_to_i8(self[x].get_values()) for x in subset] else: - values = [_m8_to_i8(self[x].values) for x in subset] + values = [_m8_to_i8(self[x].get_values()) for x in subset] else: - values = [self[subset].values] + values = [self[subset].get_values()] keys = lib.fast_zip_fillna(values) duplicated = lib.duplicated(keys, take_last=take_last) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 9b95aff465d55..f3f88583b2445 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -58,6 +58,7 @@ class Block(PandasObject): _verify_integrity = True _validate_ndim = True _ftype = 'dense' + _holder = None def __init__(self, values, placement, ndim=None, fastpath=False): if ndim is None: @@ -476,6 +477,14 @@ def to_native_types(self, slicer=None, na_rep='', **kwargs): def _concat_blocks(self, blocks, values): """ return the block concatenation """ + + # dispatch to a categorical to handle the concat + if self._holder is None: + + for b in blocks: + if b.is_categorical: + return b._concat_blocks(blocks,values) + return self._holder(values[0]) # block actions #### @@ -1739,10 +1748,24 @@ def _concat_blocks(self, blocks, values): return the block concatenation """ - categories = self.values.categories - for b in blocks: + # we could have object blocks and categorical's here + # if we only have a single cateogoricals then combine everything + # else its a non-compat categorical + + categoricals = [ b for b in blocks if b.is_categorical ] + objects = [ b for b in blocks if not b.is_categorical and b.is_object ] + + # convert everything to object and call it a day + if len(objects) + len(categoricals) != len(blocks): + raise ValueError("try to combine non-object blocks and categoricals") + + # validate the categories + categories = None + for b in categoricals: + if categories is None: + categories = b.values.categories if not categories.equals(b.values.categories): - raise ValueError("incompatible levels in categorical block merge") + raise ValueError("incompatible categories in categorical block merge") return self._holder(values[0], categories=categories) diff --git a/pandas/core/series.py b/pandas/core/series.py index f5d729b61e770..68bf4f0f022d7 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -475,7 +475,13 @@ def _ixs(self, i, axis=0): value : scalar (int) or Series (slice, sequence) """ try: - return _index.get_value_at(self.values, i) + + # dispatch to the values if we need + values = self.values + if isinstance(values, np.ndarray): + return _index.get_value_at(values, i) + else: + return values[i] except IndexError: raise except: diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 3b84d4aa34756..444eb87a399e5 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1030,6 +1030,21 @@ def test_basic(self): str(df.values) str(df) + # GH8623 + x = pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']], + columns=['person_id','person_name']) + x['person_name'] = pd.Categorical(x.person_name) # doing this breaks transform + + expected = x.iloc[0].person_name + result = x.person_name.iloc[0] + self.assertEqual(result,expected) + + result = x.person_name[0] + self.assertEqual(result,expected) + + result = x.person_name.loc[0] + self.assertEqual(result,expected) + def test_creation_astype(self): l = ["a","b","c","a"] s = pd.Series(l) @@ -1477,6 +1492,28 @@ def test_groupby(self): result = gb.sum() tm.assert_frame_equal(result, expected) + # GH 8623 + x=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']], + columns=['person_id','person_name']) + x['person_name'] = pd.Categorical(x.person_name) + + g = x.groupby(['person_id']) + result = g.transform(lambda x:x) + tm.assert_frame_equal(result, x[['person_name']]) + + result = x.drop_duplicates('person_name') + expected = x.iloc[[0,1]] + tm.assert_frame_equal(result, expected) + + def f(x): + return x.drop_duplicates('person_name').iloc[0] + + result = g.apply(f) + expected = x.iloc[[0,1]].copy() + expected.index = Index([1,2],name='person_id') + expected['person_name'] = expected['person_name'].astype('object') + tm.assert_frame_equal(result, expected) + def test_pivot_table(self): raw_cat1 = Categorical(["a","a","b","b"], categories=["a","b","z"])
BUG: bug in selecting from a Categorical with iloc (GH8623) BUG: bug in groupby-transform with a Categorical (GH8623) BUG: bug in duplicated/drop_duplicates with a Categorical (GH8623) closes #8623
https://api.github.com/repos/pandas-dev/pandas/pulls/8700
2014-10-31T21:29:04Z
2014-10-31T22:35:39Z
2014-10-31T22:35:39Z
2014-11-01T13:11:51Z
BUG: incorrect serialization of a CustomBusinessDay in HDF5 (GH8591)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 25909f385e7da..2b7a75f29705e 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -1,14 +1,12 @@ .. _whatsnew_0151: -v0.15.1 (November ??, 2014) ------------------------ +v0.15.1 (November 8, 2014) +-------------------------- -This is a minor release from 0.15.0 and includes a small number of API changes, several new features, +This is a minor bug-fix release from 0.15.0 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. -- Highlights include: - - :ref:`Enhancements <whatsnew_0151.enhancements>` - :ref:`API Changes <whatsnew_0151.api>` - :ref:`Performance Improvements <whatsnew_0151.performance>` @@ -30,10 +28,10 @@ API changes .. code-block:: python - # this was underreported and actually took (in < 0.15.1) about 24008 bytes + # this was underreported in prior versions In [1]: dfi.memory_usage(index=True) Out[1]: - Index 8000 + Index 8000 # took about 24008 bytes in < 0.15.1 A 8000 dtype: int64 @@ -178,7 +176,7 @@ Experimental Bug Fixes ~~~~~~~~~ - +- Bug in unpickling of a ``CustomBusinessDay`` object (:issue:`8591`) - Bug in coercing ``Categorical`` to a records array, e.g. ``df.to_records()`` (:issue:`8626`) - Bug in ``Categorical`` not created properly with ``Series.to_frame()`` (:issue:`8626`) - Bug in coercing in astype of a ``Categorical`` of a passed ``pd.Categorical`` (this now raises ``TypeError`` correctly), (:issue:`8626`) diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 7fe7c83988b84..8cbad9ab2b3cb 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -2047,6 +2047,28 @@ def compare(a,b): result = store.select('df') assert_frame_equal(result,df) + def test_calendar_roundtrip_issue(self): + + # 8591 + # doc example from tseries holiday section + weekmask_egypt = 'Sun Mon Tue Wed Thu' + holidays = ['2012-05-01', datetime.datetime(2013, 5, 1), np.datetime64('2014-05-01')] + bday_egypt = pandas.offsets.CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt) + dt = datetime.datetime(2013, 4, 30) + dts = date_range(dt, periods=5, freq=bday_egypt) + + s = (Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split()))) + + with ensure_clean_store(self.path) as store: + + store.put('fixed',s) + result = store.select('fixed') + assert_series_equal(result, s) + + store.append('table',s) + result = store.select('table') + assert_series_equal(result, s) + def test_append_with_timezones_dateutil(self): from datetime import timedelta diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 55aad38c10fae..81daa2b451c6b 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -614,6 +614,14 @@ def __getstate__(self): """Return a pickleable state""" state = self.__dict__.copy() del state['calendar'] + + # we don't want to actually pickle the calendar object + # as its a np.busyday; we recreate on deserilization + try: + state['kwds'].pop('calendar') + except: + pass + return state def __setstate__(self, state): diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 3b2e8f203c313..ef4288b28e9e4 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -12,12 +12,13 @@ from pandas.core.datetools import ( bday, BDay, CDay, BQuarterEnd, BMonthEnd, CBMonthEnd, CBMonthBegin, - BYearEnd, MonthEnd, MonthBegin, BYearBegin, + BYearEnd, MonthEnd, MonthBegin, BYearBegin, CustomBusinessDay, QuarterBegin, BQuarterBegin, BMonthBegin, DateOffset, Week, YearBegin, YearEnd, Hour, Minute, Second, Day, Micro, Milli, Nano, Easter, WeekOfMonth, format, ole2datetime, QuarterEnd, to_datetime, normalize_date, get_offset, get_offset_name, get_standard_freq) +from pandas import Series from pandas.tseries.frequencies import _offset_map from pandas.tseries.index import _to_m8, DatetimeIndex, _daterange_cache, date_range from pandas.tseries.tools import parse_time_string @@ -867,7 +868,6 @@ def test_pickle_compat_0_14_1(self): cday = CDay(holidays=hdays) self.assertEqual(cday, cday0_14_1) - class CustomBusinessMonthBase(object): _multiprocess_can_split_ = True
closes #8591
https://api.github.com/repos/pandas-dev/pandas/pulls/8699
2014-10-31T19:34:33Z
2014-10-31T20:18:44Z
2014-10-31T20:18:44Z
2014-10-31T20:18:45Z
API/BUG: return np.nan rather than -1 for invalid datetime accessors values (GH8689)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index ab7f3992cf37b..25909f385e7da 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -121,6 +121,33 @@ API changes pd.concat(deque((df1, df2))) +- ``s.dt.hour`` and other ``.dt`` accessors will now return ``np.nan`` for missing values (rather than previously -1), (:issue:`8689`) + + .. ipython:: python + + s = Series(date_range('20130101',periods=5,freq='D')) + s.iloc[2] = np.nan + s + + previous behavior: + + .. code-block:: python + + In [6]: s.dt.hour + Out[6]: + 0 0 + 1 0 + 2 -1 + 3 0 + 4 0 + dtype: int64 + + current behavior: + + .. ipython:: python + + s.dt.hour + .. _whatsnew_0151.enhancements: Enhancements diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 68590e1597bbc..018d8c614eaae 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -208,6 +208,29 @@ def f(): s.dt.hour[0] = 5 self.assertRaises(com.SettingWithCopyError, f) + def test_valid_dt_with_missing_values(self): + + from datetime import date, time + + # GH 8689 + s = Series(date_range('20130101',periods=5,freq='D')) + s_orig = s.copy() + s.iloc[2] = pd.NaT + + for attr in ['microsecond','nanosecond','second','minute','hour','day']: + expected = getattr(s.dt,attr).copy() + expected.iloc[2] = np.nan + result = getattr(s.dt,attr) + tm.assert_series_equal(result, expected) + + result = s.dt.date + expected = Series([date(2013,1,1),date(2013,1,2),np.nan,date(2013,1,4),date(2013,1,5)],dtype='object') + tm.assert_series_equal(result, expected) + + result = s.dt.time + expected = Series([time(0),time(0),np.nan,time(0),time(0)],dtype='object') + tm.assert_series_equal(result, expected) + def test_binop_maybe_preserve_name(self): # names match, preserve diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 3e51b55821fba..0a446919e95d2 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -174,6 +174,31 @@ def asobject(self): from pandas.core.index import Index return Index(self._box_values(self.asi8), name=self.name, dtype=object) + def _maybe_mask_results(self, result, fill_value=None, convert=None): + """ + Parameters + ---------- + result : a ndarray + convert : string/dtype or None + + Returns + ------- + result : ndarray with values replace by the fill_value + + mask the result if needed, convert to the provided dtype if its not None + + This is an internal routine + """ + + if self.hasnans: + mask = self.asi8 == tslib.iNaT + if convert: + result = result.astype(convert) + if fill_value is None: + fill_value = np.nan + result[mask] = fill_value + return result + def tolist(self): """ return a list of the underlying data diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 4ab48b2db98d0..52ab217cbffc6 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -46,13 +46,17 @@ def f(self): utc = _utc() if self.tz is not utc: values = self._local_timestamps() + if field in ['is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', 'is_year_end']: month_kw = self.freq.kwds.get('startingMonth', self.freq.kwds.get('month', 12)) if self.freq else 12 - return tslib.get_start_end_field(values, field, self.freqstr, month_kw) + result = tslib.get_start_end_field(values, field, self.freqstr, month_kw) else: - return tslib.get_date_field(values, field) + result = tslib.get_date_field(values, field) + + return self._maybe_mask_results(result,convert='float64') + f.__name__ = name f.__doc__ = docstring return property(f) @@ -643,9 +647,7 @@ def _sub_datelike(self, other): other = Timestamp(other) i8 = self.asi8 result = i8 - other.value - if self.hasnans: - mask = i8 == tslib.iNaT - result[mask] = tslib.iNaT + result = self._maybe_mask_results(result,fill_value=tslib.iNaT) return TimedeltaIndex(result,name=self.name,copy=False) def _add_delta(self, delta): @@ -1329,15 +1331,14 @@ def time(self): """ # can't call self.map() which tries to treat func as ufunc # and causes recursion warnings on python 2.6 - return _algos.arrmap_object(self.asobject.values, lambda x: x.time()) + return self._maybe_mask_results(_algos.arrmap_object(self.asobject.values, lambda x: x.time())) @property def date(self): """ Returns numpy array of datetime.date. The date part of the Timestamps. """ - return _algos.arrmap_object(self.asobject.values, lambda x: x.date()) - + return self._maybe_mask_results(_algos.arrmap_object(self.asobject.values, lambda x: x.date())) def normalize(self): """ diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 2c452b2fa7ded..5a041ed09fb27 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -307,10 +307,7 @@ def _evaluate_with_timedelta_like(self, other, op, opstr): i8 = self.asi8 result = i8/float(other.value) - if self.hasnans: - mask = i8 == tslib.iNaT - result = result.astype('float64') - result[mask] = np.nan + result = self._maybe_mask_results(result,convert='float64') return Index(result,name=self.name,copy=False) raise TypeError("can only perform ops with timedelta like values") @@ -322,9 +319,7 @@ def _add_datelike(self, other): other = Timestamp(other) i8 = self.asi8 result = i8 + other.value - if self.hasnans: - mask = i8 == tslib.iNaT - result[mask] = tslib.iNaT + result = self._maybe_mask_results(result,fill_value=tslib.iNaT) return DatetimeIndex(result,name=self.name,copy=False) def _sub_datelike(self, other): @@ -455,9 +450,7 @@ def astype(self, dtype): # return an index (essentially this is division) result = self.values.astype(dtype) if self.hasnans: - result = result.astype('float64') - result[self.asi8 == tslib.iNaT] = np.nan - return Index(result,name=self.name) + return Index(self._maybe_mask_results(result,convert='float64'),name=self.name) return Index(result.astype('i8'),name=self.name) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index e6e6b48ccb573..e046d687435e7 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -500,11 +500,11 @@ def test_properties_nat(self): # confirm Period('NaT') work identical with Timestamp('NaT') for f in ['year', 'month', 'day', 'hour', 'minute', 'second', 'week', 'dayofyear', 'quarter']: - self.assertEqual(getattr(p_nat, f), -1) - self.assertEqual(getattr(t_nat, f), -1) + self.assertTrue(np.isnan(getattr(p_nat, f))) + self.assertTrue(np.isnan(getattr(t_nat, f))) for f in ['weekofyear', 'dayofweek', 'weekday', 'qyear']: - self.assertEqual(getattr(p_nat, f), -1) + self.assertTrue(np.isnan(getattr(p_nat, f))) def test_pnow(self): dt = datetime.now() diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 282301499dcbc..11bf22a055b8f 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -1,7 +1,7 @@ # pylint: disable-msg=E1101,W0612 from __future__ import division -from datetime import datetime, timedelta +from datetime import datetime, timedelta, time import nose import numpy as np @@ -460,6 +460,9 @@ def testit(unit, transform): self.assertRaises(ValueError, lambda : to_timedelta([1,2],unit='foo')) self.assertRaises(ValueError, lambda : to_timedelta(1,unit='foo')) + # time not supported ATM + self.assertRaises(ValueError, lambda :to_timedelta(time(second=1))) + def test_to_timedelta_via_apply(self): # GH 5458 expected = Series([np.timedelta64(1,'s')]) diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 1980924483bfb..bf1f0d31e8d3e 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -10,7 +10,7 @@ from pandas import (Index, Series, TimeSeries, DataFrame, isnull, date_range, Timestamp, Period, DatetimeIndex, - Int64Index, to_datetime, bdate_range, Float64Index) + Int64Index, to_datetime, bdate_range, Float64Index, TimedeltaIndex) import pandas.core.datetools as datetools import pandas.tseries.offsets as offsets @@ -939,9 +939,9 @@ def test_nat_vector_field_access(self): 'week', 'dayofyear'] for field in fields: result = getattr(idx, field) - expected = [getattr(x, field) if x is not NaT else -1 + expected = [getattr(x, field) if x is not NaT else np.nan for x in idx] - self.assert_numpy_array_equal(result, expected) + self.assert_numpy_array_equivalent(result, np.array(expected)) def test_nat_scalar_field_access(self): fields = ['year', 'quarter', 'month', 'day', 'hour', @@ -949,9 +949,9 @@ def test_nat_scalar_field_access(self): 'week', 'dayofyear'] for field in fields: result = getattr(NaT, field) - self.assertEqual(result, -1) + self.assertTrue(np.isnan(result)) - self.assertEqual(NaT.weekday(), -1) + self.assertTrue(np.isnan(NaT.weekday())) def test_to_datetime_types(self): @@ -3376,6 +3376,33 @@ def check(val,unit=None,h=1,s=1,us=0): result = Timestamp(NaT) self.assertIs(result, NaT) + def test_roundtrip(self): + + # test value to string and back conversions + # further test accessors + base = Timestamp('20140101 00:00:00') + + result = Timestamp(base.value + pd.Timedelta('5ms').value) + self.assertEqual(result,Timestamp(str(base) + ".005000")) + self.assertEqual(result.microsecond,5000) + + result = Timestamp(base.value + pd.Timedelta('5us').value) + self.assertEqual(result,Timestamp(str(base) + ".000005")) + self.assertEqual(result.microsecond,5) + + result = Timestamp(base.value + pd.Timedelta('5ns').value) + self.assertEqual(result,Timestamp(str(base) + ".000000005")) + self.assertEqual(result.nanosecond,5) + self.assertEqual(result.microsecond,0) + + result = Timestamp(base.value + pd.Timedelta('6ms 5us').value) + self.assertEqual(result,Timestamp(str(base) + ".006005")) + self.assertEqual(result.microsecond,5+6*1000) + + result = Timestamp(base.value + pd.Timedelta('200ms 5us').value) + self.assertEqual(result,Timestamp(str(base) + ".200005")) + self.assertEqual(result.microsecond,5+200*1000) + def test_comparison(self): # 5-18-2012 00:00:00.000 stamp = long(1337299200000000000) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index e88d88c86cf48..ffe94a94b15b5 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -289,8 +289,7 @@ class Timestamp(_Timestamp): result = '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second) if self.nanosecond != 0: - nanos = self.nanosecond + 1000 * self.microsecond - result += '.%.9d' % nanos + result += '.%.9d' % (self.nanosecond + 1000 * self.microsecond) elif self.microsecond != 0: result += '.%.6d' % self.microsecond @@ -345,6 +344,10 @@ class Timestamp(_Timestamp): weekofyear = week + @property + def microsecond(self): + return self._get_field('us') + @property def quarter(self): return self._get_field('q') @@ -546,7 +549,7 @@ class NaTType(_NaT): return NPY_NAT def weekday(self): - return -1 + return np.nan def toordinal(self): return -1 @@ -555,10 +558,10 @@ class NaTType(_NaT): return (__nat_unpickle, (None, )) fields = ['year', 'quarter', 'month', 'day', 'hour', - 'minute', 'second', 'microsecond', 'nanosecond', + 'minute', 'second', 'millisecond', 'microsecond', 'nanosecond', 'week', 'dayofyear'] for field in fields: - prop = property(fget=lambda self: -1) + prop = property(fget=lambda self: np.nan) setattr(NaTType, field, prop) def __nat_unpickle(*args): @@ -3002,6 +3005,7 @@ def get_date_field(ndarray[int64_t] dtindex, object field): pandas_datetime_to_datetimestruct(dtindex[i], PANDAS_FR_ns, &dts) out[i] = dts.us return out + elif field == 'ns': for i in range(count): if dtindex[i] == NPY_NAT: out[i] = -1; continue @@ -3855,7 +3859,7 @@ def get_period_field(int code, int64_t value, int freq): if f is NULL: raise ValueError('Unrecognized period code: %d' % code) if value == iNaT: - return -1 + return np.nan return f(value, freq) def get_period_field_arr(int code, ndarray[int64_t] arr, int freq):
BUG: millisecond Timestamp/DatetimeIndex accessor fixed closes #8689
https://api.github.com/repos/pandas-dev/pandas/pulls/8695
2014-10-31T16:22:55Z
2014-10-31T19:04:34Z
2014-10-31T19:04:34Z
2014-10-31T20:11:29Z
VIS: register datetime64 in matplotlib units registry (GH8614)
diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt index b6b36ce8c1bf9..8397d2fcac2e9 100644 --- a/doc/source/whatsnew/v0.15.0.txt +++ b/doc/source/whatsnew/v0.15.0.txt @@ -801,21 +801,8 @@ a transparent change with only very limited API implications (:issue:`5080`, :is - MultiIndexes will now raise similary to other pandas objects w.r.t. truth testing, see :ref:`here <gotchas.truth>` (:issue:`7897`). - When plotting a DatetimeIndex directly with matplotlib's `plot` function, the axis labels will no longer be formatted as dates but as integers (the - internal representation of a ``datetime64``). To keep the old behaviour you - should add the :meth:`~DatetimeIndex.to_pydatetime()` method: - - .. code-block:: python - - import matplotlib.pyplot as plt - df = pd.DataFrame({'col': np.random.randint(1, 50, 60)}, - index=pd.date_range("2012-01-01", periods=60)) - - # this will now format the x axis labels as integers - plt.plot(df.index, df['col']) - - # to keep the date formating, convert the index explicitely to python datetime values - plt.plot(df.index.to_pydatetime(), df['col']) - + internal representation of a ``datetime64``). **UPDATE** This is fixed + in 0.15.1, see :ref:`here <whatsnew_0151.datetime64_plotting>`. .. _whatsnew_0150.deprecations: diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index ab1be57934ce7..5db62c737aeba 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -169,6 +169,7 @@ API changes - added Index properties `is_monotonic_increasing` and `is_monotonic_decreasing` (:issue:`8680`). + .. note:: io.data.Options has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`) As a result of a change in Yahoo's option page layout, when an expiry date is given, @@ -202,6 +203,15 @@ API changes See the Options documentation in :ref:`Remote Data <remote_data.yahoo_options>` +.. _whatsnew_0151.datetime64_plotting: + +- pandas now also registers the ``datetime64`` dtype in matplotlib's units registry + to plot such values as datetimes. This is activated once pandas is imported. In + previous versions, plotting an array of ``datetime64`` values will have resulted + in plotted integer values. To keep the previous behaviour, you can do + ``del matplotlib.units.registry[np.datetime64]`` (:issue:`8614`). + + .. _whatsnew_0151.enhancements: Enhancements @@ -283,8 +293,15 @@ Bug Fixes +<<<<<<< HEAD - Fixed a bug where plotting a column ``y`` and specifying a label would mutate the index name of the original DataFrame (:issue:`8494`) - Bug in ``date_range`` where partially-specified dates would incorporate current date (:issue:`6961`) - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue:`8702`) +======= +- Fixed a bug where plotting a column ``y`` and specifying a label +would mutate the index name of the DataFrame ``y`` came from (:issue:`8494`) + +- Fix regression in plotting of a DatetimeIndex directly with matplotlib (:issue:`8614`). +>>>>>>> VIS: register datetime64 in matplotlib units registry (GH8614) diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index b014e718d5411..4b3fdfd5e3303 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -30,6 +30,7 @@ def register(): units.registry[pydt.datetime] = DatetimeConverter() units.registry[pydt.date] = DatetimeConverter() units.registry[pydt.time] = TimeConverter() + units.registry[np.datetime64] = DatetimeConverter() def _to_ordinalf(tm): @@ -165,6 +166,8 @@ def try_parse(values): if isinstance(values, (datetime, pydt.date)): return _dt_to_float_ordinal(values) + elif isinstance(values, np.datetime64): + return _dt_to_float_ordinal(lib.Timestamp(values)) elif isinstance(values, pydt.time): return dates.date2num(values) elif (com.is_integer(values) or com.is_float(values)): diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tseries/tests/test_converter.py index d3287a01cd1da..b5a284d5f50ea 100644 --- a/pandas/tseries/tests/test_converter.py +++ b/pandas/tseries/tests/test_converter.py @@ -52,6 +52,17 @@ def test_conversion(self): rs = self.dtc.convert(Timestamp('2012-1-1'), None, None) self.assertEqual(rs, xp) + # also testing datetime64 dtype (GH8614) + rs = self.dtc.convert(np.datetime64('2012-01-01'), None, None) + self.assertEqual(rs, xp) + + rs = self.dtc.convert(np.datetime64('2012-01-01 00:00:00+00:00'), None, None) + self.assertEqual(rs, xp) + + rs = self.dtc.convert(np.array([np.datetime64('2012-01-01 00:00:00+00:00'), + np.datetime64('2012-01-02 00:00:00+00:00')]), None, None) + self.assertEqual(rs[0], xp) + def test_conversion_float(self): decimals = 9
Closes #8614
https://api.github.com/repos/pandas-dev/pandas/pulls/8693
2014-10-31T12:47:35Z
2014-11-05T14:34:26Z
2014-11-05T14:34:26Z
2014-11-05T14:46:49Z
CLN: fix grammar in extract_index error message
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index f998a01a1a165..524c485db218b 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4707,7 +4707,7 @@ def extract_index(data): raw_lengths.append(len(v)) if not indexes and not raw_lengths: - raise ValueError('If using all scalar values, you must must pass' + raise ValueError('If using all scalar values, you must pass' ' an index') if have_series or have_dicts: diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index a19d9d651a656..1aa734c4834de 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -2625,7 +2625,7 @@ def test_constructor_error_msgs(self): with assertRaisesRegexp(ValueError, "Shape of passed values is \(3, 2\), indices imply \(2, 2\)"): DataFrame(np.random.rand(2,3), columns=['A', 'B'], index=[1, 2]) - with assertRaisesRegexp(ValueError, 'If using all scalar values, you must must pass an index'): + with assertRaisesRegexp(ValueError, 'If using all scalar values, you must pass an index'): DataFrame({'a': False, 'b': True}) def test_constructor_with_embedded_frames(self):
https://api.github.com/repos/pandas-dev/pandas/pulls/8691
2014-10-31T08:21:04Z
2014-10-31T09:45:29Z
2014-10-31T09:45:29Z
2014-10-31T09:45:29Z
BUG: fix Categorical comparison to work with datetime
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 74e24a3a7a462..fcc371bb0e2c3 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -156,6 +156,7 @@ Bug Fixes - Bug in ``Categorical`` not created properly with ``Series.to_frame()`` (:issue:`8626`) - Bug in coercing in astype of a ``Categorical`` of a passed ``pd.Categorical`` (this now raises ``TypeError`` correctly), (:issue:`8626`) - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) +- Bug in comparing ``Categorical`` of datetime raising when being compared to a scalar datetime (:issue:`8687`) @@ -165,7 +166,7 @@ Bug Fixes -- Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608` +- Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) - Bug in setitem with empty indexer and unwanted coercion of dtypes (:issue:`8669`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 00128bd977911..598b29bf77e47 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -4,7 +4,7 @@ from warnings import warn import types -from pandas import compat +from pandas import compat, lib from pandas.compat import u from pandas.core.algorithms import factorize @@ -42,7 +42,7 @@ def f(self, other): # In other series, the leads to False, so do that here too ret[na_mask] = False return ret - elif np.isscalar(other): + elif lib.isscalar(other): if other in self.categories: i = self.categories.get_loc(other) return getattr(self._codes, op)(i) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index e47d8aaa52c9b..3b84d4aa34756 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -912,6 +912,11 @@ def test_deprecated_levels(self): self.assertFalse(LooseVersion(pd.__version__) >= '0.18') + def test_datetime_categorical_comparison(self): + dt_cat = pd.Categorical(pd.date_range('2014-01-01', periods=3)) + self.assert_numpy_array_equal(dt_cat > dt_cat[0], [False, True, True]) + self.assert_numpy_array_equal(dt_cat[0] < dt_cat, [False, True, True]) + class TestCategoricalAsBlock(tm.TestCase): _multiprocess_can_split_ = True
`pd.Timestamp` is one of types that don't pass `np.isscalar` test previously used in _cat_compare_op, `pd.lib.isscalar` should be used instead. ``` python In [1]: cat = pd.Categorical(pd.date_range('2014', periods=5)) In [2]: cat > cat[0] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-2-77883e4783fe> in <module>() ----> 1 cat > cat[0] /home/dshpektorov/sources/pandas/pandas/core/categorical.py in f(self, other) 52 msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \ 53 "compare values, use 'np.asarray(cat) <op> other'." ---> 54 raise TypeError(msg.format(op=op,typ=type(other))) 55 56 f.__name__ = op TypeError: Cannot compare a Categorical for op __gt__ with type <class 'pandas.tslib.Timestamp'>. If you want to compare values, use 'np.asarray(cat) <op> other'. ```
https://api.github.com/repos/pandas-dev/pandas/pulls/8687
2014-10-30T20:28:58Z
2014-10-31T13:48:48Z
2014-10-31T13:48:48Z
2014-11-02T07:44:47Z
BUG: fix writing of Categorical with to_sql (GH8624)
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index 3ee660bb85691..1b5b4746336ad 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -573,6 +573,7 @@ relevant columns back to `category` and assign the right categories and categori df2.dtypes df2["cats"] +The same holds for writing to a SQL database with ``to_sql``. Missing Data ------------ diff --git a/doc/source/io.rst b/doc/source/io.rst index e0c6c79380bea..066a9af472c24 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3337,6 +3337,14 @@ With some databases, writing large DataFrames can result in errors due to packet flavors, columns with type ``timedelta64`` will be written as integer values as nanoseconds to the database and a warning will be raised. +.. note:: + + Columns of ``category`` dtype will be converted to the dense representation + as you would get with ``np.asarray(categorical)`` (e.g. for string categories + this gives an array of strings). + Because of this, reading the database table back in does **not** generate + a categorical. + Reading Tables ~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 0755931bed990..c87c56953f2a2 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -131,7 +131,7 @@ Bug Fixes - Bug in ``Categorical`` not created properly with ``Series.to_frame()`` (:issue:`8626`) - Bug in coercing in astype of a ``Categorical`` of a passed ``pd.Categorical`` (this now raises ``TypeError`` correctly), (:issue:`8626`) - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) - +- Bug in writing Categorical columns to an SQL database with ``to_sql`` (:issue:`8624`). diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 09acfcaee976b..0ae82bec38e26 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -670,7 +670,7 @@ def insert_data(self): # datetime.datetime d = b.values.astype('M8[us]').astype(object) else: - d = np.array(b.values, dtype=object) + d = np.array(b.get_values(), dtype=object) # replace NaN with None if b._can_hold_na: diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 2099a8d0de82e..74f1602a0f603 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -678,6 +678,20 @@ def test_chunksize_read(self): tm.assert_frame_equal(res1, res3) + def test_categorical(self): + # GH8624 + # test that categorical gets written correctly as dense column + df = DataFrame( + {'person_id': [1, 2, 3], + 'person_name': ['John P. Doe', 'Jane Dove', 'John P. Doe']}) + df2 = df.copy() + df2['person_name'] = df2['person_name'].astype('category') + + df2.to_sql('test_categorical', self.conn, index=False) + res = sql.read_sql_query('SELECT * FROM test_categorical', self.conn) + + tm.assert_frame_equal(res, df) + class TestSQLApi(_TestSQLApi): """
Closes #8624 Use .get_values() instead of .values on the block, this ensures that NonConsolidatable blocks (non-dense blocks like categorical or sparse) are densified instead of just returning the raw .values.
https://api.github.com/repos/pandas-dev/pandas/pulls/8682
2014-10-30T14:46:32Z
2014-10-31T12:50:09Z
2014-10-31T12:50:09Z
2014-10-31T12:50:09Z
ENH: slicing with decreasing monotonic indexes
diff --git a/doc/source/api.rst b/doc/source/api.rst index f8068ebc38fa9..60f98cab96c4f 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1166,6 +1166,8 @@ Attributes Index.values Index.is_monotonic + Index.is_monotonic_increasing + Index.is_monotonic_decreasing Index.is_unique Index.dtype Index.inferred_type diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 343c6451d15ac..a371b5c830b2e 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -146,6 +146,29 @@ API changes s.dt.hour +- support for slicing with monotonic decreasing indexes, even if ``start`` or ``stop`` is + not found in the index (:issue:`7860`): + + .. ipython:: python + + s = pd.Series(['a', 'b', 'c', 'd'], [4, 3, 2, 1]) + s + + previous behavior: + + .. code-block:: python + + In [8]: s.loc[3.5:1.5] + KeyError: 3.5 + + current behavior: + + .. ipython:: python + + s.loc[3.5:1.5] + +- added Index properties `is_monotonic_increasing` and `is_monotonic_decreasing` (:issue:`8680`). + .. _whatsnew_0151.enhancements: Enhancements @@ -208,8 +231,9 @@ Bug Fixes - Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, e.g. datetime64) (:issue:`8607`) - - +- Bug when doing label based indexing with integers not found in the index for + non-unique but monotonic indexes (:issue:`8680`). +- Bug when indexing a Float64Index with ``np.nan`` on numpy 1.7 (:issue:`8980`). diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 71668a73d9286..bccc0e7b6be14 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1461,7 +1461,7 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): name=self.index[loc]) else: - result = self[loc] + result = self.iloc[loc] result.index = new_index # this could be a view diff --git a/pandas/core/index.py b/pandas/core/index.py index 4f5a0c1f212c2..154410bbebf01 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -573,8 +573,22 @@ def _mpl_repr(self): @property def is_monotonic(self): - """ return if the index has monotonic (only equaly or increasing) values """ - return self._engine.is_monotonic + """ alias for is_monotonic_increasing (deprecated) """ + return self._engine.is_monotonic_increasing + + @property + def is_monotonic_increasing(self): + """ return if the index is monotonic increasing (only equal or + increasing) values + """ + return self._engine.is_monotonic_increasing + + @property + def is_monotonic_decreasing(self): + """ return if the index is monotonic decreasing (only equal or + decreasing values + """ + return self._engine.is_monotonic_decreasing def is_lexsorted_for_tuple(self, tup): return True @@ -1988,16 +2002,12 @@ def _get_slice(starting_value, offset, search_side, slice_property, slc += offset except KeyError: - if self.is_monotonic: - - # we are duplicated but non-unique - # so if we have an indexer then we are done - # else search for it (GH 7523) - if not is_unique and is_integer(search_value): - slc = search_value - else: - slc = self.searchsorted(search_value, - side=search_side) + if self.is_monotonic_increasing: + slc = self.searchsorted(search_value, side=search_side) + elif self.is_monotonic_decreasing: + search_side = 'right' if search_side == 'left' else 'left' + slc = len(self) - self[::-1].searchsorted(search_value, + side=search_side) else: raise return slc @@ -2431,10 +2441,13 @@ def __contains__(self, other): def get_loc(self, key): try: if np.all(np.isnan(key)): + nan_idxs = self._nan_idxs try: - return self._nan_idxs.item() - except ValueError: - return self._nan_idxs + return nan_idxs.item() + except (ValueError, IndexError): + # should only need to catch ValueError here but on numpy + # 1.7 .item() can raise IndexError when NaNs are present + return nan_idxs except (TypeError, NotImplementedError): pass return super(Float64Index, self).get_loc(key) diff --git a/pandas/index.pyx b/pandas/index.pyx index d6e358a96e904..73d886f10b241 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -77,7 +77,7 @@ cdef class IndexEngine: bint over_size_threshold cdef: - bint unique, monotonic + bint unique, monotonic_inc, monotonic_dec bint initialized, monotonic_check, unique_check def __init__(self, vgetter, n): @@ -89,7 +89,8 @@ cdef class IndexEngine: self.monotonic_check = 0 self.unique = 0 - self.monotonic = 0 + self.monotonic_inc = 0 + self.monotonic_dec = 0 def __contains__(self, object val): self._ensure_mapping_populated() @@ -134,7 +135,7 @@ cdef class IndexEngine: if is_definitely_invalid_key(val): raise TypeError - if self.over_size_threshold and self.is_monotonic: + if self.over_size_threshold and self.is_monotonic_increasing: if not self.is_unique: return self._get_loc_duplicates(val) values = self._get_index_values() @@ -158,7 +159,7 @@ cdef class IndexEngine: cdef: Py_ssize_t diff - if self.is_monotonic: + if self.is_monotonic_increasing: values = self._get_index_values() left = values.searchsorted(val, side='left') right = values.searchsorted(val, side='right') @@ -210,25 +211,35 @@ cdef class IndexEngine: return self.unique == 1 - property is_monotonic: + property is_monotonic_increasing: def __get__(self): if not self.monotonic_check: self._do_monotonic_check() - return self.monotonic == 1 + return self.monotonic_inc == 1 + + property is_monotonic_decreasing: + + def __get__(self): + if not self.monotonic_check: + self._do_monotonic_check() + + return self.monotonic_dec == 1 cdef inline _do_monotonic_check(self): try: values = self._get_index_values() - self.monotonic, unique = self._call_monotonic(values) + self.monotonic_inc, self.monotonic_dec, unique = \ + self._call_monotonic(values) if unique is not None: self.unique = unique self.unique_check = 1 except TypeError: - self.monotonic = 0 + self.monotonic_inc = 0 + self.monotonic_dec = 0 self.monotonic_check = 1 cdef _get_index_values(self): @@ -345,7 +356,7 @@ cdef class Int64Engine(IndexEngine): return _hash.Int64HashTable(n) def _call_monotonic(self, values): - return algos.is_monotonic_int64(values) + return algos.is_monotonic_int64(values, timelike=False) def get_pad_indexer(self, other, limit=None): return algos.pad_int64(self._get_index_values(), other, @@ -435,7 +446,7 @@ cdef class Float64Engine(IndexEngine): return result def _call_monotonic(self, values): - return algos.is_monotonic_float64(values) + return algos.is_monotonic_float64(values, timelike=False) def get_pad_indexer(self, other, limit=None): return algos.pad_float64(self._get_index_values(), other, @@ -489,7 +500,7 @@ cdef class ObjectEngine(IndexEngine): return _hash.PyObjectHashTable(n) def _call_monotonic(self, values): - return algos.is_monotonic_object(values) + return algos.is_monotonic_object(values, timelike=False) def get_pad_indexer(self, other, limit=None): return algos.pad_object(self._get_index_values(), other, @@ -506,7 +517,7 @@ cdef class DatetimeEngine(Int64Engine): return 'M8[ns]' def __contains__(self, object val): - if self.over_size_threshold and self.is_monotonic: + if self.over_size_threshold and self.is_monotonic_increasing: if not self.is_unique: return self._get_loc_duplicates(val) values = self._get_index_values() @@ -521,7 +532,7 @@ cdef class DatetimeEngine(Int64Engine): return self.vgetter().view('i8') def _call_monotonic(self, values): - return algos.is_monotonic_int64(values) + return algos.is_monotonic_int64(values, timelike=True) cpdef get_loc(self, object val): if is_definitely_invalid_key(val): @@ -529,7 +540,7 @@ cdef class DatetimeEngine(Int64Engine): # Welcome to the spaghetti factory - if self.over_size_threshold and self.is_monotonic: + if self.over_size_threshold and self.is_monotonic_increasing: if not self.is_unique: val = _to_i8(val) return self._get_loc_duplicates(val) diff --git a/pandas/src/generate_code.py b/pandas/src/generate_code.py index f7aede92d635d..d04f55bb19fff 100644 --- a/pandas/src/generate_code.py +++ b/pandas/src/generate_code.py @@ -539,31 +539,51 @@ def diff_2d_%(name)s(ndarray[%(c_type)s, ndim=2] arr, is_monotonic_template = """@cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_%(name)s(ndarray[%(c_type)s] arr): +def is_monotonic_%(name)s(ndarray[%(c_type)s] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n %(c_type)s prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique """ map_indices_template = """@cython.wraparound(False) diff --git a/pandas/src/generated.pyx b/pandas/src/generated.pyx index 50eefa5e783cf..01c80518ca21a 100644 --- a/pandas/src/generated.pyx +++ b/pandas/src/generated.pyx @@ -1799,166 +1799,286 @@ def backfill_2d_inplace_bool(ndarray[uint8_t, ndim=2] values, @cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_float64(ndarray[float64_t] arr): +def is_monotonic_float64(ndarray[float64_t] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n float64_t prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique @cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_float32(ndarray[float32_t] arr): +def is_monotonic_float32(ndarray[float32_t] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n float32_t prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique @cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_object(ndarray[object] arr): +def is_monotonic_object(ndarray[object] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n object prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique @cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_int32(ndarray[int32_t] arr): +def is_monotonic_int32(ndarray[int32_t] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n int32_t prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique @cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_int64(ndarray[int64_t] arr): +def is_monotonic_int64(ndarray[int64_t] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n int64_t prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique @cython.boundscheck(False) @cython.wraparound(False) -def is_monotonic_bool(ndarray[uint8_t] arr): +def is_monotonic_bool(ndarray[uint8_t] arr, bint timelike): ''' Returns ------- - is_monotonic, is_unique + is_monotonic_inc, is_monotonic_dec, is_unique ''' cdef: Py_ssize_t i, n uint8_t prev, cur bint is_unique = 1 + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 n = len(arr) - if n < 2: - return True, True + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False, True + else: + return True, True, True + elif n < 2: + return True, True, True + + if timelike and arr[0] == iNaT: + return False, False, None prev = arr[0] for i in range(1, n): cur = arr[i] + if timelike and cur == iNaT: + return False, False, None if cur < prev: - return False, None + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 elif cur == prev: is_unique = 0 + else: + # cur or prev is NaN + return False, False, None + if not is_monotonic_inc and not is_monotonic_dec: + return False, False, None prev = cur - return True, is_unique + return is_monotonic_inc, is_monotonic_dec, is_unique @cython.wraparound(False) @cython.boundscheck(False) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1aa734c4834de..d46a219f3b1eb 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -866,7 +866,7 @@ def test_getitem_setitem_integer_slice_keyerrors(self): assert_frame_equal(result2, expected) # non-monotonic, raise KeyError - df2 = df[::-1] + df2 = df.iloc[lrange(5) + lrange(5, 10)[::-1]] self.assertRaises(KeyError, df2.ix.__getitem__, slice(3, 11)) self.assertRaises(KeyError, df2.ix.__setitem__, slice(3, 11), 0) diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index fe92cd55f1573..8ab5c30c49f10 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -848,33 +848,56 @@ def test_get_indexer(self): assert_almost_equal(r1, rbfill1) def test_slice_locs(self): - idx = Index([0, 1, 2, 5, 6, 7, 9, 10]) - n = len(idx) - - self.assertEqual(idx.slice_locs(start=2), (2, n)) - self.assertEqual(idx.slice_locs(start=3), (3, n)) - self.assertEqual(idx.slice_locs(3, 8), (3, 6)) - self.assertEqual(idx.slice_locs(5, 10), (3, n)) - self.assertEqual(idx.slice_locs(end=8), (0, 6)) - self.assertEqual(idx.slice_locs(end=9), (0, 7)) - - idx2 = idx[::-1] - self.assertRaises(KeyError, idx2.slice_locs, 8, 2) - self.assertRaises(KeyError, idx2.slice_locs, 7, 3) + for dtype in [int, float]: + idx = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype)) + n = len(idx) + + self.assertEqual(idx.slice_locs(start=2), (2, n)) + self.assertEqual(idx.slice_locs(start=3), (3, n)) + self.assertEqual(idx.slice_locs(3, 8), (3, 6)) + self.assertEqual(idx.slice_locs(5, 10), (3, n)) + self.assertEqual(idx.slice_locs(5.0, 10.0), (3, n)) + self.assertEqual(idx.slice_locs(4.5, 10.5), (3, 8)) + self.assertEqual(idx.slice_locs(end=8), (0, 6)) + self.assertEqual(idx.slice_locs(end=9), (0, 7)) + + idx2 = idx[::-1] + self.assertEqual(idx2.slice_locs(8, 2), (2, 6)) + self.assertEqual(idx2.slice_locs(8.5, 1.5), (2, 6)) + self.assertEqual(idx2.slice_locs(7, 3), (2, 5)) + self.assertEqual(idx2.slice_locs(10.5, -1), (0, n)) def test_slice_locs_dup(self): idx = Index(['a', 'a', 'b', 'c', 'd', 'd']) - rs = idx.slice_locs('a', 'd') - self.assertEqual(rs, (0, 6)) - - rs = idx.slice_locs(end='d') - self.assertEqual(rs, (0, 6)) + self.assertEqual(idx.slice_locs('a', 'd'), (0, 6)) + self.assertEqual(idx.slice_locs(end='d'), (0, 6)) + self.assertEqual(idx.slice_locs('a', 'c'), (0, 4)) + self.assertEqual(idx.slice_locs('b', 'd'), (2, 6)) - rs = idx.slice_locs('a', 'c') - self.assertEqual(rs, (0, 4)) - - rs = idx.slice_locs('b', 'd') - self.assertEqual(rs, (2, 6)) + idx2 = idx[::-1] + self.assertEqual(idx2.slice_locs('d', 'a'), (0, 6)) + self.assertEqual(idx2.slice_locs(end='a'), (0, 6)) + self.assertEqual(idx2.slice_locs('d', 'b'), (0, 4)) + self.assertEqual(idx2.slice_locs('c', 'a'), (2, 6)) + + for dtype in [int, float]: + idx = Index(np.array([10, 12, 12, 14], dtype=dtype)) + self.assertEqual(idx.slice_locs(12, 12), (1, 3)) + self.assertEqual(idx.slice_locs(11, 13), (1, 3)) + + idx2 = idx[::-1] + self.assertEqual(idx2.slice_locs(12, 12), (1, 3)) + self.assertEqual(idx2.slice_locs(13, 11), (1, 3)) + + def test_slice_locs_na(self): + idx = Index([np.nan, 1, 2]) + self.assertRaises(KeyError, idx.slice_locs, start=1.5) + self.assertRaises(KeyError, idx.slice_locs, end=1.5) + self.assertEqual(idx.slice_locs(1), (1, 3)) + self.assertEqual(idx.slice_locs(np.nan), (0, 3)) + + idx = Index([np.nan, np.nan, 1, 2]) + self.assertRaises(KeyError, idx.slice_locs, np.nan) def test_drop(self): n = len(self.strIndex) @@ -922,6 +945,7 @@ def test_tuple_union_bug(self): def test_is_monotonic_incomparable(self): index = Index([5, datetime.now(), 7]) self.assertFalse(index.is_monotonic) + self.assertFalse(index.is_monotonic_decreasing) def test_get_set_value(self): values = np.random.randn(100) @@ -1286,6 +1310,15 @@ def test_equals(self): i2 = Float64Index([1.0,np.nan]) self.assertTrue(i.equals(i2)) + def test_get_loc_na(self): + idx = Float64Index([np.nan, 1, 2]) + self.assertEqual(idx.get_loc(1), 1) + self.assertEqual(idx.get_loc(np.nan), 0) + + idx = Float64Index([np.nan, 1, np.nan]) + self.assertEqual(idx.get_loc(1), 1) + self.assertRaises(KeyError, idx.slice_locs, np.nan) + def test_contains_nans(self): i = Float64Index([1.0, 2.0, np.nan]) self.assertTrue(np.nan in i) @@ -1403,9 +1436,31 @@ def test_dtype(self): def test_is_monotonic(self): self.assertTrue(self.index.is_monotonic) + self.assertTrue(self.index.is_monotonic_increasing) + self.assertFalse(self.index.is_monotonic_decreasing) index = Int64Index([4, 3, 2, 1]) self.assertFalse(index.is_monotonic) + self.assertTrue(index.is_monotonic_decreasing) + + index = Int64Index([1]) + self.assertTrue(index.is_monotonic) + self.assertTrue(index.is_monotonic_increasing) + self.assertTrue(index.is_monotonic_decreasing) + + def test_is_monotonic_na(self): + examples = [Index([np.nan]), + Index([np.nan, 1]), + Index([1, 2, np.nan]), + Index(['a', 'b', np.nan]), + pd.to_datetime(['NaT']), + pd.to_datetime(['NaT', '2000-01-01']), + pd.to_datetime(['2000-01-01', 'NaT', '2000-01-02']), + pd.to_timedelta(['1 day', 'NaT']), + ] + for index in examples: + self.assertFalse(index.is_monotonic_increasing) + self.assertFalse(index.is_monotonic_decreasing) def test_equals(self): same_values = Index(self.index, dtype=object) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 018d8c614eaae..938d171506461 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -1544,7 +1544,7 @@ def test_ix_getitem(self): def test_ix_getitem_not_monotonic(self): d1, d2 = self.ts.index[[5, 15]] - ts2 = self.ts[::2][::-1] + ts2 = self.ts[::2][[1, 2, 0]] self.assertRaises(KeyError, ts2.ix.__getitem__, slice(d1, d2)) self.assertRaises(KeyError, ts2.ix.__setitem__, slice(d1, d2), 0) @@ -1570,7 +1570,7 @@ def test_ix_getitem_setitem_integer_slice_keyerrors(self): assert_series_equal(result2, expected) # non-monotonic, raise KeyError - s2 = s[::-1] + s2 = s.iloc[lrange(5) + lrange(5, 10)[::-1]] self.assertRaises(KeyError, s2.ix.__getitem__, slice(3, 11)) self.assertRaises(KeyError, s2.ix.__setitem__, slice(3, 11), 0) diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 367ea276646ee..917e10c4b7706 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -85,7 +85,7 @@ def test_asobject_tolist(self): def test_minmax(self): for tz in self.tz: # monotonic - idx1 = pd.DatetimeIndex([pd.NaT, '2011-01-01', '2011-01-02', + idx1 = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz=tz) self.assertTrue(idx1.is_monotonic) @@ -305,7 +305,7 @@ def test_asobject_tolist(self): def test_minmax(self): # monotonic - idx1 = TimedeltaIndex(['nat', '1 days', '2 days', '3 days']) + idx1 = TimedeltaIndex(['1 days', '2 days', '3 days']) self.assertTrue(idx1.is_monotonic) # non-monotonic
Fixes #7860 The first commit adds `Index.is_monotonic_decreasing` and `Index.is_monotonic_increasing` (alias for `is_monotonic`). `is_monotonic` will have a performance degradation (still O(n) time) in cases where the Index is decreasing monotonic. If necessary, we could work around this, but I think we can probably get away with this because the fall-back options are much slower and in many cases (e.g., for slice indexing) the next thing we'll want to know is if it's decreasing monotonic, anyways. Next up will be handling indexing monotonic decreasing indexes with reversed slices, e.g., `s.loc[30:10]`. CC @jreback @hugadams @immerrr (thank you, specialities wiki!)
https://api.github.com/repos/pandas-dev/pandas/pulls/8680
2014-10-30T06:15:51Z
2014-11-02T22:41:28Z
2014-11-02T22:41:28Z
2014-11-06T11:04:00Z
PERF: set multiindex labels with a coerced dtype (GH8456)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index c666a19bcd133..d57f4c7e2a9d3 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -20,6 +20,30 @@ users upgrade to this version. API changes ~~~~~~~~~~~ +- Represent ``MultiIndex`` labels with a dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the *reported* memory usage was incorrect as it didn't show the usage for the memory occupied by the underling data array. (:issue:`8456`) + + .. ipython:: python + + dfi = DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']) + + previous behavior: + + .. code-block:: python + + # this was underreported and actually took (in < 0.15.1) about 24008 bytes + In [1]: dfi.memory_usage(index=True) + Out[1]: + Index 8000 + A 8000 + dtype: int64 + + + current behavior: + + .. ipython:: python + + dfi.memory_usage(index=True) + - ``groupby`` with ``as_index=False`` will not add erroneous extra columns to result (:issue:`8582`): diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 8c4f45fdeb57a..364a3fa13801b 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -166,8 +166,7 @@ def factorize(values, sort=False, order=None, na_sentinel=-1): elif is_timedelta: uniques = uniques.astype('m8[ns]') if isinstance(values, Index): - uniques = values._simple_new(uniques, None, freq=getattr(values, 'freq', None), - tz=getattr(values, 'tz', None)) + uniques = values._shallow_copy(uniques, name=None) elif isinstance(values, Series): uniques = Index(uniques) return labels, uniques diff --git a/pandas/core/base.py b/pandas/core/base.py index 71a08e0dd553d..fba83be6fcadf 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -232,7 +232,6 @@ def __repr__(self): __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled pop = append = extend = remove = sort = insert = _disabled - class FrozenNDArray(PandasObject, np.ndarray): # no __array_finalize__ for now because no metadata @@ -540,4 +539,3 @@ def duplicated(self, take_last=False): def _update_inplace(self, result, **kwargs): raise NotImplementedError - diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index c7cc065a965a0..00128bd977911 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -196,7 +196,7 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa if fastpath: # fast path - self._codes = _coerce_codes_dtype(values, categories) + self._codes = com._coerce_indexer_dtype(values, categories) self.name = name self.categories = categories self.ordered = ordered @@ -289,7 +289,7 @@ def __init__(self, values, categories=None, ordered=None, name=None, fastpath=Fa self.ordered = False if ordered is None else ordered self.categories = categories self.name = name - self._codes = _coerce_codes_dtype(codes, categories) + self._codes = com._coerce_indexer_dtype(codes, categories) def copy(self): """ Copy constructor. """ @@ -609,7 +609,7 @@ def add_categories(self, new_categories, inplace=False): new_categories = self._validate_categories(new_categories) cat = self if inplace else self.copy() cat._categories = new_categories - cat._codes = _coerce_codes_dtype(cat._codes, new_categories) + cat._codes = com._coerce_indexer_dtype(cat._codes, new_categories) if not inplace: return cat @@ -1422,22 +1422,6 @@ def _delegate_method(self, name, *args, **kwargs): ##### utility routines ##### -_int8_max = np.iinfo(np.int8).max -_int16_max = np.iinfo(np.int16).max -_int32_max = np.iinfo(np.int32).max - -def _coerce_codes_dtype(codes, categories): - """ coerce the code input array to an appropriate dtype """ - codes = np.array(codes,copy=False) - l = len(categories) - if l < _int8_max: - return codes.astype('int8') - elif l < _int16_max: - return codes.astype('int16') - elif l < _int32_max: - return codes.astype('int32') - return codes.astype('int64') - def _get_codes_for_values(values, categories): """" utility routine to turn values into codes given the specified categories @@ -1450,7 +1434,7 @@ def _get_codes_for_values(values, categories): (hash_klass, vec_klass), vals = _get_data_algo(values, _hashtables) t = hash_klass(len(categories)) t.map_locations(com._values_from_object(categories)) - return _coerce_codes_dtype(t.lookup(values), categories) + return com._coerce_indexer_dtype(t.lookup(values), categories) def _convert_to_list_like(list_like): if hasattr(list_like, "dtype"): diff --git a/pandas/core/common.py b/pandas/core/common.py index 2839b54b7d71a..51464e1809e75 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -49,7 +49,9 @@ class AmbiguousIndexError(PandasError, KeyError): _INT64_DTYPE = np.dtype(np.int64) _DATELIKE_DTYPES = set([np.dtype(t) for t in ['M8[ns]', '<M8[ns]', '>M8[ns]', 'm8[ns]', '<m8[ns]', '>m8[ns]']]) - +_int8_max = np.iinfo(np.int8).max +_int16_max = np.iinfo(np.int16).max +_int32_max = np.iinfo(np.int32).max # define abstract base classes to enable isinstance type checking on our # objects @@ -723,6 +725,7 @@ def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): return func def func(arr, indexer, out, fill_value=np.nan): + indexer = _ensure_int64(indexer) _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info) return func @@ -815,6 +818,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info) + indexer = _ensure_int64(indexer) func(arr, indexer, out, fill_value) if flip_order: @@ -961,6 +965,16 @@ def diff(arr, n, axis=0): return out_arr +def _coerce_indexer_dtype(indexer, categories): + """ coerce the indexer input array to the smallest dtype possible """ + l = len(categories) + if l < _int8_max: + return _ensure_int8(indexer) + elif l < _int16_max: + return _ensure_int16(indexer) + elif l < _int32_max: + return _ensure_int32(indexer) + return _ensure_int64(indexer) def _coerce_to_dtypes(result, dtypes): """ given a dtypes and a result set, coerce the result elements to the diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 182d9c4c2620b..f998a01a1a165 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1564,7 +1564,7 @@ def memory_usage(self, index=False): result = Series([ c.values.nbytes for col, c in self.iteritems() ], index=self.columns) if index: - result = Series(self.index.values.nbytes, + result = Series(self.index.nbytes, index=['Index']).append(result) return result diff --git a/pandas/core/index.py b/pandas/core/index.py index d56354833012a..4f5a0c1f212c2 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -7,6 +7,7 @@ from pandas import compat import numpy as np +from sys import getsizeof import pandas.tslib as tslib import pandas.lib as lib import pandas.algos as _algos @@ -17,7 +18,7 @@ from pandas.core.common import isnull, array_equivalent import pandas.core.common as com from pandas.core.common import (_values_from_object, is_float, is_integer, - ABCSeries, _ensure_object) + ABCSeries, _ensure_object, _ensure_int64) from pandas.core.config import get_option # simplify @@ -2680,13 +2681,13 @@ def _set_labels(self, labels, level=None, copy=False, validate=True, raise ValueError('Length of labels must match length of levels.') if level is None: - new_labels = FrozenList(_ensure_frozen(v, copy=copy)._shallow_copy() - for v in labels) + new_labels = FrozenList(_ensure_frozen(lab, lev, copy=copy)._shallow_copy() + for lev, lab in zip(self.levels, labels)) else: level = [self._get_level_number(l) for l in level] new_labels = list(self._labels) - for l, v in zip(level, labels): - new_labels[l] = _ensure_frozen(v, copy=copy)._shallow_copy() + for l, lev, lab in zip(level, self.levels, labels): + new_labels[l] = _ensure_frozen(lab, lev, copy=copy)._shallow_copy() new_labels = FrozenList(new_labels) self._labels = new_labels @@ -2824,6 +2825,14 @@ def _array_values(self): def dtype(self): return np.dtype('O') + @cache_readonly + def nbytes(self): + """ return the number of bytes in the underlying data """ + level_nbytes = sum(( i.nbytes for i in self.levels )) + label_nbytes = sum(( i.nbytes for i in self.labels )) + names_nbytes = sum(( getsizeof(i) for i in self.names )) + return level_nbytes + label_nbytes + names_nbytes + def __repr__(self): encoding = get_option('display.encoding') attrs = [('levels', default_pprint(self.levels)), @@ -4361,7 +4370,7 @@ def insert(self, loc, item): lev_loc = level.get_loc(k) new_levels.append(level) - new_labels.append(np.insert(labels, loc, lev_loc)) + new_labels.append(np.insert(_ensure_int64(labels), loc, lev_loc)) return MultiIndex(levels=new_levels, labels=new_labels, names=self.names, verify_integrity=False) @@ -4474,8 +4483,8 @@ def _ensure_index(index_like, copy=False): return Index(index_like) -def _ensure_frozen(array_like, copy=False): - array_like = np.asanyarray(array_like, dtype=np.int_) +def _ensure_frozen(array_like, categories, copy=False): + array_like = com._coerce_indexer_dtype(array_like, categories) array_like = array_like.view(FrozenNDArray) if copy: array_like = array_like.copy() diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index fb9124bf19958..a19d9d651a656 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -6766,6 +6766,15 @@ def test_info_memory_usage(self): size_df = np.size(df.columns.values) # index=False; default self.assertEqual(size_df, np.size(df.memory_usage())) + # test for validity + DataFrame(1,index=['a'],columns=['A']).memory_usage(index=True) + DataFrame(1,index=['a'],columns=['A']).index.nbytes + DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']).index.nbytes + DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']).index.values.nbytes + DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']).memory_usage(index=True) + DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']).index.nbytes + DataFrame(1,index=pd.MultiIndex.from_product([['a'],range(1000)]),columns=['A']).index.values.nbytes + def test_dtypes(self): self.mixed_frame['bool'] = self.mixed_frame['A'] > 0 result = self.mixed_frame.dtypes diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 75af8f4e26302..fe92cd55f1573 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -37,6 +37,7 @@ class Base(object): """ base class for index sub-class tests """ _holder = None + _compat_props = ['shape', 'ndim', 'size', 'itemsize', 'nbytes'] def verify_pickle(self,index): unpickled = self.round_trip_pickle(index) @@ -90,9 +91,12 @@ def test_ndarray_compat_properties(self): self.assertTrue(idx.transpose().equals(idx)) values = idx.values - for prop in ['shape', 'ndim', 'size', 'itemsize', 'nbytes']: + for prop in self._compat_props: self.assertEqual(getattr(idx, prop), getattr(values, prop)) + # test for validity + idx.nbytes + idx.values.nbytes class TestIndex(Base, tm.TestCase): _holder = Index @@ -1837,6 +1841,7 @@ def test_pickle_compat_construction(self): class TestMultiIndex(Base, tm.TestCase): _holder = MultiIndex _multiprocess_can_split_ = True + _compat_props = ['shape', 'ndim', 'size', 'itemsize'] def setUp(self): major_axis = Index(['foo', 'bar', 'baz', 'qux']) @@ -1865,6 +1870,24 @@ def f(): pass tm.assertRaisesRegexp(ValueError,'The truth value of a',f) + def test_labels_dtypes(self): + + # GH 8456 + i = MultiIndex.from_tuples([('A', 1), ('A', 2)]) + self.assertTrue(i.labels[0].dtype == 'int8') + self.assertTrue(i.labels[1].dtype == 'int8') + + i = MultiIndex.from_product([['a'],range(40)]) + self.assertTrue(i.labels[1].dtype == 'int8') + i = MultiIndex.from_product([['a'],range(400)]) + self.assertTrue(i.labels[1].dtype == 'int16') + i = MultiIndex.from_product([['a'],range(40000)]) + self.assertTrue(i.labels[1].dtype == 'int32') + + i = pd.MultiIndex.from_product([['a'],range(1000)]) + self.assertTrue((i.labels[0]>=0).all()) + self.assertTrue((i.labels[1]>=0).all()) + def test_hash_error(self): with tm.assertRaisesRegexp(TypeError, "unhashable type: %r" %
closes #8456
https://api.github.com/repos/pandas-dev/pandas/pulls/8676
2014-10-29T23:01:54Z
2014-10-30T11:19:17Z
2014-10-30T11:19:17Z
2014-12-13T21:52:27Z
TST: fix up for 32-bit indexers w.r.t. (GH8669)
diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 6dd123996b125..9b95aff465d55 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -551,29 +551,31 @@ def setitem(self, indexer, value): try: def _is_scalar_indexer(indexer): - # treat a len 0 array like a scalar # return True if we are all scalar indexers if arr_value.ndim == 1: if not isinstance(indexer, tuple): indexer = tuple([indexer]) + return all([ np.isscalar(idx) for idx in indexer ]) + return False - def _is_ok(idx): - - if np.isscalar(idx): - return True - elif isinstance(idx, slice): - return False - return len(idx) == 0 + def _is_empty_indexer(indexer): + # return a boolean if we have an empty indexer - return all([ _is_ok(idx) for idx in indexer ]) + if arr_value.ndim == 1: + if not isinstance(indexer, tuple): + indexer = tuple([indexer]) + return all([ isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer ]) return False + # empty indexers + # 8669 (empty) + if _is_empty_indexer(indexer): + pass # setting a single element for each dim and with a rhs that could be say a list - # or empty indexers (so no astyping) - # GH 6043, 8669 (empty) - if _is_scalar_indexer(indexer): + # GH 6043 + elif _is_scalar_indexer(indexer): values[indexer] = value # if we are an exact match (ex-broadcasting),
xref #8669
https://api.github.com/repos/pandas-dev/pandas/pulls/8675
2014-10-29T22:45:27Z
2014-10-29T22:56:36Z
2014-10-29T22:56:36Z
2016-02-12T17:40:27Z
BUG: years-only in date_range uses current date (GH6961)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 0755931bed990..c666a19bcd133 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -171,3 +171,5 @@ Bug Fixes - Fixed a bug where plotting a column ``y`` and specifying a label would mutate the index name of the original DataFrame (:issue:`8494`) + +- Bug in ``date_range`` where partially-specified dates would incorporate current date (:issue:`6961`) diff --git a/pandas/tseries/tests/test_daterange.py b/pandas/tseries/tests/test_daterange.py index b109f6585092a..500e19d36fff6 100644 --- a/pandas/tseries/tests/test_daterange.py +++ b/pandas/tseries/tests/test_daterange.py @@ -470,6 +470,12 @@ def test_range_closed(self): self.assertTrue(expected_left.equals(left)) self.assertTrue(expected_right.equals(right)) + def test_years_only(self): + # GH 6961 + dr = date_range('2014', '2015', freq='M') + self.assertEqual(dr[0], datetime(2014, 1, 31)) + self.assertEqual(dr[-1], datetime(2014, 12, 31)) + class TestCustomDateRange(tm.TestCase):
closes #6961 this was probably fixed by #7907 but just adding an explicit test and release note. (in a sense, it should really be the 0.15.0 release note, but that release has shipped...)
https://api.github.com/repos/pandas-dev/pandas/pulls/8672
2014-10-29T03:48:02Z
2014-10-29T11:39:26Z
2014-10-29T11:39:26Z
2014-10-30T11:20:24Z
BUG: Bug in setitem with empty indexer and unwanted coercion of dtypes (GH8669)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 04aec92f448c7..af312e3e22275 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -118,7 +118,8 @@ Bug Fixes -- Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) +- Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608` +- Bug in setitem with empty indexer and unwanted coercion of dtypes (:issue:`8669`) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 89e1cd6ce0fb6..6dd123996b125 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -549,10 +549,31 @@ def setitem(self, indexer, value): "different length than the value") try: + + def _is_scalar_indexer(indexer): + # treat a len 0 array like a scalar + # return True if we are all scalar indexers + + if arr_value.ndim == 1: + if not isinstance(indexer, tuple): + indexer = tuple([indexer]) + + def _is_ok(idx): + + if np.isscalar(idx): + return True + elif isinstance(idx, slice): + return False + return len(idx) == 0 + + return all([ _is_ok(idx) for idx in indexer ]) + return False + + # setting a single element for each dim and with a rhs that could be say a list - # GH 6043 - if arr_value.ndim == 1 and ( - np.isscalar(indexer) or (isinstance(indexer, tuple) and all([ np.isscalar(idx) for idx in indexer ]))): + # or empty indexers (so no astyping) + # GH 6043, 8669 (empty) + if _is_scalar_indexer(indexer): values[indexer] = value # if we are an exact match (ex-broadcasting), diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index a2f3284efcb82..7bc5ca0bfb2e7 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1043,6 +1043,13 @@ def test_loc_setitem_frame(self): expected = DataFrame(dict(A = Series(val1,index=keys1), B = Series(val2,index=keys2))).reindex(index=index) assert_frame_equal(df, expected) + # GH 8669 + # invalid coercion of nan -> int + df = DataFrame({'A' : [1,2,3], 'B' : np.nan }) + df.loc[df.B > df.A, 'B'] = df.A + expected = DataFrame({'A' : [1,2,3], 'B' : np.nan}) + assert_frame_equal(df, expected) + # GH 6546 # setting with mixed labels df = DataFrame({1:[1,2],2:[3,4],'a':['a','b']}) @@ -1055,7 +1062,6 @@ def test_loc_setitem_frame(self): df.loc[0,[1,2]] = [5,6] assert_frame_equal(df, expected) - def test_loc_setitem_frame_multiples(self): # multiple setting df = DataFrame({ 'A' : ['foo','bar','baz'],
closes #8669
https://api.github.com/repos/pandas-dev/pandas/pulls/8671
2014-10-29T01:10:58Z
2014-10-29T01:31:56Z
2014-10-29T01:31:56Z
2014-10-30T11:20:24Z
BUG: fix concat to work with more iterables (GH8645)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 04aec92f448c7..76099a39d9aba 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -75,6 +75,28 @@ API changes gr.apply(sum) +- ``concat`` permits a wider variety of iterables of pandas objects to be + passed as the first parameter (:issue:`8645`): + + .. ipython:: python + + from collections import deque + df1 = pd.DataFrame([1, 2, 3]) + df2 = pd.DataFrame([4, 5, 6]) + + previous behavior: + + .. code-block:: python + + In [7]: pd.concat(deque((df1, df2))) + TypeError: first argument must be a list-like of pandas objects, you passed an object of type "deque" + + current behavior: + + .. ipython:: python + + pd.concat(deque((df1, df2))) + .. _whatsnew_0151.enhancements: Enhancements diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 8fddfdda797c6..7a89c317a69c6 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -675,7 +675,7 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, Parameters ---------- - objs : list or dict of Series, DataFrame, or Panel objects + objs : a sequence or mapping of Series, DataFrame, or Panel objects If a dict is passed, the sorted keys will be used as the `keys` argument, unless it is passed, in which case the values will be selected (see below). Any None objects will be dropped silently unless @@ -731,8 +731,8 @@ class _Concatenator(object): def __init__(self, objs, axis=0, join='outer', join_axes=None, keys=None, levels=None, names=None, ignore_index=False, verify_integrity=False, copy=True): - if not isinstance(objs, (list,tuple,types.GeneratorType,dict,TextFileReader)): - raise TypeError('first argument must be a list-like of pandas ' + if isinstance(objs, (NDFrame, compat.string_types)): + raise TypeError('first argument must be an iterable of pandas ' 'objects, you passed an object of type ' '"{0}"'.format(type(objs).__name__)) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index b9c7fdfeb6c48..8f375ca168edd 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -2203,6 +2203,33 @@ def test_concat_series_axis1_same_names_ignore_index(self): result = concat([s1, s2], axis=1, ignore_index=True) self.assertTrue(np.array_equal(result.columns, [0, 1])) + def test_concat_iterables(self): + from collections import deque, Iterable + + # GH8645 check concat works with tuples, list, generators, and weird + # stuff like deque and custom iterables + df1 = DataFrame([1, 2, 3]) + df2 = DataFrame([4, 5, 6]) + expected = DataFrame([1, 2, 3, 4, 5, 6]) + assert_frame_equal(pd.concat((df1, df2), ignore_index=True), expected) + assert_frame_equal(pd.concat([df1, df2], ignore_index=True), expected) + assert_frame_equal(pd.concat((df for df in (df1, df2)), ignore_index=True), expected) + assert_frame_equal(pd.concat(deque((df1, df2)), ignore_index=True), expected) + class CustomIterator1(object): + def __len__(self): + return 2 + def __getitem__(self, index): + try: + return {0: df1, 1: df2}[index] + except KeyError: + raise IndexError + assert_frame_equal(pd.concat(CustomIterator1(), ignore_index=True), expected) + class CustomIterator2(Iterable): + def __iter__(self): + yield df1 + yield df2 + assert_frame_equal(pd.concat(CustomIterator2(), ignore_index=True), expected) + def test_concat_invalid(self): # trying to concat a ndframe with a non-ndframe
closes #8645 Enhances `pd.concat` to work with any iterable (except specifically undesired ones like pandas objects and strings). A new test is included covering tuples, lists, generator expressions, deques, and custom sequences, and all existing tests still pass. Finally, a "what's new" entry is included (not sure this last is correct - but [pull requests guidelines](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md) mentioned it)
https://api.github.com/repos/pandas-dev/pandas/pulls/8668
2014-10-28T22:28:44Z
2014-10-29T01:12:58Z
2014-10-29T01:12:58Z
2014-10-30T17:39:16Z
ENH: Series.str.split can return a DataFrame instead of Series of lists
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index c666a19bcd133..ac788967f1c18 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -109,6 +109,7 @@ Enhancements - Added support for 3-character ISO and non-standard country codes in :func:``io.wb.download()`` (:issue:`8482`) - :ref:`World Bank data requests <remote_data.wb>` now will warn/raise based on an ``errors`` argument, as well as a list of hard-coded country codes and the World Bank's JSON response. In prior versions, the error messages didn't look at the World Bank's JSON response. Problem-inducing input were simply dropped prior to the request. The issue was that many good countries were cropped in the hard-coded approach. All countries will work now, but some bad countries will raise exceptions because some edge cases break the entire response. (:issue:`8482`) +- Added option to ``Series.str.split()`` to return a ``DataFrame`` rather than a ``Series`` (:issue:`8428`) .. _whatsnew_0151.performance: diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 2d8b8f8b2edff..78780bc9618f7 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -621,7 +621,7 @@ def str_center(arr, width): return str_pad(arr, width, side='both') -def str_split(arr, pat=None, n=None): +def str_split(arr, pat=None, n=None, return_type='series'): """ Split each string (a la re.split) in array by given pattern, propagating NA values @@ -631,6 +631,9 @@ def str_split(arr, pat=None, n=None): pat : string, default None String or regular expression to split on. If None, splits on whitespace n : int, default None (all) + return_type : {'series', 'frame'}, default 'series + If frame, returns a DataFrame (elements are strings) + If series, returns an Series (elements are lists of strings). Notes ----- @@ -640,6 +643,8 @@ def str_split(arr, pat=None, n=None): ------- split : array """ + if return_type not in ('series', 'frame'): + raise ValueError("return_type must be {'series', 'frame'}") if pat is None: if n is None or n == 0: n = -1 @@ -654,8 +659,11 @@ def str_split(arr, pat=None, n=None): n = 0 regex = re.compile(pat) f = lambda x: regex.split(x, maxsplit=n) - - return _na_map(f, arr) + if return_type == 'frame': + res = DataFrame((Series(x) for x in _na_map(f, arr)), index=arr.index) + else: + res = _na_map(f, arr) + return res def str_slice(arr, start=None, stop=None, step=1): @@ -937,8 +945,8 @@ def cat(self, others=None, sep=None, na_rep=None): return self._wrap_result(result) @copy(str_split) - def split(self, pat=None, n=-1): - result = str_split(self.series, pat, n=n) + def split(self, pat=None, n=-1, return_type='series'): + result = str_split(self.series, pat, n=n, return_type=return_type) return self._wrap_result(result) @copy(str_get) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 41594a1655d18..02808ebf0b340 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -873,6 +873,34 @@ def test_split_no_pat_with_nonzero_n(self): expected = Series({0: ['split', 'once'], 1: ['split', 'once too!']}) tm.assert_series_equal(expected, result) + def test_split_to_dataframe(self): + s = Series(['nosplit', 'alsonosplit']) + result = s.str.split('_', return_type='frame') + exp = DataFrame({0: Series(['nosplit', 'alsonosplit'])}) + tm.assert_frame_equal(result, exp) + + s = Series(['some_equal_splits', 'with_no_nans']) + result = s.str.split('_', return_type='frame') + exp = DataFrame({0: ['some', 'with'], 1: ['equal', 'no'], + 2: ['splits', 'nans']}) + tm.assert_frame_equal(result, exp) + + s = Series(['some_unequal_splits', 'one_of_these_things_is_not']) + result = s.str.split('_', return_type='frame') + exp = DataFrame({0: ['some', 'one'], 1: ['unequal', 'of'], + 2: ['splits', 'these'], 3: [NA, 'things'], + 4: [NA, 'is'], 5: [NA, 'not']}) + tm.assert_frame_equal(result, exp) + + s = Series(['some_splits', 'with_index'], index=['preserve', 'me']) + result = s.str.split('_', return_type='frame') + exp = DataFrame({0: ['some', 'with'], 1: ['splits', 'index']}, + index=['preserve', 'me']) + tm.assert_frame_equal(result, exp) + + with tm.assertRaisesRegexp(ValueError, "return_type must be"): + s.str.split('_', return_type="some_invalid_type") + def test_pipe_failures(self): # #2119 s = Series(['A|B|C'])
closes #8428. Adds a flag which when True returns a DataFrame with columns being the index of the lists generated by the string splitting operation. When False, it returns a 1D numpy array, as before. Defaults to false to not break compatibility. In the case with no splits, returns a single column DataFrame rather than squashing to a Series.
https://api.github.com/repos/pandas-dev/pandas/pulls/8663
2014-10-28T19:32:07Z
2014-10-29T23:45:11Z
2014-10-29T23:45:11Z
2014-10-30T11:20:23Z
BUG: various categorical fixes (GH8626)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index ed080f7f11863..59d96d0ca1c71 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -48,7 +48,41 @@ Experimental Bug Fixes ~~~~~~~~~ + +- Bug in coercing ``Categorical` to a records array, e.g. ``df.to_records()`` (:issue:`8626) +- Bug in ``Categorical`` not created properly with ``Series.to_frame()`` (:issue:`8626`) +- Bug in coercing in astype of a ``Categorical`` of a passed ``pd.Categorical`` (this now raises ``TypeError`` correctly), (:issue:`8626`) - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) + + + + + + + + + - Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) + + + + + + + - Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, e.g. datetime64) (:issue:`8607`) + + + + + + + + + + + + + + - Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index b35cfdcf7c8f1..e0d2eaa8a6e0c 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -187,6 +187,7 @@ class Categorical(PandasObject): # For comparisons, so that numpy uses our implementation if the compare ops, which raise __array_priority__ = 1000 + _typ = 'categorical' ordered = False name = None @@ -1464,4 +1465,3 @@ def _convert_to_list_like(list_like): else: # is this reached? return [list_like] - diff --git a/pandas/core/common.py b/pandas/core/common.py index 31dc58d1870e0..2839b54b7d71a 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -56,7 +56,10 @@ class AmbiguousIndexError(PandasError, KeyError): def create_pandas_abc_type(name, attr, comp): @classmethod def _check(cls, inst): - return getattr(inst, attr, None) in comp + result = getattr(inst, attr, None) + if result is None: + return False + return result in comp dct = dict(__instancecheck__=_check, __subclasscheck__=_check) meta = type("ABCBase", (type,), dct) @@ -78,6 +81,7 @@ def _check(cls, inst): 'sparse_time_series')) ABCSparseArray = create_pandas_abc_type("ABCSparseArray", "_subtyp", ('sparse_array', 'sparse_series')) +ABCCategorical = create_pandas_abc_type("ABCCategorical","_typ",("categorical")) class _ABCGeneric(type): diff --git a/pandas/core/frame.py b/pandas/core/frame.py index d90ef76ddfa5e..e2c53be1d0cd4 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -26,7 +26,8 @@ from pandas.core.common import (isnull, notnull, PandasError, _try_sort, _default_index, _maybe_upcast, _is_sequence, _infer_dtype_from_scalar, _values_from_object, - is_list_like, _get_dtype, _maybe_box_datetimelike) + is_list_like, _get_dtype, _maybe_box_datetimelike, + is_categorical_dtype) from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import (_maybe_droplevels, @@ -332,6 +333,8 @@ def _init_dict(self, data, index, columns, dtype=None): def _init_ndarray(self, values, index, columns, dtype=None, copy=False): + # input must be a ndarray, list, Series, index + if isinstance(values, Series): if columns is None: if values.name is not None: @@ -345,9 +348,41 @@ def _init_ndarray(self, values, index, columns, dtype=None, if not len(values) and columns is not None and len(columns): values = np.empty((0, 1), dtype=object) + # helper to create the axes as indexes + def _get_axes(N, K, index=index, columns=columns): + # return axes or defaults + + if index is None: + index = _default_index(N) + else: + index = _ensure_index(index) + + if columns is None: + columns = _default_index(K) + else: + columns = _ensure_index(columns) + return index, columns + + # we could have a categorical type passed or coerced to 'category' + # recast this to an _arrays_to_mgr + if is_categorical_dtype(getattr(values,'dtype',None)) or is_categorical_dtype(dtype): + + if not hasattr(values,'dtype'): + values = _prep_ndarray(values, copy=copy) + values = values.ravel() + elif copy: + values = values.copy() + + index, columns = _get_axes(len(values),1) + return _arrays_to_mgr([ values ], columns, index, columns, + dtype=dtype) + + # by definition an array here + # the dtypes will be coerced to a single dtype values = _prep_ndarray(values, copy=copy) if dtype is not None: + if values.dtype != dtype: try: values = values.astype(dtype) @@ -356,18 +391,7 @@ def _init_ndarray(self, values, index, columns, dtype=None, % (dtype, orig)) raise_with_traceback(e) - N, K = values.shape - - if index is None: - index = _default_index(N) - else: - index = _ensure_index(index) - - if columns is None: - columns = _default_index(K) - else: - columns = _ensure_index(columns) - + index, columns = _get_axes(*values.shape) return create_block_manager_from_blocks([values.T], [columns, index]) @property @@ -877,7 +901,7 @@ def to_records(self, index=True, convert_datetime64=True): else: ix_vals = [self.index.values] - arrays = ix_vals + [self[c].values for c in self.columns] + arrays = ix_vals + [self[c].get_values() for c in self.columns] count = 0 index_names = list(self.index.names) @@ -890,7 +914,7 @@ def to_records(self, index=True, convert_datetime64=True): index_names = ['index'] names = index_names + lmap(str, self.columns) else: - arrays = [self[c].values for c in self.columns] + arrays = [self[c].get_values() for c in self.columns] names = lmap(str, self.columns) dtype = np.dtype([(x, v.dtype) for x, v in zip(names, arrays)]) @@ -4729,6 +4753,7 @@ def convert(v): values = convert(values) else: + # drop subclass info, do not copy data values = np.asarray(values) if copy: diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 9be680d998216..89e1cd6ce0fb6 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -92,6 +92,21 @@ def is_datelike(self): """ return True if I am a non-datelike """ return self.is_datetime or self.is_timedelta + def is_categorical_astype(self, dtype): + """ + validate that we have a astypeable to categorical, + returns a boolean if we are a categorical + """ + if com.is_categorical_dtype(dtype): + if dtype == com.CategoricalDtype(): + return True + + # this is a pd.Categorical, but is not + # a valid type for astypeing + raise TypeError("invalid type {0} for astype".format(dtype)) + + return False + def to_dense(self): return self.values.view() @@ -345,7 +360,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, # may need to convert to categorical # this is only called for non-categoricals - if com.is_categorical_dtype(dtype): + if self.is_categorical_astype(dtype): return make_block(Categorical(self.values), ndim=self.ndim, placement=self.mgr_locs) @@ -1682,7 +1697,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, raise on an except if raise == True """ - if dtype == com.CategoricalDtype(): + if self.is_categorical_astype(dtype): values = self.values else: values = np.array(self.values).astype(dtype) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 03c73232f13bb..e47d8aaa52c9b 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1072,6 +1072,41 @@ def test_construction_series(self): df = DataFrame({'x': Series(['a', 'b', 'c'],dtype='category')}, index=index) tm.assert_frame_equal(df, expected) + def test_construction_frame(self): + + # GH8626 + + # dict creation + df = DataFrame({ 'A' : list('abc') },dtype='category') + expected = Series(list('abc'),dtype='category') + tm.assert_series_equal(df['A'],expected) + + # to_frame + s = Series(list('abc'),dtype='category') + result = s.to_frame() + expected = Series(list('abc'),dtype='category') + tm.assert_series_equal(result[0],expected) + result = s.to_frame(name='foo') + expected = Series(list('abc'),dtype='category') + tm.assert_series_equal(result['foo'],expected) + + # list-like creation + df = DataFrame(list('abc'),dtype='category') + expected = Series(list('abc'),dtype='category') + tm.assert_series_equal(df[0],expected) + + # these coerces back to object as its spread across columns + + # ndim != 1 + df = DataFrame([pd.Categorical(list('abc'))]) + expected = DataFrame([list('abc')]) + tm.assert_frame_equal(df,expected) + + # mixed + df = DataFrame([pd.Categorical(list('abc')),list('def')]) + expected = DataFrame([list('abc'),list('def')]) + tm.assert_frame_equal(df,expected) + def test_reindex(self): index = pd.date_range('20000101', periods=3) @@ -2223,6 +2258,42 @@ def cmp(a,b): # array conversion tm.assert_almost_equal(np.array(s),np.array(s.values)) + # valid conversion + for valid in [lambda x: x.astype('category'), + lambda x: x.astype(com.CategoricalDtype()), + lambda x: x.astype('object').astype('category'), + lambda x: x.astype('object').astype(com.CategoricalDtype())]: + + result = valid(s) + tm.assert_series_equal(result,s) + + # invalid conversion (these are NOT a dtype) + for invalid in [lambda x: x.astype(pd.Categorical), + lambda x: x.astype('object').astype(pd.Categorical)]: + self.assertRaises(TypeError, lambda : invalid(s)) + + + def test_to_records(self): + + # GH8626 + + # dict creation + df = DataFrame({ 'A' : list('abc') },dtype='category') + expected = Series(list('abc'),dtype='category') + tm.assert_series_equal(df['A'],expected) + + # list-like creation + df = DataFrame(list('abc'),dtype='category') + expected = Series(list('abc'),dtype='category') + tm.assert_series_equal(df[0],expected) + + # to record array + # this coerces + result = df.to_records() + expected = np.rec.array([(0, 'a'), (1, 'b'), (2, 'c')], + dtype=[('index', '<i8'), ('0', 'O')]) + tm.assert_almost_equal(result,expected) + def test_numeric_like_ops(self): # numeric ops should not succeed @@ -2262,7 +2333,7 @@ def get_dir(s): def test_pickle_v0_14_1(self): cat = pd.Categorical(values=['a', 'b', 'c'], - levels=['a', 'b', 'c', 'd'], + categories=['a', 'b', 'c', 'd'], name='foobar', ordered=False) pickle_path = os.path.join(tm.get_data_path(), 'categorical_0_14_1.pickle')
BUG: coerce Categorical in record array creation (GH8626) BUG: Categorical not created properly with to_frame() from Series (GH8626) BUG: handle astype with passed pd.Categorical (GH8626) closes #8626
https://api.github.com/repos/pandas-dev/pandas/pulls/8652
2014-10-27T13:14:02Z
2014-10-28T00:03:02Z
2014-10-28T00:03:02Z
2014-10-30T11:20:23Z
TST: skip tests on incompatible PyTables version (GH8649, GH8650)
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index da9d39ae82617..7fe7c83988b84 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -11,6 +11,14 @@ import pandas from pandas import (Series, DataFrame, Panel, MultiIndex, Categorical, bdate_range, date_range, Index, DatetimeIndex, isnull) + +from pandas.io.pytables import _tables +try: + _tables() +except ImportError as e: + raise nose.SkipTest(e) + + from pandas.io.pytables import (HDFStore, get_store, Term, read_hdf, IncompatibilityWarning, PerformanceWarning, AttributeConflictWarning, DuplicateWarning, @@ -290,7 +298,7 @@ def test_api(self): #File path doesn't exist path = "" - self.assertRaises(IOError, read_hdf, path, 'df') + self.assertRaises(IOError, read_hdf, path, 'df') def test_api_default_format(self):
closes #8649 closes #8650
https://api.github.com/repos/pandas-dev/pandas/pulls/8651
2014-10-27T12:09:24Z
2014-10-27T12:37:35Z
2014-10-27T12:37:35Z
2014-10-30T11:20:23Z
BUG: Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, eg. datetime64) (GH8607)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index dc69bd9f55752..ed080f7f11863 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -50,4 +50,5 @@ Bug Fixes - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) - Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) +- Bug in ix/loc block splitting on setitem (manifests with integer-like dtypes, e.g. datetime64) (:issue:`8607`) - Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 6d002bc8d633a..954acb0f95159 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -482,6 +482,14 @@ def can_do_equal_len(): if isinstance(indexer, tuple): indexer = _maybe_convert_ix(*indexer) + # if we are setting on the info axis ONLY + # set using those methods to avoid block-splitting + # logic here + if len(indexer) > info_axis and com.is_integer(indexer[info_axis]) and all( + _is_null_slice(idx) for i, idx in enumerate(indexer) if i != info_axis): + self.obj[item_labels[indexer[info_axis]]] = value + return + if isinstance(value, ABCSeries): value = self._align_series(indexer, value) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 3f4d825a4b82e..fb9124bf19958 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -1000,7 +1000,10 @@ def test_fancy_setitem_int_labels(self): tmp = df.copy() exp = df.copy() tmp.ix[:, 2] = 5 - exp.values[:, 2] = 5 + + # tmp correctly sets the dtype + # so match the exp way + exp[2] = 5 assert_frame_equal(tmp, exp) def test_fancy_getitem_int_labels(self): diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 4eb06db57b054..a2f3284efcb82 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -605,7 +605,8 @@ def test_iloc_setitem(self): expected = Series([0,1,0],index=[4,5,6]) assert_series_equal(s, expected) - def test_loc_setitem(self): + def test_ix_loc_setitem(self): + # GH 5771 # loc with slice and series s = Series(0,index=[4,5,6]) @@ -627,6 +628,31 @@ def test_loc_setitem(self): expected = DataFrame({'a' : [0.5,-0.5,-1.5], 'b' : [0,1,2] }) assert_frame_equal(df,expected) + # GH 8607 + # ix setitem consistency + df = DataFrame( + {'timestamp':[1413840976, 1413842580, 1413760580], + 'delta':[1174, 904, 161], + 'elapsed':[7673, 9277, 1470] + }) + expected = DataFrame( + {'timestamp':pd.to_datetime([1413840976, 1413842580, 1413760580], unit='s'), + 'delta':[1174, 904, 161], + 'elapsed':[7673, 9277, 1470] + }) + + df2 = df.copy() + df2['timestamp'] = pd.to_datetime(df['timestamp'], unit='s') + assert_frame_equal(df2,expected) + + df2 = df.copy() + df2.loc[:,'timestamp'] = pd.to_datetime(df['timestamp'], unit='s') + assert_frame_equal(df2,expected) + + df2 = df.copy() + df2.ix[:,2] = pd.to_datetime(df['timestamp'], unit='s') + assert_frame_equal(df2,expected) + def test_loc_setitem_multiindex(self): # GH7190
closes #8607
https://api.github.com/repos/pandas-dev/pandas/pulls/8644
2014-10-27T00:57:48Z
2014-10-27T12:13:31Z
2014-10-27T12:13:31Z
2014-10-30T11:20:23Z
BUG: bug in Float/Int index with ndarray for add/sub numeric ops (GH8608)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index fd34099ffe75b..dc69bd9f55752 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -49,5 +49,5 @@ Bug Fixes ~~~~~~~~~ - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) - +- Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) - Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`) diff --git a/pandas/core/index.py b/pandas/core/index.py index c2c7e28a7a7f4..d56354833012a 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -1147,14 +1147,7 @@ def __add__(self, other): "use '|' or .union()",FutureWarning) return self.union(other) return Index(np.array(self) + other) - __iadd__ = __add__ - __eq__ = _indexOp('__eq__') - __ne__ = _indexOp('__ne__') - __lt__ = _indexOp('__lt__') - __gt__ = _indexOp('__gt__') - __le__ = _indexOp('__le__') - __ge__ = _indexOp('__ge__') def __sub__(self, other): if isinstance(other, Index): @@ -1162,6 +1155,13 @@ def __sub__(self, other): "use .difference()",FutureWarning) return self.difference(other) + __eq__ = _indexOp('__eq__') + __ne__ = _indexOp('__ne__') + __lt__ = _indexOp('__lt__') + __gt__ = _indexOp('__gt__') + __le__ = _indexOp('__le__') + __ge__ = _indexOp('__ge__') + def __and__(self, other): return self.intersection(other) @@ -2098,7 +2098,7 @@ def _invalid_op(self, other=None): def _add_numeric_methods(cls): """ add in numeric methods """ - def _make_evaluate_binop(op, opstr): + def _make_evaluate_binop(op, opstr, reversed=False): def _evaluate_numeric_binop(self, other): import pandas.tseries.offsets as offsets @@ -2128,7 +2128,13 @@ def _evaluate_numeric_binop(self, other): else: if not (com.is_float(other) or com.is_integer(other)): raise TypeError("can only perform ops with scalar values") - return self._shallow_copy(op(self.values, other)) + + # if we are a reversed non-communative op + values = self.values + if reversed: + values, other = other, values + + return self._shallow_copy(op(values, other)) return _evaluate_numeric_binop @@ -2145,16 +2151,23 @@ def _evaluate_numeric_unary(self): return _evaluate_numeric_unary + cls.__add__ = cls.__radd__ = _make_evaluate_binop(operator.add,'__add__') + cls.__sub__ = _make_evaluate_binop(operator.sub,'__sub__') + cls.__rsub__ = _make_evaluate_binop(operator.sub,'__sub__',reversed=True) cls.__mul__ = cls.__rmul__ = _make_evaluate_binop(operator.mul,'__mul__') - cls.__floordiv__ = cls.__rfloordiv__ = _make_evaluate_binop(operator.floordiv,'__floordiv__') - cls.__truediv__ = cls.__rtruediv__ = _make_evaluate_binop(operator.truediv,'__truediv__') + cls.__floordiv__ = _make_evaluate_binop(operator.floordiv,'__floordiv__') + cls.__rfloordiv__ = _make_evaluate_binop(operator.floordiv,'__floordiv__',reversed=True) + cls.__truediv__ = _make_evaluate_binop(operator.truediv,'__truediv__') + cls.__rtruediv__ = _make_evaluate_binop(operator.truediv,'__truediv__',reversed=True) if not compat.PY3: - cls.__div__ = cls.__rdiv__ = _make_evaluate_binop(operator.div,'__div__') + cls.__div__ = _make_evaluate_binop(operator.div,'__div__') + cls.__rdiv__ = _make_evaluate_binop(operator.div,'__div__',reversed=True) cls.__neg__ = _make_evaluate_unary(lambda x: -x,'__neg__') cls.__pos__ = _make_evaluate_unary(lambda x: x,'__pos__') cls.__abs__ = _make_evaluate_unary(lambda x: np.abs(x),'__abs__') cls.__inv__ = _make_evaluate_unary(lambda x: -x,'__inv__') + Index._add_numeric_methods_disabled() class NumericIndex(Index): @@ -2166,7 +2179,6 @@ class NumericIndex(Index): """ _is_numeric_dtype = True - class Int64Index(NumericIndex): """ diff --git a/pandas/stats/plm.py b/pandas/stats/plm.py index 54b687e277a38..53b8cce64b74a 100644 --- a/pandas/stats/plm.py +++ b/pandas/stats/plm.py @@ -240,7 +240,7 @@ def _add_entity_effects(self, panel): self.log('-- Excluding dummy for entity: %s' % to_exclude) - dummies = dummies.filter(dummies.columns - [to_exclude]) + dummies = dummies.filter(dummies.columns.difference([to_exclude])) dummies = dummies.add_prefix('FE_') panel = panel.join(dummies) @@ -286,7 +286,7 @@ def _add_categorical_dummies(self, panel, cat_mappings): self.log( '-- Excluding dummy for %s: %s' % (effect, to_exclude)) - dummies = dummies.filter(dummies.columns - [mapped_name]) + dummies = dummies.filter(dummies.columns.difference([mapped_name])) dropped_dummy = True dummies = _convertDummies(dummies, cat_mappings.get(effect)) diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index daea405a873ae..75af8f4e26302 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -595,7 +595,7 @@ def test_add(self): # - API change GH 8226 with tm.assert_produces_warning(): - self.strIndex + self.dateIndex + self.strIndex + self.strIndex firstCat = self.strIndex.union(self.dateIndex) secondCat = self.strIndex.union(self.strIndex) @@ -729,7 +729,7 @@ def test_symmetric_diff(self): # other isn't iterable with tm.assertRaises(TypeError): - idx1 - 1 + Index(idx1,dtype='object') - 1 def test_pickle(self): @@ -1132,12 +1132,37 @@ def test_numeric_compat(self): tm.assert_index_equal(result, Float64Index(np.arange(5,dtype='float64')*(np.arange(5,dtype='float64')+0.1))) - # invalid self.assertRaises(TypeError, lambda : idx * date_range('20130101',periods=5)) self.assertRaises(ValueError, lambda : idx * self._holder(np.arange(3))) self.assertRaises(ValueError, lambda : idx * np.array([1,2])) + + def test_explicit_conversions(self): + + # GH 8608 + # add/sub are overriden explicity for Float/Int Index + idx = self._holder(np.arange(5,dtype='int64')) + + # float conversions + arr = np.arange(5,dtype='int64')*3.2 + expected = Float64Index(arr) + fidx = idx * 3.2 + tm.assert_index_equal(fidx,expected) + fidx = 3.2 * idx + tm.assert_index_equal(fidx,expected) + + # interops with numpy arrays + expected = Float64Index(arr) + a = np.zeros(5,dtype='float64') + result = fidx - a + tm.assert_index_equal(result,expected) + + expected = Float64Index(-arr) + a = np.zeros(5,dtype='float64') + result = a - fidx + tm.assert_index_equal(result,expected) + def test_ufunc_compat(self): idx = self._holder(np.arange(5,dtype='int64')) result = np.sin(idx) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 78ed19a7049a5..3e51b55821fba 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -317,50 +317,56 @@ def _add_datelike(self, other): def _sub_datelike(self, other): raise NotImplementedError - def __add__(self, other): - from pandas.core.index import Index - from pandas.tseries.tdi import TimedeltaIndex - from pandas.tseries.offsets import DateOffset - if isinstance(other, TimedeltaIndex): - return self._add_delta(other) - elif isinstance(self, TimedeltaIndex) and isinstance(other, Index): - if hasattr(other,'_add_delta'): - return other._add_delta(self) - raise TypeError("cannot add TimedeltaIndex and {typ}".format(typ=type(other))) - elif isinstance(other, Index): - return self.union(other) - elif isinstance(other, (DateOffset, timedelta, np.timedelta64, tslib.Timedelta)): - return self._add_delta(other) - elif com.is_integer(other): - return self.shift(other) - elif isinstance(other, (tslib.Timestamp, datetime)): - return self._add_datelike(other) - else: # pragma: no cover - return NotImplemented - - def __sub__(self, other): - from pandas.core.index import Index - from pandas.tseries.tdi import TimedeltaIndex - from pandas.tseries.offsets import DateOffset - if isinstance(other, TimedeltaIndex): - return self._add_delta(-other) - elif isinstance(self, TimedeltaIndex) and isinstance(other, Index): - if not isinstance(other, TimedeltaIndex): - raise TypeError("cannot subtract TimedeltaIndex and {typ}".format(typ=type(other))) - return self._add_delta(-other) - elif isinstance(other, Index): - return self.difference(other) - elif isinstance(other, (DateOffset, timedelta, np.timedelta64, tslib.Timedelta)): - return self._add_delta(-other) - elif com.is_integer(other): - return self.shift(-other) - elif isinstance(other, (tslib.Timestamp, datetime)): - return self._sub_datelike(other) - else: # pragma: no cover - return NotImplemented - - __iadd__ = __add__ - __isub__ = __sub__ + @classmethod + def _add_datetimelike_methods(cls): + """ add in the datetimelike methods (as we may have to override the superclass) """ + + def __add__(self, other): + from pandas.core.index import Index + from pandas.tseries.tdi import TimedeltaIndex + from pandas.tseries.offsets import DateOffset + if isinstance(other, TimedeltaIndex): + return self._add_delta(other) + elif isinstance(self, TimedeltaIndex) and isinstance(other, Index): + if hasattr(other,'_add_delta'): + return other._add_delta(self) + raise TypeError("cannot add TimedeltaIndex and {typ}".format(typ=type(other))) + elif isinstance(other, Index): + return self.union(other) + elif isinstance(other, (DateOffset, timedelta, np.timedelta64, tslib.Timedelta)): + return self._add_delta(other) + elif com.is_integer(other): + return self.shift(other) + elif isinstance(other, (tslib.Timestamp, datetime)): + return self._add_datelike(other) + else: # pragma: no cover + return NotImplemented + cls.__add__ = __add__ + + def __sub__(self, other): + from pandas.core.index import Index + from pandas.tseries.tdi import TimedeltaIndex + from pandas.tseries.offsets import DateOffset + if isinstance(other, TimedeltaIndex): + return self._add_delta(-other) + elif isinstance(self, TimedeltaIndex) and isinstance(other, Index): + if not isinstance(other, TimedeltaIndex): + raise TypeError("cannot subtract TimedeltaIndex and {typ}".format(typ=type(other))) + return self._add_delta(-other) + elif isinstance(other, Index): + return self.difference(other) + elif isinstance(other, (DateOffset, timedelta, np.timedelta64, tslib.Timedelta)): + return self._add_delta(-other) + elif com.is_integer(other): + return self.shift(-other) + elif isinstance(other, (tslib.Timestamp, datetime)): + return self._sub_datelike(other) + else: # pragma: no cover + return NotImplemented + cls.__sub__ = __sub__ + + cls.__iadd__ = __add__ + cls.__isub__ = __sub__ def _add_delta(self, other): return NotImplemented @@ -469,5 +475,3 @@ def repeat(self, repeats, axis=None): """ return self._simple_new(self.values.repeat(repeats), name=self.name) - - diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 7aaec511b82bf..4ab48b2db98d0 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1665,6 +1665,7 @@ def to_julian_date(self): self.nanosecond/3600.0/1e+9 )/24.0) DatetimeIndex._add_numeric_methods_disabled() +DatetimeIndex._add_datetimelike_methods() def _generate_regular_range(start, end, periods, offset): if isinstance(offset, Tick): diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index b4d8a6547950d..cba449b9596e1 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -1263,6 +1263,7 @@ def tz_localize(self, tz, infer_dst=False): raise NotImplementedError("Not yet implemented for PeriodIndex") PeriodIndex._add_numeric_methods_disabled() +PeriodIndex._add_datetimelike_methods() def _get_ordinal_range(start, end, periods, freq): if com._count_not_none(start, end, periods) < 2: diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 4822be828a2c3..2c452b2fa7ded 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -898,6 +898,7 @@ def delete(self, loc): return TimedeltaIndex(new_tds, name=self.name, freq=freq) TimedeltaIndex._add_numeric_methods() +TimedeltaIndex._add_datetimelike_methods() def _is_convertible_to_index(other): """ return a boolean whether I can attempt conversion to a TimedeltaIndex """ @@ -978,5 +979,3 @@ def timedelta_range(start=None, end=None, periods=None, freq='D', return TimedeltaIndex(start=start, end=end, periods=periods, freq=freq, name=name, closed=closed) - -
closes #8608
https://api.github.com/repos/pandas-dev/pandas/pulls/8634
2014-10-25T15:45:59Z
2014-10-25T23:35:27Z
2014-10-25T23:35:27Z
2014-10-30T11:20:23Z
BUG: Fix pandas.io.data.Options for change in format of Yahoo Option page
diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index bba3db86d837c..f4c9d6a24cea2 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -86,8 +86,29 @@ If you don't want to download all the data, more specific requests can be made. data = aapl.get_call_data(expiry=expiry) data.iloc[0:5:, 0:5] -Note that if you call ``get_all_data`` first, this second call will happen much faster, as the data is cached. +Note that if you call ``get_all_data`` first, this second call will happen much faster, +as the data is cached. +If a given expiry date is not available, data for the next available expiry will be +returned (January 15, 2015 in the above example). + +Available expiry dates can be accessed from the ``expiry_dates`` property. + +.. ipython:: python + + aapl.expiry_dates + data = aapl.get_call_data(expiry=aapl.expiry_dates[0]) + data.iloc[0:5:, 0:5] + +A list-like object containing dates can also be passed to the expiry parameter, +returning options data for all expiry dates in the list. + +.. ipython:: python + + data = aapl.get_near_stock_price(expiry=aapl.expiry_dates[0:3]) + data.iloc[0:5:, 0:5] + +The ``month`` and ``year`` parameters can be used to get all options data for a given month. .. _remote_data.google: diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 16c8bf12b99c0..0869f1c712b44 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -169,6 +169,39 @@ API changes - added Index properties `is_monotonic_increasing` and `is_monotonic_decreasing` (:issue:`8680`). +.. note:: io.data.Options has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`) + + As a result of a change in Yahoo's option page layout, when an expiry date is given, + ``Options`` methods now return data for a single expiry date. Previously, methods returned all + data for the selected month. + + The ``month`` and ``year`` parameters have been undeprecated and can be used to get all + options data for a given month. + + If an expiry date that is not valid is given, data for the next expiry after the given + date is returned. + + Option data frames are now saved on the instance as ``callsYYMMDD`` or ``putsYYMMDD``. Previously + they were saved as ``callsMMYY`` and ``putsMMYY``. The next expiry is saved as ``calls`` and ``puts``. + + New features: + + The expiry parameter can now be a single date or a list-like object containing dates. + + A new property ``expiry_dates`` was added, which returns all available expiry dates. + + current behavior: + + .. ipython:: python + + from pandas.io.data import Options + aapl = Options('aapl','yahoo') + aapl.get_call_data().iloc[0:5,0:1] + aapl.expiry_dates + aapl.get_near_stock_price(expiry=aapl.expiry_dates[0:3]).iloc[0:5,0:1] + + See the Options documentation in :ref:`Remote Data <remote_data.yahoo_options>` + .. _whatsnew_0151.enhancements: Enhancements diff --git a/pandas/io/data.py b/pandas/io/data.py index f0b078944d8ea..6cad478ee841b 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -13,16 +13,15 @@ import numpy as np from pandas.compat import( - StringIO, bytes_to_str, range, lrange, lmap, zip + StringIO, bytes_to_str, range, lmap, zip ) import pandas.compat as compat -from pandas import Panel, DataFrame, Series, read_csv, concat, to_datetime +from pandas import Panel, DataFrame, Series, read_csv, concat, to_datetime, DatetimeIndex, DateOffset 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.tseries.offsets import MonthEnd from pandas.util.testing import _network_error_classes - +from pandas.io.html import read_html class SymbolWarning(UserWarning): pass @@ -521,30 +520,8 @@ def get_data_famafrench(name): CUR_YEAR = dt.datetime.now().year CUR_DAY = dt.datetime.now().day -def _unpack(row, kind): - 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') - header = _unpack(rows[0], kind='th') - data = [_unpack(row, kind='td') for row in rows[1:]] - # Use ',' as a thousands separator as we're pulling from the US site. - return TextParser(data, names=header, na_values=['N/A'], - thousands=',').get_chunk() - -def _two_char_month(s): +def _two_char(s): return '{0:0>2}'.format(s) @@ -568,15 +545,14 @@ class Options(object): # Instantiate object with ticker >>> aapl = Options('aapl', 'yahoo') - # Fetch May 2014 call data - >>> expiry = datetime.date(2014, 5, 1) - >>> calls = aapl.get_call_data(expiry=expiry) + # Fetch next expiry call data + >>> calls = aapl.get_call_data() # Can now access aapl.calls instance variable >>> aapl.calls - # Fetch May 2014 put data - >>> puts = aapl.get_put_data(expiry=expiry) + # Fetch next expiry put data + >>> puts = aapl.get_put_data() # Can now access aapl.puts instance variable >>> aapl.puts @@ -591,7 +567,9 @@ class Options(object): >>> all_data = aapl.get_all_data() """ - _TABLE_LOC = {'calls': 9, 'puts': 13} + _TABLE_LOC = {'calls': 1, 'puts': 2} + _OPTIONS_BASE_URL = 'http://finance.yahoo.com/q/op?s={sym}' + _FINANCE_BASE_URL = 'http://finance.yahoo.com' def __init__(self, symbol, data_source=None): """ Instantiates options_data with a ticker saved as symbol """ @@ -611,8 +589,15 @@ def get_options_data(self, month=None, year=None, expiry=None): Parameters ---------- - expiry: datetime.date, optional(default=None) - The date when options expire (defaults to current month) + month : number, int, optional(default=None) + The month the options expire. This should be either 1 or 2 + digits. + + year : number, int, optional(default=None) + The year the options expire. This should be a 4 digit int. + + expiry : date-like or convertible or list-like object, optional (default=None) + The date (or dates) when options expire (defaults to current month) Returns ------- @@ -621,7 +606,7 @@ def get_options_data(self, month=None, year=None, expiry=None): Index: Strike: Option strike, int - Expiry: Option expiry, datetime.date + Expiry: Option expiry, Timestamp Type: Call or Put, string Symbol: Option symbol as reported on Yahoo, string Columns: @@ -650,105 +635,84 @@ 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, respectively, two digit - representations of the month and year for the expiry of the - options. + a different expiry, the ivar will be named callsYYMMDD or putsYYMMDD, + where YY, MM and DD are, respectively, two digit representations of + the year, month and day for the expiry of the options. + """ 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_tables(self, expiry): - root = self._get_option_page_from_yahoo(expiry) - tables = self._parse_option_page_from_yahoo(root) - m1 = _two_char_month(expiry.month) - table_name = '_tables' + m1 + str(expiry.year)[-2:] - setattr(self, table_name, tables) - return tables + def _get_option_frames_from_yahoo(self, expiry): + url = self._yahoo_url_from_expiry(expiry) + option_frames = self._option_frames_from_url(url) + frame_name = '_frames' + self._expiry_to_string(expiry) + setattr(self, frame_name, option_frames) + return option_frames - def _get_option_page_from_yahoo(self, expiry): + @staticmethod + def _expiry_to_string(expiry): + m1 = _two_char(expiry.month) + d1 = _two_char(expiry.day) + return str(expiry.year)[-2:] + m1 + d1 - url = self._OPTIONS_BASE_URL.format(sym=self.symbol) + def _yahoo_url_from_expiry(self, expiry): + try: + expiry_links = self._expiry_links - m1 = _two_char_month(expiry.month) + except AttributeError: + _, expiry_links = self._get_expiry_dates_and_links() - # if this month use other url - if expiry.month == CUR_MONTH and expiry.year == CUR_YEAR: - url += '+Options' - else: - url += '&m={year}-{m1}'.format(year=expiry.year, m1=m1) + return self._FINANCE_BASE_URL + expiry_links[expiry] - root = self._parse_url(url) - return root + def _option_frames_from_url(self, url): + frames = read_html(url) + nframes = len(frames) + frames_req = max(self._TABLE_LOC.values()) + if nframes < frames_req: + raise RemoteDataError("%s options tables found (%s expected)" % (nframes, frames_req)) - def _parse_option_page_from_yahoo(self, root): + if not hasattr(self, 'underlying_price'): + try: + self.underlying_price, self.quote_time = self._get_underlying_price(url) + except IndexError: + self.underlying_price, self.quote_time = np.nan, np.nan - tables = root.xpath('.//table') - ntables = len(tables) - if ntables == 0: - raise RemoteDataError("No tables found") + calls = self._process_data(frames[self._TABLE_LOC['calls']], 'call') + puts = self._process_data(frames[self._TABLE_LOC['puts']], 'put') - try: - self.underlying_price, self.quote_time = self._get_underlying_price(root) - except IndexError: - self.underlying_price, self.quote_time = np.nan, np.nan + return {'calls': calls, 'puts': puts} - return tables - - def _get_underlying_price(self, root): - underlying_price = float(root.xpath('.//*[@class="time_rtq_ticker"]')[0]\ + def _get_underlying_price(self, url): + root = self._parse_url(url) + underlying_price = float(root.xpath('.//*[@class="time_rtq_ticker Fz-30 Fw-b"]')[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: - 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") + try: + quote_time_text = root.xpath('.//*[@class="time_rtq Fz-m"]')[0].getchildren()[1].getchildren()[0].text + quote_time = dt.datetime.strptime(quote_time_text, "%I:%M%p EDT") quote_time = quote_time.replace(year=CUR_YEAR, month=CUR_MONTH, day=CUR_DAY) + except ValueError: + raise RemoteDataError('Unable to determine time of quote for page %s' % url) 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:] + def _get_option_data(self, expiry, name): + frame_name = '_frames' + self._expiry_to_string(expiry) try: - tables = getattr(self, table_name) + frames = getattr(self, frame_name) except AttributeError: - tables = self._get_option_tables(expiry) + frames = self._get_option_frames_from_yahoo(expiry) - ntables = len(tables) - table_loc = self._TABLE_LOC[name] - if table_loc - 1 > ntables: - raise RemoteDataError("Table location {0} invalid, {1} tables" - " found".format(table_loc, ntables)) + option_data = frames[name] + if expiry != self.expiry_dates[0]: + name += self._expiry_to_string(expiry) - try: - option_data = _parse_options_data(tables[table_loc]) - option_data['Type'] = name[:-1] - option_data = self._process_data(option_data, name[:-1]) - - 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 - - except (Exception) as e: - raise RemoteDataError("Cannot retrieve Table data {0}".format(str(e))) + setattr(self, name, option_data) + return option_data def get_call_data(self, month=None, year=None, expiry=None): """ @@ -758,8 +722,15 @@ def get_call_data(self, month=None, year=None, expiry=None): Parameters ---------- - expiry: datetime.date, optional(default=None) - The date when options expire (defaults to current month) + month : number, int, optional(default=None) + The month the options expire. This should be either 1 or 2 + digits. + + year : number, int, optional(default=None) + The year the options expire. This should be a 4 digit int. + + expiry : date-like or convertible or list-like object, optional (default=None) + The date (or dates) when options expire (defaults to current month) Returns ------- @@ -768,7 +739,7 @@ def get_call_data(self, month=None, year=None, expiry=None): Index: Strike: Option strike, int - Expiry: Option expiry, datetime.date + Expiry: Option expiry, Timestamp Type: Call or Put, string Symbol: Option symbol as reported on Yahoo, string Columns: @@ -797,11 +768,12 @@ 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, - respectively, two digit representations of the month and year + or year, the ivar will be named callsYYMMDD where YY, MM and DD are, + respectively, two digit representations of the year, month and day for the expiry of the options. """ - return self._get_option_data(month, year, expiry, 'calls').sortlevel() + expiry = self._try_parse_dates(year, month, expiry) + return self._get_data_in_date_range(expiry, call=True, put=False) def get_put_data(self, month=None, year=None, expiry=None): """ @@ -811,8 +783,15 @@ def get_put_data(self, month=None, year=None, expiry=None): Parameters ---------- - expiry: datetime.date, optional(default=None) - The date when options expire (defaults to current month) + month : number, int, optional(default=None) + The month the options expire. This should be either 1 or 2 + digits. + + year : number, int, optional(default=None) + The year the options expire. This should be a 4 digit int. + + expiry : date-like or convertible or list-like object, optional (default=None) + The date (or dates) when options expire (defaults to current month) Returns ------- @@ -821,7 +800,7 @@ def get_put_data(self, month=None, year=None, expiry=None): Index: Strike: Option strike, int - Expiry: Option expiry, datetime.date + Expiry: Option expiry, Timestamp Type: Call or Put, string Symbol: Option symbol as reported on Yahoo, string Columns: @@ -852,11 +831,12 @@ def get_put_data(self, month=None, year=None, expiry=None): Also note that aapl.puts will always be the puts for the next expiry. If the user calls this method with a different month - or year, the ivar will be named putsMMYY where MM and YY are, - repsectively, two digit representations of the month and year + or year, the ivar will be named putsYYMMDD where YY, MM and DD are, + respectively, two digit representations of the year, month and day for the expiry of the options. """ - return self._get_option_data(month, year, expiry, 'puts').sortlevel() + expiry = self._try_parse_dates(year, month, expiry) + return self._get_data_in_date_range(expiry, put=True, call=False) def get_near_stock_price(self, above_below=2, call=True, put=False, month=None, year=None, expiry=None): @@ -866,20 +846,25 @@ def get_near_stock_price(self, above_below=2, call=True, put=False, Parameters ---------- - above_below: number, int, optional (default=2) + above_below : number, int, optional (default=2) The number of strike prices above and below the stock price that should be taken - call: bool - Tells the function whether or not it should be using - self.calls + call : bool + Tells the function whether or not it should be using calls + + put : bool + Tells the function weather or not it should be using puts - put: bool - Tells the function weather or not it should be using - self.puts + month : number, int, optional(default=None) + The month the options expire. This should be either 1 or 2 + digits. - expiry: datetime.date, optional(default=None) - The date when options expire (defaults to current month) + year : number, int, optional(default=None) + The year the options expire. This should be a 4 digit int. + + expiry : date-like or convertible or list-like object, optional (default=None) + The date (or dates) when options expire (defaults to current month) Returns ------- @@ -891,17 +876,9 @@ def get_near_stock_price(self, above_below=2, call=True, put=False, 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 - - data = {} - - for nam in to_ret: - df = self._get_option_data(month, year, expiry, nam) - data[nam] = self.chop_data(df, above_below, self.underlying_price) - - return concat([data[nam] for nam in to_ret]).sortlevel() + expiry = self._try_parse_dates(year, month, expiry) + data = self._get_data_in_date_range(expiry, call=call, put=put) + return self.chop_data(data, above_below, self.underlying_price) def chop_data(self, df, above_below=2, underlying_price=None): """Returns a data frame only options that are near the current stock price.""" @@ -912,7 +889,10 @@ def chop_data(self, df, above_below=2, underlying_price=None): except AttributeError: underlying_price = np.nan - if not np.isnan(underlying_price): + max_strike = max(df.index.get_level_values('Strike')) + min_strike = min(df.index.get_level_values('Strike')) + + if not np.isnan(underlying_price) and min_strike < underlying_price < max_strike: start_index = np.where(df.index.get_level_values('Strike') > underlying_price)[0][0] @@ -922,47 +902,70 @@ def chop_data(self, df, above_below=2, underlying_price=None): return df - - - @staticmethod - def _try_parse_dates(year, month, expiry): + def _try_parse_dates(self, 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) + year : int + Calendar year - month: Calendar month, int (deprecated) + month : int + Calendar month - expiry: Expiry date (month and year), datetime.date, (preferred) + expiry : date-like or convertible, (preferred) + Expiry date Returns ------- - Tuple of year (int), month (int), expiry (datetime.date) + list of expiry dates (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." + "or none of these options for the next expiry." 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 + if hasattr(expiry, '__iter__'): + expiry = [self._validate_expiry(exp) for exp in expiry] + else: + expiry = [self._validate_expiry(expiry)] + + if len(expiry) == 0: + raise ValueError('No expiries available for given input.') + elif year is None and month is None: + #No arguments passed, provide next expiry year = CUR_YEAR month = CUR_MONTH expiry = dt.date(year, month, 1) + expiry = [self._validate_expiry(expiry)] + else: - expiry = dt.date(year, month, 1) + #Year and month passed, provide all expiries in that month + expiry = [expiry for expiry in self.expiry_dates if expiry.year == year and expiry.month == month] + if len(expiry) == 0: + raise ValueError('No expiries available in %s-%s' % (year, month)) + + return expiry - return year, month, expiry + def _validate_expiry(self, expiry): + """Ensures that an expiry date has data available on Yahoo + If the expiry date does not have options that expire on that day, return next expiry""" + + expiry_dates = self.expiry_dates + expiry = to_datetime(expiry) + if hasattr(expiry, 'date'): + expiry = expiry.date() + + if expiry in expiry_dates: + return expiry + else: + index = DatetimeIndex(expiry_dates).order() + return index[index.date >= expiry][0].date() def get_forward_data(self, months, call=True, put=False, near=False, above_below=2): @@ -973,21 +976,21 @@ def get_forward_data(self, months, call=True, put=False, near=False, Parameters ---------- - months: number, int + months : number, int How many months to go out in the collection of the data. This is inclusive. - call: bool, optional (default=True) + call : bool, optional (default=True) Whether or not to collect data for call options - put: bool, optional (default=False) + put : bool, optional (default=False) Whether or not to collect data for put options. - near: bool, optional (default=False) + near : bool, optional (default=False) Whether this function should get only the data near the current stock price. Uses Options.get_near_stock_price - above_below: number, int, optional (default=2) + above_below : number, int, optional (default=2) The number of strike prices above and below the stock price that should be taken if the near option is set to True @@ -998,7 +1001,7 @@ def get_forward_data(self, months, call=True, put=False, near=False, Index: Strike: Option strike, int - Expiry: Option expiry, datetime.date + Expiry: Option expiry, Timestamp Type: Call or Put, string Symbol: Option symbol as reported on Yahoo, string Columns: @@ -1017,48 +1020,12 @@ def get_forward_data(self, months, call=True, put=False, near=False, """ warnings.warn("get_forward_data() is deprecated", FutureWarning) - in_months = lrange(CUR_MONTH, CUR_MONTH + months + 1) - in_years = [CUR_YEAR] * (months + 1) - - # Figure out how many items in in_months go past 12 - to_change = 0 - for i in range(months): - if in_months[i] > 12: - in_months[i] -= 12 - to_change += 1 - - # Change the corresponding items in the in_years list. - for i in range(1, to_change + 1): - in_years[-i] += 1 - - to_ret = Series({'calls': call, 'puts': put}) - to_ret = to_ret[to_ret].index - all_data = [] - - for name in to_ret: - - for mon in range(months): - m2 = in_months[mon] - y2 = in_years[mon] - - if not near: - 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)(m2, y2) - else: - frame = self.get_near_stock_price(call=call, put=put, - above_below=above_below, - month=m2, year=y2) - frame = self._process_data(frame, name[:-1]) - - all_data.append(frame) - - return concat(all_data).sortlevel() + end_date = dt.date.today() + MonthEnd(months) + dates = (date for date in self.expiry_dates if date <= end_date.date()) + data = self._get_data_in_date_range(dates, call=call, put=put) + if near: + data = self.chop_data(data, above_below=above_below) + return data def get_all_data(self, call=True, put=True): """ @@ -1068,10 +1035,10 @@ def get_all_data(self, call=True, put=True): Parameters ---------- - call: bool, optional (default=True) + call : bool, optional (default=True) Whether or not to collect data for call options - put: bool, optional (default=True) + put : bool, optional (default=True) Whether or not to collect data for put options. Returns @@ -1081,7 +1048,7 @@ def get_all_data(self, call=True, put=True): Index: Strike: Option strike, int - Expiry: Option expiry, datetime.date + Expiry: Option expiry, Timestamp Type: Call or Put, string Symbol: Option symbol as reported on Yahoo, string Columns: @@ -1099,67 +1066,68 @@ def get_all_data(self, call=True, put=True): 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 + expiry_dates = self.expiry_dates except AttributeError: - months = self._get_expiry_months() + expiry_dates, _ = self._get_expiry_dates_and_links() - all_data = [] + return self._get_data_in_date_range(dates=expiry_dates, call=call, put=put) - for name in to_ret: - - for month in months: - m2 = month.month - y2 = month.year + def _get_data_in_date_range(self, dates, call=True, put=True): - m1 = _two_char_month(m2) - nam = name + str(m1) + str(y2)[2:] + to_ret = Series({'calls': call, 'puts': put}) + to_ret = to_ret[to_ret].index + data = [] + for name in to_ret: + for expiry_date in dates: + nam = name + self._expiry_to_string(expiry_date) 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) + frame = self._get_option_data(expiry=expiry_date, name=name) + data.append(frame) - all_data.append(frame) + return concat(data).sortlevel() - return concat(all_data).sortlevel() + @property + def expiry_dates(self): + """ + Returns a list of available expiry dates + """ + try: + expiry_dates = self._expiry_dates + except AttributeError: + expiry_dates, _ = self._get_expiry_dates_and_links() + return expiry_dates - def _get_expiry_months(self): + def _get_expiry_dates_and_links(self): """ - Gets available expiry months. + Gets available expiry dates. Returns ------- - months : List of datetime objects + Tuple of: + List of datetime.date objects + Dict of datetime.date objects as keys and corresponding links """ - url = 'http://finance.yahoo.com/q/op?s={sym}'.format(sym=self.symbol) + url = self._OPTIONS_BASE_URL.format(sym=self.symbol) root = self._parse_url(url) try: - links = root.xpath('.//*[@id="yfncsumtab"]')[0].xpath('.//a') + links = root.xpath('//*[@id="options_menu"]/form/select/option') except IndexError: - raise RemoteDataError('Expiry months not available') + raise RemoteDataError('Expiry dates not available') - 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']) + expiry_dates = [dt.datetime.strptime(element.text, "%B %d, %Y").date() for element in links] + links = [element.attrib['data-selectbox-link'] for element in links] + expiry_links = dict(zip(expiry_dates, links)) + self._expiry_links = expiry_links + self._expiry_dates = expiry_dates - 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 + return expiry_dates, expiry_links def _parse_url(self, url): """ @@ -1183,23 +1151,27 @@ def _parse_url(self, url): "element".format(url)) return root - def _process_data(self, frame, type): """ Adds columns for Expiry, IsNonstandard (ie: deliverable is not 100 shares) and Tag (the tag indicating what is actually deliverable, None if standard). """ + frame.columns = ['Strike', 'Symbol', 'Last', 'Bid', 'Ask', 'Chg', 'PctChg', 'Vol', 'Open_Int', 'IV'] 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('-','') + 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 + try: + frame['Underlying_Price'] = self.underlying_price + frame["Quote_Time"] = self.quote_time + except AttributeError: + frame['Underlying_Price'] = np.nan + frame["Quote_Time"] = np.nan frame.rename(columns={'Open Int': 'Open_Int'}, inplace=True) frame['Type'] = type frame.set_index(['Strike', 'Expiry', 'Type', 'Symbol'], inplace=True) diff --git a/pandas/io/tests/data/yahoo_options1.html b/pandas/io/tests/data/yahoo_options1.html index 987072b15e280..2846a2bd12732 100644 --- a/pandas/io/tests/data/yahoo_options1.html +++ b/pandas/io/tests/data/yahoo_options1.html @@ -1,52 +1,210 @@ -<!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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-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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font-size: 11px !important; + padding-top: 10px; + padding-right: 5px; + padding-bottom: 10px; + vertical-align: middle; +} +#quote-table tr th * { + font-size: 11px; +} +#quote-table tr th .expand-icon { + display: block !important; + margin: 0 auto; + border: 1px solid #e2e2e6; + background-color: #fcfcfc; + -webkit-border-radius: 2px; + border-radius: 2px; + padding: 2px 0; +} +#quote-table tr th.column-strike { + width: 82px; +} +#quote-table tr th .sort-icons { + position: absolute; + margin-left: 2px; +} +#quote-table tr th .Icon { + display: none; +} +#quote-table tr th.low-high .up { + display: block !important; +} +#quote-table tr th.high-low .down { + display: block !important; +} +#quote-table td { + text-align: center; + padding: 7px 5px 7px 5px; +} +#quote-table td:first-child, +#quote-table th:first-child { + border-right: 1px solid #e2e2e6; +} +#quote-table .D-ib .Icon { + color: #66aeb2; +} +#quote-table caption { + background-color: #454545 !important; + color: #fff; + 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-5px 0 0 0 #cff3ec; + box-shadow: inset -5px 0 0 0 #cff3ec; +} +#quote-table .column-strike { + text-align: center; + padding: 4px 20px; +} +#quote-table .column-strike .header_text, +#quote-table .column-expires .cell .expiration{ + color: #454545; + font-size: 15px; + font-weight: bold; + max-width: 100%; +} +#quote-table .column-strike .header_text { + width: 100%; +} +#quote-table .column-strike .filter { + border: 1px solid #e2e2e6; + background-color: #fcfcfc; + color: #858585; + display: inline-block; + padding: 1px 10px; + -webkit-border-radius: 3px; + border-radius: 3px; + margin-top: 4px; +} +#quote-table .column-strike .filter span { + position: relative; + top: -2px; + font-weight: bold; + margin-left: -5px; +} + +#quote-table .column-strike .sort-icons { + top: 35px; +} +#quote-table .column-expires .sort-icons { + top: 45px; +} +#optionsStraddlesTable .column-expires .sort-icons { + top: 40px; +} +#quote-table #options_menu { + width: 100%; +} +#quote-table #options_menu .SelectBox-Pick { + background-color: #fcfcfc !important; + border: 1px solid #e2e2e6; + color: #128086; + font-size: 14px; + padding: 5px; + padding-top: 8px; +} +#quote-table #options_menu .SelectBox-Text { + font-weight: bold; +} +#quote-table .size-toggle-menu { + margin-left: 15px !important; +} +#quote-table .options-menu-item { + top: -9px; +} +#quote-table .option_view { + float: right; +} +#quote-table .option-change-pos { + color: #2ac194; +} +#quote-table .option-change-neg { + color: #f90f31; +} +#quote-table .toggle li { + color: #128086; + background-color: #fcfcfc; +} +#quote-table .toggle li.active { + color: #fff; + background-color: #35d2b6; +} +#quote-table .expand-icon { + color: #b5b5b5; + font-size: 12px; + cursor: pointer; +} +#quote-table .straddleCallContractName { + padding-left: 25px; +} +#quote-table .straddlePutContractName { + padding-left: 20px; +} +#quote-table .straddle-row-expand { + display: none; + border-bottom: 1px solid #f9f9f9; +} +#quote-table .straddle-row-expand td { + padding-right: 5px; +} +#quote-table .straddle-row-expand label { + color: #454545; + font-size: 11px; + margin-bottom: 2px; + color: #888; +} +#quote-table .straddle-row-expand label, +#quote-table .straddle-row-expand div { + display: block; + font-weight: 400; + text-align: left; + padding-left: 5px; +} +#quote-table .expand-icon-up { + display: none; +} +#quote-table tr.expanded + .straddle-row-expand { + display: table-row; +} +#quote-table tr.expanded .expand-icon-up { + display: inline-block; +} +#quote-table tr.expanded .expand-icon-down { + display: none; +} +.in-the-money-banner { + color: #7f8584; + font-size: 11px; + background-color: #eefcfa; + border-left: 12px solid #e0faf6; + border-right: 12px solid #e0faf6; + width: 76px !important; + text-align: center; + padding: 5px !important; + margin-top: 5px; + margin-left: 15px; +} +#optionsStraddlesTable td div { + text-align: center; +} +#optionsStraddlesTable .straddle-strike, +#optionsStraddlesTable .column-strike, +#optionsStraddlesTable .straddle-expire{ + border-right: 1px solid #e2e2e6; + border-left: 1px solid #e2e2e6; +} +#optionsStraddlesTable td:first-child, +#optionsStraddlesTable th:first-child { + border-right: none !important; +} +#optionsStraddlesTable .odd td.in-the-money { + background-color: #ecf6f4; +} +#optionsStraddlesTable .odd td.in-the-money:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #cff3ec; + box-shadow: inset 5px 0 0 0 #cff3ec; +} +#optionsStraddlesTable .odd td.in-the-money:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #cff3ec; + box-shadow: inset -5px 0 0 0 #cff3ec; +} +#optionsStraddlesTable .even td.in-the-money { + background-color: #f3fdfc; +} +#optionsStraddlesTable .even td.in-the-money:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #d5f8f3; + box-shadow: inset 5px 0 0 0 #d5f8f3; +} +#optionsStraddlesTable .even td.in-the-money:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #d5f8f3; + box-shadow: inset -5px 0 0 0 #d5f8f3; +} +.column-expand-all { + cursor: pointer; +} +.options-table.expand-all tr + .straddle-row-expand { + display: table-row !important; +} +.options-table.expand-all tr .expand-icon-up { + display: inline-block !important; +} +.options-table.expand-all tr .expand-icon-down { + display: none !important; +} +.options_menu .toggle a { + color: #128086; +} +.options_menu .toggle a:hover { + text-decoration: none; +} +.options_menu .toggle .active a { + color: #fff; +} +#options_menu .symbol_lookup { + float: right; + top: -11px; +} +.symbol_lookup .options-ac-input { + border-radius: 0; + height: 26px; + width: 79%; +} +.goto-icon { + border-left: 1px solid #e2e2e6; + color: #028087; + cursor: pointer; +} +.symbol_lookup .goto-icon { + height: 27px; + line-height: 2.1em; +} +#finAcOutput { + left: 10px; + top: -10px; +} +#finAcOutput .yui3-fin-ac-hidden { + display: none; +} +#finAcOutput .yui3-aclist { + border: 1px solid #DDD; + background: #fefefe; + font-size: 92%; + left: 0 !important; + overflow: visible; + padding: .5em; + position: absolute !important; + text-align: left; + top: 0 !important; + +} +#finAcOutput li.yui3-fin-ac-item-active, +#finAcOutput li.yui3-fin-ac-item-hover { + background: #F1F1F1; + cursor: pointer; +} +#finAcOutput div:first-child { + width: 30em !important; +} +#finAcOutput b.yui3-highlight { + font-weight: bold; +} +#finAcOutput li .name { + display: inline-block; + left: 0; + width: 25em; + overflow: hidden; + position: relative; +} + +#finAcOutput li .symbol { + width: 8.5em; + display: inline-block; + margin: 0 1em 0 0; + overflow: hidden; +} + +#finAcOutput li { + color: #444; + cursor: default; + font-weight: 300; + list-style: none; + margin: 0; + padding: .15em .38em; + position: relative; + vertical-align: bottom; + white-space: nowrap; +} + +.yui3-fin-ac-hidden { + visibility: hidden; +} + +.filterRangeRow { + line-height: 5px; +} +.filterRangeTitle { + padding-bottom: 5px; + font-size: 12px !important; +} +.clear-filter { + padding-left: 20px; +} +.closeFilter { + font-size: 10px !important; + color: red !important; +} +.modify-filter { + font-size: 11px !important; +} +.showModifyFilter { + top: 80px; + left: 630px; +} + +#options_menu { + margin-bottom: -15px; +} + +#optionsTableApplet { + margin-top: 9px; + width: 1070px; +} + +#yfi_charts.desktop #yfi_doc { + width: 1440px; +} +#sky { + float: right; + margin-left: 30px; + margin-top: 50px; + width: 170px; +} +</style> +<div id="applet_4971909175742216" class="App_v2 js-applet" data-applet-guid="4971909175742216" data-applet-type="td-applet-options-table"> + + + + + + <div class="App-Bd"> + <div class="App-Main" data-region="main"> + <div class="js-applet-view-container-main"> + + <div id="quote-table"> + <div id="options_menu" class="Grid-U options_menu"> + + <form class="Grid-U SelectBox"> + <div class="SelectBox-Pick"><b class='SelectBox-Text '>October 24, 2014</b><i class='Icon Va-m'>&#xe002;</i></div> + <select class='Start-0' data-plugin="selectbox"> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1414108800" value="1414108800" selected >October 24, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1414713600" value="1414713600" >October 31, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1415318400" value="1415318400" >November 7, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1415923200" value="1415923200" >November 14, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1416614400" value="1416614400" >November 22, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1417132800" value="1417132800" >November 28, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1419033600" value="1419033600" >December 20, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1421452800" value="1421452800" >January 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1424390400" value="1424390400" >February 20, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1429228800" value="1429228800" >April 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1437091200" value="1437091200" >July 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1452816000" value="1452816000" >January 15, 2016</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1484870400" value="1484870400" >January 20, 2017</option> + + </select> + </form> + + + <div class="Grid-U options-menu-item size-toggle-menu"> + <ul class="toggle size-toggle"> + <li data-size="REGULAR" class="size-toggle-option toggle-regular active Cur-p"> + <a href="/q/op?s=AAPL&date=1414108800">Regular</a> + </li> + <li data-size="MINI" class="size-toggle-option toggle-mini Cur-p"> + <a href="/q/op?s=AAPL&size=mini&date=1414108800">Mini</a> + </li> + </ul> + </div> + + + <div class="Grid-U options-menu-item symbol_lookup"> + <div class="Cf"> + <div class="fin-ac-container Bd-1 Pos-r M-10"> + <input placeholder="Lookup Option" type="text" autocomplete="off" value="" name="s" class="options-ac-input Bd-0" id="finAcOptions"> + <i class="Icon Fl-end W-20 goto-icon">&#xe015;</i> + </div> + <div id="finAcOutput" class="yui-ac-container Pos-r"></div> + </div> + </div> + <div class="Grid-U option_view options-menu-item"> + <ul class="toggle toggle-view-mode"> + <li class="toggle-list active"> + <a href="/q/op?s=AAPL&date=1414108800">List</a> + </li> + <li class="toggle-straddle "> + <a href="/q/op?s=AAPL&straddle=true&date=1414108800">Straddle</a> + </li> + </ul> + + </div> + <div class="Grid-U in_the_money in-the-money-banner"> + In The Money + </div> + </div> + + + + <div class="options-table " id="optionsCallsTable" data-sec="options-calls-table"> + <div class="strike-filter option-filter-overlay"> + <p>Show Me Strikes From</p> + <div class="My-6"> + $ <input class="filter-low strike-filter" data-filter-type="low" type="text"> + to $ <input class="filter-high strike-filter" data-filter-type="high" type="text"> + </div> + <a data-table-filter="optionsCalls" class="Cur-p apply-filter">Apply Filter</a> + <a class="Cur-p clear-filter">Clear Filter</a> +</div> + + +<div class="follow-quote-area"> + <div class="quote-table-overflow"> + <table class="details-table quote-table Fz-m"> + + + <caption> + Calls + </caption> + + + <thead class="details-header quote-table-headers"> + <tr> + + + + + <th class='column-strike Pstart-38 low-high Fz-xs filterable sortable option_column' style='color: #454545;' data-sort-column='strike' data-col-pos='0'> + <div class="cell"> + <div class="D-ib header_text strike">Strike</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + <div class="filter Cur-p "><span>&#8757;</span> Filter</div> + </th> + + + + + + <th class='column-contractName Pstart-10 '>Contract Name</th> + + + + + + + <th class='column-last Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='lastPrice' data-col-pos='2'> + <div class="cell"> + <div class="D-ib lastPrice">Last</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-bid Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='bid' data-col-pos='3'> + <div class="cell"> + <div class="D-ib bid">Bid</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-ask Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='ask' data-col-pos='4'> + <div class="cell"> + <div class="D-ib ask">Ask</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-change Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='change' data-col-pos='5'> + <div class="cell"> + <div class="D-ib change">Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-percentChange Pstart-16 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='percentChange' data-col-pos='6'> + <div class="cell"> + <div class="D-ib percentChange">%Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-volume Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='volume' data-col-pos='7'> + <div class="cell"> + <div class="D-ib volume">Volume</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-openInterest Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='openInterest' data-col-pos='8'> + <div class="cell"> + <div class="D-ib openInterest">Open Interest</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-impliedVolatility Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='impliedVolatility' data-col-pos='9'> + <div class="cell"> + <div class="D-ib impliedVolatility">Implied Volatility</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + </tr> + + <tr class="filterRangeRow D-n"> + <td colspan="10"> + <div> + <span class="filterRangeTitle"></span> + <span class="closeFilter Cur-p">&#10005;</span> + <span class="modify-filter Cur-p">[modify]</span> + </div> + </td> + </tr> + + </thead> + + <tbody> + + + + <tr data-row="0" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=55.00">55.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00055000">AAPL141024C00055000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >44.16</div> + </td> + <td> + <div class="option_entry Fz-m" >49.70</div> + </td> + <td> + <div class="option_entry Fz-m" >49.95</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="25">25</strong> + </td> + <td> + <div class="option_entry Fz-m" >0</div> + </td> + <td> + <div class="option_entry Fz-m" >568.75%</div> + </td> + </tr> + + <tr data-row="1" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00075000">AAPL141024C00075000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >27.71</div> + </td> + <td> + <div class="option_entry Fz-m" >29.75</div> + </td> + <td> + <div class="option_entry Fz-m" >29.90</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >247</div> + </td> + <td> + <div class="option_entry Fz-m" >293.75%</div> + </td> + </tr> + + <tr data-row="2" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00080000">AAPL141024C00080000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >24.95</div> + </td> + <td> + <div class="option_entry Fz-m" >24.75</div> + </td> + <td> + <div class="option_entry Fz-m" >24.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.60</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+6.85%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1">1</strong> + </td> + <td> + <div class="option_entry Fz-m" >77</div> + </td> + <td> + <div class="option_entry Fz-m" >242.97%</div> + </td> + </tr> + + <tr data-row="3" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=83.00">83.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00083000">AAPL141024C00083000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >19.84</div> + </td> + <td> + <div class="option_entry Fz-m" >21.75</div> + </td> + <td> + <div class="option_entry Fz-m" >21.90</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="1">1</strong> + </td> + <td> + <div class="option_entry Fz-m" >6</div> + </td> + <td> + <div class="option_entry Fz-m" >214.06%</div> + </td> + </tr> + + <tr data-row="4" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00085000">AAPL141024C00085000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >19.86</div> + </td> + <td> + <div class="option_entry Fz-m" >19.75</div> + </td> + <td> + <div class="option_entry Fz-m" >19.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.36</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+7.35%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="13">13</strong> + </td> + <td> + <div class="option_entry Fz-m" >233</div> + </td> + <td> + <div class="option_entry Fz-m" >194.53%</div> + </td> + </tr> + + <tr data-row="5" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=86.00">86.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00086000">AAPL141024C00086000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >16.72</div> + </td> + <td> + <div class="option_entry Fz-m" >18.75</div> + </td> + <td> + <div class="option_entry Fz-m" >18.90</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >16</div> + </td> + <td> + <div class="option_entry Fz-m" >185.16%</div> + </td> + </tr> + + <tr data-row="6" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=87.00">87.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00087000">AAPL141024C00087000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >12.50</div> + </td> + <td> + <div class="option_entry Fz-m" >17.75</div> + </td> + <td> + <div class="option_entry Fz-m" >17.90</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="20">20</strong> + </td> + <td> + <div class="option_entry Fz-m" >15</div> + </td> + <td> + <div class="option_entry Fz-m" >175.78%</div> + </td> + </tr> + + <tr data-row="7" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00088000">AAPL141024C00088000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >16.95</div> + </td> + <td> + <div class="option_entry Fz-m" >16.75</div> + </td> + <td> + <div class="option_entry Fz-m" >16.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.75</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+11.51%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >346</div> + </td> + <td> + <div class="option_entry Fz-m" >166.41%</div> + </td> + </tr> + + <tr data-row="8" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00089000">AAPL141024C00089000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >13.80</div> + </td> + <td> + <div class="option_entry Fz-m" >15.75</div> + </td> + <td> + <div class="option_entry Fz-m" >15.90</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >314</div> + </td> + <td> + <div class="option_entry Fz-m" >157.42%</div> + </td> + </tr> + + <tr data-row="9" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00090000">AAPL141024C00090000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >14.80</div> + </td> + <td> + <div class="option_entry Fz-m" >14.75</div> + </td> + <td> + <div class="option_entry Fz-m" >14.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.38</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+10.28%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="198">198</strong> + </td> + <td> + <div class="option_entry Fz-m" >617</div> + </td> + <td> + <div class="option_entry Fz-m" >148.44%</div> + </td> + </tr> + + <tr data-row="10" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00091000">AAPL141024C00091000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >13.60</div> + </td> + <td> + <div class="option_entry Fz-m" >13.75</div> + </td> + <td> + <div class="option_entry Fz-m" >13.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.20</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+9.68%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="8">8</strong> + </td> + <td> + <div class="option_entry Fz-m" >385</div> + </td> + <td> + <div class="option_entry Fz-m" >139.06%</div> + </td> + </tr> + + <tr data-row="11" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00092000">AAPL141024C00092000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >12.95</div> + </td> + <td> + <div class="option_entry Fz-m" >12.75</div> + </td> + <td> + <div class="option_entry Fz-m" >12.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.47</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+12.80%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="3">3</strong> + </td> + <td> + <div class="option_entry Fz-m" >1036</div> + </td> + <td> + <div class="option_entry Fz-m" >129.69%</div> + </td> + </tr> + + <tr data-row="12" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00093000">AAPL141024C00093000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >12.00</div> + </td> + <td> + <div class="option_entry Fz-m" >11.75</div> + </td> + <td> + <div class="option_entry Fz-m" >11.90</div> + </td> + <td> + <div class="option_entry Fz-m" >2.00</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+20.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="22">22</strong> + </td> + <td> + <div class="option_entry Fz-m" >613</div> + </td> + <td> + <div class="option_entry Fz-m" >120.70%</div> + </td> + </tr> + + <tr data-row="13" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00094000">AAPL141024C00094000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >10.80</div> + </td> + <td> + <div class="option_entry Fz-m" >10.75</div> + </td> + <td> + <div class="option_entry Fz-m" >10.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.20</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+12.50%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="19">19</strong> + </td> + <td> + <div class="option_entry Fz-m" >1726</div> + </td> + <td> + <div class="option_entry Fz-m" >111.72%</div> + </td> + </tr> + + <tr data-row="14" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00095000">AAPL141024C00095000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >9.85</div> + </td> + <td> + <div class="option_entry Fz-m" >9.75</div> + </td> + <td> + <div class="option_entry Fz-m" >9.85</div> + </td> + <td> + <div class="option_entry Fz-m" >1.34</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+15.75%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="3799">3799</strong> + </td> + <td> + <div class="option_entry Fz-m" >14099</div> + </td> + <td> + <div class="option_entry Fz-m" >84.38%</div> + </td> + </tr> + + <tr data-row="15" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00096000">AAPL141024C00096000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >8.80</div> + </td> + <td> + <div class="option_entry Fz-m" >8.75</div> + </td> + <td> + <div class="option_entry Fz-m" >8.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.45</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+19.73%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="374">374</strong> + </td> + <td> + <div class="option_entry Fz-m" >8173</div> + </td> + <td> + <div class="option_entry Fz-m" >93.36%</div> + </td> + </tr> + + <tr data-row="16" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00097000">AAPL141024C00097000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >7.85</div> + </td> + <td> + <div class="option_entry Fz-m" >7.75</div> + </td> + <td> + <div class="option_entry Fz-m" >7.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.66</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+26.82%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="5094">5094</strong> + </td> + <td> + <div class="option_entry Fz-m" >21122</div> + </td> + <td> + <div class="option_entry Fz-m" >84.38%</div> + </td> + </tr> + + <tr data-row="17" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00098000">AAPL141024C00098000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >6.80</div> + </td> + <td> + <div class="option_entry Fz-m" >6.75</div> + </td> + <td> + <div class="option_entry Fz-m" >6.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.81</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+36.27%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1039">1039</strong> + </td> + <td> + <div class="option_entry Fz-m" >18540</div> + </td> + <td> + <div class="option_entry Fz-m" >75.00%</div> + </td> + </tr> + + <tr data-row="18" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00099000">AAPL141024C00099000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >5.75</div> + </td> + <td> + <div class="option_entry Fz-m" >5.75</div> + </td> + <td> + <div class="option_entry Fz-m" >5.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.70</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+41.98%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2602">2602</strong> + </td> + <td> + <div class="option_entry Fz-m" >15608</div> + </td> + <td> + <div class="option_entry Fz-m" >65.63%</div> + </td> + </tr> + + <tr data-row="19" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00100000">AAPL141024C00100000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >4.85</div> + </td> + <td> + <div class="option_entry Fz-m" >4.80</div> + </td> + <td> + <div class="option_entry Fz-m" >4.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.85</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+61.67%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="8867">8867</strong> + </td> + <td> + <div class="option_entry Fz-m" >31290</div> + </td> + <td> + <div class="option_entry Fz-m" >56.25%</div> + </td> + </tr> + + <tr data-row="20" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00101000">AAPL141024C00101000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >3.70</div> + </td> + <td> + <div class="option_entry Fz-m" >3.80</div> + </td> + <td> + <div class="option_entry Fz-m" >3.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.58</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+74.53%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="5232">5232</strong> + </td> + <td> + <div class="option_entry Fz-m" >19255</div> + </td> + <td> + <div class="option_entry Fz-m" >46.68%</div> + </td> + </tr> + + <tr data-row="21" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00102000">AAPL141024C00102000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >2.85</div> + </td> + <td> + <div class="option_entry Fz-m" >2.84</div> + </td> + <td> + <div class="option_entry Fz-m" >2.91</div> + </td> + <td> + <div class="option_entry Fz-m" >1.52</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+114.29%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="11311">11311</strong> + </td> + <td> + <div class="option_entry Fz-m" >32820</div> + </td> + <td> + <div class="option_entry Fz-m" >38.09%</div> + </td> + </tr> + + <tr data-row="22" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00103000">AAPL141024C00103000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.88</div> + </td> + <td> + <div class="option_entry Fz-m" >1.87</div> + </td> + <td> + <div class="option_entry Fz-m" >1.90</div> + </td> + <td> + <div class="option_entry Fz-m" >1.21</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+180.60%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="26745">26745</strong> + </td> + <td> + <div class="option_entry Fz-m" >40149</div> + </td> + <td> + <div class="option_entry Fz-m" >26.56%</div> + </td> + </tr> + + <tr data-row="23" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00104000">AAPL141024C00104000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.05</div> + </td> + <td> + <div class="option_entry Fz-m" >1.00</div> + </td> + <td> + <div class="option_entry Fz-m" >1.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.77</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+275.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="38966">38966</strong> + </td> + <td> + <div class="option_entry Fz-m" >38899</div> + </td> + <td> + <div class="option_entry Fz-m" >23.44%</div> + </td> + </tr> + + <tr data-row="24" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00105000">AAPL141024C00105000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.40</div> + </td> + <td> + <div class="option_entry Fz-m" >0.38</div> + </td> + <td> + <div class="option_entry Fz-m" >0.40</div> + </td> + <td> + <div class="option_entry Fz-m" >0.30</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+300.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="66026">66026</strong> + </td> + <td> + <div class="option_entry Fz-m" >42521</div> + </td> + <td> + <div class="option_entry Fz-m" >21.88%</div> + </td> + </tr> + + <tr data-row="25" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00106000">AAPL141024C00106000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.12</div> + </td> + <td> + <div class="option_entry Fz-m" >0.11</div> + </td> + <td> + <div class="option_entry Fz-m" >0.12</div> + </td> + <td> + <div class="option_entry Fz-m" >0.08</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+200.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="54624">54624</strong> + </td> + <td> + <div class="option_entry Fz-m" >22789</div> + </td> + <td> + <div class="option_entry Fz-m" >22.85%</div> + </td> + </tr> + + <tr data-row="26" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00107000">AAPL141024C00107000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+100.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="15310">15310</strong> + </td> + <td> + <div class="option_entry Fz-m" >60738</div> + </td> + <td> + <div class="option_entry Fz-m" >25.78%</div> + </td> + </tr> + + <tr data-row="27" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00108000">AAPL141024C00108000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+100.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="10333">10333</strong> + </td> + <td> + <div class="option_entry Fz-m" >20808</div> + </td> + <td> + <div class="option_entry Fz-m" >32.81%</div> + </td> + </tr> + + <tr data-row="28" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00109000">AAPL141024C00109000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+100.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="343">343</strong> + </td> + <td> + <div class="option_entry Fz-m" >8606</div> + </td> + <td> + <div class="option_entry Fz-m" >40.63%</div> + </td> + </tr> + + <tr data-row="29" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00110000">AAPL141024C00110000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+100.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1151">1151</strong> + </td> + <td> + <div class="option_entry Fz-m" >32265</div> + </td> + <td> + <div class="option_entry Fz-m" >45.31%</div> + </td> + </tr> + + <tr data-row="30" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00111000">AAPL141024C00111000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="14">14</strong> + </td> + <td> + <div class="option_entry Fz-m" >4228</div> + </td> + <td> + <div class="option_entry Fz-m" >47.66%</div> + </td> + </tr> + + <tr data-row="31" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=112.00">112.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00112000">AAPL141024C00112000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="22">22</strong> + </td> + <td> + <div class="option_entry Fz-m" >3281</div> + </td> + <td> + <div class="option_entry Fz-m" >54.69%</div> + </td> + </tr> + + <tr data-row="32" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=113.00">113.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00113000">AAPL141024C00113000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="13">13</strong> + </td> + <td> + <div class="option_entry Fz-m" >1734</div> + </td> + <td> + <div class="option_entry Fz-m" >56.25%</div> + </td> + </tr> + + <tr data-row="33" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=114.00">114.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00114000">AAPL141024C00114000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="20">20</strong> + </td> + <td> + <div class="option_entry Fz-m" >1306</div> + </td> + <td> + <div class="option_entry Fz-m" >62.50%</div> + </td> + </tr> + + <tr data-row="34" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00115000">AAPL141024C00115000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="16">16</strong> + </td> + <td> + <div class="option_entry Fz-m" >1968</div> + </td> + <td> + <div class="option_entry Fz-m" >65.63%</div> + </td> + </tr> + + <tr data-row="35" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=116.00">116.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00116000">AAPL141024C00116000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >733</div> + </td> + <td> + <div class="option_entry Fz-m" >71.88%</div> + </td> + </tr> + + <tr data-row="36" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=117.00">117.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00117000">AAPL141024C00117000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="127">127</strong> + </td> + <td> + <div class="option_entry Fz-m" >183</div> + </td> + <td> + <div class="option_entry Fz-m" >78.13%</div> + </td> + </tr> + + <tr data-row="37" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=118.00">118.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00118000">AAPL141024C00118000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="4">4</strong> + </td> + <td> + <div class="option_entry Fz-m" >203</div> + </td> + <td> + <div class="option_entry Fz-m" >84.38%</div> + </td> + </tr> + + <tr data-row="38" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=119.00">119.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00119000">AAPL141024C00119000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="215">215</strong> + </td> + <td> + <div class="option_entry Fz-m" >225</div> + </td> + <td> + <div class="option_entry Fz-m" >87.50%</div> + </td> + </tr> + + <tr data-row="39" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=120.00">120.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00120000">AAPL141024C00120000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="526">526</strong> + </td> + <td> + <div class="option_entry Fz-m" >523</div> + </td> + <td> + <div class="option_entry Fz-m" >93.75%</div> + </td> + </tr> + + <tr data-row="40" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=122.00">122.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00122000">AAPL141024C00122000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="0">0</strong> + </td> + <td> + <div class="option_entry Fz-m" >5</div> + </td> + <td> + <div class="option_entry Fz-m" >103.13%</div> + </td> + </tr> + + <tr data-row="41" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=130.00">130.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024C00130000">AAPL141024C00130000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="1">1</strong> + </td> + <td> + <div class="option_entry Fz-m" >1</div> + </td> + <td> + <div class="option_entry Fz-m" >143.75%</div> + </td> + </tr> + + + + + + + + </tbody> + </table> + </div> +</div> + + + </div> + + <div class="options-table " id="optionsPutsTable" data-sec="options-puts-table"> + <div class="strike-filter option-filter-overlay"> + <p>Show Me Strikes From</p> + <div class="My-6"> + $ <input class="filter-low strike-filter" data-filter-type="low" type="text"> + to $ <input class="filter-high strike-filter" data-filter-type="high" type="text"> + </div> + <a data-table-filter="optionsPuts" class="Cur-p apply-filter">Apply Filter</a> + <a class="Cur-p clear-filter">Clear Filter</a> +</div> + + +<div class="follow-quote-area"> + <div class="quote-table-overflow"> + <table class="details-table quote-table Fz-m"> + + + <caption> + Puts + </caption> + + + <thead class="details-header quote-table-headers"> + <tr> + + + + + <th class='column-strike Pstart-38 low-high Fz-xs filterable sortable option_column' style='color: #454545;' data-sort-column='strike' data-col-pos='0'> + <div class="cell"> + <div class="D-ib header_text strike">Strike</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + <div class="filter Cur-p "><span>&#8757;</span> Filter</div> + </th> + + + + + + <th class='column-contractName Pstart-10 '>Contract Name</th> + + + + + + + <th class='column-last Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='lastPrice' data-col-pos='2'> + <div class="cell"> + <div class="D-ib lastPrice">Last</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-bid Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='bid' data-col-pos='3'> + <div class="cell"> + <div class="D-ib bid">Bid</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-ask Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='ask' data-col-pos='4'> + <div class="cell"> + <div class="D-ib ask">Ask</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-change Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='change' data-col-pos='5'> + <div class="cell"> + <div class="D-ib change">Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-percentChange Pstart-16 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='percentChange' data-col-pos='6'> + <div class="cell"> + <div class="D-ib percentChange">%Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-volume Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='volume' data-col-pos='7'> + <div class="cell"> + <div class="D-ib volume">Volume</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-openInterest Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='openInterest' data-col-pos='8'> + <div class="cell"> + <div class="D-ib openInterest">Open Interest</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-impliedVolatility Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='impliedVolatility' data-col-pos='9'> + <div class="cell"> + <div class="D-ib impliedVolatility">Implied Volatility</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + </tr> + + <tr class="filterRangeRow D-n"> + <td colspan="10"> + <div> + <span class="filterRangeTitle"></span> + <span class="closeFilter Cur-p">&#10005;</span> + <span class="modify-filter Cur-p">[modify]</span> + </div> + </td> + </tr> + + </thead> + + <tbody> + + + <tr data-row="0" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=55.00">55.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00055000">AAPL141024P00055000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >2</div> + </td> + <td> + <div class="option_entry Fz-m" >400.00%</div> + </td> + </tr> + + <tr data-row="1" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=60.00">60.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00060000">AAPL141024P00060000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="161">161</strong> + </td> + <td> + <div class="option_entry Fz-m" >162</div> + </td> + <td> + <div class="option_entry Fz-m" >350.00%</div> + </td> + </tr> + + <tr data-row="2" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=70.00">70.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00070000">AAPL141024P00070000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="101">101</strong> + </td> + <td> + <div class="option_entry Fz-m" >104</div> + </td> + <td> + <div class="option_entry Fz-m" >262.50%</div> + </td> + </tr> + + <tr data-row="3" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00075000">AAPL141024P00075000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >1670</div> + </td> + <td> + <div class="option_entry Fz-m" >218.75%</div> + </td> + </tr> + + <tr data-row="4" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00080000">AAPL141024P00080000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="1200">1200</strong> + </td> + <td> + <div class="option_entry Fz-m" >1754</div> + </td> + <td> + <div class="option_entry Fz-m" >181.25%</div> + </td> + </tr> + + <tr data-row="5" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=83.00">83.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00083000">AAPL141024P00083000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="34">34</strong> + </td> + <td> + <div class="option_entry Fz-m" >4148</div> + </td> + <td> + <div class="option_entry Fz-m" >156.25%</div> + </td> + </tr> + + <tr data-row="6" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=84.00">84.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00084000">AAPL141024P00084000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="1325">1325</strong> + </td> + <td> + <div class="option_entry Fz-m" >2296</div> + </td> + <td> + <div class="option_entry Fz-m" >150.00%</div> + </td> + </tr> + + <tr data-row="7" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00085000">AAPL141024P00085000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >7442</div> + </td> + <td> + <div class="option_entry Fz-m" >143.75%</div> + </td> + </tr> + + <tr data-row="8" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=86.00">86.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00086000">AAPL141024P00086000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="32">32</strong> + </td> + <td> + <div class="option_entry Fz-m" >1782</div> + </td> + <td> + <div class="option_entry Fz-m" >134.38%</div> + </td> + </tr> + + <tr data-row="9" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=87.00">87.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00087000">AAPL141024P00087000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="20">20</strong> + </td> + <td> + <div class="option_entry Fz-m" >3490</div> + </td> + <td> + <div class="option_entry Fz-m" >125.00%</div> + </td> + </tr> + + <tr data-row="10" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00088000">AAPL141024P00088000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="36">36</strong> + </td> + <td> + <div class="option_entry Fz-m" >5516</div> + </td> + <td> + <div class="option_entry Fz-m" >118.75%</div> + </td> + </tr> + + <tr data-row="11" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00089000">AAPL141024P00089000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="20">20</strong> + </td> + <td> + <div class="option_entry Fz-m" >3694</div> + </td> + <td> + <div class="option_entry Fz-m" >112.50%</div> + </td> + </tr> + + <tr data-row="12" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00090000">AAPL141024P00090000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+100.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="149">149</strong> + </td> + <td> + <div class="option_entry Fz-m" >13444</div> + </td> + <td> + <div class="option_entry Fz-m" >106.25%</div> + </td> + </tr> + + <tr data-row="13" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00091000">AAPL141024P00091000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="402">402</strong> + </td> + <td> + <div class="option_entry Fz-m" >7670</div> + </td> + <td> + <div class="option_entry Fz-m" >98.44%</div> + </td> + </tr> + + <tr data-row="14" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00092000">AAPL141024P00092000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="81">81</strong> + </td> + <td> + <div class="option_entry Fz-m" >12728</div> + </td> + <td> + <div class="option_entry Fz-m" >93.75%</div> + </td> + </tr> + + <tr data-row="15" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00093000">AAPL141024P00093000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="409">409</strong> + </td> + <td> + <div class="option_entry Fz-m" >15985</div> + </td> + <td> + <div class="option_entry Fz-m" >84.38%</div> + </td> + </tr> + + <tr data-row="16" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00094000">AAPL141024P00094000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="673">673</strong> + </td> + <td> + <div class="option_entry Fz-m" >17398</div> + </td> + <td> + <div class="option_entry Fz-m" >78.13%</div> + </td> + </tr> + + <tr data-row="17" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00095000">AAPL141024P00095000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="480">480</strong> + </td> + <td> + <div class="option_entry Fz-m" >22751</div> + </td> + <td> + <div class="option_entry Fz-m" >71.88%</div> + </td> + </tr> + + <tr data-row="18" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00096000">AAPL141024P00096000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="818">818</strong> + </td> + <td> + <div class="option_entry Fz-m" >18293</div> + </td> + <td> + <div class="option_entry Fz-m" >65.63%</div> + </td> + </tr> + + <tr data-row="19" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00097000">AAPL141024P00097000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-66.67%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1534">1534</strong> + </td> + <td> + <div class="option_entry Fz-m" >17302</div> + </td> + <td> + <div class="option_entry Fz-m" >57.81%</div> + </td> + </tr> + + <tr data-row="20" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00098000">AAPL141024P00098000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-66.67%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1945">1945</strong> + </td> + <td> + <div class="option_entry Fz-m" >26469</div> + </td> + <td> + <div class="option_entry Fz-m" >54.69%</div> + </td> + </tr> + + <tr data-row="21" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00099000">AAPL141024P00099000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-50.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="3821">3821</strong> + </td> + <td> + <div class="option_entry Fz-m" >21769</div> + </td> + <td> + <div class="option_entry Fz-m" >50.78%</div> + </td> + </tr> + + <tr data-row="22" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00100000">AAPL141024P00100000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.04</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-57.14%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="4979">4979</strong> + </td> + <td> + <div class="option_entry Fz-m" >21891</div> + </td> + <td> + <div class="option_entry Fz-m" >44.53%</div> + </td> + </tr> + + <tr data-row="23" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00101000">AAPL141024P00101000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.12</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-80.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="10032">10032</strong> + </td> + <td> + <div class="option_entry Fz-m" >15354</div> + </td> + <td> + <div class="option_entry Fz-m" >39.45%</div> + </td> + </tr> + + <tr data-row="24" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00102000">AAPL141024P00102000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.30</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-90.91%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="12599">12599</strong> + </td> + <td> + <div class="option_entry Fz-m" >15053</div> + </td> + <td> + <div class="option_entry Fz-m" >32.42%</div> + </td> + </tr> + + <tr data-row="25" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00103000">AAPL141024P00103000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.07</div> + </td> + <td> + <div class="option_entry Fz-m" >0.07</div> + </td> + <td> + <div class="option_entry Fz-m" >0.08</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.64</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-90.14%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="21356">21356</strong> + </td> + <td> + <div class="option_entry Fz-m" >10473</div> + </td> + <td> + <div class="option_entry Fz-m" >27.54%</div> + </td> + </tr> + + <tr data-row="26" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00104000">AAPL141024P00104000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.18</div> + </td> + <td> + <div class="option_entry Fz-m" >0.18</div> + </td> + <td> + <div class="option_entry Fz-m" >0.19</div> + </td> + <td> + <div class="option_entry Fz-m" >-1.09</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-85.83%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="50078">50078</strong> + </td> + <td> + <div class="option_entry Fz-m" >4619</div> + </td> + <td> + <div class="option_entry Fz-m" >22.85%</div> + </td> + </tr> + + <tr data-row="27" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00105000">AAPL141024P00105000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.56</div> + </td> + <td> + <div class="option_entry Fz-m" >0.55</div> + </td> + <td> + <div class="option_entry Fz-m" >0.58</div> + </td> + <td> + <div class="option_entry Fz-m" >-1.46</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-72.28%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="25194">25194</strong> + </td> + <td> + <div class="option_entry Fz-m" >1483</div> + </td> + <td> + <div class="option_entry Fz-m" >22.36%</div> + </td> + </tr> + + <tr data-row="28" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00106000">AAPL141024P00106000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.31</div> + </td> + <td> + <div class="option_entry Fz-m" >1.26</div> + </td> + <td> + <div class="option_entry Fz-m" >1.31</div> + </td> + <td> + <div class="option_entry Fz-m" >-1.54</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-54.04%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2558">2558</strong> + </td> + <td> + <div class="option_entry Fz-m" >709</div> + </td> + <td> + <div class="option_entry Fz-m" >24.22%</div> + </td> + </tr> + + <tr data-row="29" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> + </td> + <td> + <div 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+ <div class="option_entry Fz-m" >3.15</div> + </td> + <td> + <div class="option_entry Fz-m" >3.25</div> + </td> + <td> + <div class="option_entry Fz-m" >-1.55</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-32.98%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="84">84</strong> + </td> + <td> + <div class="option_entry Fz-m" >445</div> + </td> + <td> + <div class="option_entry Fz-m" >40.23%</div> + </td> + </tr> + + <tr data-row="31" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00109000">AAPL141024P00109000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >4.25</div> + </td> + <td> + <div class="option_entry Fz-m" >4.10</div> + </td> + <td> + <div class="option_entry Fz-m" >4.25</div> + </td> + <td> + <div class="option_entry Fz-m" >-2.35</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-35.61%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="41">41</strong> + </td> + <td> + <div class="option_entry Fz-m" >62</div> + </td> + <td> + <div class="option_entry Fz-m" >49.61%</div> + </td> + </tr> + + <tr data-row="32" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00110000">AAPL141024P00110000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >5.10</div> + </td> + <td> + <div class="option_entry Fz-m" >5.10</div> + </td> + <td> + <div class="option_entry Fz-m" >5.25</div> + </td> + <td> + <div class="option_entry Fz-m" >-2.50</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-32.89%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="258">258</strong> + </td> + 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class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=112.00">112.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00112000">AAPL141024P00112000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >7.05</div> + </td> + <td> + <div class="option_entry Fz-m" >7.10</div> + </td> + <td> + <div class="option_entry Fz-m" >7.25</div> + </td> + <td> + <div class="option_entry Fz-m" >-4.65</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-39.74%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >83</div> + </td> + <td> + <div class="option_entry Fz-m" >50.00%</div> + </td> + </tr> + + <tr data-row="35" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=113.00">113.00</a></strong> + </td> + <td> + <div 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<div class="option_entry Fz-m" >11.10</div> + </td> + <td> + <div class="option_entry Fz-m" >11.25</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="57">57</strong> + </td> + <td> + <div class="option_entry Fz-m" >0</div> + </td> + <td> + <div class="option_entry Fz-m" >71.88%</div> + </td> + </tr> + + <tr data-row="37" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=118.00">118.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141024P00118000">AAPL141024P00118000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >15.60</div> + </td> + <td> + <div class="option_entry Fz-m" >13.10</div> + </td> + <td> + <div class="option_entry Fz-m" >13.25</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div 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+ + if (!route) { return ''; } + + path = route.path; + keys = route.keys; + + if (context && (len = keys.length)) { + for (i = 0; i < len; i += 1) { + key = keys[i]; + param = key.name || key; + regex = new RegExp('[:*]' + param + '\\b'); + path = path.replace(regex, context[param]); + } + } + + // Replace missing params with empty strings. + return path.replace(/([:*])([\w\-]+)?/g, ''); + }; +root.App || (root.App = {}); +root.App.Cache || (root.App.Cache = {}); +root.App.Cache.globals = {"config":{"hosts":{"_default":"finance.yahoo.com","production":"finance.yahoo.com","staging":"stage.finance.yahoo.com","functional.test":"qa1.finance.yahoo.com","smoke.test":"int1.finance.yahoo.com","development":"int1.finance.yahoo.com"},"dss":{"assetPath":"\u002Fpv\u002Fstatic\u002Flib\u002Fios-default-set_201312031214.js","pn":"yahoo_finance_us_web","secureAssetHost":"https:\u002F\u002Fs.yimg.com","assetHost":"http:\u002F\u002Fl.yimg.com","cookieName":"DSS"},"mrs":{"mrs_host":"mrs-ynews.mrs.o.yimg.com","key":"mrs.ynews.crumbkey","app_id":"ynews"},"title":"Yahoo Finance - Business Finance, Stock Market, Quotes, News","crumbKey":"touchdown.crumbkey","asset_combo":true,"asset_mode":"prod","asset_filter":"min","assets":{"js":[{"location":"bottom","value":"https:\u002F\u002Fs.yimg.com\u002Fos\u002Fmit\u002Fmedia\u002Fm\u002Fheader\u002Fheader-uh3-finance-hardcoded-jsonblob-min-1583812.js"}],"css":["css.master",{"location":"top","value":"https:\u002F\u002Fs.yimg.com\u002Fos\u002Fmit\u002Fmedia\u002Fm\u002Fquotes\u002Fquotes-search-gs-smartphone-min-1680382.css"}],"options":{"inc_init_bottom":"0","inc_rapid":"1","rapid_version":"3.21","yui_instance_location":"bottom"}},"cdn":{"comboBase":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?","comboSep":"&","prefixMap":{"http:\u002F\u002Fl.yimg.com\u002F":""},"base":"https:\u002F\u002Fs.yimg.com"},"prefix_map":{"http:\u002F\u002Fl.yimg.com\u002F":""},"xhrPath":"_td_charts_api","adsEnabled":true,"ads":{"position":{"LREC":{"w":"300","h":"265"},"FB2-1":{"w":"198","h":"60"},"FB2-2":{"w":"198","h":"60"},"FB2-3":{"w":"198","h":"60"},"FB2-4":{"w":"198","h":"60"},"LDRP":{"w":"320","h":"76","metaSize":true},"WBTN":{"w":"120","h":"60"},"WBTN-1":{"w":"120","h":"60"},"FB2-0":{"w":"120","h":"60"},"SKY":{"w":"160","h":"600"}}},"spaceid":"2022773886","urlSpaceId":"true","urlSpaceIdMap":{"quotes":"980779717","q\u002Fop":"28951412","q":"980779724"},"rapidSettings":{"webworker_file":"\u002Frapid-worker.js","client_only":1,"keys":{"version":"td app","site":"mobile-web-quotes"},"ywa":{"project_id":"1000911397279","document_group":"interactive-chart","host":"y.analytics.yahoo.com"},"ywaMappingAction":{"click":12,"hvr":115,"rottn":128,"drag":105},"ywaMappingCf":{"_p":20,"ad":58,"authfb":11,"bpos":24,"camp":54,"cat":25,"code":55,"cpos":21,"ct":23,"dcl":26,"dir":108,"domContentLoadedEventEnd":44,"elm":56,"elmt":57,"f":40,"ft":51,"grpt":109,"ilc":39,"itc":111,"loadEventEnd":45,"ltxt":17,"mpos":110,"mrkt":12,"pcp":67,"pct":48,"pd":46,"pkgt":22,"pos":20,"prov":114,"psp":72,"pst":68,"pstcat":47,"pt":13,"rescode":27,"responseEnd":43,"responseStart":41,"rspns":107,"sca":53,"sec":18,"site":42,"slk":19,"sort":28,"t1":121,"t2":122,"t3":123,"t4":124,"t5":125,"t6":126,"t7":127,"t8":128,"t9":129,"tar":113,"test":14,"v":52,"ver":49,"x":50},"tracked_mods":["yfi_investing_nav","chart-details"],"nofollow_class":[]},"property":"finance","uh":{"experience":"GS"},"loginRedirectHost":"finance.yahoo.com","default_ticker":"YHOO","default_market_tickers":["^DJI","^IXIC"],"uhAssetsBase":"https:\u002F\u002Fs.yimg.com","sslEnabled":true,"layout":"options","packageName":"finance-td-app-mobile-web","customActions":{"before":[function (req, res, data, callback) { + var header, + config = req.config(), + path = req.path; + + if (req.i13n && req.i13n.stampNonClassified) { + //console.log('=====> [universal_header] page stamped: ' + req.i13n.isStamped() + ' with spaceid ' + req.i13n.getSpaceid()); + req.i13n.stampNonClassified(config.spaceid); + } + config.uh = config.uh || {}; + config.uh.experience = config.uh.experience || 'uh3'; + + req.query.experience = config.uh.experience; + req.query.property = 'finance'; + header = finUH.getMarkup(req); + + res.locals = res.locals || {}; + + if (header.sidebar) { + res.locals.sidebar_css = header.sidebar.uh_css; + res.locals.sidebar_js = header.sidebar.uh_js; + data.sidebar_markup = header.sidebar.uh_markup; + } + + res.locals.uh_css = header.uh_css; + res.locals.uh_js = header.uh_js; + data.uh_markup = header.uh_markup; + //TODO - localize these strings + if (path && path.indexOf('op') > -1) { + res.locals.page_title = parseSymbol(req.query.s) + " Options | Yahoo! Inc. Stock - Yahoo! Finance"; + } else if (path && ((path.indexOf('echarts') > -1) || (path.indexOf('q') > -1))) { + res.locals.page_title = parseSymbol(req.query.s) + " Interactive Chart | Yahoo! Inc. Stock - Yahoo! Finance"; + } else { + res.locals.page_title = config.title; + } + callback(); +},function (req, res, data, next) { + /* this would invoke the ESI plugin on YTS */ + res.parentRes.set('X-Esi', '1'); + + var hosts = req.config().hosts, + hostToSet = hosts._default; + + Object.keys(hosts).some(function (host) { + if (req.headers.host.indexOf(host) >= 0) { + hostToSet = hosts[host]; + return true; + } + }); + + /* saving request host server name for esi end point */ + res.locals.requesturl = { + host: hostToSet + }; + + /* saving header x-yahoo-request-url for Darla configuration */ + res.locals.requestxhosturl = req.headers['x-env-host'] ? {host: req.headers['x-env-host']} : {host: hostToSet}; + + //urlPath is used for ./node_modules/assembler/node_modules/dust-helpers/lib/util.js::getSpaceId() + //see: https://git.corp.yahoo.com/sports/sportacular-web + req.context.urlPath = req.path; + + // console.log(JSON.stringify({ + // requesturl: res.locals.requesturl.host, + // requestxhosturl: res.locals.requestxhosturl, + // urlPath: req.context.urlPath + // })); + + next(); +},function (req, res, data, callback) { + + res.locals = res.locals || {}; + if (req.query && req.query.s) { + res.locals.quote = req.query.s; + } + + callback(); +},function (req, res, data, callback) { + var params, + ticker, + config, i; + + req = req || {}; + req.params = req.params || {}; + + config = req.config() || {}; + + + data = data || {}; + + params = req.params || {}; + ticker = (params.ticker || (req.query && req.query.s) || 'YHOO').toUpperCase(); + ticker = ticker.split('+')[0];//Split on + if it's in the ticker + ticker = ticker.split(' ')[0];//Split on space if it's in the ticker + + params.tickers = []; + if (config.default_market_tickers) { + params.tickers = params.tickers.concat(config.default_market_tickers); + } + params.tickers.push(ticker); + params.tickers = params.tickers.join(','); + params.format = 'inflated'; + + //Move this into a new action + res.locals.isTablet = config.isTablet; + + quoteStore.read('finance_quote', params, req, function (err, qData) { + if (!err && qData.quotes && qData.quotes.length > 0) { + res.locals.quoteData = qData; + for (i = 0; i < qData.quotes.length; i = i + 1) { + if (qData.quotes[i].symbol.toUpperCase() === ticker.toUpperCase()) { + params.ticker_securityType = qData.quotes[i].type; + } + } + params.tickers = ticker; + } + callback(); + }); +},function (req, res, data, callback) { + + marketTimeStore.read('markettime', {}, req, function (err, data) { + if (data && data.index) { + res.parentRes.locals.markettime = data.index.markettime; + } + callback(); + }); +}],"after":[]}},"context":{"authed":"0","ynet":"0","ssl":"1","spdy":"0","bucket":"gs513","colo":"gq1","device":"desktop","environment":"prod","lang":"en-US","partner":"none","site":"finance","region":"US","intl":"us","tz":"America\u002FLos_Angeles","edgepipeEnabled":false,"urlPath":"\u002Fq\u002Fop"},"intl":{"locales":"en-US"},"user":{"crumb":"Mo4ghtv8vTn"}}; +root.YUI_config = {"version":"3.17.2","base":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?yui:3.17.2\u002F","comboBase":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?","comboSep":"&","root":"yui:3.17.2\u002F","filter":"min","logLevel":"error","combine":true,"patches":[function patchLangBundlesRequires(Y, loader) { + var getRequires = loader.getRequires; + loader.getRequires = function (mod) { + var i, j, m, name, mods, loadDefaultBundle, + locales = Y.config.lang || [], + r = getRequires.apply(this, arguments); + // expanding requirements with optional requires + if (mod.langBundles && !mod.langBundlesExpanded) { + mod.langBundlesExpanded = []; + locales = typeof locales === 'string' ? 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- document.activeElement = e.target; - } - } - // Handle !IE browsers that do not support the .activeElement property - if(!document.activeElement){ - if (document.addEventListener){ - document.addEventListener("focus", focusHandler, true); - } - } - })(document); - - </script><div class="y-nav-legobg"><div class="yfi_doc"><div id="y-nav-sec" class="clear"><div id="searchQuotes" class="ticker-search mod" mode="search"><div class="hd"></div><div class="bd"><form id="quote" name="quote" action="/q"><h2 class="yfi_signpost">Search for share prices</h2><label for="txtQuotes">Search for share prices</label><input id="txtQuotes" name="s" value="" type="text" autocomplete="off" placeholder="Enter Symbol"><input id="get_quote_logic_opt" name="ql" value="1" type="hidden" autocomplete="off"><div id="yfi_quotes_submit"><span><span><span><input type="submit" value="Look Up" id="btnQuotes" class="rapid-nf"></span></span></span></div></form></div><div class="ft"><a href="http://finance.search.yahoo.com?fr=fin-v1">Finance Search</a><p><span id="yfs_market_time">Tue, May 13, 2014, 9:14PM EDT - U.S. Markets closed</span></p></div></div></div></div></div></div><div id="screen"><span id="yfs_ad_" ></span><style type="text/css"> - .yfi-price-change-up{ - color:#008800 - } - - .yfi-price-change-down{ - color:#cc0000 - } - </style><div id="marketindices">&nbsp;<a href="/q?s=%5EDJI">Dow</a>&nbsp;<span id="yfs_pp0_^dji"><img width="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>&nbsp;<a href="/q?s=%5EIXIC">Nasdaq</a>&nbsp;<span id="yfs_pp0_^ixic"><img width="10" height="14" style="margin-right:-2px;" border="0" 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></div><div id="companynav"><table cellpadding="0" cellspacing="0" border="0"><tr><td height="5"></td></tr></table></div><div id="leftcol"><div id="yfi_investing_nav"><div class="hd"><h2>More On AAPL</h2></div><div class="bd"><h3>Quotes</h3><ul><li><a href="/q?s=AAPL">Summary</a></li><li><a href="/q/ecn?s=AAPL+Order+Book">Order Book</a></li><li class="selected">Options</li><li><a href="/q/hp?s=AAPL+Historical+Prices">Historical Prices</a></li></ul><h3>Charts</h3><ul><li><a href="/echarts?s=AAPL+Interactive#symbol=AAPL;range=">Interactive</a></li><li><a href="/q/bc?s=AAPL+Basic+Chart">Basic Chart</a></li><li><a href="/q/ta?s=AAPL+Basic+Tech.+Analysis">Basic Tech. 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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 --> + + .app_promo.after_hours, .app_promo.pre_market { + top: 8px; + } + </style> + <div class="rtq_leaf"> + <div class="rtq_div"> + <div class="yui-g quote_summary"> + <div class="yfi_rt_quote_summary" id="yfi_rt_quote_summary"> + <div class="hd"> + <div class="title Fz-xl"> + <h2 class="symbol-name">Apple Inc. (AAPL)</h2> + <span class="wl_sign Invisible"><button class="follow-quote follow-quote-follow follow-quote-always-visible D-ib Bd-0 O-0 Cur-p Sprite P-0 M-0 Fz-s" data-flw-quote="AAPL"><i class="Icon">&#xe023;</i></button> <span class="follow-quote-txt Fz-m" data-flw-quote="AAPL"> + Watchlist + </span></span> + </div> + </div> + <div class="yfi_rt_quote_summary_rt_top sigfig_promo_1"> + <div> + <span class="time_rtq_ticker Fz-30 Fw-b"> + <span id="yfs_l84_AAPL" data-sq="AAPL:value">104.8999</span> + </span> + + + + <span class="down_r time_rtq_content Fz-2xl Fw-b"><span id="yfs_c63_AAPL"><img width="10" height="14" border="0" style="margin-right:-2px;" src="https://s.yimg.com/lq/i/us/fi/03rd/down_r.gif" alt="Down"> <span class="yfi-price-change-red" data-sq="AAPL:chg">-0.3201</span></span><span id="yfs_p43_AAPL">(<span class="yfi-price-change-red" data-sq="AAPL:pctChg">0.30%</span>)</span> </span> + + + <span class="time_rtq Fz-m"><span class="rtq_exch">NasdaqGS - </span><span id="yfs_t53_AAPL">As of <span data-sq="AAPL:lstTrdTime">10:14AM EDT</span></span></span> + + </div> + <div><span class="rtq_separator">|</span> + + + </div> + </div> + <style> + #yfi_toolbox_mini_rtq.sigfig_promo { + bottom:45px !important; + } + </style> + <div class="app_promo " > + <a href="https://mobile.yahoo.com/finance/?src=gta" title="Get the App" target="_blank" ></a> + + </div> + </div> + </div> + </div> + </div> + + + + + </div> + </div> + </div> + + + + + +</div> + +</div><!--END td-applet-mw-quote-details--> + + + + <div id="optionsTableApplet"> + + + <div data-region="td-applet-options-table"><style>.App_v2 { + border: none; + margin: 0; + padding: 0; +} + +.options-table { + position: relative; +} + +/*.Icon.up {*/ + /*display: none;*/ +/*}*/ + +.option_column { + width: auto; +} + +.header_text { + float: left; + max-width: 50px; +} +.header_sorts { + color: #00be8c; + float: left; +} + +.size-toggle-menu { + margin-left: 600px; +} + +.in-the-money-banner { + background-color: rgba(224,241,231,1); + padding: 7px; + position: relative; + top: -3px; + width: 95px; +} + +.in-the-money.odd { + background-color: rgba(232,249,239,1); +} + +.in-the-money.even { + background-color: rgba(224,241,231,1); +} + +.toggle li{ + display: inline-block; + cursor: pointer; + border: 1px solid #e2e2e6; + border-right-width: 0; + color: #454545; + background-color: #fff; + float: left; + padding: 0px; + margin: 0px; +} + +.toggle li a { + padding: 7px; + display: block; +} + +.toggle li:hover{ + background-color: #e2e2e6; +} + +.toggle li.active{ + color: #fff; + background-color: #30d3b6; + border-color: #30d3b6; + border-bottom-color: #0c8087; +} + +.toggle li:first-child{ + border-radius: 3px 0 0 3px; +} + +.toggle li:last-child{ + border-radius: 0 3px 3px 0; + border-right-width: 1px; +} + +.high-low .up { + display: none; +} + +.high-low .down { + display: block; +} + +.low-high .down { + display: none; +} + +.low-high .up { + display: block; +} + +.option_column.sortable { + cursor: pointer; +} + +.option-filter-overlay { + background-color: #fff; + border: 1px solid #979ba2; + border-radius: 3px; + float: left; + padding: 15px; + position: absolute; + top: 60px; + z-index: 10; + display: none; +} + +#optionsStraddlesTable .option-filter-overlay { + left: 430px; +} + +.option-filter-overlay.active { + display: block; +} + +.option-filter-overlay .strike-filter{ + height: 25px; + width: 75px; +} + +#straddleTable .column-strike .cell{ + width: 30px; +} + +/**columns**/ + +#quote-table th.column-expires { + width: 102px; +} +.straddle-expire div.option_entry { + min-width: 65px; +} +.column-last .cell { + width: 55px; +} + +.column-change .cell { + width: 70px; +} + +.cell .change { + width: 35px; +} + +.column-percentChange .cell { + width: 85px; +} + +.column-volume .cell { + width: 70px; +} + +.cell .sessionVolume { + width: 37px; +} + +.column-session-volume .cell { + width: 75px; +} + +.column-openInterest .cell, .column-openInterestChange .cell { + width: 75px; +} +.cell .openInterest, .cell .openInterestChange { + width: 37px; +} + +.column-bid .cell { + width: 50px; +} + +.column-ask .cell { + width: 55px; +} + +.column-impliedVolatility .cell { + width: 75px; +} + +.cell .impliedVolatility { + width: 37px; +} + +.column-contractName .cell { + width: 170px; +} + +.options-menu-item { + position: relative; + top: -11px; +} + +.options-table { + margin-bottom: 30px; +} +.options-table.hidden { + display: none; +} +#quote-table table { + width: 100%; +} +#quote-table tr * { + font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; + font-size: 15px; + color: #454545; + font-weight: 200; +} +#quote-table tr a { + color: #1D1DA3; +} +#quote-table tr .Icon { + font-family: YGlyphs; +} +#quote-table tr.odd { + background-color: #f7f7f7; +} +#quote-table tr th { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + text-align: center; + width: 60px; + font-size: 11px !important; + padding-top: 10px; + padding-right: 5px; + padding-bottom: 10px; + vertical-align: middle; +} +#quote-table tr th * { + font-size: 11px; +} +#quote-table tr th .expand-icon { + display: block !important; + margin: 0 auto; + border: 1px solid #e2e2e6; + background-color: #fcfcfc; + -webkit-border-radius: 2px; + border-radius: 2px; + padding: 2px 0; +} +#quote-table tr th.column-strike { + width: 82px; +} +#quote-table tr th .sort-icons { + position: absolute; + margin-left: 2px; +} +#quote-table tr th .Icon { + display: none; +} +#quote-table tr th.low-high .up { + display: block !important; +} +#quote-table tr th.high-low .down { + display: block !important; +} +#quote-table td { + text-align: center; + padding: 7px 5px 7px 5px; +} +#quote-table td:first-child, +#quote-table th:first-child { + border-right: 1px solid #e2e2e6; +} +#quote-table .D-ib .Icon { + color: #66aeb2; +} +#quote-table caption { + background-color: #454545 !important; + color: #fff; + font-size: medium; + padding: 4px; + padding-left: 20px !important; + text-rendering: antialiased; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +#quote-table caption .callStraddles { + width:50%; + text-align:center; + float:left; +} +#quote-table caption .putStraddles { + width:50%; + text-align:center; + float:right; +} +#quote-table .in-the-money.even { + background-color: #f3fdfc; +} +#quote-table .in-the-money.even td:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #d5f8f3; + box-shadow: inset 5px 0 0 0 #d5f8f3; +} +#quote-table .in-the-money.even td:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #d5f8f3; + box-shadow: inset -5px 0 0 0 #d5f8f3; +} +#quote-table .in-the-money.odd { + background-color: #ecf6f4; +} +#quote-table .in-the-money.odd td:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #cff3ec; + box-shadow: inset 5px 0 0 0 #cff3ec; +} +#quote-table .in-the-money.odd td:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #cff3ec; + box-shadow: inset -5px 0 0 0 #cff3ec; +} +#quote-table .column-strike { + text-align: center; + padding: 4px 20px; +} +#quote-table .column-strike .header_text, +#quote-table .column-expires .cell .expiration{ + color: #454545; + font-size: 15px; + font-weight: bold; + max-width: 100%; +} +#quote-table .column-strike .header_text { + width: 100%; +} +#quote-table .column-strike .filter { + border: 1px solid #e2e2e6; + background-color: #fcfcfc; + color: #858585; + display: inline-block; + padding: 1px 10px; + -webkit-border-radius: 3px; + border-radius: 3px; + margin-top: 4px; +} +#quote-table .column-strike .filter span { + position: relative; + top: -2px; + font-weight: bold; + margin-left: -5px; +} + +#quote-table .column-strike .sort-icons { + top: 35px; +} +#quote-table .column-expires .sort-icons { + top: 45px; +} +#optionsStraddlesTable .column-expires .sort-icons { + top: 40px; +} +#quote-table #options_menu { + width: 100%; +} +#quote-table #options_menu .SelectBox-Pick { + background-color: #fcfcfc !important; + border: 1px solid #e2e2e6; + color: #128086; + font-size: 14px; + padding: 5px; + padding-top: 8px; +} +#quote-table #options_menu .SelectBox-Text { + font-weight: bold; +} +#quote-table .size-toggle-menu { + margin-left: 15px !important; +} +#quote-table .options-menu-item { + top: -9px; +} +#quote-table .option_view { + float: right; +} +#quote-table .option-change-pos { + color: #2ac194; +} +#quote-table .option-change-neg { + color: #f90f31; +} +#quote-table .toggle li { + color: #128086; + background-color: #fcfcfc; +} +#quote-table .toggle li.active { + color: #fff; + background-color: #35d2b6; +} +#quote-table .expand-icon { + color: #b5b5b5; + font-size: 12px; + cursor: pointer; +} +#quote-table .straddleCallContractName { + padding-left: 25px; +} +#quote-table .straddlePutContractName { + padding-left: 20px; +} +#quote-table .straddle-row-expand { + display: none; + border-bottom: 1px solid #f9f9f9; +} +#quote-table .straddle-row-expand td { + padding-right: 5px; +} +#quote-table .straddle-row-expand label { + color: #454545; + font-size: 11px; + margin-bottom: 2px; + color: #888; +} +#quote-table .straddle-row-expand label, +#quote-table .straddle-row-expand div { + display: block; + font-weight: 400; + text-align: left; + padding-left: 5px; +} +#quote-table .expand-icon-up { + display: none; +} +#quote-table tr.expanded + .straddle-row-expand { + display: table-row; +} +#quote-table tr.expanded .expand-icon-up { + display: inline-block; +} +#quote-table tr.expanded .expand-icon-down { + display: none; +} +.in-the-money-banner { + color: #7f8584; + font-size: 11px; + background-color: #eefcfa; + border-left: 12px solid #e0faf6; + border-right: 12px solid #e0faf6; + width: 76px !important; + text-align: center; + padding: 5px !important; + margin-top: 5px; + margin-left: 15px; +} +#optionsStraddlesTable td div { + text-align: center; +} +#optionsStraddlesTable .straddle-strike, +#optionsStraddlesTable .column-strike, +#optionsStraddlesTable .straddle-expire{ + border-right: 1px solid #e2e2e6; + border-left: 1px solid #e2e2e6; +} +#optionsStraddlesTable td:first-child, +#optionsStraddlesTable th:first-child { + border-right: none !important; +} +#optionsStraddlesTable .odd td.in-the-money { + background-color: #ecf6f4; +} +#optionsStraddlesTable .odd td.in-the-money:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #cff3ec; + box-shadow: inset 5px 0 0 0 #cff3ec; +} +#optionsStraddlesTable .odd td.in-the-money:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #cff3ec; + box-shadow: inset -5px 0 0 0 #cff3ec; +} +#optionsStraddlesTable .even td.in-the-money { + background-color: #f3fdfc; +} +#optionsStraddlesTable .even td.in-the-money:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #d5f8f3; + box-shadow: inset 5px 0 0 0 #d5f8f3; +} +#optionsStraddlesTable .even td.in-the-money:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #d5f8f3; + box-shadow: inset -5px 0 0 0 #d5f8f3; +} +.column-expand-all { + cursor: pointer; +} +.options-table.expand-all tr + .straddle-row-expand { + display: table-row !important; +} +.options-table.expand-all tr .expand-icon-up { + display: inline-block !important; +} +.options-table.expand-all tr .expand-icon-down { + display: none !important; +} +.options_menu .toggle a { + color: #128086; +} +.options_menu .toggle a:hover { + text-decoration: none; +} +.options_menu .toggle .active a { + color: #fff; +} +#options_menu .symbol_lookup { + float: right; + top: -11px; +} +.symbol_lookup .options-ac-input { + border-radius: 0; + height: 26px; + width: 79%; +} +.goto-icon { + border-left: 1px solid #e2e2e6; + color: #028087; + cursor: pointer; +} +.symbol_lookup .goto-icon { + height: 27px; + line-height: 2.1em; +} +#finAcOutput { + left: 10px; + top: -10px; +} +#finAcOutput .yui3-fin-ac-hidden { + display: none; +} +#finAcOutput .yui3-aclist { + border: 1px solid #DDD; + background: #fefefe; + font-size: 92%; + left: 0 !important; + overflow: visible; + padding: .5em; + position: absolute !important; + text-align: left; + top: 0 !important; + +} +#finAcOutput li.yui3-fin-ac-item-active, +#finAcOutput li.yui3-fin-ac-item-hover { + background: #F1F1F1; + cursor: pointer; +} +#finAcOutput div:first-child { + width: 30em !important; +} +#finAcOutput b.yui3-highlight { + font-weight: bold; +} +#finAcOutput li .name { + display: inline-block; + left: 0; + width: 25em; + overflow: hidden; + position: relative; +} + +#finAcOutput li .symbol { + width: 8.5em; + display: inline-block; + margin: 0 1em 0 0; + overflow: hidden; +} + +#finAcOutput li { + color: #444; + cursor: default; + font-weight: 300; + list-style: none; + margin: 0; + padding: .15em .38em; + position: relative; + vertical-align: bottom; + white-space: nowrap; +} + +.yui3-fin-ac-hidden { + visibility: hidden; +} + +.filterRangeRow { + line-height: 5px; +} +.filterRangeTitle { + padding-bottom: 5px; + font-size: 12px !important; +} +.clear-filter { + padding-left: 20px; +} +.closeFilter { + font-size: 10px !important; + color: red !important; +} +.modify-filter { + font-size: 11px !important; +} +.showModifyFilter { + top: 80px; + left: 630px; +} + +#options_menu { + margin-bottom: -15px; +} + +#optionsTableApplet { + margin-top: 9px; + width: 1070px; +} + +#yfi_charts.desktop #yfi_doc { + width: 1440px; +} +#sky { + float: right; + margin-left: 30px; + margin-top: 50px; + width: 170px; +} +</style> +<div id="applet_5264156462795343" class="App_v2 js-applet" data-applet-guid="5264156462795343" data-applet-type="td-applet-options-table"> + + + + + + <div class="App-Bd"> + <div class="App-Main" data-region="main"> + <div class="js-applet-view-container-main"> + + <div id="quote-table"> + <div id="options_menu" class="Grid-U options_menu"> + + <form class="Grid-U SelectBox"> + <div class="SelectBox-Pick"><b class='SelectBox-Text '>October 31, 2014</b><i class='Icon Va-m'>&#xe002;</i></div> + <select class='Start-0' data-plugin="selectbox"> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1414713600" value="1414713600" >October 31, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1415318400" value="1415318400" >November 7, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1415923200" value="1415923200" >November 14, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1416614400" value="1416614400" >November 22, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1417132800" value="1417132800" >November 28, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1417737600" value="1417737600" >December 5, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1419033600" value="1419033600" >December 20, 2014</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1421452800" value="1421452800" >January 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1424390400" value="1424390400" >February 20, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1429228800" value="1429228800" >April 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1437091200" value="1437091200" >July 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1452816000" value="1452816000" >January 15, 2016</option> + + + <option data-selectbox-link="/q/op?s=AAPL&date=1484870400" value="1484870400" >January 20, 2017</option> + + </select> + </form> + + + <div class="Grid-U options-menu-item size-toggle-menu"> + <ul class="toggle size-toggle"> + <li data-size="REGULAR" class="size-toggle-option toggle-regular active Cur-p"> + <a href="/q/op?s=AAPL&date=1414713600">Regular</a> + </li> + <li data-size="MINI" class="size-toggle-option toggle-mini Cur-p"> + <a href="/q/op?s=AAPL&size=mini&date=1414713600">Mini</a> + </li> + </ul> + </div> + + + <div class="Grid-U options-menu-item symbol_lookup"> + <div class="Cf"> + <div class="fin-ac-container Bd-1 Pos-r M-10"> + <input placeholder="Lookup Option" type="text" autocomplete="off" value="" name="s" class="options-ac-input Bd-0" id="finAcOptions"> + <i class="Icon Fl-end W-20 goto-icon">&#xe015;</i> + </div> + <div id="finAcOutput" class="yui-ac-container Pos-r"></div> + </div> + </div> + <div class="Grid-U option_view options-menu-item"> + <ul class="toggle toggle-view-mode"> + <li class="toggle-list active"> + <a href="/q/op?s=AAPL&date=1414713600">List</a> + </li> + <li class="toggle-straddle "> + <a href="/q/op?s=AAPL&straddle=true&date=1414713600">Straddle</a> + </li> + </ul> + + </div> + <div class="Grid-U in_the_money in-the-money-banner"> + In The Money + </div> + </div> + + + + <div class="options-table " id="optionsCallsTable" data-sec="options-calls-table"> + <div class="strike-filter option-filter-overlay"> + <p>Show Me Strikes From</p> + <div class="My-6"> + $ <input class="filter-low strike-filter" data-filter-type="low" type="text"> + to $ <input class="filter-high strike-filter" data-filter-type="high" type="text"> + </div> + <a data-table-filter="optionsCalls" class="Cur-p apply-filter">Apply Filter</a> + <a class="Cur-p clear-filter">Clear Filter</a> +</div> + + +<div class="follow-quote-area"> + <div class="quote-table-overflow"> + <table class="details-table quote-table Fz-m"> + + + <caption> + Calls + </caption> + + + <thead class="details-header quote-table-headers"> + <tr> + + + + + <th class='column-strike Pstart-38 low-high Fz-xs filterable sortable option_column' style='color: #454545;' data-sort-column='strike' data-col-pos='0'> + <div class="cell"> + <div class="D-ib header_text strike">Strike</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + <div class="filter Cur-p "><span>&#8757;</span> Filter</div> + </th> + + + + + + <th class='column-contractName Pstart-10 '>Contract Name</th> + + + + + + + <th class='column-last Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='lastPrice' data-col-pos='2'> + <div class="cell"> + <div class="D-ib lastPrice">Last</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-bid Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='bid' data-col-pos='3'> + <div class="cell"> + <div class="D-ib bid">Bid</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-ask Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='ask' data-col-pos='4'> + <div class="cell"> + <div class="D-ib ask">Ask</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-change Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='change' data-col-pos='5'> + <div class="cell"> + <div class="D-ib change">Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-percentChange Pstart-16 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='percentChange' data-col-pos='6'> + <div class="cell"> + <div class="D-ib percentChange">%Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-volume Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='volume' data-col-pos='7'> + <div class="cell"> + <div class="D-ib volume">Volume</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-openInterest Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='openInterest' data-col-pos='8'> + <div class="cell"> + <div class="D-ib openInterest">Open Interest</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-impliedVolatility Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='impliedVolatility' data-col-pos='9'> + <div class="cell"> + <div class="D-ib impliedVolatility">Implied Volatility</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + </tr> + + <tr class="filterRangeRow D-n"> + <td colspan="10"> + <div> + <span class="filterRangeTitle"></span> + <span class="closeFilter Cur-p">&#10005;</span> + <span class="modify-filter Cur-p">[modify]</span> + </div> + </td> + </tr> + + </thead> + + <tbody> + + + + <tr data-row="0" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00075000">AAPL141031C00075000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >26.90</div> + </td> + <td> + <div class="option_entry Fz-m" >29.35</div> + </td> + <td> + <div class="option_entry Fz-m" >30.45</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >2</div> + </td> + <td> + <div class="option_entry Fz-m" >50.00%</div> + </td> + </tr> + + <tr data-row="1" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00080000">AAPL141031C00080000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >25.03</div> + </td> + <td> + <div class="option_entry Fz-m" >24.00</div> + </td> + <td> + <div class="option_entry Fz-m" >25.45</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="191">191</strong> + </td> + <td> + <div class="option_entry Fz-m" >250</div> + </td> + <td> + <div class="option_entry Fz-m" >158.98%</div> + </td> + </tr> + + <tr data-row="2" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00085000">AAPL141031C00085000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >20.20</div> + </td> + <td> + <div class="option_entry Fz-m" >19.60</div> + </td> + <td> + <div class="option_entry Fz-m" >20.55</div> + </td> + <td> + <div class="option_entry Fz-m" >0.21</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+1.05%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >729</div> + </td> + <td> + <div class="option_entry Fz-m" >101.76%</div> + </td> + </tr> + + <tr data-row="3" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=86.00">86.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00086000">AAPL141031C00086000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >19.00</div> + </td> + <td> + <div class="option_entry Fz-m" >18.20</div> + </td> + <td> + <div class="option_entry Fz-m" >19.35</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="3">3</strong> + </td> + <td> + <div class="option_entry Fz-m" >13</div> + </td> + <td> + <div class="option_entry Fz-m" >118.56%</div> + </td> + </tr> + + <tr data-row="4" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=87.00">87.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00087000">AAPL141031C00087000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >18.20</div> + </td> + <td> + <div class="option_entry Fz-m" >18.15</div> + </td> + <td> + <div class="option_entry Fz-m" >18.30</div> + </td> + <td> + <div class="option_entry Fz-m" >0.30</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+1.68%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="21">21</strong> + </td> + <td> + <div class="option_entry Fz-m" >161</div> + </td> + <td> + <div class="option_entry Fz-m" >104.88%</div> + </td> + </tr> + + <tr data-row="5" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00088000">AAPL141031C00088000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >17.00</div> + </td> + <td> + <div class="option_entry Fz-m" >16.20</div> + </td> + <td> + <div class="option_entry Fz-m" >17.35</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="19">19</strong> + </td> + <td> + <div class="option_entry Fz-m" >148</div> + </td> + <td> + <div class="option_entry Fz-m" >107.72%</div> + </td> + </tr> + + <tr data-row="6" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00089000">AAPL141031C00089000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >13.95</div> + </td> + <td> + <div class="option_entry Fz-m" >15.20</div> + </td> + <td> + <div class="option_entry Fz-m" >16.35</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >65</div> + </td> + <td> + <div class="option_entry Fz-m" >102.34%</div> + </td> + </tr> + + <tr data-row="7" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00090000">AAPL141031C00090000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >15.20</div> + </td> + <td> + <div class="option_entry Fz-m" >14.40</div> + </td> + <td> + <div class="option_entry Fz-m" >15.60</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="168">168</strong> + </td> + <td> + <div class="option_entry Fz-m" >687</div> + </td> + <td> + <div class="option_entry Fz-m" >70.70%</div> + </td> + </tr> + + <tr data-row="8" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00091000">AAPL141031C00091000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >14.00</div> + </td> + <td> + <div class="option_entry Fz-m" >13.20</div> + </td> + <td> + <div class="option_entry Fz-m" >14.35</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="3">3</strong> + </td> + <td> + <div class="option_entry Fz-m" >222</div> + </td> + <td> + <div class="option_entry Fz-m" >91.60%</div> + </td> + </tr> + + <tr data-row="9" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00092000">AAPL141031C00092000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >13.02</div> + </td> + <td> + <div class="option_entry Fz-m" >12.20</div> + </td> + <td> + <div class="option_entry Fz-m" >13.35</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >237</div> + </td> + <td> + <div class="option_entry Fz-m" >86.13%</div> + </td> + </tr> + + <tr data-row="10" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00093000">AAPL141031C00093000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >12.00</div> + </td> + <td> + <div class="option_entry Fz-m" >11.35</div> + </td> + <td> + <div class="option_entry Fz-m" >12.60</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="36">36</strong> + </td> + <td> + <div class="option_entry Fz-m" >293</div> + </td> + <td> + <div class="option_entry Fz-m" >54.88%</div> + </td> + </tr> + + <tr data-row="11" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00094000">AAPL141031C00094000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >11.04</div> + </td> + <td> + <div class="option_entry Fz-m" >10.60</div> + </td> + <td> + <div class="option_entry Fz-m" >11.20</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="109">109</strong> + </td> + <td> + <div class="option_entry Fz-m" >678</div> + </td> + <td> + <div class="option_entry Fz-m" >67.77%</div> + </td> + </tr> + + <tr data-row="12" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00095000">AAPL141031C00095000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >10.25</div> + </td> + <td> + <div class="option_entry Fz-m" >9.80</div> + </td> + <td> + <div class="option_entry Fz-m" >10.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="338">338</strong> + </td> + <td> + <div class="option_entry Fz-m" >6269</div> + </td> + <td> + <div class="option_entry Fz-m" >53.42%</div> + </td> + </tr> + + <tr data-row="13" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00096000">AAPL141031C00096000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >9.30</div> + </td> + <td> + <div class="option_entry Fz-m" >9.25</div> + </td> + <td> + <div class="option_entry Fz-m" >9.40</div> + </td> + <td> + <div class="option_entry Fz-m" >0.10</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+1.09%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1">1</strong> + </td> + <td> + <div class="option_entry Fz-m" >1512</div> + </td> + <td> + <div class="option_entry Fz-m" >63.53%</div> + </td> + </tr> + + <tr data-row="14" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00097000">AAPL141031C00097000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >8.31</div> + </td> + <td> + <div class="option_entry Fz-m" >7.80</div> + </td> + <td> + <div class="option_entry Fz-m" >8.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="280">280</strong> + </td> + <td> + <div class="option_entry Fz-m" >2129</div> + </td> + <td> + <div class="option_entry Fz-m" >44.34%</div> + </td> + </tr> + + <tr data-row="15" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00098000">AAPL141031C00098000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >7.33</div> + </td> + <td> + <div class="option_entry Fz-m" >7.25</div> + </td> + <td> + <div class="option_entry Fz-m" >7.40</div> + </td> + <td> + <div class="option_entry Fz-m" >0.17</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+2.37%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="31">31</strong> + </td> + <td> + <div class="option_entry Fz-m" >3441</div> + </td> + <td> + <div class="option_entry Fz-m" >52.64%</div> + </td> + </tr> + + <tr data-row="16" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00099000">AAPL141031C00099000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >6.24</div> + </td> + <td> + <div class="option_entry Fz-m" >6.05</div> + </td> + <td> + <div class="option_entry Fz-m" >6.25</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+0.65%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="41">41</strong> + </td> + <td> + <div class="option_entry Fz-m" >7373</div> + </td> + <td> + <div class="option_entry Fz-m" >44.24%</div> + </td> + </tr> + + <tr data-row="17" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00100000">AAPL141031C00100000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >5.34</div> + </td> + <td> + <div class="option_entry Fz-m" >5.25</div> + </td> + <td> + <div class="option_entry Fz-m" >5.45</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-0.19%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="48">48</strong> + </td> + <td> + <div class="option_entry Fz-m" >12778</div> + </td> + <td> + <div class="option_entry Fz-m" >45.51%</div> + </td> + </tr> + + <tr data-row="18" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00101000">AAPL141031C00101000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >4.40</div> + </td> + <td> + <div class="option_entry Fz-m" >4.30</div> + </td> + <td> + <div class="option_entry Fz-m" >4.50</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="125">125</strong> + </td> + <td> + <div class="option_entry Fz-m" >10047</div> + </td> + <td> + <div class="option_entry Fz-m" >40.87%</div> + </td> + </tr> + + <tr data-row="19" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00102000">AAPL141031C00102000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >3.40</div> + </td> + <td> + <div class="option_entry Fz-m" >3.40</div> + </td> + <td> + <div class="option_entry Fz-m" >3.55</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.10</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-2.86%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1344">1344</strong> + </td> + <td> + <div class="option_entry Fz-m" >11868</div> + </td> + <td> + <div class="option_entry Fz-m" >35.74%</div> + </td> + </tr> + + <tr data-row="20" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00103000">AAPL141031C00103000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >2.59</div> + </td> + <td> + <div class="option_entry Fz-m" >2.56</div> + </td> + <td> + <div class="option_entry Fz-m" >2.59</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.06</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-2.26%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="932">932</strong> + </td> + <td> + <div class="option_entry Fz-m" >12198</div> + </td> + <td> + <div class="option_entry Fz-m" >29.79%</div> + </td> + </tr> + + <tr data-row="21" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00104000">AAPL141031C00104000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.83</div> + </td> + <td> + <div class="option_entry Fz-m" >1.78</div> + </td> + <td> + <div class="option_entry Fz-m" >1.83</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.06</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-3.17%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1292">1292</strong> + </td> + <td> + <div class="option_entry Fz-m" >13661</div> + </td> + <td> + <div class="option_entry Fz-m" >27.30%</div> + </td> + </tr> + + <tr data-row="22" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00105000">AAPL141031C00105000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.18</div> + </td> + <td> + <div class="option_entry Fz-m" >1.18</div> + </td> + <td> + <div class="option_entry Fz-m" >1.19</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.07</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-5.60%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2104">2104</strong> + </td> + <td> + <div class="option_entry Fz-m" >25795</div> + </td> + <td> + <div class="option_entry Fz-m" >25.29%</div> + </td> + </tr> + + <tr data-row="23" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00106000">AAPL141031C00106000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.67</div> + </td> + <td> + <div class="option_entry Fz-m" >0.67</div> + </td> + <td> + <div class="option_entry Fz-m" >0.70</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.05</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-6.94%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="950">950</strong> + </td> + <td> + <div class="option_entry Fz-m" >16610</div> + </td> + <td> + <div class="option_entry Fz-m" >23.73%</div> + </td> + </tr> + + <tr data-row="24" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00107000">AAPL141031C00107000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.36</div> + </td> + <td> + <div class="option_entry Fz-m" >0.36</div> + </td> + <td> + <div class="option_entry Fz-m" >0.37</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.05</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-12.20%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="7129">7129</strong> + </td> + <td> + <div class="option_entry Fz-m" >15855</div> + </td> + <td> + <div class="option_entry Fz-m" >22.66%</div> + </td> + </tr> + + <tr data-row="25" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00108000">AAPL141031C00108000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.19</div> + </td> + <td> + <div class="option_entry Fz-m" >0.18</div> + </td> + <td> + <div class="option_entry Fz-m" >0.20</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.04</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-17.39%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2455">2455</strong> + </td> + <td> + <div class="option_entry Fz-m" >8253</div> + </td> + <td> + <div class="option_entry Fz-m" >22.85%</div> + </td> + </tr> + + <tr data-row="26" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00109000">AAPL141031C00109000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.10</div> + </td> + <td> + <div class="option_entry Fz-m" >0.09</div> + </td> + <td> + <div class="option_entry Fz-m" >0.10</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-9.09%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="382">382</strong> + </td> + <td> + <div class="option_entry Fz-m" >3328</div> + </td> + <td> + <div class="option_entry Fz-m" >23.05%</div> + </td> + </tr> + + <tr data-row="27" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00110000">AAPL141031C00110000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.06</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-28.57%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="803">803</strong> + </td> + <td> + <div class="option_entry Fz-m" >9086</div> + </td> + <td> + <div class="option_entry Fz-m" >24.22%</div> + </td> + </tr> + + <tr data-row="28" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00111000">AAPL141031C00111000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-40.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="73">73</strong> + </td> + <td> + <div class="option_entry Fz-m" >1275</div> + </td> + <td> + <div class="option_entry Fz-m" >25.98%</div> + </td> + </tr> + + <tr data-row="29" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=112.00">112.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00112000">AAPL141031C00112000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="187">187</strong> + </td> + <td> + <div class="option_entry Fz-m" >775</div> + </td> + <td> + <div class="option_entry Fz-m" >29.30%</div> + </td> + </tr> + + <tr data-row="30" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=113.00">113.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00113000">AAPL141031C00113000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="33">33</strong> + </td> + <td> + <div class="option_entry Fz-m" >1198</div> + </td> + <td> + <div class="option_entry Fz-m" >32.42%</div> + </td> + </tr> + + <tr data-row="31" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=114.00">114.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00114000">AAPL141031C00114000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="170">170</strong> + </td> + <td> + <div class="option_entry Fz-m" >931</div> + </td> + <td> + <div class="option_entry Fz-m" >35.74%</div> + </td> + </tr> + + <tr data-row="32" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00115000">AAPL141031C00115000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="8">8</strong> + </td> + <td> + <div class="option_entry Fz-m" >550</div> + </td> + <td> + <div class="option_entry Fz-m" >35.16%</div> + </td> + </tr> + + <tr data-row="33" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=116.00">116.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00116000">AAPL141031C00116000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="43">43</strong> + </td> + <td> + <div class="option_entry Fz-m" >86</div> + </td> + <td> + <div class="option_entry Fz-m" >37.89%</div> + </td> + </tr> + + <tr data-row="34" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=118.00">118.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00118000">AAPL141031C00118000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="49">49</strong> + </td> + <td> + <div class="option_entry Fz-m" >49</div> + </td> + <td> + <div class="option_entry Fz-m" >47.66%</div> + </td> + </tr> + + <tr data-row="35" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=119.00">119.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00119000">AAPL141031C00119000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.09</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >22</div> + </td> + <td> + <div class="option_entry Fz-m" >46.09%</div> + </td> + </tr> + + <tr data-row="36" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=120.00">120.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00120000">AAPL141031C00120000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="43">43</strong> + </td> + <td> + <div class="option_entry Fz-m" >69</div> + </td> + <td> + <div class="option_entry Fz-m" >48.83%</div> + </td> + </tr> + + <tr data-row="37" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=121.00">121.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00121000">AAPL141031C00121000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="0">0</strong> + </td> + <td> + <div class="option_entry Fz-m" >10</div> + </td> + <td> + <div class="option_entry Fz-m" >51.56%</div> + </td> + </tr> + + <tr data-row="38" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=122.00">122.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031C00122000">AAPL141031C00122000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="1">1</strong> + </td> + <td> + <div class="option_entry Fz-m" >3</div> + </td> + <td> + <div class="option_entry Fz-m" >50.00%</div> + </td> + </tr> + + + + + + + + </tbody> + </table> + </div> +</div> + + + </div> + + <div class="options-table " id="optionsPutsTable" data-sec="options-puts-table"> + <div class="strike-filter option-filter-overlay"> + <p>Show Me Strikes From</p> + <div class="My-6"> + $ <input class="filter-low strike-filter" data-filter-type="low" type="text"> + to $ <input class="filter-high strike-filter" data-filter-type="high" type="text"> + </div> + <a data-table-filter="optionsPuts" class="Cur-p apply-filter">Apply Filter</a> + <a class="Cur-p clear-filter">Clear Filter</a> +</div> + + +<div class="follow-quote-area"> + <div class="quote-table-overflow"> + <table class="details-table quote-table Fz-m"> + + + <caption> + Puts + </caption> + + + <thead class="details-header quote-table-headers"> + <tr> + + + + + <th class='column-strike Pstart-38 low-high Fz-xs filterable sortable option_column' style='color: #454545;' data-sort-column='strike' data-col-pos='0'> + <div class="cell"> + <div class="D-ib header_text strike">Strike</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + <div class="filter Cur-p "><span>&#8757;</span> Filter</div> + </th> + + + + + + <th class='column-contractName Pstart-10 '>Contract Name</th> + + + + + + + <th class='column-last Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='lastPrice' data-col-pos='2'> + <div class="cell"> + <div class="D-ib lastPrice">Last</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-bid Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='bid' data-col-pos='3'> + <div class="cell"> + <div class="D-ib bid">Bid</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-ask Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='ask' data-col-pos='4'> + <div class="cell"> + <div class="D-ib ask">Ask</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-change Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='change' data-col-pos='5'> + <div class="cell"> + <div class="D-ib change">Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-percentChange Pstart-16 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='percentChange' data-col-pos='6'> + <div class="cell"> + <div class="D-ib percentChange">%Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-volume Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='volume' data-col-pos='7'> + <div class="cell"> + <div class="D-ib volume">Volume</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-openInterest Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='openInterest' data-col-pos='8'> + <div class="cell"> + <div class="D-ib openInterest">Open Interest</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-impliedVolatility Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='impliedVolatility' data-col-pos='9'> + <div class="cell"> + <div class="D-ib impliedVolatility">Implied Volatility</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + </tr> + + <tr class="filterRangeRow D-n"> + <td colspan="10"> + <div> + <span class="filterRangeTitle"></span> + <span class="closeFilter Cur-p">&#10005;</span> + <span class="modify-filter Cur-p">[modify]</span> + </div> + </td> + </tr> + + </thead> + + <tbody> + + + <tr data-row="0" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=75.00">75.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00075000">AAPL141031P00075000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="3">3</strong> + </td> + <td> + <div class="option_entry Fz-m" >365</div> + </td> + <td> + <div class="option_entry Fz-m" >96.88%</div> + </td> + </tr> + + <tr data-row="1" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=80.00">80.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00080000">AAPL141031P00080000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="500">500</strong> + </td> + <td> + <div class="option_entry Fz-m" >973</div> + </td> + <td> + <div class="option_entry Fz-m" >85.94%</div> + </td> + </tr> + + <tr data-row="2" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=85.00">85.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00085000">AAPL141031P00085000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="50">50</strong> + </td> + <td> + <div class="option_entry Fz-m" >1303</div> + </td> + <td> + <div class="option_entry Fz-m" >68.75%</div> + </td> + </tr> + + <tr data-row="3" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=86.00">86.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00086000">AAPL141031P00086000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="3">3</strong> + </td> + <td> + <div class="option_entry Fz-m" >655</div> + </td> + <td> + <div class="option_entry Fz-m" >64.84%</div> + </td> + </tr> + + <tr data-row="4" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=87.00">87.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00087000">AAPL141031P00087000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="39">39</strong> + </td> + <td> + <div class="option_entry Fz-m" >808</div> + </td> + <td> + <div class="option_entry Fz-m" >60.94%</div> + </td> + </tr> + + <tr data-row="5" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=88.00">88.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00088000">AAPL141031P00088000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-pos">+100.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="30">30</strong> + </td> + <td> + <div class="option_entry Fz-m" >1580</div> + </td> + <td> + <div class="option_entry Fz-m" >57.81%</div> + </td> + </tr> + + <tr data-row="6" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=89.00">89.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00089000">AAPL141031P00089000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="350">350</strong> + </td> + <td> + <div class="option_entry Fz-m" >794</div> + </td> + <td> + <div class="option_entry Fz-m" >60.94%</div> + </td> + </tr> + + <tr data-row="7" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=90.00">90.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00090000">AAPL141031P00090000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="162">162</strong> + </td> + <td> + <div class="option_entry Fz-m" >7457</div> + </td> + <td> + <div class="option_entry Fz-m" >53.91%</div> + </td> + </tr> + + <tr data-row="8" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=91.00">91.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00091000">AAPL141031P00091000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.01</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="109">109</strong> + </td> + <td> + <div class="option_entry Fz-m" >2469</div> + </td> + <td> + <div class="option_entry Fz-m" >53.91%</div> + </td> + </tr> + + <tr data-row="9" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=92.00">92.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00092000">AAPL141031P00092000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-33.33%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="702">702</strong> + </td> + <td> + <div class="option_entry Fz-m" >21633</div> + </td> + <td> + <div class="option_entry Fz-m" >50.00%</div> + </td> + </tr> + + <tr data-row="10" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=93.00">93.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00093000">AAPL141031P00093000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-25.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1150">1150</strong> + </td> + <td> + <div class="option_entry Fz-m" >21876</div> + </td> + <td> + <div class="option_entry Fz-m" >49.61%</div> + </td> + </tr> + + <tr data-row="11" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=94.00">94.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00094000">AAPL141031P00094000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.02</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-40.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="30">30</strong> + </td> + <td> + <div class="option_entry Fz-m" >23436</div> + </td> + <td> + <div class="option_entry Fz-m" >45.70%</div> + </td> + </tr> + + <tr data-row="12" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=95.00">95.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00095000">AAPL141031P00095000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.03</div> + </td> + <td> + <div class="option_entry Fz-m" >0.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="178">178</strong> + </td> + <td> + <div class="option_entry Fz-m" >30234</div> + </td> + <td> + <div class="option_entry Fz-m" >43.56%</div> + </td> + </tr> + + <tr data-row="13" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=96.00">96.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00096000">AAPL141031P00096000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.04</div> + </td> + <td> + <div class="option_entry Fz-m" >0.06</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-20.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >13213</div> + </td> + <td> + <div class="option_entry Fz-m" >40.82%</div> + </td> + </tr> + + <tr data-row="14" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=97.00">97.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00097000">AAPL141031P00097000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.05</div> + </td> + <td> + <div class="option_entry Fz-m" >0.06</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-16.67%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="150">150</strong> + </td> + <td> + <div class="option_entry Fz-m" >3145</div> + </td> + <td> + <div class="option_entry Fz-m" >36.91%</div> + </td> + </tr> + + <tr data-row="15" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=98.00">98.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00098000">AAPL141031P00098000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.07</div> + </td> + <td> + <div class="option_entry Fz-m" >0.06</div> + </td> + <td> + <div class="option_entry Fz-m" >0.07</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.01</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-12.50%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="29">29</strong> + </td> + <td> + <div class="option_entry Fz-m" >5478</div> + </td> + <td> + <div class="option_entry Fz-m" >33.79%</div> + </td> + </tr> + + <tr data-row="16" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=99.00">99.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00099000">AAPL141031P00099000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.08</div> + </td> + <td> + <div class="option_entry Fz-m" >0.08</div> + </td> + <td> + <div class="option_entry Fz-m" >0.09</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-20.00%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="182">182</strong> + </td> + <td> + <div class="option_entry Fz-m" >4769</div> + </td> + <td> + <div class="option_entry Fz-m" >31.25%</div> + </td> + </tr> + + <tr data-row="17" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=100.00">100.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00100000">AAPL141031P00100000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.11</div> + </td> + <td> + <div class="option_entry Fz-m" >0.10</div> + </td> + <td> + <div class="option_entry Fz-m" >0.11</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-15.38%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1294">1294</strong> + </td> + <td> + <div class="option_entry Fz-m" >13038</div> + </td> + <td> + <div class="option_entry Fz-m" >28.13%</div> + </td> + </tr> + + <tr data-row="18" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=101.00">101.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00101000">AAPL141031P00101000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.14</div> + </td> + <td> + <div class="option_entry Fz-m" >0.14</div> + </td> + <td> + <div class="option_entry Fz-m" >0.15</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.03</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-17.65%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="219">219</strong> + </td> + <td> + <div class="option_entry Fz-m" >9356</div> + </td> + <td> + <div class="option_entry Fz-m" >25.54%</div> + </td> + </tr> + + <tr data-row="19" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=102.00">102.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00102000">AAPL141031P00102000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.21</div> + </td> + <td> + <div class="option_entry Fz-m" >0.21</div> + </td> + <td> + <div class="option_entry Fz-m" >0.22</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.03</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-12.50%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1614">1614</strong> + </td> + <td> + <div class="option_entry Fz-m" >10835</div> + </td> + <td> + <div class="option_entry Fz-m" >23.19%</div> + </td> + </tr> + + <tr data-row="20" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=103.00">103.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00103000">AAPL141031P00103000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.35</div> + </td> + <td> + <div class="option_entry Fz-m" >0.34</div> + </td> + <td> + <div class="option_entry Fz-m" >0.35</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.03</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-7.89%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="959">959</strong> + </td> + <td> + <div class="option_entry Fz-m" >11228</div> + </td> + <td> + <div class="option_entry Fz-m" >21.29%</div> + </td> + </tr> + + <tr data-row="21" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=104.00">104.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00104000">AAPL141031P00104000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.58</div> + </td> + <td> + <div class="option_entry Fz-m" >0.58</div> + </td> + <td> + <div class="option_entry Fz-m" >0.60</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-3.33%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="1424">1424</strong> + </td> + <td> + <div class="option_entry Fz-m" >9823</div> + </td> + <td> + <div class="option_entry Fz-m" >20.22%</div> + </td> + </tr> + + <tr data-row="22" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=105.00">105.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00105000">AAPL141031P00105000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.94</div> + </td> + <td> + <div class="option_entry Fz-m" >0.93</div> + </td> + <td> + <div class="option_entry Fz-m" >0.96</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.05</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-5.05%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="2934">2934</strong> + </td> + <td> + <div class="option_entry Fz-m" >7424</div> + </td> + <td> + <div class="option_entry Fz-m" >18.56%</div> + </td> + </tr> + + <tr data-row="23" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=106.00">106.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00106000">AAPL141031P00106000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.49</div> + </td> + <td> + <div class="option_entry Fz-m" >1.45</div> + </td> + <td> + <div class="option_entry Fz-m" >1.50</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-1.32%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="384">384</strong> + </td> + <td> + <div class="option_entry Fz-m" >1441</div> + </td> + <td> + <div class="option_entry Fz-m" >16.99%</div> + </td> + </tr> + + <tr data-row="24" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=107.00">107.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00107000">AAPL141031P00107000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >2.15</div> + </td> + <td> + <div class="option_entry Fz-m" >2.13</div> + </td> + <td> + <div class="option_entry Fz-m" >2.15</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.03</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-1.38%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="104">104</strong> + </td> + <td> + <div class="option_entry Fz-m" >573</div> + </td> + <td> + <div class="option_entry Fz-m" >11.82%</div> + </td> + </tr> + + <tr data-row="25" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=108.00">108.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00108000">AAPL141031P00108000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >2.99</div> + </td> + <td> + <div class="option_entry Fz-m" >2.88</div> + </td> + <td> + <div class="option_entry Fz-m" >3.05</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.04</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-1.32%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="5">5</strong> + </td> + <td> + <div class="option_entry Fz-m" >165</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00%</div> + </td> + </tr> + + <tr data-row="26" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=109.00">109.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00109000">AAPL141031P00109000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >3.88</div> + </td> + <td> + <div class="option_entry Fz-m" >3.80</div> + </td> + <td> + <div class="option_entry Fz-m" >3.95</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.02</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-0.51%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="10">10</strong> + </td> + <td> + <div class="option_entry Fz-m" >115</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00%</div> + </td> + </tr> + + <tr data-row="27" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=110.00">110.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00110000">AAPL141031P00110000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >4.95</div> + </td> + <td> + <div class="option_entry Fz-m" >4.95</div> + </td> + <td> + <div class="option_entry Fz-m" >5.20</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="275">275</strong> + </td> + <td> + <div class="option_entry Fz-m" >302</div> + </td> + <td> + <div class="option_entry Fz-m" >27.05%</div> + </td> + </tr> + + <tr data-row="28" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=111.00">111.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00111000">AAPL141031P00111000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >6.05</div> + </td> + <td> + <div class="option_entry Fz-m" >5.95</div> + </td> + <td> + <div class="option_entry Fz-m" >6.20</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="2">2</strong> + </td> + <td> + <div class="option_entry Fz-m" >7</div> + </td> + <td> + <div class="option_entry Fz-m" >30.96%</div> + </td> + </tr> + + <tr data-row="29" data-row-quote="_" class="in-the-money + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=AAPL&strike=115.00">115.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=AAPL141031P00115000">AAPL141031P00115000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >10.10</div> + </td> + <td> + <div class="option_entry Fz-m" >9.85</div> + </td> + <td> + <div class="option_entry Fz-m" >10.40</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="0">0</strong> + </td> + <td> + <div class="option_entry Fz-m" >52</div> + </td> + <td> + <div class="option_entry Fz-m" >57.91%</div> + </td> + </tr> + + <tr data-row="30" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" 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Inc. Stock - Yahoo! Finance"; + } else if (path && ((path.indexOf('echarts') > -1) || (path.indexOf('q') > -1))) { + res.locals.page_title = parseSymbol(req.query.s) + " Interactive Chart | Yahoo! Inc. Stock - Yahoo! Finance"; + } else { + res.locals.page_title = config.title; + } + callback(); +},function (req, res, data, next) { + /* this would invoke the ESI plugin on YTS */ + res.parentRes.set('X-Esi', '1'); + + var hosts = req.config().hosts, + hostToSet = hosts._default; + + Object.keys(hosts).some(function (host) { + if (req.headers.host.indexOf(host) >= 0) { + hostToSet = hosts[host]; + return true; + } + }); + + /* saving request host server name for esi end point */ + res.locals.requesturl = { + host: hostToSet + }; + + /* saving header x-yahoo-request-url for Darla configuration */ + res.locals.requestxhosturl = req.headers['x-env-host'] ? {host: req.headers['x-env-host']} : {host: hostToSet}; + + //urlPath is used for ./node_modules/assembler/node_modules/dust-helpers/lib/util.js::getSpaceId() + //see: https://git.corp.yahoo.com/sports/sportacular-web + req.context.urlPath = req.path; + + // console.log(JSON.stringify({ + // requesturl: res.locals.requesturl.host, + // requestxhosturl: res.locals.requestxhosturl, + // urlPath: req.context.urlPath + // })); + + next(); +},function (req, res, data, callback) { + + res.locals = res.locals || {}; + if (req.query && req.query.s) { + res.locals.quote = req.query.s; + } + + callback(); +},function (req, res, data, callback) { + var params, + ticker, + config, i; + + req = req || {}; + req.params = req.params || {}; + + config = req.config() || {}; + + + data = data || {}; + + params = req.params || {}; + ticker = (params.ticker || (req.query && req.query.s) || 'YHOO').toUpperCase(); + ticker = ticker.split('+')[0];//Split on + if it's in the ticker + ticker = ticker.split(' ')[0];//Split on space if it's in the ticker + + params.tickers = []; + if (config.default_market_tickers) { + params.tickers = params.tickers.concat(config.default_market_tickers); + } + params.tickers.push(ticker); + params.tickers = params.tickers.join(','); + params.format = 'inflated'; + + //Move this into a new action + res.locals.isTablet = config.isTablet; + + quoteStore.read('finance_quote', params, req, function (err, qData) { + if (!err && qData.quotes && qData.quotes.length > 0) { + res.locals.quoteData = qData; + for (i = 0; i < qData.quotes.length; i = i + 1) { + if (qData.quotes[i].symbol.toUpperCase() === ticker.toUpperCase()) { + params.ticker_securityType = qData.quotes[i].type; + } + } + params.tickers = ticker; + } + callback(); + }); +},function (req, res, data, callback) { + + marketTimeStore.read('markettime', {}, req, function (err, data) { + if (data && data.index) { + res.parentRes.locals.markettime = data.index.markettime; + } + callback(); + }); +}],"after":[]}},"context":{"authed":"0","ynet":"0","ssl":"1","spdy":"0","bucket":"","colo":"gq1","device":"desktop","environment":"prod","lang":"en-US","partner":"none","site":"finance","region":"US","intl":"us","tz":"America\u002FLos_Angeles","edgepipeEnabled":false,"urlPath":"\u002Fq\u002Fop"},"intl":{"locales":"en-US"},"user":{"crumb":"7UQ1gVX3rPU"}}; +root.YUI_config = {"version":"3.17.2","base":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?yui:3.17.2\u002F","comboBase":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?","comboSep":"&","root":"yui:3.17.2\u002F","filter":"min","logLevel":"error","combine":true,"patches":[function patchLangBundlesRequires(Y, loader) { + var getRequires = loader.getRequires; + loader.getRequires = function (mod) { + var i, j, m, name, mods, loadDefaultBundle, + locales = Y.config.lang || [], + r = getRequires.apply(this, arguments); + // expanding requirements with optional requires + if (mod.langBundles && !mod.langBundlesExpanded) { + mod.langBundlesExpanded = []; + locales = typeof locales === 'string' ? 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year = today.year - month = today.month + 1 - if month > 12: - year = year + 1 - month = 1 - cls.expiry = datetime(year, month, 1) + cls.year = today.year + cls.month = today.month + 1 + if cls.month > 12: + cls.year = cls.year + 1 + cls.month = 1 + cls.expiry = datetime(cls.year, cls.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) - cls.tables1 = cls.aapl._parse_option_page_from_yahoo(cls.root1) - cls.unprocessed_data1 = web._parse_options_data(cls.tables1[cls.aapl._TABLE_LOC['puts']]) - cls.data1 = cls.aapl._process_data(cls.unprocessed_data1, 'put') + cls.data1 = cls.aapl._option_frames_from_url(cls.html1)['puts'] @classmethod def tearDownClass(cls): @@ -297,9 +293,9 @@ def test_get_put_data(self): self.assertTrue(len(puts) > 1) @network - def test_get_expiry_months(self): + def test_get_expiry_dates(self): try: - dates = self.aapl._get_expiry_months() + dates, _ = self.aapl._get_expiry_dates_and_links() except RemoteDataError as e: raise nose.SkipTest(e) self.assertTrue(len(dates) > 1) @@ -312,6 +308,14 @@ def test_get_all_data(self): raise nose.SkipTest(e) self.assertTrue(len(data) > 1) + @network + def test_get_data_with_list(self): + try: + data = self.aapl.get_call_data(expiry=self.aapl.expiry_dates) + except RemoteDataError as e: + raise nose.SkipTest(e) + self.assertTrue(len(data) > 1) + @network def test_get_all_data_calls_only(self): try: @@ -323,21 +327,29 @@ def test_get_all_data_calls_only(self): @network def test_sample_page_price_quote_time1(self): #Tests the weekend quote time format - price, quote_time = self.aapl._get_underlying_price(self.root1) + price, quote_time = self.aapl._get_underlying_price(self.html1) self.assertIsInstance(price, (int, float, complex)) self.assertIsInstance(quote_time, (datetime, Timestamp)) def test_chop(self): #regression test for #7625 self.aapl.chop_data(self.data1, above_below=2, underlying_price=np.nan) - chopped = self.aapl.chop_data(self.data1, above_below=2, underlying_price=300) + chopped = self.aapl.chop_data(self.data1, above_below=2, underlying_price=100) self.assertIsInstance(chopped, DataFrame) self.assertTrue(len(chopped) > 1) + def test_chop_out_of_strike_range(self): + #regression test for #7625 + self.aapl.chop_data(self.data1, above_below=2, underlying_price=np.nan) + chopped = self.aapl.chop_data(self.data1, above_below=2, underlying_price=100000) + self.assertIsInstance(chopped, DataFrame) + self.assertTrue(len(chopped) > 1) + + @network def test_sample_page_price_quote_time2(self): #Tests the weekday quote time format - price, quote_time = self.aapl._get_underlying_price(self.root2) + price, quote_time = self.aapl._get_underlying_price(self.html2) self.assertIsInstance(price, (int, float, complex)) self.assertIsInstance(quote_time, (datetime, Timestamp)) @@ -346,62 +358,29 @@ def test_sample_page_chg_float(self): #Tests that numeric columns with comma's are appropriately dealt with self.assertEqual(self.data1['Chg'].dtype, 'float64') + @network + def test_month_year(self): + try: + data = self.aapl.get_call_data(month=self.month, year=self.year) + except RemoteDataError as e: + raise nose.SkipTest(e) + + self.assertTrue(len(data) > 1) + class TestOptionsWarnings(tm.TestCase): @classmethod def setUpClass(cls): super(TestOptionsWarnings, cls).setUpClass() - _skip_if_no_lxml() - - with assert_produces_warning(FutureWarning): - cls.aapl = web.Options('aapl') - - today = datetime.today() - cls.year = today.year - cls.month = today.month + 1 - if cls.month > 12: - cls.year += 1 - cls.month = 1 @classmethod def tearDownClass(cls): super(TestOptionsWarnings, cls).tearDownClass() - del cls.aapl, cls.year, cls.month - - @network - def test_get_options_data_warning(self): - with assert_produces_warning(): - try: - self.aapl.get_options_data(month=self.month, year=self.year) - except RemoteDataError as e: - raise nose.SkipTest(e) - - @network - def test_get_near_stock_price_warning(self): - with assert_produces_warning(): - try: - options_near = self.aapl.get_near_stock_price(call=True, - put=True, - month=self.month, - year=self.year) - except RemoteDataError as e: - raise nose.SkipTest(e) - - @network - def test_get_call_data_warning(self): - with assert_produces_warning(): - try: - self.aapl.get_call_data(month=self.month, year=self.year) - except RemoteDataError as e: - raise nose.SkipTest(e) @network - def test_get_put_data_warning(self): + def test_options_source_warning(self): with assert_produces_warning(): - try: - self.aapl.get_put_data(month=self.month, year=self.year) - except RemoteDataError as e: - raise nose.SkipTest(e) + aapl = web.Options('aapl') class TestDataReader(tm.TestCase):
This should fix #8612. Will need a sample of a during the week HTML to make sure that the underlying price and quote time work with that format. I'll update for that on Monday.
https://api.github.com/repos/pandas-dev/pandas/pulls/8631
2014-10-25T04:46:36Z
2014-11-05T11:41:57Z
2014-11-05T11:41:57Z
2014-11-10T15:25:50Z
BUG: cut/qcut on Series with "retbins" (GH8589)
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index fa1b8b24e75b5..7b3aaa00e7ea9 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -47,3 +47,4 @@ Experimental Bug Fixes ~~~~~~~~~ +- Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py index 3bdd49673ca71..4a0218bef6001 100644 --- a/pandas/tools/tests/test_tile.py +++ b/pandas/tools/tests/test_tile.py @@ -248,6 +248,16 @@ def test_qcut_return_categorical(self): ordered=True)) tm.assert_series_equal(res, exp) + def test_series_retbins(self): + # GH 8589 + s = Series(np.arange(4)) + result, bins = cut(s, 2, retbins=True) + assert_equal(result.cat.codes.values, [0, 0, 1, 1]) + assert_almost_equal(bins, [-0.003, 1.5, 3]) + + result, bins = qcut(s, 2, retbins=True) + assert_equal(result.cat.codes.values, [0, 0, 1, 1]) + assert_almost_equal(bins, [0, 1.5, 3]) def curpath(): diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index 5eddd2f8dec33..6830919d9c09f 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -109,11 +109,8 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3, if (np.diff(bins) < 0).any(): raise ValueError('bins must increase monotonically.') - res = _bins_to_cuts(x, bins, right=right, labels=labels,retbins=retbins, precision=precision, - include_lowest=include_lowest) - if isinstance(x, Series): - res = Series(res, index=x.index) - return res + return _bins_to_cuts(x, bins, right=right, labels=labels,retbins=retbins, precision=precision, + include_lowest=include_lowest) @@ -168,18 +165,21 @@ def qcut(x, q, labels=None, retbins=False, precision=3): else: quantiles = q bins = algos.quantile(x, quantiles) - res = _bins_to_cuts(x, bins, labels=labels, retbins=retbins,precision=precision, - include_lowest=True) - if isinstance(x, Series): - res = Series(res, index=x.index) - return res + return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,precision=precision, + include_lowest=True) def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False, precision=3, name=None, include_lowest=False): - if name is None and isinstance(x, Series): - name = x.name + x_is_series = isinstance(x, Series) + series_index = None + + if x_is_series: + series_index = x.index + if name is None: + name = x.name + x = np.asarray(x) side = 'left' if right else 'right' @@ -224,6 +224,9 @@ def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False, fac = fac.astype(np.float64) np.putmask(fac, na_mask, np.nan) + if x_is_series: + fac = Series(fac, index=series_index) + if not retbins: return fac
Fixes #8589. I centralized a bit of logic into tile.py's `_bins_to_cuts()` so that this change could be made in one place, hope that's okay.
https://api.github.com/repos/pandas-dev/pandas/pulls/8622
2014-10-24T04:56:35Z
2014-10-24T18:36:38Z
2014-10-24T18:36:38Z
2014-10-30T11:20:23Z
Fix shape attribute for MultiIndex
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index 7b3aaa00e7ea9..fd34099ffe75b 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -47,4 +47,7 @@ Experimental Bug Fixes ~~~~~~~~~ + - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) + +- Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`) diff --git a/pandas/core/base.py b/pandas/core/base.py index 5d6f39e1792c3..71a08e0dd553d 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -297,7 +297,7 @@ def transpose(self): @property def shape(self): """ return a tuple of the shape of the underlying data """ - return self._data.shape + return self.values.shape @property def ndim(self): diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 3c5f3a8d6b6d3..daea405a873ae 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -83,6 +83,17 @@ def f(): pass tm.assertRaisesRegexp(ValueError,'The truth value of a',f) + def test_ndarray_compat_properties(self): + + idx = self.create_index() + self.assertTrue(idx.T.equals(idx)) + self.assertTrue(idx.transpose().equals(idx)) + + values = idx.values + for prop in ['shape', 'ndim', 'size', 'itemsize', 'nbytes']: + self.assertEqual(getattr(idx, prop), getattr(values, prop)) + + class TestIndex(Base, tm.TestCase): _holder = Index _multiprocess_can_split_ = True
This fix ensures that `idx.shape == (len(idx),)` if `idx` is a MultiIndex. Also added tests for other ndarray compat properties.
https://api.github.com/repos/pandas-dev/pandas/pulls/8609
2014-10-23T02:29:08Z
2014-10-24T23:51:37Z
2014-10-24T23:51:37Z
2014-10-30T11:20:23Z
DOC: reorg whatsnew files to subfolder
diff --git a/doc/source/whatsnew.rst b/doc/source/whatsnew.rst index e79f36231e2a7..6ec72ad3a951c 100644 --- a/doc/source/whatsnew.rst +++ b/doc/source/whatsnew.rst @@ -18,46 +18,46 @@ What's New These are new features and improvements of note in each release. -.. include:: v0.15.1.txt +.. include:: whatsnew/v0.15.1.txt -.. include:: v0.15.0.txt +.. include:: whatsnew/v0.15.0.txt -.. include:: v0.14.1.txt +.. include:: whatsnew/v0.14.1.txt -.. include:: v0.14.0.txt +.. include:: whatsnew/v0.14.0.txt -.. include:: v0.13.1.txt +.. include:: whatsnew/v0.13.1.txt -.. include:: v0.13.0.txt +.. include:: whatsnew/v0.13.0.txt -.. include:: v0.12.0.txt +.. include:: whatsnew/v0.12.0.txt -.. include:: v0.11.0.txt +.. include:: whatsnew/v0.11.0.txt -.. include:: v0.10.1.txt +.. include:: whatsnew/v0.10.1.txt -.. include:: v0.10.0.txt +.. include:: whatsnew/v0.10.0.txt -.. include:: v0.9.1.txt +.. include:: whatsnew/v0.9.1.txt -.. include:: v0.9.0.txt +.. include:: whatsnew/v0.9.0.txt -.. include:: v0.8.1.txt +.. include:: whatsnew/v0.8.1.txt -.. include:: v0.8.0.txt +.. include:: whatsnew/v0.8.0.txt -.. include:: v0.7.3.txt +.. include:: whatsnew/v0.7.3.txt -.. include:: v0.7.2.txt +.. include:: whatsnew/v0.7.2.txt -.. include:: v0.7.1.txt +.. include:: whatsnew/v0.7.1.txt -.. include:: v0.7.0.txt +.. include:: whatsnew/v0.7.0.txt -.. include:: v0.6.1.txt +.. include:: whatsnew/v0.6.1.txt -.. include:: v0.6.0.txt +.. include:: whatsnew/v0.6.0.txt -.. include:: v0.5.0.txt +.. include:: whatsnew/v0.5.0.txt -.. include:: v0.4.x.txt +.. include:: whatsnew/v0.4.x.txt diff --git a/doc/source/v0.10.0.txt b/doc/source/whatsnew/v0.10.0.txt similarity index 100% rename from doc/source/v0.10.0.txt rename to doc/source/whatsnew/v0.10.0.txt diff --git a/doc/source/v0.10.1.txt b/doc/source/whatsnew/v0.10.1.txt similarity index 100% rename from doc/source/v0.10.1.txt rename to doc/source/whatsnew/v0.10.1.txt diff --git a/doc/source/v0.11.0.txt b/doc/source/whatsnew/v0.11.0.txt similarity index 100% rename from doc/source/v0.11.0.txt rename to doc/source/whatsnew/v0.11.0.txt diff --git a/doc/source/v0.12.0.txt b/doc/source/whatsnew/v0.12.0.txt similarity index 100% rename from doc/source/v0.12.0.txt rename to doc/source/whatsnew/v0.12.0.txt diff --git a/doc/source/v0.13.0.txt b/doc/source/whatsnew/v0.13.0.txt similarity index 100% rename from doc/source/v0.13.0.txt rename to doc/source/whatsnew/v0.13.0.txt diff --git a/doc/source/v0.13.1.txt b/doc/source/whatsnew/v0.13.1.txt similarity index 100% rename from doc/source/v0.13.1.txt rename to doc/source/whatsnew/v0.13.1.txt diff --git a/doc/source/v0.14.0.txt b/doc/source/whatsnew/v0.14.0.txt similarity index 100% rename from doc/source/v0.14.0.txt rename to doc/source/whatsnew/v0.14.0.txt diff --git a/doc/source/v0.14.1.txt b/doc/source/whatsnew/v0.14.1.txt similarity index 100% rename from doc/source/v0.14.1.txt rename to doc/source/whatsnew/v0.14.1.txt diff --git a/doc/source/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt similarity index 100% rename from doc/source/v0.15.0.txt rename to doc/source/whatsnew/v0.15.0.txt diff --git a/doc/source/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt similarity index 100% rename from doc/source/v0.15.1.txt rename to doc/source/whatsnew/v0.15.1.txt diff --git a/doc/source/v0.4.x.txt b/doc/source/whatsnew/v0.4.x.txt similarity index 100% rename from doc/source/v0.4.x.txt rename to doc/source/whatsnew/v0.4.x.txt diff --git a/doc/source/v0.5.0.txt b/doc/source/whatsnew/v0.5.0.txt similarity index 100% rename from doc/source/v0.5.0.txt rename to doc/source/whatsnew/v0.5.0.txt diff --git a/doc/source/v0.6.0.txt b/doc/source/whatsnew/v0.6.0.txt similarity index 100% rename from doc/source/v0.6.0.txt rename to doc/source/whatsnew/v0.6.0.txt diff --git a/doc/source/v0.6.1.txt b/doc/source/whatsnew/v0.6.1.txt similarity index 100% rename from doc/source/v0.6.1.txt rename to doc/source/whatsnew/v0.6.1.txt diff --git a/doc/source/v0.7.0.txt b/doc/source/whatsnew/v0.7.0.txt similarity index 100% rename from doc/source/v0.7.0.txt rename to doc/source/whatsnew/v0.7.0.txt diff --git a/doc/source/v0.7.1.txt b/doc/source/whatsnew/v0.7.1.txt similarity index 100% rename from doc/source/v0.7.1.txt rename to doc/source/whatsnew/v0.7.1.txt diff --git a/doc/source/v0.7.2.txt b/doc/source/whatsnew/v0.7.2.txt similarity index 100% rename from doc/source/v0.7.2.txt rename to doc/source/whatsnew/v0.7.2.txt diff --git a/doc/source/v0.7.3.txt b/doc/source/whatsnew/v0.7.3.txt similarity index 100% rename from doc/source/v0.7.3.txt rename to doc/source/whatsnew/v0.7.3.txt diff --git a/doc/source/v0.8.0.txt b/doc/source/whatsnew/v0.8.0.txt similarity index 100% rename from doc/source/v0.8.0.txt rename to doc/source/whatsnew/v0.8.0.txt diff --git a/doc/source/v0.8.1.txt b/doc/source/whatsnew/v0.8.1.txt similarity index 100% rename from doc/source/v0.8.1.txt rename to doc/source/whatsnew/v0.8.1.txt diff --git a/doc/source/v0.9.0.txt b/doc/source/whatsnew/v0.9.0.txt similarity index 100% rename from doc/source/v0.9.0.txt rename to doc/source/whatsnew/v0.9.0.txt diff --git a/doc/source/v0.9.1.txt b/doc/source/whatsnew/v0.9.1.txt similarity index 100% rename from doc/source/v0.9.1.txt rename to doc/source/whatsnew/v0.9.1.txt
Moved all whatsnew files into a subfolder, because it are becoming a bit too many files in one folder (doc/source/) I think. @jreback What do you think?
https://api.github.com/repos/pandas-dev/pandas/pulls/8606
2014-10-22T20:48:17Z
2014-10-23T06:57:57Z
2014-10-23T06:57:57Z
2014-10-23T06:57:57Z
DOC: read_html docstring update link to read_csv and infer_types mention
diff --git a/pandas/io/html.py b/pandas/io/html.py index 402758815e95b..7ef1e2d2df374 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -786,10 +786,7 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, latest information on table attributes for the modern web. parse_dates : bool, optional - See :func:`~pandas.io.parsers.read_csv` for more details. In 0.13, this - parameter can sometimes interact strangely with ``infer_types``. If you - get a large number of ``NaT`` values in your results, consider passing - ``infer_types=False`` and manually converting types afterwards. + See :func:`~pandas.read_csv` for more details. tupleize_cols : bool, optional If ``False`` try to parse multiple header rows into a @@ -837,7 +834,7 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, See Also -------- - pandas.io.parsers.read_csv + pandas.read_csv """ if infer_types is not None: warnings.warn("infer_types has no effect since 0.15", FutureWarning)
- changed link to toplevel read_csv - since `infer_types` is in essence removed, the comment made no sense anymore I think
https://api.github.com/repos/pandas-dev/pandas/pulls/8605
2014-10-22T20:43:13Z
2014-10-27T15:39:02Z
2014-10-27T15:39:02Z
2014-10-27T15:39:02Z
Fix typo in visualization.rst doc
diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 83b862e11e438..f30d6c9d5d4c0 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -1186,7 +1186,7 @@ with "(right)" in the legend. To turn off the automatic marking, use the Suppressing Tick Resolution Adjustment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -pandas includes automatically tick resolution adjustment for regular frequency +pandas includes automatic tick resolution adjustment for regular frequency time-series data. For limited cases where pandas cannot infer the frequency information (e.g., in an externally created ``twinx``), you can choose to suppress this behavior for alignment purposes.
https://api.github.com/repos/pandas-dev/pandas/pulls/8604
2014-10-22T19:28:18Z
2014-10-22T20:47:38Z
2014-10-22T20:47:38Z
2014-10-22T20:47:45Z
Use '+' to qualify memory usage in df.info() if appropriate
diff --git a/doc/source/faq.rst b/doc/source/faq.rst index 2befb22ab5de4..b93e5ae9c922a 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -50,6 +50,10 @@ when calling ``df.info()``: df.info() +The ``+`` symbol indicates that the true memory usage could be higher, because +pandas does not count the memory used by values in columns with +``dtype=object``. + By default the display option is set to ``True`` but can be explicitly overridden by passing the ``memory_usage`` argument when invoking ``df.info()``. diff --git a/doc/source/v0.15.1.txt b/doc/source/v0.15.1.txt index 2bd88531f92d9..fa1b8b24e75b5 100644 --- a/doc/source/v0.15.1.txt +++ b/doc/source/v0.15.1.txt @@ -28,6 +28,8 @@ Enhancements - Added option to select columns when importing Stata files (:issue:`7935`) +- Qualify memory usage in ``DataFrame.info()`` by adding ``+`` if it is a lower bound (:issue:`8578`) + .. _whatsnew_0151.performance: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 2c172d6fe1af0..d90ef76ddfa5e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1478,13 +1478,13 @@ def _verbose_repr(): def _non_verbose_repr(): lines.append(self.columns.summary(name='Columns')) - def _sizeof_fmt(num): + def _sizeof_fmt(num, size_qualifier): # returns size in human readable format for x in ['bytes', 'KB', 'MB', 'GB', 'TB']: if num < 1024.0: - return "%3.1f %s" % (num, x) + return "%3.1f%s %s" % (num, size_qualifier, x) num /= 1024.0 - return "%3.1f %s" % (num, 'PB') + return "%3.1f%s %s" % (num, size_qualifier, 'PB') if verbose: _verbose_repr() @@ -1502,8 +1502,14 @@ def _sizeof_fmt(num): if memory_usage is None: memory_usage = get_option('display.memory_usage') if memory_usage: # append memory usage of df to display + # size_qualifier is just a best effort; not guaranteed to catch all + # cases (e.g., it misses categorical data even with object + # categories) + size_qualifier = ('+' if 'object' in counts + or self.index.dtype.kind == 'O' else '') + mem_usage = self.memory_usage(index=True).sum() lines.append("memory usage: %s\n" % - _sizeof_fmt(self.memory_usage(index=True).sum())) + _sizeof_fmt(mem_usage, size_qualifier)) _put_lines(buf, lines) def memory_usage(self, index=False): diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index e5064544b292e..3f4d825a4b82e 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -6732,6 +6732,21 @@ def test_info_memory_usage(self): res = buf.getvalue().splitlines() self.assertTrue("memory usage: " not in res[-1]) + df.info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + # memory usage is a lower bound, so print it as XYZ+ MB + self.assertTrue(re.match(r"memory usage: [^+]+\+", res[-1])) + + df.iloc[:, :5].info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + # excluded column with object dtype, so estimate is accurate + self.assertFalse(re.match(r"memory usage: [^+]+\+", res[-1])) + + df_with_object_index = pd.DataFrame({'a': [1]}, index=['foo']) + df_with_object_index.info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + self.assertTrue(re.match(r"memory usage: [^+]+\+", res[-1])) + # Test a DataFrame with duplicate columns dtypes = ['int64', 'int64', 'int64', 'float64'] data = {}
This should resolve @kay1793's complaint in #8578.
https://api.github.com/repos/pandas-dev/pandas/pulls/8599
2014-10-22T07:36:49Z
2014-10-22T10:54:40Z
2014-10-22T10:54:40Z
2014-10-30T11:20:23Z
Function pointer misspecified in io documentaion
diff --git a/doc/source/io.rst b/doc/source/io.rst index ae07e0af10c0e..e0c6c79380bea 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3411,7 +3411,7 @@ Of course, you can specify a more "complex" query. pd.read_sql_query("SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", engine) -The func:`~pandas.read_sql_query` function supports a ``chunksize`` argument. +The :func:`~pandas.read_sql_query` function supports a ``chunksize`` argument. Specifying this will return an iterator through chunks of the query result: .. ipython:: python
The pointer to `read_sql_query` is not specified correctly in `doc/source/io.rst` .
https://api.github.com/repos/pandas-dev/pandas/pulls/8598
2014-10-21T21:40:19Z
2014-10-21T23:20:26Z
2014-10-21T23:20:26Z
2014-10-30T11:20:23Z
ENH: Raise error in certain unhandled _reduce cases.
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index dc69bd9f55752..7d5ce1952e7b2 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -30,6 +30,8 @@ Enhancements - Qualify memory usage in ``DataFrame.info()`` by adding ``+`` if it is a lower bound (:issue:`8578`) +- Raise errors in certain aggregation cases where an argument such as ``numeric_only`` is not handled (:issue:`8592`). + .. _whatsnew_0151.performance: diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index b35cfdcf7c8f1..d343dccacb6b8 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -1210,8 +1210,8 @@ def __setitem__(self, key, value): self._codes[key] = lindexer #### reduction ops #### - def _reduce(self, op, axis=0, skipna=True, numeric_only=None, - filter_type=None, name=None, **kwds): + def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): """ perform the reduction type operation """ func = getattr(self,name,None) if func is None: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index d90ef76ddfa5e..c1b92147612aa 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4129,7 +4129,7 @@ def any(self, axis=None, bool_only=None, skipna=True, level=None, if level is not None: return self._agg_by_level('any', axis=axis, level=level, skipna=skipna) - return self._reduce(nanops.nanany, axis=axis, skipna=skipna, + return self._reduce(nanops.nanany, 'any', axis=axis, skipna=skipna, numeric_only=bool_only, filter_type='bool') def all(self, axis=None, bool_only=None, skipna=True, level=None, @@ -4160,11 +4160,11 @@ def all(self, axis=None, bool_only=None, skipna=True, level=None, if level is not None: return self._agg_by_level('all', axis=axis, level=level, skipna=skipna) - return self._reduce(nanops.nanall, axis=axis, skipna=skipna, + return self._reduce(nanops.nanall, 'all', axis=axis, skipna=skipna, numeric_only=bool_only, filter_type='bool') - def _reduce(self, op, axis=0, skipna=True, numeric_only=None, - filter_type=None, name=None, **kwds): + def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): axis = self._get_axis_number(axis) f = lambda x: op(x, axis=axis, skipna=skipna, **kwds) labels = self._get_agg_axis(axis) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 53abfe10fe8ea..71668a73d9286 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3934,9 +3934,8 @@ def stat_func(self, axis=None, skipna=None, level=None, if level is not None: return self._agg_by_level(name, axis=axis, level=level, skipna=skipna) - return self._reduce(f, axis=axis, - skipna=skipna, numeric_only=numeric_only, - name=name) + return self._reduce(f, name, axis=axis, + skipna=skipna, numeric_only=numeric_only) stat_func.__name__ = name return stat_func @@ -4005,7 +4004,7 @@ def stat_func(self, axis=None, skipna=None, level=None, ddof=1, if level is not None: return self._agg_by_level(name, axis=axis, level=level, skipna=skipna, ddof=ddof) - return self._reduce(f, axis=axis, + return self._reduce(f, name, axis=axis, skipna=skipna, ddof=ddof) stat_func.__name__ = name return stat_func diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 686a0c4f6cca4..72f9c5bd00cb7 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -1045,8 +1045,12 @@ def _apply_2d(self, func, axis): return self._construct_return_type(dict(results)) - def _reduce(self, op, axis=0, skipna=True, numeric_only=None, - filter_type=None, name=None, **kwds): + def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): + if numeric_only: + raise NotImplementedError( + 'Panel.{0} does not implement numeric_only.'.format(name)) + axis_name = self._get_axis_name(axis) axis_number = self._get_axis_number(axis_name) f = lambda x: op(x, axis=axis_number, skipna=skipna, **kwds) diff --git a/pandas/core/series.py b/pandas/core/series.py index 0408d62ce302c..f5d729b61e770 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2056,8 +2056,8 @@ def apply(self, func, convert_dtype=True, args=(), **kwds): return self._constructor(mapped, index=self.index).__finalize__(self) - def _reduce(self, op, axis=0, skipna=True, numeric_only=None, - filter_type=None, name=None, **kwds): + def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): """ perform a reduction operation @@ -2067,10 +2067,16 @@ def _reduce(self, op, axis=0, skipna=True, numeric_only=None, """ delegate = self.values if isinstance(delegate, np.ndarray): + # Validate that 'axis' is consistent with Series's single axis. + self._get_axis_number(axis) + if numeric_only: + raise NotImplementedError( + 'Series.{0} does not implement numeric_only.'.format(name)) return op(delegate, skipna=skipna, **kwds) - return delegate._reduce(op=op, axis=axis, skipna=skipna, numeric_only=numeric_only, - filter_type=filter_type, name=name, **kwds) + return delegate._reduce(op=op, name=name, axis=axis, skipna=skipna, + numeric_only=numeric_only, + filter_type=filter_type, **kwds) def _maybe_box(self, func, dropna=False): """ diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index bb428b7e4c6bb..39d286f3744e1 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -303,8 +303,8 @@ def __array_finalize__(self, obj): self.name = getattr(obj, 'name', None) self.fill_value = getattr(obj, 'fill_value', None) - def _reduce(self, op, axis=0, skipna=True, numeric_only=None, - filter_type=None, name=None, **kwds): + def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): """ perform a reduction operation """ return op(self.get_values(), skipna=skipna, **kwds) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 7ead8b30e8671..27171984f2873 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1669,11 +1669,11 @@ def test_groupby_multiple_key(self): lambda x: x.month, lambda x: x.day], axis=1) - agged = grouped.agg(lambda x: x.sum(1)) + agged = grouped.agg(lambda x: x.sum()) self.assertTrue(agged.index.equals(df.columns)) assert_almost_equal(df.T.values, agged.values) - agged = grouped.agg(lambda x: x.sum(1)) + agged = grouped.agg(lambda x: x.sum()) assert_almost_equal(df.T.values, agged.values) def test_groupby_multi_corner(self): @@ -1708,7 +1708,7 @@ def test_omit_nuisance(self): # won't work with axis = 1 grouped = df.groupby({'A': 0, 'C': 0, 'D': 1, 'E': 1}, axis=1) result = self.assertRaises(TypeError, grouped.agg, - lambda x: x.sum(1, numeric_only=False)) + lambda x: x.sum(0, numeric_only=False)) def test_omit_nuisance_python_multiple(self): grouped = self.three_group.groupby(['A', 'B']) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 736cdf312b361..14e4e32acae9f 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1,6 +1,7 @@ # pylint: disable=W0612,E1101 from datetime import datetime +from inspect import getargspec import operator import nose @@ -169,6 +170,11 @@ def wrapper(x): self.assertRaises(Exception, f, axis=obj.ndim) + # Unimplemented numeric_only parameter. + if 'numeric_only' in getargspec(f).args: + self.assertRaisesRegexp(NotImplementedError, name, f, + numeric_only=True) + class SafeForSparse(object): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 2d3961a643991..68590e1597bbc 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -5,6 +5,7 @@ from datetime import datetime, timedelta import operator import string +from inspect import getargspec from itertools import product, starmap from distutils.version import LooseVersion @@ -2338,6 +2339,14 @@ def testit(): exp = alternate(s) self.assertEqual(res, exp) + # Invalid axis. + self.assertRaises(ValueError, f, self.series, axis=1) + + # Unimplemented numeric_only parameter. + if 'numeric_only' in getargspec(f).args: + self.assertRaisesRegexp(NotImplementedError, name, f, + self.series, numeric_only=True) + testit() try:
https://api.github.com/repos/pandas-dev/pandas/pulls/8592
2014-10-21T03:14:00Z
2014-10-28T00:03:46Z
2014-10-28T00:03:46Z
2014-10-30T11:20:23Z
DOC/REL: clean-up and restructure v0.15.0 whatsnew file (GH8477)
diff --git a/doc/source/api.rst b/doc/source/api.rst index 2e913d8aae4da..f8068ebc38fa9 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -190,6 +190,8 @@ Standard moving window functions rolling_quantile rolling_window +.. _api.functions_expanding: + Standard expanding window functions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.10.0.txt b/doc/source/whatsnew/v0.10.0.txt index 93ab3b912030d..04159186084f5 100644 --- a/doc/source/whatsnew/v0.10.0.txt +++ b/doc/source/whatsnew/v0.10.0.txt @@ -48,6 +48,7 @@ want to broadcast, we are phasing out this special case (Zen of Python: talking about: .. ipython:: python + :okwarning: import pandas as pd df = pd.DataFrame(np.random.randn(6, 4), diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt index c8c7ed3b5011e..5d7598b749feb 100644 --- a/doc/source/whatsnew/v0.15.0.txt +++ b/doc/source/whatsnew/v0.15.0.txt @@ -17,27 +17,23 @@ users upgrade to this version. - The ``Categorical`` type was integrated as a first-class pandas type, see :ref:`here <whatsnew_0150.cat>` - New scalar type ``Timedelta``, and a new index type ``TimedeltaIndex``, see :ref:`here <whatsnew_0150.timedeltaindex>` - - New DataFrame default display for ``df.info()`` to include memory usage, see :ref:`Memory Usage <whatsnew_0150.memory>` - New datetimelike properties accessor ``.dt`` for Series, see :ref:`Datetimelike Properties <whatsnew_0150.dt>` - - Split indexing documentation into :ref:`Indexing and Selecting Data <indexing>` and :ref:`MultiIndex / Advanced Indexing <advanced>` - - Split out string methods documentation into :ref:`Working with Text Data <text>` + - New DataFrame default display for ``df.info()`` to include memory usage, see :ref:`Memory Usage <whatsnew_0150.memory>` - ``read_csv`` will now by default ignore blank lines when parsing, see :ref:`here <whatsnew_0150.blanklines>` - API change in using Indexes in set operations, see :ref:`here <whatsnew_0150.index_set_ops>` + - Enhancements in the handling of timezones, see :ref:`here <whatsnew_0150.tz>` + - A lot of improvements to the rolling and expanding moment funtions, see :ref:`here <whatsnew_0150.roll>` - Internal refactoring of the ``Index`` class to no longer sub-class ``ndarray``, see :ref:`Internal Refactoring <whatsnew_0150.refactoring>` - dropping support for ``PyTables`` less than version 3.0.0, and ``numexpr`` less than version 2.1 (:issue:`7990`) + - Split indexing documentation into :ref:`Indexing and Selecting Data <indexing>` and :ref:`MultiIndex / Advanced Indexing <advanced>` + - Split out string methods documentation into :ref:`Working with Text Data <text>` +- Check the :ref:`API Changes <whatsnew_0150.api>` and :ref:`deprecations <whatsnew_0150.deprecations>` before updating + - :ref:`Other Enhancements <whatsnew_0150.enhancements>` -- :ref:`API Changes <whatsnew_0150.api>` - -- :ref:`Timezone API Change <whatsnew_0150.tz>` - -- :ref:`Rolling/Expanding Moments API Changes <whatsnew_0150.roll>` - - :ref:`Performance Improvements <whatsnew_0150.performance>` -- :ref:`Deprecations <whatsnew_0150.deprecations>` - - :ref:`Bug Fixes <whatsnew_0150.bug_fixes>` .. warning:: @@ -49,285 +45,169 @@ users upgrade to this version. .. warning:: The refactorings in :class:`~pandas.Categorical` changed the two argument constructor from - "codes/labels and levels" to "values and levels". This can lead to subtle bugs. If you use + "codes/labels and levels" to "values and levels (now called 'categories')". This can lead to subtle bugs. If you use :class:`~pandas.Categorical` directly, please audit your code before updating to this pandas version and change it to use the :meth:`~pandas.Categorical.from_codes` constructor. See more on ``Categorical`` :ref:`here <whatsnew_0150.cat>` -.. _whatsnew_0150.api: - -API changes -~~~~~~~~~~~ -- :func:`describe` on mixed-types DataFrames is more flexible. Type-based column filtering is now possible via the ``include``/``exclude`` arguments. - See the :ref:`docs <basics.describe>` (:issue:`8164`). - - .. ipython:: python - - df = DataFrame({'catA': ['foo', 'foo', 'bar'] * 8, - 'catB': ['a', 'b', 'c', 'd'] * 6, - 'numC': np.arange(24), - 'numD': np.arange(24.) + .5}) - df.describe(include=["object"]) - df.describe(include=["number", "object"], exclude=["float"]) - - Requesting all columns is possible with the shorthand 'all' - - .. ipython:: python - - df.describe(include='all') - - Without those arguments, 'describe` will behave as before, including only numerical columns or, if none are, only categorical columns. See also the :ref:`docs <basics.describe>` - -- Passing multiple levels to :meth:`~pandas.DataFrame.stack()` will now work when multiple level - numbers are passed (:issue:`7660`), and will raise a ``ValueError`` when the - levels aren't all level names or all level numbers. See - :ref:`Reshaping by stacking and unstacking <reshaping.stack_multiple>`. - -- :func:`set_names`, :func:`set_labels`, and :func:`set_levels` methods now take an optional ``level`` keyword argument to all modification of specific level(s) of a MultiIndex. Additionally :func:`set_names` now accepts a scalar string value when operating on an ``Index`` or on a specific level of a ``MultiIndex`` (:issue:`7792`) - - .. ipython:: python - - idx = MultiIndex.from_product([['a'], range(3), list("pqr")], names=['foo', 'bar', 'baz']) - idx.set_names('qux', level=0) - idx.set_names(['qux','baz'], level=[0,1]) - idx.set_levels(['a','b','c'], level='bar') - idx.set_levels([['a','b','c'],[1,2,3]], level=[1,2]) - -- Raise a ``ValueError`` in ``df.to_hdf`` with 'fixed' format, if ``df`` has non-unique columns as the resulting file will be broken (:issue:`7761`) - -.. _whatsnew_0150.blanklines: - -- Made both the C-based and Python engines for `read_csv` and `read_table` ignore empty lines in input as well as - whitespace-filled lines, as long as ``sep`` is not whitespace. This is an API change - that can be controlled by the keyword parameter ``skip_blank_lines``. See :ref:`the docs <io.skiplines>` (:issue:`4466`) - -- Bug in passing a ``DatetimeIndex`` with a timezone that was not being retained in DataFrame construction from a dict (:issue:`7822`) - - In prior versions this would drop the timezone. - - .. ipython:: python - - i = date_range('1/1/2011', periods=3, freq='10s', tz = 'US/Eastern') - i - df = DataFrame( {'a' : i } ) - df - df.dtypes - - This behavior is unchanged. - - .. ipython:: python - - df = DataFrame( ) - df['a'] = i - df - df.dtypes - -- ``SettingWithCopy`` raise/warnings (according to the option ``mode.chained_assignment``) will now be issued when setting a value on a sliced mixed-dtype DataFrame using chained-assignment. (:issue:`7845`, :issue:`7950`) - - .. code-block:: python - - In [1]: df = DataFrame(np.arange(0,9), columns=['count']) - - In [2]: df['group'] = 'b' - - In [3]: df.iloc[0:5]['group'] = 'a' - /usr/local/bin/ipython:1: SettingWithCopyWarning: - A value is trying to be set on a copy of a slice from a DataFrame. - Try using .loc[row_indexer,col_indexer] = value instead - - See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy - -- The ``infer_types`` argument to :func:`~pandas.io.html.read_html` now has no - effect (:issue:`7762`, :issue:`7032`). - -- ``DataFrame.to_stata`` and ``StataWriter`` check string length for - compatibility with limitations imposed in dta files where fixed-width - strings must contain 244 or fewer characters. Attempting to write Stata - dta files with strings longer than 244 characters raises a ``ValueError``. (:issue:`7858`) - -- ``read_stata`` and ``StataReader`` can import missing data information into a - ``DataFrame`` by setting the argument ``convert_missing`` to ``True``. When - using this options, missing values are returned as ``StataMissingValue`` - objects and columns containing missing values have ``object`` data type. (:issue:`8045`) - -- ``Index.isin`` now supports a ``level`` argument to specify which index level - to use for membership tests (:issue:`7892`, :issue:`7890`) - - .. code-block:: python - - In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) - - In [2]: idx.values - Out[2]: array([(0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')], dtype=object) - - In [3]: idx.isin(['a', 'c', 'e'], level=1) - Out[3]: array([ True, False, True, True, False, True], dtype=bool) - -- ``merge``, ``DataFrame.merge``, and ``ordered_merge`` now return the same type - as the ``left`` argument. (:issue:`7737`) -- Histogram from ``DataFrame.plot`` with ``kind='hist'`` (:issue:`7809`), See :ref:`the docs<visualization.hist>`. -- Boxplot from ``DataFrame.plot`` with ``kind='box'`` (:issue:`7998`), See :ref:`the docs<visualization.box>`. -- Consistency when indexing with ``.loc`` and a list-like indexer when no values are found. - - .. ipython:: python +New features +~~~~~~~~~~~~ - df = DataFrame([['a'],['b']],index=[1,2]) - df +.. _whatsnew_0150.cat: - In prior versions there was a difference in these two constructs: +Categoricals in Series/DataFrame +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - ``df.loc[[3]]`` would return a frame reindexed by 3 (with all ``np.nan`` values) - - ``df.loc[[3],:]`` would raise ``KeyError``. +:class:`~pandas.Categorical` can now be included in `Series` and `DataFrames` and gained new +methods to manipulate. Thanks to Jan Schulz for much of this API/implementation. (:issue:`3943`, :issue:`5313`, :issue:`5314`, +:issue:`7444`, :issue:`7839`, :issue:`7848`, :issue:`7864`, :issue:`7914`, :issue:`7768`, :issue:`8006`, :issue:`3678`, +:issue:`8075`, :issue:`8076`, :issue:`8143`, :issue:`8453`, :issue:`8518`). - Both will now raise a ``KeyError``. The rule is that *at least 1* indexer must be found when using a list-like and ``.loc`` (:issue:`7999`) +For full docs, see the :ref:`categorical introduction <categorical>` and the +:ref:`API documentation <api.categorical>`. - Furthermore in prior versions these were also different: +.. ipython:: python - - ``df.loc[[1,3]]`` would return a frame reindexed by [1,3] - - ``df.loc[[1,3],:]`` would raise ``KeyError``. + df = DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']}) - Both will now return a frame reindex by [1,3]. E.g. + df["grade"] = df["raw_grade"].astype("category") + df["grade"] - .. ipython:: python + # Rename the categories + df["grade"].cat.categories = ["very good", "good", "very bad"] - df.loc[[1,3]] - df.loc[[1,3],:] + # Reorder the categories and simultaneously add the missing categories + df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) + df["grade"] + df.sort("grade") + df.groupby("grade").size() - This can also be seen in multi-axis indexing with a ``Panel``. +- ``pandas.core.group_agg`` and ``pandas.core.factor_agg`` were removed. As an alternative, construct + a dataframe and use ``df.groupby(<group>).agg(<func>)``. - .. ipython:: python +- Supplying "codes/labels and levels" to the :class:`~pandas.Categorical` constructor is not + supported anymore. Supplying two arguments to the constructor is now interpreted as + "values and levels (now called 'categories')". Please change your code to use the :meth:`~pandas.Categorical.from_codes` + constructor. - p = Panel(np.arange(2*3*4).reshape(2,3,4), - items=['ItemA','ItemB'], - major_axis=[1,2,3], - minor_axis=['A','B','C','D']) - p +- The ``Categorical.labels`` attribute was renamed to ``Categorical.codes`` and is read + only. If you want to manipulate codes, please use one of the + :ref:`API methods on Categoricals <api.categorical>`. - The following would raise ``KeyError`` prior to 0.15.0: +- The ``Categorical.levels`` attribute is renamed to ``Categorical.categories``. - .. ipython:: python - p.loc[['ItemA','ItemD'],:,'D'] +.. _whatsnew_0150.timedeltaindex: - Furthermore, ``.loc`` will raise If no values are found in a multi-index with a list-like indexer: +TimedeltaIndex/Scalar +^^^^^^^^^^^^^^^^^^^^^ - .. ipython:: python - :okexcept: +We introduce a new scalar type ``Timedelta``, which is a subclass of ``datetime.timedelta``, and behaves in a similar manner, +but allows compatibility with ``np.timedelta64`` types as well as a host of custom representation, parsing, and attributes. +This type is very similar to how ``Timestamp`` works for ``datetimes``. It is a nice-API box for the type. See the :ref:`docs <timedeltas.timedeltas>`. +(:issue:`3009`, :issue:`4533`, :issue:`8209`, :issue:`8187`, :issue:`8190`, :issue:`7869`, :issue:`7661`, :issue:`8345`, :issue:`8471`) - s = Series(np.arange(3,dtype='int64'), - index=MultiIndex.from_product([['A'],['foo','bar','baz']], - names=['one','two']) - ).sortlevel() - s - try: - s.loc[['D']] - except KeyError as e: - print("KeyError: " + str(e)) +.. warning:: -- ``Index`` now supports ``duplicated`` and ``drop_duplicates``. (:issue:`4060`) + ``Timedelta`` scalars (and ``TimedeltaIndex``) component fields are *not the same* as the component fields on a ``datetime.timedelta`` object. For example, ``.seconds`` on a ``datetime.timedelta`` object returns the total number of seconds combined between ``hours``, ``minutes`` and ``seconds``. In contrast, the pandas ``Timedelta`` breaks out hours, minutes, microseconds and nanoseconds separately. - .. ipython:: python + .. ipython:: python - idx = Index([1, 2, 3, 4, 1, 2]) - idx - idx.duplicated() - idx.drop_duplicates() + # Timedelta accessor + tds = Timedelta('31 days 5 min 3 sec') + tds.minutes + tds.seconds -- Assigning values to ``None`` now considers the dtype when choosing an 'empty' value (:issue:`7941`). + # datetime.timedelta accessor + # this is 5 minutes * 60 + 3 seconds + tds.to_pytimedelta().seconds - Previously, assigning to ``None`` in numeric containers changed the - dtype to object (or errored, depending on the call). It now uses - ``NaN``: +.. warning:: - .. ipython:: python + Prior to 0.15.0 ``pd.to_timedelta`` would return a ``Series`` for list-like/Series input, and a ``np.timedelta64`` for scalar input. + It will now return a ``TimedeltaIndex`` for list-like input, ``Series`` for Series input, and ``Timedelta`` for scalar input. - s = Series([1, 2, 3]) - s.loc[0] = None - s + The arguments to ``pd.to_timedelta`` are now ``(arg,unit='ns',box=True,coerce=False)``, previously were ``(arg,box=True,unit='ns')`` as these are more logical. - ``NaT`` is now used similarly for datetime containers. +Consruct a scalar - For object containers, we now preserve ``None`` values (previously these - were converted to ``NaN`` values). +.. ipython:: python - .. ipython:: python + Timedelta('1 days 06:05:01.00003') + Timedelta('15.5us') + Timedelta('1 hour 15.5us') - s = Series(["a", "b", "c"]) - s.loc[0] = None - s + # negative Timedeltas have this string repr + # to be more consistent with datetime.timedelta conventions + Timedelta('-1us') - To insert a ``NaN``, you must explicitly use ``np.nan``. See the :ref:`docs <missing.inserting>`. + # a NaT + Timedelta('nan') -- Previously an enlargement with a mixed-dtype frame would act unlike ``.append`` which will preserve dtypes (related :issue:`2578`, :issue:`8176`): +Access fields for a ``Timedelta`` - .. ipython:: python +.. ipython:: python - df = DataFrame([[True, 1],[False, 2]], - columns=["female","fitness"]) - df - df.dtypes + td = Timedelta('1 hour 3m 15.5us') + td.hours + td.minutes + td.microseconds + td.nanoseconds - # dtypes are now preserved - df.loc[2] = df.loc[1] - df - df.dtypes +Construct a ``TimedeltaIndex`` -- In prior versions, updating a pandas object inplace would not reflect in other python references to this object. (:issue:`8511`,:issue:`5104`) +.. ipython:: python + :suppress: - .. ipython:: python + import datetime + from datetime import timedelta - s = Series([1, 2, 3]) - s2 = s - s += 1.5 +.. ipython:: python - Behavior prior to v0.15.0 + TimedeltaIndex(['1 days','1 days, 00:00:05', + np.timedelta64(2,'D'),timedelta(days=2,seconds=2)]) - .. code-block:: python +Constructing a ``TimedeltaIndex`` with a regular range +.. ipython:: python - # the original object - In [5]: s - Out[5]: - 0 2.5 - 1 3.5 - 2 4.5 - dtype: float64 + timedelta_range('1 days',periods=5,freq='D') + timedelta_range(start='1 days',end='2 days',freq='30T') +You can now use a ``TimedeltaIndex`` as the index of a pandas object - # a reference to the original object - In [7]: s2 - Out[7]: - 0 1 - 1 2 - 2 3 - dtype: int64 +.. ipython:: python - This is now the correct behavior + s = Series(np.arange(5), + index=timedelta_range('1 days',periods=5,freq='s')) + s - .. ipython:: python +You can select with partial string selections - # the original object - s +.. ipython:: python - # a reference to the original object - s2 + s['1 day 00:00:02'] + s['1 day':'1 day 00:00:02'] -- ``Series.to_csv()`` now returns a string when ``path=None``, matching the behaviour of ``DataFrame.to_csv()`` (:issue:`8215`). +Finally, the combination of ``TimedeltaIndex`` with ``DatetimeIndex`` allow certain combination operations that are ``NaT`` preserving: -- ``read_hdf`` now raises ``IOError`` when a file that doesn't exist is passed in. Previously, a new, empty file was created, and a ``KeyError`` raised (:issue:`7715`). +.. ipython:: python -- ``DataFrame.info()`` now ends its output with a newline character (:issue:`8114`) -- add ``copy=True`` argument to ``pd.concat`` to enable pass thru of complete blocks (:issue:`8252`) + tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days']) + tdi.tolist() + dti = date_range('20130101',periods=3) + dti.tolist() + + (dti + tdi).tolist() + (dti - tdi).tolist() + +- iteration of a ``Series`` e.g. ``list(Series(...))`` of ``timedelta64[ns]`` would prior to v0.15.0 return ``np.timedelta64`` for each element. These will now be wrapped in ``Timedelta``. -- Added support for numpy 1.8+ data types (``bool_``, ``int_``, ``float_``, ``string_``) for conversion to R dataframe (:issue:`8400`) -- Concatenating no objects will now raise a ``ValueError`` rather than a bare ``Exception``. -- Merge errors will now be sub-classes of ``ValueError`` rather than raw ``Exception`` (:issue:`8501`) -- ``DataFrame.plot`` and ``Series.plot`` keywords are now have consistent orders (:issue:`8037`) .. _whatsnew_0150.memory: Memory Usage -~~~~~~~~~~~~~ +^^^^^^^^^^^^ Implemented methods to find memory usage of a DataFrame. See the :ref:`FAQ <df-memory-usage>` for more. (:issue:`6852`). @@ -351,10 +231,11 @@ Additionally :meth:`~pandas.DataFrame.memory_usage` is an available method for a df.memory_usage(index=True) + .. _whatsnew_0150.dt: .dt accessor -~~~~~~~~~~~~ +^^^^^^^^^^^^ ``Series`` has gained an accessor to succinctly return datetime like properties for the *values* of the Series, if its a datetime/period like Series. (:issue:`7207`) This will return a Series, indexed like the existing Series. See the :ref:`docs <basics.dt_accessors>` @@ -408,10 +289,11 @@ The ``.dt`` accessor works for period and timedelta dtypes. s.dt.seconds s.dt.components + .. _whatsnew_0150.tz: -Timezone API changes -~~~~~~~~~~~~~~~~~~~~ +Timezone handling improvements +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - ``tz_localize(None)`` for tz-aware ``Timestamp`` and ``DatetimeIndex`` now removes timezone holding local time, previously this resulted in ``Exception`` or ``TypeError`` (:issue:`7812`) @@ -439,14 +321,15 @@ Timezone API changes - ``Timestamp.__repr__`` displays ``dateutil.tz.tzoffset`` info (:issue:`7907`) + .. _whatsnew_0150.roll: -Rolling/Expanding Moments API changes -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Rolling/Expanding Moments improvements +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - :func:`rolling_min`, :func:`rolling_max`, :func:`rolling_cov`, and :func:`rolling_corr` now return objects with all ``NaN`` when ``len(arg) < min_periods <= window`` rather - than raising. (This makes all rolling functions consistent in this behavior), (:issue:`7766`) + than raising. (This makes all rolling functions consistent in this behavior). (:issue:`7766`) Prior to 0.15.0 @@ -520,10 +403,7 @@ Rolling/Expanding Moments API changes rolling_window(s, window=3, win_type='triang', center=True) -- Removed ``center`` argument from :func:`expanding_max`, :func:`expanding_min`, :func:`expanding_sum`, - :func:`expanding_mean`, :func:`expanding_median`, :func:`expanding_std`, :func:`expanding_var`, - :func:`expanding_skew`, :func:`expanding_kurt`, :func:`expanding_quantile`, :func:`expanding_count`, - :func:`expanding_cov`, :func:`expanding_corr`, :func:`expanding_corr_pairwise`, and :func:`expanding_apply`, +- Removed ``center`` argument from all :func:`expanding_ <expanding_apply>` functions (see :ref:`list <api.functions_expanding>`), as the results produced when ``center=True`` did not make much sense. (:issue:`7925`) - Added optional ``ddof`` argument to :func:`expanding_cov` and :func:`rolling_cov`. @@ -643,178 +523,307 @@ Rolling/Expanding Moments API changes See :ref:`Exponentially weighted moment functions <stats.moments.exponentially_weighted>` for details. (:issue:`7912`) -.. _whatsnew_0150.refactoring: - -Internal Refactoring -~~~~~~~~~~~~~~~~~~~~ -In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray`` -but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be -a transparent change with only very limited API implications (:issue:`5080`, :issue:`7439`, :issue:`7796`, :issue:`8024`, :issue:`8367`, :issue:`7997`, :issue:`8522`) +.. _whatsnew_0150.sql: -- you may need to unpickle pandas version < 0.15.0 pickles using ``pd.read_pickle`` rather than ``pickle.load``. See :ref:`pickle docs <io.pickle>` -- when plotting with a ``PeriodIndex``. The ``matplotlib`` internal axes will now be arrays of ``Period`` rather than a ``PeriodIndex``. (this is similar to how a ``DatetimeIndex`` passes arrays of ``datetimes`` now) -- MultiIndexes will now raise similary to other pandas objects w.r.t. truth testing, See :ref:`here <gotchas.truth>` (:issue:`7897`). +Improvements in the sql io module +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. _whatsnew_0150.cat: +- Added support for a ``chunksize`` parameter to ``to_sql`` function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (:issue:`8062`). +- Added support for a ``chunksize`` parameter to ``read_sql`` function. Specifying this argument will return an iterator through chunks of the query result (:issue:`2908`). +- Added support for writing ``datetime.date`` and ``datetime.time`` object columns with ``to_sql`` (:issue:`6932`). +- Added support for specifying a ``schema`` to read from/write to with ``read_sql_table`` and ``to_sql`` (:issue:`7441`, :issue:`7952`). + For example: -Categoricals in Series/DataFrame -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + .. code-block:: python -:class:`~pandas.Categorical` can now be included in `Series` and `DataFrames` and gained new -methods to manipulate. Thanks to Jan Schulz for much of this API/implementation. (:issue:`3943`, :issue:`5313`, :issue:`5314`, -:issue:`7444`, :issue:`7839`, :issue:`7848`, :issue:`7864`, :issue:`7914`, :issue:`7768`, :issue:`8006`, :issue:`3678`, -:issue:`8075`, :issue:`8076`, :issue:`8143`, :issue:`8453`, :issue:`8518`). + df.to_sql('table', engine, schema='other_schema') + pd.read_sql_table('table', engine, schema='other_schema') -For full docs, see the :ref:`categorical introduction <categorical>` and the -:ref:`API documentation <api.categorical>`. +- Added support for writing ``NaN`` values with ``to_sql`` (:issue:`2754`). +- Added support for writing datetime64 columns with ``to_sql`` for all database flavors (:issue:`7103`). -.. ipython:: python - df = DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']}) +.. _whatsnew_0150.api: - df["grade"] = df["raw_grade"].astype("category") - df["grade"] +Backwards incompatible API changes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - # Rename the categories - df["grade"].cat.categories = ["very good", "good", "very bad"] +.. _whatsnew_0150.api_breaking: - # Reorder the categories and simultaneously add the missing categories - df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) - df["grade"] - df.sort("grade") - df.groupby("grade").size() +Breaking changes +^^^^^^^^^^^^^^^^ -- ``pandas.core.group_agg`` and ``pandas.core.factor_agg`` were removed. As an alternative, construct - a dataframe and use ``df.groupby(<group>).agg(<func>)``. +API changes related to ``Categorical`` (see :ref:`here <whatsnew_0150.cat>` +for more details): -- Supplying "codes/labels and levels" to the :class:`~pandas.Categorical` constructor is not - supported anymore. Supplying two arguments to the constructor is now interpreted as - "values and levels". Please change your code to use the :meth:`~pandas.Categorical.from_codes` +- The ``Categorical`` constructor with two arguments changed from + "codes/labels and levels" to "values and levels (now called 'categories')". + This can lead to subtle bugs. If you use :class:`~pandas.Categorical` directly, + please audit your code by changing it to use the :meth:`~pandas.Categorical.from_codes` constructor. -- The ``Categorical.labels`` attribute was renamed to ``Categorical.codes`` and is read - only. If you want to manipulate codes, please use one of the - :ref:`API methods on Categoricals <api.categorical>`. + An old function call like (prior to 0.15.0): -.. _whatsnew_0150.timedeltaindex: + .. code-block:: python -TimedeltaIndex/Scalar -~~~~~~~~~~~~~~~~~~~~~ + pd.Categorical([0,1,0,2,1], levels=['a', 'b', 'c']) -We introduce a new scalar type ``Timedelta``, which is a subclass of ``datetime.timedelta``, and behaves in a similar manner, -but allows compatibility with ``np.timedelta64`` types as well as a host of custom representation, parsing, and attributes. -This type is very similar to how ``Timestamp`` works for ``datetimes``. It is a nice-API box for the type. See the :ref:`docs <timedeltas.timedeltas>`. -(:issue:`3009`, :issue:`4533`, :issue:`8209`, :issue:`8187`, :issue:`8190`, :issue:`7869`, :issue:`7661`, :issue:`8345`, :issue:`8471`) + will have to adapted to the following to keep the same behaviour: -.. warning:: + .. code-block:: python - ``Timedelta`` scalars (and ``TimedeltaIndex``) component fields are *not the same* as the component fields on a ``datetime.timedelta`` object. For example, ``.seconds`` on a ``datetime.timedelta`` object returns the total number of seconds combined between ``hours``, ``minutes`` and ``seconds``. In contrast, the pandas ``Timedelta`` breaks out hours, minutes, microseconds and nanoseconds separately. + In [2]: pd.Categorical.from_codes([0,1,0,2,1], categories=['a', 'b', 'c']) + Out[2]: + [a, b, a, c, b] + Categories (3, object): [a, b, c] - .. ipython:: python +API changes related to the introduction of the ``Timedelta`` scalar (see +:ref:`above <whatsnew_0150.timedeltaindex>` for more details): + +- Prior to 0.15.0 :func:`to_timedelta` would return a ``Series`` for list-like/Series input, + and a ``np.timedelta64`` for scalar input. It will now return a ``TimedeltaIndex`` for + list-like input, ``Series`` for Series input, and ``Timedelta`` for scalar input. - # Timedelta accessor - tds = Timedelta('31 days 5 min 3 sec') - tds.minutes - tds.seconds +For API changes related to the rolling and expanding functions, see detailed overview :ref:`above <whatsnew_0150.roll>`. - # datetime.timedelta accessor - # this is 5 minutes * 60 + 3 seconds - tds.to_pytimedelta().seconds +Other notable API changes: -.. warning:: +- Consistency when indexing with ``.loc`` and a list-like indexer when no values are found. - Prior to 0.15.0 ``pd.to_timedelta`` would return a ``Series`` for list-like/Series input, and a ``np.timedelta64`` for scalar input. - It will now return a ``TimedeltaIndex`` for list-like input, ``Series`` for Series input, and ``Timedelta`` for scalar input. + .. ipython:: python - The arguments to ``pd.to_timedelta`` are now ``(arg,unit='ns',box=True,coerce=False)``, previously were ``(arg,box=True,unit='ns')`` as these are more logical. + df = DataFrame([['a'],['b']],index=[1,2]) + df -Consruct a scalar + In prior versions there was a difference in these two constructs: -.. ipython:: python + - ``df.loc[[3]]`` would return a frame reindexed by 3 (with all ``np.nan`` values) + - ``df.loc[[3],:]`` would raise ``KeyError``. - Timedelta('1 days 06:05:01.00003') - Timedelta('15.5us') - Timedelta('1 hour 15.5us') + Both will now raise a ``KeyError``. The rule is that *at least 1* indexer must be found when using a list-like and ``.loc`` (:issue:`7999`) - # negative Timedeltas have this string repr - # to be more consistent with datetime.timedelta conventions - Timedelta('-1us') + Furthermore in prior versions these were also different: - # a NaT - Timedelta('nan') + - ``df.loc[[1,3]]`` would return a frame reindexed by [1,3] + - ``df.loc[[1,3],:]`` would raise ``KeyError``. + + Both will now return a frame reindex by [1,3]. E.g. + + .. ipython:: python + + df.loc[[1,3]] + df.loc[[1,3],:] + + This can also be seen in multi-axis indexing with a ``Panel``. + + .. ipython:: python + + p = Panel(np.arange(2*3*4).reshape(2,3,4), + items=['ItemA','ItemB'], + major_axis=[1,2,3], + minor_axis=['A','B','C','D']) + p + + The following would raise ``KeyError`` prior to 0.15.0: + + .. ipython:: python + + p.loc[['ItemA','ItemD'],:,'D'] + + Furthermore, ``.loc`` will raise If no values are found in a multi-index with a list-like indexer: + + .. ipython:: python + :okexcept: + + s = Series(np.arange(3,dtype='int64'), + index=MultiIndex.from_product([['A'],['foo','bar','baz']], + names=['one','two']) + ).sortlevel() + s + try: + s.loc[['D']] + except KeyError as e: + print("KeyError: " + str(e)) + +- Assigning values to ``None`` now considers the dtype when choosing an 'empty' value (:issue:`7941`). + + Previously, assigning to ``None`` in numeric containers changed the + dtype to object (or errored, depending on the call). It now uses + ``NaN``: + + .. ipython:: python + + s = Series([1, 2, 3]) + s.loc[0] = None + s + + ``NaT`` is now used similarly for datetime containers. + + For object containers, we now preserve ``None`` values (previously these + were converted to ``NaN`` values). + + .. ipython:: python + + s = Series(["a", "b", "c"]) + s.loc[0] = None + s + + To insert a ``NaN``, you must explicitly use ``np.nan``. See the :ref:`docs <missing.inserting>`. + +- In prior versions, updating a pandas object inplace would not reflect in other python references to this object. (:issue:`8511`, :issue:`5104`) + + .. ipython:: python + + s = Series([1, 2, 3]) + s2 = s + s += 1.5 + + Behavior prior to v0.15.0 + + .. code-block:: python + + + # the original object + In [5]: s + Out[5]: + 0 2.5 + 1 3.5 + 2 4.5 + dtype: float64 + + + # a reference to the original object + In [7]: s2 + Out[7]: + 0 1 + 1 2 + 2 3 + dtype: int64 + + This is now the correct behavior + + .. ipython:: python + + # the original object + s + + # a reference to the original object + s2 + +.. _whatsnew_0150.blanklines: + +- Made both the C-based and Python engines for `read_csv` and `read_table` ignore empty lines in input as well as + whitespace-filled lines, as long as ``sep`` is not whitespace. This is an API change + that can be controlled by the keyword parameter ``skip_blank_lines``. See :ref:`the docs <io.skiplines>` (:issue:`4466`) + +- A timeseries/index localized to UTC when inserted into a Series/DataFrame will preserve the UTC timezone + and inserted as ``object`` dtype rather than being converted to a naive ``datetime64[ns]`` (:issue:`8411`). + +- Bug in passing a ``DatetimeIndex`` with a timezone that was not being retained in DataFrame construction from a dict (:issue:`7822`) + + In prior versions this would drop the timezone, now it retains the timezone, + but gives a column of ``object`` dtype: + + .. ipython:: python + + i = date_range('1/1/2011', periods=3, freq='10s', tz = 'US/Eastern') + i + df = DataFrame( {'a' : i } ) + df + df.dtypes + + Previously this would have yielded a column of ``datetime64`` dtype, but without timezone info. + + The behaviour of assigning a column to an existing dataframe as `df['a'] = i` + remains unchanged (this already returned an ``object`` column with a timezone). + +- When passing multiple levels to :meth:`~pandas.DataFrame.stack()`, it will now raise a ``ValueError`` when the + levels aren't all level names or all level numbers (:issue:`7660`). See + :ref:`Reshaping by stacking and unstacking <reshaping.stack_multiple>`. -Access fields for a ``Timedelta`` +- Raise a ``ValueError`` in ``df.to_hdf`` with 'fixed' format, if ``df`` has non-unique columns as the resulting file will be broken (:issue:`7761`) -.. ipython:: python +- ``SettingWithCopy`` raise/warnings (according to the option ``mode.chained_assignment``) will now be issued when setting a value on a sliced mixed-dtype DataFrame using chained-assignment. (:issue:`7845`, :issue:`7950`) - td = Timedelta('1 hour 3m 15.5us') - td.hours - td.minutes - td.microseconds - td.nanoseconds + .. code-block:: python -Construct a ``TimedeltaIndex`` + In [1]: df = DataFrame(np.arange(0,9), columns=['count']) -.. ipython:: python - :suppress: + In [2]: df['group'] = 'b' - import datetime - from datetime import timedelta + In [3]: df.iloc[0:5]['group'] = 'a' + /usr/local/bin/ipython:1: SettingWithCopyWarning: + A value is trying to be set on a copy of a slice from a DataFrame. + Try using .loc[row_indexer,col_indexer] = value instead -.. ipython:: python + See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy - TimedeltaIndex(['1 days','1 days, 00:00:05', - np.timedelta64(2,'D'),timedelta(days=2,seconds=2)]) +- ``merge``, ``DataFrame.merge``, and ``ordered_merge`` now return the same type + as the ``left`` argument (:issue:`7737`). -Constructing a ``TimedeltaIndex`` with a regular range +- Previously an enlargement with a mixed-dtype frame would act unlike ``.append`` which will preserve dtypes (related :issue:`2578`, :issue:`8176`): -.. ipython:: python + .. ipython:: python - timedelta_range('1 days',periods=5,freq='D') - timedelta_range(start='1 days',end='2 days',freq='30T') + df = DataFrame([[True, 1],[False, 2]], + columns=["female","fitness"]) + df + df.dtypes -You can now use a ``TimedeltaIndex`` as the index of a pandas object + # dtypes are now preserved + df.loc[2] = df.loc[1] + df + df.dtypes -.. ipython:: python +- ``Series.to_csv()`` now returns a string when ``path=None``, matching the behaviour of ``DataFrame.to_csv()`` (:issue:`8215`). - s = Series(np.arange(5), - index=timedelta_range('1 days',periods=5,freq='s')) - s +- ``read_hdf`` now raises ``IOError`` when a file that doesn't exist is passed in. Previously, a new, empty file was created, and a ``KeyError`` raised (:issue:`7715`). -You can select with partial string selections +- ``DataFrame.info()`` now ends its output with a newline character (:issue:`8114`) +- Concatenating no objects will now raise a ``ValueError`` rather than a bare ``Exception``. +- Merge errors will now be sub-classes of ``ValueError`` rather than raw ``Exception`` (:issue:`8501`) +- ``DataFrame.plot`` and ``Series.plot`` keywords are now have consistent orders (:issue:`8037`) -.. ipython:: python - s['1 day 00:00:02'] - s['1 day':'1 day 00:00:02'] +.. _whatsnew_0150.refactoring: -Finally, the combination of ``TimedeltaIndex`` with ``DatetimeIndex`` allow certain combination operations that are ``NaT`` preserving: +Internal Refactoring +^^^^^^^^^^^^^^^^^^^^ -.. ipython:: python +In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray`` +but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This +change allows very easy sub-classing and creation of new index types. This should be +a transparent change with only very limited API implications (:issue:`5080`, :issue:`7439`, :issue:`7796`, :issue:`8024`, :issue:`8367`, :issue:`7997`, :issue:`8522`): - tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days']) - tdi.tolist() - dti = date_range('20130101',periods=3) - dti.tolist() +- you may need to unpickle pandas version < 0.15.0 pickles using ``pd.read_pickle`` rather than ``pickle.load``. See :ref:`pickle docs <io.pickle>` +- when plotting with a ``PeriodIndex``, the matplotlib internal axes will now be arrays of ``Period`` rather than a ``PeriodIndex`` (this is similar to how a ``DatetimeIndex`` passes arrays of ``datetimes`` now) +- MultiIndexes will now raise similary to other pandas objects w.r.t. truth testing, see :ref:`here <gotchas.truth>` (:issue:`7897`). +- When plotting a DatetimeIndex directly with matplotlib's `plot` function, + the axis labels will no longer be formatted as dates but as integers (the + internal representation of a ``datetime64``). To keep the old behaviour you + should add the :meth:`~DatetimeIndex.to_pydatetime()` method: - (dti + tdi).tolist() - (dti - tdi).tolist() + .. code-block:: python -- iteration of a ``Series`` e.g. ``list(Series(...))`` of ``timedelta64[ns]`` would prior to v0.15.0 return ``np.timedelta64`` for each element. These will now be wrapped in ``Timedelta``. + import matplotlib.pyplot as plt + df = pd.DataFrame({'col': np.random.randint(1, 50, 60)}, + index=pd.date_range("2012-01-01", periods=60)) -.. _whatsnew_0150.prior_deprecations: + # this will now format the x axis labels as integers + plt.plot(df.index, df['col']) -Prior Version Deprecations/Changes -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # to keep the date formating, convert the index explicitely to python datetime values + plt.plot(df.index.to_pydatetime(), df['col']) -- Remove ``DataFrame.delevel`` method in favor of ``DataFrame.reset_index`` .. _whatsnew_0150.deprecations: Deprecations -~~~~~~~~~~~~ +^^^^^^^^^^^^ +- The attributes ``Categorical`` ``labels`` and ``levels`` attributes are + deprecated and renamed to ``codes`` and ``categories``. - The ``outtype`` argument to ``pd.DataFrame.to_dict`` has been deprecated in favor of ``orient``. (:issue:`7840`) - The ``convert_dummies`` method has been deprecated in favor of ``get_dummies`` (:issue:`8140`) @@ -826,7 +835,7 @@ Deprecations .. _whatsnew_0150.index_set_ops: -- The ``Index`` set operations ``+`` and ``-`` were deprecated in order to provide these for numeric type operations on certain index types. ``+`` can be replace by ``.union()`` or ``|``, and ``-`` by ``.difference()``. Further the method name ``Index.diff()`` is deprecated and can be replaced by ``Index.difference()`` (:issue:`8226`) +- The ``Index`` set operations ``+`` and ``-`` were deprecated in order to provide these for numeric type operations on certain index types. ``+`` can be replaced by ``.union()`` or ``|``, and ``-`` by ``.difference()``. Further the method name ``Index.diff()`` is deprecated and can be replaced by ``Index.difference()`` (:issue:`8226`) .. code-block:: python @@ -844,34 +853,83 @@ Deprecations # should be replaced by Index(['a','b','c']).difference(Index(['b','c','d'])) -.. _whatsnew_0150.enhancements: +- The ``infer_types`` argument to :func:`~pandas.read_html` now has no + effect and is deprecated (:issue:`7762`, :issue:`7032`). -Enhancements -~~~~~~~~~~~~ -- Added support for a ``chunksize`` parameter to ``to_sql`` function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (:issue:`8062`). -- Added support for a ``chunksize`` parameter to ``read_sql`` function. Specifying this argument will return an iterator through chunks of the query result (:issue:`2908`). -- Added support for writing ``datetime.date`` and ``datetime.time`` object columns with ``to_sql`` (:issue:`6932`). -- Added support for specifying a ``schema`` to read from/write to with ``read_sql_table`` and ``to_sql`` (:issue:`7441`, :issue:`7952`). - For example: +.. _whatsnew_0150.prior_deprecations: - .. code-block:: python +Removal of prior version deprecations/changes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - df.to_sql('table', engine, schema='other_schema') - pd.read_sql_table('table', engine, schema='other_schema') +- Remove ``DataFrame.delevel`` method in favor of ``DataFrame.reset_index`` -- Added support for writing ``NaN`` values with ``to_sql`` (:issue:`2754`). -- Added support for writing datetime64 columns with ``to_sql`` for all database flavors (:issue:`7103`). + + +.. _whatsnew_0150.enhancements: + +Enhancements +~~~~~~~~~~~~ + +Enhancements in the importing/exporting of Stata files: - Added support for bool, uint8, uint16 and uint32 datatypes in ``to_stata`` (:issue:`7097`, :issue:`7365`) - Added conversion option when importing Stata files (:issue:`8527`) +- ``DataFrame.to_stata`` and ``StataWriter`` check string length for + compatibility with limitations imposed in dta files where fixed-width + strings must contain 244 or fewer characters. Attempting to write Stata + dta files with strings longer than 244 characters raises a ``ValueError``. (:issue:`7858`) +- ``read_stata`` and ``StataReader`` can import missing data information into a + ``DataFrame`` by setting the argument ``convert_missing`` to ``True``. When + using this options, missing values are returned as ``StataMissingValue`` + objects and columns containing missing values have ``object`` data type. (:issue:`8045`) +Enhancements in the plotting functions: + - Added ``layout`` keyword to ``DataFrame.plot``. You can pass a tuple of ``(rows, columns)``, one of which can be ``-1`` to automatically infer (:issue:`6667`, :issue:`8071`). - Allow to pass multiple axes to ``DataFrame.plot``, ``hist`` and ``boxplot`` (:issue:`5353`, :issue:`6970`, :issue:`7069`) - Added support for ``c``, ``colormap`` and ``colorbar`` arguments for ``DataFrame.plot`` with ``kind='scatter'`` (:issue:`7780`) +- Histogram from ``DataFrame.plot`` with ``kind='hist'`` (:issue:`7809`), See :ref:`the docs<visualization.hist>`. +- Boxplot from ``DataFrame.plot`` with ``kind='box'`` (:issue:`7998`), See :ref:`the docs<visualization.box>`. + +Other: - ``read_csv`` now has a keyword parameter ``float_precision`` which specifies which floating-point converter the C engine should use during parsing, see :ref:`here <io.float_precision>` (:issue:`8002`, :issue:`8044`) +- Added ``searchsorted`` method to ``Series`` objects (:issue:`7447`) + +- :func:`describe` on mixed-types DataFrames is more flexible. Type-based column filtering is now possible via the ``include``/``exclude`` arguments. + See the :ref:`docs <basics.describe>` (:issue:`8164`). + + .. ipython:: python + + df = DataFrame({'catA': ['foo', 'foo', 'bar'] * 8, + 'catB': ['a', 'b', 'c', 'd'] * 6, + 'numC': np.arange(24), + 'numD': np.arange(24.) + .5}) + df.describe(include=["object"]) + df.describe(include=["number", "object"], exclude=["float"]) + + Requesting all columns is possible with the shorthand 'all' + + .. ipython:: python + + df.describe(include='all') + + Without those arguments, 'describe` will behave as before, including only numerical columns or, if none are, only categorical columns. See also the :ref:`docs <basics.describe>` + +- Added ``split`` as an option to the ``orient`` argument in ``pd.DataFrame.to_dict``. (:issue:`7840`) + +- The ``get_dummies`` method can now be used on DataFrames. By default only + catagorical columns are encoded as 0's and 1's, while other columns are + left untouched. + + .. ipython:: python + + df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'], + 'C': [1, 2, 3]}) + pd.get_dummies(df) + - ``PeriodIndex`` supports ``resolution`` as the same as ``DatetimeIndex`` (:issue:`7708`) - ``pandas.tseries.holiday`` has added support for additional holidays and ways to observe holidays (:issue:`7070`) - ``pandas.tseries.holiday.Holiday`` now supports a list of offsets in Python3 (:issue:`7070`) @@ -900,20 +958,6 @@ Enhancements idx idx + pd.offsets.MonthEnd(3) -- Added ``split`` as an option to the ``orient`` argument in ``pd.DataFrame.to_dict``. (:issue:`7840`) - -- The ``get_dummies`` method can now be used on DataFrames. By default only - catagorical columns are encoded as 0's and 1's, while other columns are - left untouched. - - .. ipython:: python - - df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'], - 'C': [1, 2, 3]}) - pd.get_dummies(df) - - - - Added experimental compatibility with ``openpyxl`` for versions >= 2.0. The ``DataFrame.to_excel`` method ``engine`` keyword now recognizes ``openpyxl1`` and ``openpyxl2`` which will explicitly require openpyxl v1 and v2 respectively, failing if @@ -923,41 +967,64 @@ Enhancements - ``DataFrame.fillna`` can now accept a ``DataFrame`` as a fill value (:issue:`8377`) -- Added ``searchsorted`` method to ``Series`` objects (:issue:`7447`) - -.. _whatsnew_0150.performance: - -Performance -~~~~~~~~~~~ +- Passing multiple levels to :meth:`~pandas.DataFrame.stack()` will now work when multiple level + numbers are passed (:issue:`7660`). See + :ref:`Reshaping by stacking and unstacking <reshaping.stack_multiple>`. -- Performance improvements in ``DatetimeIndex.__iter__`` to allow faster iteration (:issue:`7683`) -- Performance improvements in ``Period`` creation (and ``PeriodIndex`` setitem) (:issue:`5155`) -- Improvements in Series.transform for significant performance gains (revised) (:issue:`6496`) -- Performance improvements in ``StataReader`` when reading large files (:issue:`8040`, :issue:`8073`) -- Performance improvements in ``StataWriter`` when writing large files (:issue:`8079`) -- Performance and memory usage improvements in multi-key ``groupby`` (:issue:`8128`) -- Performance improvements in groupby ``.agg`` and ``.apply`` where builtins max/min were not mapped to numpy/cythonized versions (:issue:`7722`) -- Performance improvement in writing to sql (``to_sql``) of up to 50% (:issue:`8208`). -- Performance benchmarking of groupby for large value of ngroups (:issue:`6787`) -- Performance improvement in ``CustomBusinessDay``, ``CustomBusinessMonth`` (:issue:`8236`) -- Performance improvement for ``MultiIndex.values`` for multi-level indexes containing datetimes (:issue:`8543`) +- :func:`set_names`, :func:`set_labels`, and :func:`set_levels` methods now take an optional ``level`` keyword argument to all modification of specific level(s) of a MultiIndex. Additionally :func:`set_names` now accepts a scalar string value when operating on an ``Index`` or on a specific level of a ``MultiIndex`` (:issue:`7792`) + .. ipython:: python + idx = MultiIndex.from_product([['a'], range(3), list("pqr")], names=['foo', 'bar', 'baz']) + idx.set_names('qux', level=0) + idx.set_names(['qux','baz'], level=[0,1]) + idx.set_levels(['a','b','c'], level='bar') + idx.set_levels([['a','b','c'],[1,2,3]], level=[1,2]) +- ``Index.isin`` now supports a ``level`` argument to specify which index level + to use for membership tests (:issue:`7892`, :issue:`7890`) + .. code-block:: python + In [1]: idx = MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]) + In [2]: idx.values + Out[2]: array([(0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')], dtype=object) + In [3]: idx.isin(['a', 'c', 'e'], level=1) + Out[3]: array([ True, False, True, True, False, True], dtype=bool) +- ``Index`` now supports ``duplicated`` and ``drop_duplicates``. (:issue:`4060`) + .. ipython:: python + idx = Index([1, 2, 3, 4, 1, 2]) + idx + idx.duplicated() + idx.drop_duplicates() +- add ``copy=True`` argument to ``pd.concat`` to enable pass thru of complete blocks (:issue:`8252`) +- Added support for numpy 1.8+ data types (``bool_``, ``int_``, ``float_``, ``string_``) for conversion to R dataframe (:issue:`8400`) +.. _whatsnew_0150.performance: +Performance +~~~~~~~~~~~ +- Performance improvements in ``DatetimeIndex.__iter__`` to allow faster iteration (:issue:`7683`) +- Performance improvements in ``Period`` creation (and ``PeriodIndex`` setitem) (:issue:`5155`) +- Improvements in Series.transform for significant performance gains (revised) (:issue:`6496`) +- Performance improvements in ``StataReader`` when reading large files (:issue:`8040`, :issue:`8073`) +- Performance improvements in ``StataWriter`` when writing large files (:issue:`8079`) +- Performance and memory usage improvements in multi-key ``groupby`` (:issue:`8128`) +- Performance improvements in groupby ``.agg`` and ``.apply`` where builtins max/min were not mapped to numpy/cythonized versions (:issue:`7722`) +- Performance improvement in writing to sql (``to_sql``) of up to 50% (:issue:`8208`). +- Performance benchmarking of groupby for large value of ngroups (:issue:`6787`) +- Performance improvement in ``CustomBusinessDay``, ``CustomBusinessMonth`` (:issue:`8236`) +- Performance improvement for ``MultiIndex.values`` for multi-level indexes containing datetimes (:issue:`8543`) @@ -965,6 +1032,7 @@ Performance Bug Fixes ~~~~~~~~~ + - Bug in pivot_table, when using margins and a dict aggfunc (:issue:`8349`) - Bug in ``read_csv`` where ``squeeze=True`` would return a view (:issue:`8217`) - Bug in checking of table name in ``read_sql`` in certain cases (:issue:`7826`). @@ -1002,44 +1070,26 @@ Bug Fixes - Bug in ``PeriodIndex.unique`` returns int64 ``np.ndarray`` (:issue:`7540`) - Bug in ``groupby.apply`` with a non-affecting mutation in the function (:issue:`8467`) - Bug in ``DataFrame.reset_index`` which has ``MultiIndex`` contains ``PeriodIndex`` or ``DatetimeIndex`` with tz raises ``ValueError`` (:issue:`7746`, :issue:`7793`) - - - Bug in ``DataFrame.plot`` with ``subplots=True`` may draw unnecessary minor xticks and yticks (:issue:`7801`) - Bug in ``StataReader`` which did not read variable labels in 117 files due to difference between Stata documentation and implementation (:issue:`7816`) - Bug in ``StataReader`` where strings were always converted to 244 characters-fixed width irrespective of underlying string size (:issue:`7858`) - - Bug in ``DataFrame.plot`` and ``Series.plot`` may ignore ``rot`` and ``fontsize`` keywords (:issue:`7844`) - - - Bug in ``DatetimeIndex.value_counts`` doesn't preserve tz (:issue:`7735`) - Bug in ``PeriodIndex.value_counts`` results in ``Int64Index`` (:issue:`7735`) - Bug in ``DataFrame.join`` when doing left join on index and there are multiple matches (:issue:`5391`) - - - - Bug in ``GroupBy.transform()`` where int groups with a transform that didn't preserve the index were incorrectly truncated (:issue:`7972`). - - Bug in ``groupby`` where callable objects without name attributes would take the wrong path, and produce a ``DataFrame`` instead of a ``Series`` (:issue:`7929`) - - Bug in ``groupby`` error message when a DataFrame grouping column is duplicated (:issue:`7511`) - - Bug in ``read_html`` where the ``infer_types`` argument forced coercion of date-likes incorrectly (:issue:`7762`, :issue:`7032`). - - - Bug in ``Series.str.cat`` with an index which was filtered as to not include the first item (:issue:`7857`) - - - Bug in ``Timestamp`` cannot parse ``nanosecond`` from string (:issue:`7878`) - Bug in ``Timestamp`` with string offset and ``tz`` results incorrect (:issue:`7833`) - - Bug in ``tslib.tz_convert`` and ``tslib.tz_convert_single`` may return different results (:issue:`7798`) - Bug in ``DatetimeIndex.intersection`` of non-overlapping timestamps with tz raises ``IndexError`` (:issue:`7880`) - Bug in alignment with TimeOps and non-unique indexes (:issue:`8363`) - - - Bug in ``GroupBy.filter()`` where fast path vs. slow path made the filter return a non scalar value that appeared valid but wasn't (:issue:`7870`). - Bug in ``date_range()``/``DatetimeIndex()`` when the timezone was inferred from input dates yet incorrect @@ -1048,46 +1098,23 @@ Bug Fixes - Bug in area plot draws legend with incorrect ``alpha`` when ``stacked=True`` (:issue:`8027`) - ``Period`` and ``PeriodIndex`` addition/subtraction with ``np.timedelta64`` results in incorrect internal representations (:issue:`7740`) - Bug in ``Holiday`` with no offset or observance (:issue:`7987`) - - Bug in ``DataFrame.to_latex`` formatting when columns or index is a ``MultiIndex`` (:issue:`7982`). - - Bug in ``DateOffset`` around Daylight Savings Time produces unexpected results (:issue:`5175`). - - - - - - Bug in ``DataFrame.shift`` where empty columns would throw ``ZeroDivisionError`` on numpy 1.7 (:issue:`8019`) - - - - - - Bug in installation where ``html_encoding/*.html`` wasn't installed and therefore some tests were not running correctly (:issue:`7927`). - - Bug in ``read_html`` where ``bytes`` objects were not tested for in ``_read`` (:issue:`7927`). - - Bug in ``DataFrame.stack()`` when one of the column levels was a datelike (:issue:`8039`) - Bug in broadcasting numpy scalars with ``DataFrame`` (:issue:`8116`) - - - Bug in ``pivot_table`` performed with nameless ``index`` and ``columns`` raises ``KeyError`` (:issue:`8103`) - - Bug in ``DataFrame.plot(kind='scatter')`` draws points and errorbars with different colors when the color is specified by ``c`` keyword (:issue:`8081`) - - - - - Bug in ``Float64Index`` where ``iat`` and ``at`` were not testing and were failing (:issue:`8092`). - Bug in ``DataFrame.boxplot()`` where y-limits were not set correctly when producing multiple axes (:issue:`7528`, :issue:`5517`). - - Bug in ``read_csv`` where line comments were not handled correctly given a custom line terminator or ``delim_whitespace=True`` (:issue:`8122`). - - Bug in ``read_html`` where empty tables caused a ``StopIteration`` (:issue:`7575`) - Bug in casting when setting a column in a same-dtype block (:issue:`7704`) - Bug in accessing groups from a ``GroupBy`` when the original grouper @@ -1097,7 +1124,6 @@ Bug Fixes - Bug in ``GroupBy.count`` with float32 data type were nan values were not excluded (:issue:`8169`). - Bug with stacked barplots and NaNs (:issue:`8175`). - Bug in resample with non evenly divisible offsets (e.g. '7s') (:issue:`8371`) - - Bug in interpolation methods with the ``limit`` keyword when no values needed interpolating (:issue:`7173`). - Bug where ``col_space`` was ignored in ``DataFrame.to_string()`` when ``header=False`` (:issue:`8230`). - Bug with ``DatetimeIndex.asof`` incorrectly matching partial strings and returning the wrong date (:issue:`8245`). @@ -1121,6 +1147,6 @@ Bug Fixes - Bug in ``Series`` that allows it to be indexed by a ``DataFrame`` which has unexpected results. Such indexing is no longer permitted (:issue:`8444`) - Bug in item assignment of a ``DataFrame`` with multi-index columns where right-hand-side columns were not aligned (:issue:`7655`) - Suppress FutureWarning generated by NumPy when comparing object arrays containing NaN for equality (:issue:`7065`) - - Bug in ``DataFrame.eval()`` where the dtype of the ``not`` operator (``~``) was not correctly inferred as ``bool``. +
Closes #8477 What did I change: - some general clean-up (blank lines, ..) - put the different new feature sections in front (as also in the highlights section) - the 2-level menu structure as discussed in #8477 - API section: move some entries to the 'enhancements' section (entries that were not 'real' (in sense of breaking) API changes): - describe keywords include/exclude - new 'level' keyword for Index.set_names/set_levels - new 'level' keyword for Index isin - histogram/boxplot also via `plot` - index new methods duplicated and drop_duplicates Still work in progress!
https://api.github.com/repos/pandas-dev/pandas/pulls/8586
2014-10-19T21:04:51Z
2014-10-27T14:42:02Z
2014-10-27T14:42:02Z
2014-10-30T11:20:23Z
BUG: column name conflict & as_index=False breaks groupby ops
diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index dc69bd9f55752..cc06797b3276e 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -19,7 +19,54 @@ users upgrade to this version. API changes ~~~~~~~~~~~ +- ``groupby`` with ``as_index=False`` will not add erroneous extra columns to + result (:issue:`8582`): + .. code-block:: python + + In [1]: np.random.seed(2718281) + + In [2]: df = pd.DataFrame(np.random.randint(0, 100, (10, 2)), + ...: columns=['jim', 'joe']) + + In [3]: ts = pd.Series(5 * np.random.randint(0, 3, 10)) + + In [4]: df.groupby(ts, as_index=False).max() + Out[4]: + NaN jim joe + 0 0 72 83 + 1 5 77 84 + 2 10 96 65 + +with the new release: + + .. ipython:: python + + np.random.seed(2718281) + df = pd.DataFrame(np.random.randint(0, 100, (10, 2)), + columns=['jim', 'joe']) + df.head() + + ts = pd.Series(5 * np.random.randint(0, 3, 10)) + df.groupby(ts, as_index=False).max() + +- ``groupby`` will not erroneously exclude columns if the column name conflics + with the grouper name (:issue:`8112`): + + .. code-block:: python + + In [1]: df = pd.DataFrame({'jim': range(5), 'joe': range(5, 10)}) + + In [2]: gr = df.groupby(df['jim'] < 2) + + In [3]: _ = gr.nth(0) # invokes the code path which excludes the 1st column + + In [4]: gr.apply(sum) # excludes 1st column from output + Out[4]: + joe + jim + False 24 + True 11 .. _whatsnew_0151.enhancements: @@ -51,3 +98,5 @@ Bug Fixes - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` (:issue:`8589`) - Bug in numeric index operations of add/sub with Float/Index Index with numpy arrays (:issue:`8608`) - Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`) +- Bug in ``GroupBy`` where a name conflict between the grouper and columns + would break ``groupby`` operations (:issue:`7115`, :issue:`8112`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index a141d8cebfd8e..4b85da1b7b224 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -471,7 +471,9 @@ def _set_selection_from_grouper(self): grp = self.grouper if self.as_index and getattr(grp,'groupings',None) is not None and self.obj.ndim > 1: ax = self.obj._info_axis - groupers = [ g.name for g in grp.groupings if g.level is None and g.name is not None and g.name in ax ] + groupers = [g.name for g in grp.groupings + if g.level is None and g.in_axis] + if len(groupers): self._group_selection = ax.difference(Index(groupers)).tolist() @@ -1844,6 +1846,8 @@ class Grouping(object): obj : name : level : + in_axis : if the Grouping is a column in self.obj and hence among + Groupby.exclusions list Returns ------- @@ -1857,7 +1861,7 @@ class Grouping(object): """ def __init__(self, index, grouper=None, obj=None, name=None, level=None, - sort=True): + sort=True, in_axis=False): self.name = name self.level = level @@ -1865,6 +1869,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, self.index = index self.sort = sort self.obj = obj + self.in_axis = in_axis # right place for this? if isinstance(grouper, (Series, Index)) and name is None: @@ -2096,23 +2101,43 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True): groupings = [] exclusions = [] - for i, (gpr, level) in enumerate(zip(keys, levels)): - name = None + + # if the actual grouper should be obj[key] + def is_in_axis(key): + if not _is_label_like(key): + try: + obj._data.items.get_loc(key) + except Exception: + return False + + return True + + # if the the grouper is obj[name] + def is_in_obj(gpr): try: - obj._data.items.get_loc(gpr) - in_axis = True + return id(gpr) == id(obj[gpr.name]) except Exception: - in_axis = False + return False + + for i, (gpr, level) in enumerate(zip(keys, levels)): - if _is_label_like(gpr) or in_axis: - exclusions.append(gpr) - name = gpr - gpr = obj[gpr] + if is_in_obj(gpr): # df.groupby(df['name']) + in_axis, name = True, gpr.name + exclusions.append(name) + + elif is_in_axis(gpr): # df.groupby('name') + in_axis, name, gpr = True, gpr, obj[gpr] + exclusions.append(name) + + else: + in_axis, name = False, None if isinstance(gpr, Categorical) and len(gpr) != len(obj): raise ValueError("Categorical grouper must have len(grouper) == len(data)") - ping = Grouping(group_axis, gpr, obj=obj, name=name, level=level, sort=sort) + ping = Grouping(group_axis, gpr, obj=obj, name=name, + level=level, sort=sort, in_axis=in_axis) + groupings.append(ping) if len(groupings) == 0: @@ -2647,18 +2672,7 @@ def aggregate(self, arg, *args, **kwargs): result = self._aggregate_generic(arg, *args, **kwargs) if not self.as_index: - if isinstance(result.index, MultiIndex): - zipped = zip(result.index.levels, result.index.labels, - result.index.names) - for i, (lev, lab, name) in enumerate(zipped): - result.insert(i, name, - com.take_nd(lev.values, lab, - allow_fill=False)) - result = result.consolidate() - else: - values = result.index.values - name = self.grouper.groupings[0].name - result.insert(0, name, values) + self._insert_inaxis_grouper_inplace(result) result.index = np.arange(len(result)) return result.convert_objects() @@ -3180,6 +3194,17 @@ def _get_data_to_aggregate(self): else: return obj._data, 1 + def _insert_inaxis_grouper_inplace(self, result): + # zip in reverse so we can always insert at loc 0 + izip = zip(* map(reversed, ( + self.grouper.names, + self.grouper.get_group_levels(), + [grp.in_axis for grp in self.grouper.groupings]))) + + for name, lev, in_axis in izip: + if in_axis: + result.insert(0, name, lev) + def _wrap_aggregated_output(self, output, names=None): agg_axis = 0 if self.axis == 1 else 1 agg_labels = self._obj_with_exclusions._get_axis(agg_axis) @@ -3188,11 +3213,7 @@ def _wrap_aggregated_output(self, output, names=None): if not self.as_index: result = DataFrame(output, columns=output_keys) - group_levels = self.grouper.get_group_levels() - zipped = zip(self.grouper.names, group_levels) - - for i, (name, labels) in enumerate(zipped): - result.insert(i, name, labels) + self._insert_inaxis_grouper_inplace(result) result = result.consolidate() else: index = self.grouper.result_index @@ -3209,11 +3230,7 @@ def _wrap_agged_blocks(self, items, blocks): mgr = BlockManager(blocks, [items, index]) result = DataFrame(mgr) - group_levels = self.grouper.get_group_levels() - zipped = zip(self.grouper.names, group_levels) - - for i, (name, labels) in enumerate(zipped): - result.insert(i, name, labels) + self._insert_inaxis_grouper_inplace(result) result = result.consolidate() else: index = self.grouper.result_index diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 7ead8b30e8671..303d7f99240af 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1499,6 +1499,24 @@ def test_groupby_as_index_agg(self): result3 = grouped['C'].agg({'Q': np.sum}) assert_frame_equal(result3, expected3) + # GH7115 & GH8112 & GH8582 + df = DataFrame(np.random.randint(0, 100, (50, 3)), + columns=['jim', 'joe', 'jolie']) + ts = Series(np.random.randint(5, 10, 50), name='jim') + + gr = df.groupby(ts) + _ = gr.nth(0) # invokes _set_selection_from_grouper internally + assert_frame_equal(gr.apply(sum), df.groupby(ts).apply(sum)) + + for attr in ['mean', 'max', 'count', 'idxmax', 'cumsum', 'all']: + gr = df.groupby(ts, as_index=False) + left = getattr(gr, attr)() + + gr = df.groupby(ts.values, as_index=True) + right = getattr(gr, attr)().reset_index(drop=True) + + assert_frame_equal(left, right) + def test_mulitindex_passthru(self): # GH 7997 @@ -2565,7 +2583,6 @@ def test_groupby_nonstring_columns(self): grouped = df.groupby(0) result = grouped.mean() expected = df.groupby(df[0]).mean() - del expected[0] assert_frame_equal(result, expected) def test_cython_grouper_series_bug_noncontig(self):
closes https://github.com/pydata/pandas/issues/7115 closes https://github.com/pydata/pandas/issues/8112 closes https://github.com/pydata/pandas/issues/8582
https://api.github.com/repos/pandas-dev/pandas/pulls/8585
2014-10-19T21:00:29Z
2014-10-28T00:04:25Z
2014-10-28T00:04:25Z
2014-12-07T13:23:35Z
DOC/REL: adapt make.py file to upload with other user than pandas
diff --git a/doc/make.py b/doc/make.py index 4367ac91396bb..6b424ce2814d5 100755 --- a/doc/make.py +++ b/doc/make.py @@ -31,48 +31,48 @@ SPHINX_BUILD = 'sphinxbuild' -def upload_dev(): +def upload_dev(user='pandas'): 'push a copy to the pydata dev directory' - if os.system('cd build/html; rsync -avz . pandas@pandas.pydata.org' - ':/usr/share/nginx/pandas/pandas-docs/dev/ -essh'): + if os.system('cd build/html; rsync -avz . {0}@pandas.pydata.org' + ':/usr/share/nginx/pandas/pandas-docs/dev/ -essh'.format(user)): raise SystemExit('Upload to Pydata Dev failed') -def upload_dev_pdf(): +def upload_dev_pdf(user='pandas'): 'push a copy to the pydata dev directory' - if os.system('cd build/latex; scp pandas.pdf pandas@pandas.pydata.org' - ':/usr/share/nginx/pandas/pandas-docs/dev/'): + if os.system('cd build/latex; scp pandas.pdf {0}@pandas.pydata.org' + ':/usr/share/nginx/pandas/pandas-docs/dev/'.format(user)): raise SystemExit('PDF upload to Pydata Dev failed') -def upload_stable(): +def upload_stable(user='pandas'): 'push a copy to the pydata stable directory' - if os.system('cd build/html; rsync -avz . pandas@pandas.pydata.org' - ':/usr/share/nginx/pandas/pandas-docs/stable/ -essh'): + if os.system('cd build/html; rsync -avz . {0}@pandas.pydata.org' + ':/usr/share/nginx/pandas/pandas-docs/stable/ -essh'.format(user)): raise SystemExit('Upload to stable failed') -def upload_stable_pdf(): +def upload_stable_pdf(user='pandas'): 'push a copy to the pydata dev directory' - if os.system('cd build/latex; scp pandas.pdf pandas@pandas.pydata.org' - ':/usr/share/nginx/pandas/pandas-docs/stable/'): + if os.system('cd build/latex; scp pandas.pdf {0}@pandas.pydata.org' + ':/usr/share/nginx/pandas/pandas-docs/stable/'.format(user)): raise SystemExit('PDF upload to stable failed') -def upload_prev(ver, doc_root='./'): +def upload_prev(ver, doc_root='./', user='pandas'): 'push a copy of older release to appropriate version directory' local_dir = doc_root + 'build/html' remote_dir = '/usr/share/nginx/pandas/pandas-docs/version/%s/' % ver - cmd = 'cd %s; rsync -avz . pandas@pandas.pydata.org:%s -essh' - cmd = cmd % (local_dir, remote_dir) + cmd = 'cd %s; rsync -avz . %s@pandas.pydata.org:%s -essh' + cmd = cmd % (local_dir, user, remote_dir) print(cmd) if os.system(cmd): raise SystemExit( 'Upload to %s from %s failed' % (remote_dir, local_dir)) local_dir = doc_root + 'build/latex' - pdf_cmd = 'cd %s; scp pandas.pdf pandas@pandas.pydata.org:%s' - pdf_cmd = pdf_cmd % (local_dir, remote_dir) + pdf_cmd = 'cd %s; scp pandas.pdf %s@pandas.pydata.org:%s' + pdf_cmd = pdf_cmd % (local_dir, user, remote_dir) if os.system(pdf_cmd): raise SystemExit('Upload PDF to %s from %s failed' % (ver, doc_root)) @@ -337,6 +337,10 @@ def generate_index(api=True, single=False, **kwds): type=str, default=False, help='filename of section to compile, e.g. "indexing"') +argparser.add_argument('--user', + type=str, + default=False, + help='Username to connect to the pydata server') def main(): args, unknown = argparser.parse_known_args() @@ -354,16 +358,19 @@ def main(): ver = sys.argv[2] if ftype == 'build_previous': - build_prev(ver) + build_prev(ver, user=args.user) if ftype == 'upload_previous': - upload_prev(ver) + upload_prev(ver, user=args.user) elif len(sys.argv) == 2: for arg in sys.argv[1:]: func = funcd.get(arg) if func is None: raise SystemExit('Do not know how to handle %s; valid args are %s' % ( arg, list(funcd.keys()))) - func() + if args.user: + func(user=args.user) + else: + func() else: small_docs = False all()
https://api.github.com/repos/pandas-dev/pandas/pulls/8583
2014-10-19T20:38:48Z
2014-10-22T20:43:54Z
2014-10-22T20:43:54Z
2014-10-30T11:20:23Z
DOC: fix example of GH8512 name clash with following examples
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 49959b1e4270f..7ee82cd69a257 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -322,10 +322,10 @@ equality to be True: .. ipython:: python - df = DataFrame({'col':['foo', 0, np.nan]}) + df1 = DataFrame({'col':['foo', 0, np.nan]}) df2 = DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0]) - df.equals(df2) - df.equals(df2.sort()) + df1.equals(df2) + df1.equals(df2.sort()) Combining overlapping data sets
https://api.github.com/repos/pandas-dev/pandas/pulls/8579
2014-10-18T22:42:11Z
2014-10-18T22:42:16Z
2014-10-18T22:42:16Z
2014-10-18T22:42:28Z
ENH: Allow columns selection in read_stata
diff --git a/doc/source/v0.15.1.txt b/doc/source/v0.15.1.txt index 89447121930fb..2bd88531f92d9 100644 --- a/doc/source/v0.15.1.txt +++ b/doc/source/v0.15.1.txt @@ -26,6 +26,8 @@ API changes Enhancements ~~~~~~~~~~~~ +- Added option to select columns when importing Stata files (:issue:`7935`) + .. _whatsnew_0151.performance: diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 8bf1c596b62cf..c2542594861c4 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -29,7 +29,7 @@ def read_stata(filepath_or_buffer, convert_dates=True, convert_categoricals=True, encoding=None, index=None, - convert_missing=False, preserve_dtypes=True): + convert_missing=False, preserve_dtypes=True, columns=None): """ Read Stata file into DataFrame @@ -55,11 +55,14 @@ def read_stata(filepath_or_buffer, convert_dates=True, preserve_dtypes : boolean, defaults to True Preserve Stata datatypes. If False, numeric data are upcast to pandas default types for foreign data (float64 or int64) + columns : list or None + Columns to retain. Columns will be returned in the given order. None + returns all columns """ reader = StataReader(filepath_or_buffer, encoding) return reader.data(convert_dates, convert_categoricals, index, - convert_missing, preserve_dtypes) + convert_missing, preserve_dtypes, columns) _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"] @@ -977,7 +980,7 @@ def _read_strls(self): self.path_or_buf.read(1) # zero-termination def data(self, convert_dates=True, convert_categoricals=True, index=None, - convert_missing=False, preserve_dtypes=True): + convert_missing=False, preserve_dtypes=True, columns=None): """ Reads observations from Stata file, converting them into a dataframe @@ -999,6 +1002,10 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None, preserve_dtypes : boolean, defaults to True Preserve Stata datatypes. If False, numeric data are upcast to pandas default types for foreign data (float64 or int64) + columns : list or None + Columns to retain. Columns will be returned in the given order. + None returns all columns + Returns ------- y : DataFrame instance @@ -1034,6 +1041,35 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None, data = DataFrame.from_records(data, index=index) data.columns = self.varlist + if columns is not None: + column_set = set(columns) + if len(column_set) != len(columns): + raise ValueError('columns contains duplicate entries') + unmatched = column_set.difference(data.columns) + if unmatched: + raise ValueError('The following columns were not found in the ' + 'Stata data set: ' + + ', '.join(list(unmatched))) + # Copy information for retained columns for later processing + dtyplist = [] + typlist = [] + fmtlist = [] + lbllist = [] + matched = set() + for i, col in enumerate(data.columns): + if col in column_set: + matched.update([col]) + dtyplist.append(self.dtyplist[i]) + typlist.append(self.typlist[i]) + fmtlist.append(self.fmtlist[i]) + lbllist.append(self.lbllist[i]) + + data = data[columns] + self.dtyplist = dtyplist + self.typlist = typlist + self.fmtlist = fmtlist + self.lbllist = lbllist + for col, typ in zip(data, self.typlist): if type(typ) is int: data[col] = data[col].apply(self._null_terminate, convert_dtype=True,) diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index c5727a5579b79..2cb7809166be5 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -720,12 +720,29 @@ def test_dtype_conversion(self): tm.assert_frame_equal(expected, conversion) + def test_drop_column(self): + expected = self.read_csv(self.csv15) + expected['byte_'] = expected['byte_'].astype(np.int8) + expected['int_'] = expected['int_'].astype(np.int16) + expected['long_'] = expected['long_'].astype(np.int32) + expected['float_'] = expected['float_'].astype(np.float32) + expected['double_'] = expected['double_'].astype(np.float64) + expected['date_td'] = expected['date_td'].apply(datetime.strptime, + args=('%Y-%m-%d',)) + columns = ['byte_', 'int_', 'long_'] + expected = expected[columns] + dropped = read_stata(self.dta15_117, convert_dates=True, + columns=columns) + tm.assert_frame_equal(expected, dropped) + with tm.assertRaises(ValueError): + columns = ['byte_', 'byte_'] + read_stata(self.dta15_117, convert_dates=True, columns=columns) - - - + with tm.assertRaises(ValueError): + columns = ['byte_', 'int_', 'long_', 'not_found'] + read_stata(self.dta15_117, convert_dates=True, columns=columns) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
Allow columns to be selected when importing Stata data files. Closes #7935
https://api.github.com/repos/pandas-dev/pandas/pulls/8577
2014-10-18T04:29:06Z
2014-10-20T11:03:40Z
2014-10-20T11:03:40Z
2014-11-12T15:51:47Z
TST/PERF: optimize tm.makeStringIndex
diff --git a/doc/source/merging.rst b/doc/source/merging.rst index 274bad618cb3d..7128e2dd82d6c 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -130,9 +130,9 @@ behavior: .. ipython:: python - from pandas.util.testing import rands + from pandas.util.testing import rands_array df = DataFrame(np.random.randn(10, 4), columns=['a', 'b', 'c', 'd'], - index=[rands(5) for _ in range(10)]) + index=rands_array(5, 10)) df concat([df.ix[:7, ['a', 'b']], df.ix[2:-2, ['c']], diff --git a/pandas/core/common.py b/pandas/core/common.py index 1e3d789ce206b..31dc58d1870e0 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2110,11 +2110,6 @@ def _count_not_none(*args): # miscellaneous python tools -def rands(n): - """Generates a random alphanumeric string of length *n*""" - from random import Random - import string - return ''.join(Random().sample(string.ascii_letters + string.digits, n)) def adjoin(space, *lists): diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index c097f82c6bd2f..da9d39ae82617 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -198,8 +198,8 @@ def test_long_strings(self): # GH6166 # unconversion of long strings was being chopped in earlier # versions of numpy < 1.7.2 - df = DataFrame({'a': [tm.rands(100) for _ in range(10)]}, - index=[tm.rands(100) for _ in range(10)]) + df = DataFrame({'a': tm.rands_array(100, size=10)}, + index=tm.rands_array(100, size=10)) with ensure_clean_store(self.path) as store: store.append('df', df, data_columns=['a']) diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 5e91adbe1a2fa..0d13b6513b377 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -274,11 +274,6 @@ def test_repr_binary_type(): assert_equal(res, b) -def test_rands(): - r = com.rands(10) - assert(len(r) == 10) - - def test_adjoin(): data = [['a', 'b', 'c'], ['dd', 'ee', 'ff'], diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 7d4ee05a1e64f..89d08d37e0a30 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -1201,9 +1201,8 @@ def test_pprint_thing(self): def test_wide_repr(self): with option_context('mode.sim_interactive', True, 'display.show_dimensions', True): - col = lambda l, k: [tm.rands(k) for _ in range(l)] max_cols = get_option('display.max_columns') - df = DataFrame([col(max_cols - 1, 25) for _ in range(10)]) + df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) set_option('display.expand_frame_repr', False) rep_str = repr(df) @@ -1227,9 +1226,8 @@ def test_wide_repr_wide_columns(self): def test_wide_repr_named(self): with option_context('mode.sim_interactive', True): - col = lambda l, k: [tm.rands(k) for _ in range(l)] max_cols = get_option('display.max_columns') - df = DataFrame([col(max_cols-1, 25) for _ in range(10)]) + df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) df.index.name = 'DataFrame Index' set_option('display.expand_frame_repr', False) @@ -1249,11 +1247,10 @@ def test_wide_repr_named(self): def test_wide_repr_multiindex(self): with option_context('mode.sim_interactive', True): - col = lambda l, k: [tm.rands(k) for _ in range(l)] - midx = pandas.MultiIndex.from_arrays([np.array(col(10, 5)), - np.array(col(10, 5))]) + midx = pandas.MultiIndex.from_arrays( + tm.rands_array(5, size=(2, 10))) max_cols = get_option('display.max_columns') - df = DataFrame([col(max_cols-1, 25) for _ in range(10)], + df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)), index=midx) df.index.names = ['Level 0', 'Level 1'] set_option('display.expand_frame_repr', False) @@ -1274,12 +1271,11 @@ def test_wide_repr_multiindex(self): def test_wide_repr_multiindex_cols(self): with option_context('mode.sim_interactive', True): max_cols = get_option('display.max_columns') - col = lambda l, k: [tm.rands(k) for _ in range(l)] - midx = pandas.MultiIndex.from_arrays([np.array(col(10, 5)), - np.array(col(10, 5))]) - mcols = pandas.MultiIndex.from_arrays([np.array(col(max_cols-1, 3)), - np.array(col(max_cols-1, 3))]) - df = DataFrame([col(max_cols-1, 25) for _ in range(10)], + midx = pandas.MultiIndex.from_arrays( + tm.rands_array(5, size=(2, 10))) + mcols = pandas.MultiIndex.from_arrays( + tm.rands_array(3, size=(2, max_cols - 1))) + df = DataFrame(tm.rands_array(25, (10, max_cols - 1)), index=midx, columns=mcols) df.index.names = ['Level 0', 'Level 1'] set_option('display.expand_frame_repr', False) @@ -1296,9 +1292,8 @@ def test_wide_repr_multiindex_cols(self): def test_wide_repr_unicode(self): with option_context('mode.sim_interactive', True): - col = lambda l, k: [tm.randu(k) for _ in range(l)] max_cols = get_option('display.max_columns') - df = DataFrame([col(max_cols-1, 25) for _ in range(10)]) + df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) set_option('display.expand_frame_repr', False) rep_str = repr(df) set_option('display.expand_frame_repr', True) @@ -1877,30 +1872,31 @@ def test_repr_html(self): self.reset_display_options() 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)]) + df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1))) reg_repr = df._repr_html_() assert "..." not in reg_repr - wide_df = DataFrame([row(max_cols+1, 25) for _ in range(10)]) + wide_df = DataFrame(tm.rands_array(25, size=(10, max_cols + 1))) 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) + mcols = pandas.MultiIndex.from_product([np.arange(max_cols//2), + ['foo', 'bar']], + names=['first', 'second']) + df = DataFrame(tm.rands_array(25, size=(10, len(mcols))), + 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) + mcols = pandas.MultiIndex.from_product((np.arange(1+(max_cols//2)), + ['foo', 'bar']), + names=['first', 'second']) + df = DataFrame(tm.rands_array(25, size=(10, len(mcols))), + columns=mcols) wide_repr = df._repr_html_() assert '...' in wide_repr diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 3efd399450d11..e5064544b292e 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4734,7 +4734,7 @@ def test_bytestring_with_unicode(self): def test_very_wide_info_repr(self): df = DataFrame(np.random.randn(10, 20), - columns=[tm.rands(10) for _ in range(20)]) + columns=tm.rands_array(10, 20)) repr(df) def test_repr_column_name_unicode_truncation_bug(self): diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 0e8b5d68e3fd7..7ead8b30e8671 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -8,7 +8,6 @@ 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 from pandas.core.groupby import (SpecificationError, DataError, _nargsort, _lexsort_indexer) @@ -2579,7 +2578,7 @@ def test_cython_grouper_series_bug_noncontig(self): self.assertTrue(result.isnull().all()) def test_series_grouper_noncontig_index(self): - index = Index([tm.rands(10) for _ in range(100)]) + index = Index(tm.rands_array(10, 100)) values = Series(np.random.randn(50), index=index[::2]) labels = np.random.randint(0, 5, 50) @@ -2869,8 +2868,8 @@ def test_column_select_via_attr(self): assert_frame_equal(result, expected) def test_rank_apply(self): - lev1 = np.array([rands(10) for _ in range(100)], dtype=object) - lev2 = np.array([rands(10) for _ in range(130)], dtype=object) + lev1 = tm.rands_array(10, 100) + lev2 = tm.rands_array(10, 130) lab1 = np.random.randint(0, 100, size=500) lab2 = np.random.randint(0, 130, size=500) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 29bdb2c983d61..2d3961a643991 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -327,8 +327,7 @@ def test_getitem_setitem_ellipsis(self): self.assertTrue((result == 5).all()) def test_getitem_negative_out_of_bounds(self): - s = Series([tm.rands(5) for _ in range(10)], - index=[tm.rands(10) for _ in range(10)]) + s = Series(tm.rands_array(5, 10), index=tm.rands_array(10, 10)) self.assertRaises(IndexError, s.__getitem__, -11) self.assertRaises(IndexError, s.__setitem__, -11, 'foo') @@ -3852,11 +3851,10 @@ def _check_op(arr, op): _check_op(arr, operator.floordiv) def test_series_frame_radd_bug(self): - from pandas.util.testing import rands import operator # GH 353 - vals = Series([rands(5) for _ in range(10)]) + vals = Series(tm.rands_array(5, 10)) result = 'foo_' + vals expected = vals.map(lambda x: 'foo_' + x) assert_series_equal(result, expected) diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py index 76b49a5f976bd..8c56ba0e0f548 100644 --- a/pandas/tests/test_util.py +++ b/pandas/tests/test_util.py @@ -59,6 +59,22 @@ def test_bad_deprecate_kwarg(self): def f4(new=None): pass + +def test_rands(): + r = tm.rands(10) + assert(len(r) == 10) + + +def test_rands_array(): + arr = tm.rands_array(5, size=10) + assert(arr.shape == (10,)) + assert(len(arr[0]) == 5) + + arr = tm.rands_array(7, size=(10, 10)) + assert(arr.shape == (10, 10)) + assert(len(arr[1, 1]) == 7) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index d1c6af5743e07..b9c7fdfeb6c48 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -14,7 +14,7 @@ from pandas.tseries.index import DatetimeIndex from pandas.tools.merge import merge, concat, ordered_merge, MergeError from pandas.util.testing import (assert_frame_equal, assert_series_equal, - assert_almost_equal, rands, + assert_almost_equal, makeCustomDataframe as mkdf, assertRaisesRegexp) from pandas import isnull, DataFrame, Index, MultiIndex, Panel, Series, date_range, read_table, read_csv @@ -913,7 +913,7 @@ def test_merge_right_vs_left(self): def test_compress_group_combinations(self): # ~ 40000000 possible unique groups - key1 = np.array([rands(10) for _ in range(10000)], dtype='O') + key1 = tm.rands_array(10, 10000) key1 = np.tile(key1, 2) key2 = key1[::-1] diff --git a/pandas/util/testing.py b/pandas/util/testing.py index b34bcc3c12890..38057d641fc17 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -193,15 +193,50 @@ def randbool(size=(), p=0.5): return rand(*size) <= p -def rands(n): - choices = string.ascii_letters + string.digits - return ''.join(random.choice(choices) for _ in range(n)) +RANDS_CHARS = np.array(list(string.ascii_letters + string.digits), + dtype=(np.str_, 1)) +RANDU_CHARS = np.array(list(u("").join(map(unichr, lrange(1488, 1488 + 26))) + + string.digits), dtype=(np.unicode_, 1)) + + +def rands_array(nchars, size, dtype='O'): + """Generate an array of byte strings.""" + retval = (choice(RANDS_CHARS, size=nchars * np.prod(size)) + .view((np.str_, nchars)).reshape(size)) + if dtype is None: + return retval + else: + return retval.astype(dtype) + + +def randu_array(nchars, size, dtype='O'): + """Generate an array of unicode strings.""" + retval = (choice(RANDU_CHARS, size=nchars * np.prod(size)) + .view((np.unicode_, nchars)).reshape(size)) + if dtype is None: + return retval + else: + return retval.astype(dtype) -def randu(n): - choices = u("").join(map(unichr, lrange(1488, 1488 + 26))) - choices += string.digits - return ''.join([random.choice(choices) for _ in range(n)]) +def rands(nchars): + """ + Generate one random byte string. + + See `rands_array` if you want to create an array of random strings. + + """ + return ''.join(choice(RANDS_CHARS, nchars)) + + +def randu(nchars): + """ + Generate one random unicode string. + + See `randu_array` if you want to create an array of random unicode strings. + + """ + return ''.join(choice(RANDU_CHARS, nchars)) def choice(x, size=10): @@ -743,10 +778,11 @@ def getArangeMat(): # make index def makeStringIndex(k=10): - return Index([rands(10) for _ in range(k)]) + return Index(rands_array(nchars=10, size=k)) + def makeUnicodeIndex(k=10): - return Index([randu(10) for _ in range(k)]) + return Index(randu_array(nchars=10, size=k)) def makeBoolIndex(k=10): if k == 1: diff --git a/vb_suite/frame_ctor.py b/vb_suite/frame_ctor.py index 713237779494e..b11dd6c290ae1 100644 --- a/vb_suite/frame_ctor.py +++ b/vb_suite/frame_ctor.py @@ -17,8 +17,8 @@ setup = common_setup + """ N, K = 5000, 50 -index = [rands(10) for _ in xrange(N)] -columns = [rands(10) for _ in xrange(K)] +index = tm.makeStringIndex(N) +columns = tm.makeStringIndex(K) frame = DataFrame(np.random.randn(N, K), index=index, columns=columns) try: diff --git a/vb_suite/groupby.py b/vb_suite/groupby.py index f9797def4c53b..26311920ec861 100644 --- a/vb_suite/groupby.py +++ b/vb_suite/groupby.py @@ -187,7 +187,7 @@ def f(): setup = common_setup + """ K = 1000 N = 100000 -uniques = np.array([rands(10) for x in xrange(K)], dtype='O') +uniques = tm.makeStringIndex(K).values s = Series(np.tile(uniques, N // K)) """ diff --git a/vb_suite/hdfstore_bench.py b/vb_suite/hdfstore_bench.py index 47f8e106351d4..a822ad1c614be 100644 --- a/vb_suite/hdfstore_bench.py +++ b/vb_suite/hdfstore_bench.py @@ -19,7 +19,7 @@ def remove(f): # get from a store setup1 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000)}, index=index) @@ -36,7 +36,7 @@ def remove(f): # write to a store setup2 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000)}, index=index) @@ -52,7 +52,7 @@ def remove(f): # get from a store (mixed) setup3 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000), 'string1' : ['foo'] * 25000, @@ -73,7 +73,7 @@ def remove(f): # write to a store (mixed) setup4 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000), 'string1' : ['foo'] * 25000, @@ -93,7 +93,7 @@ def remove(f): setup5 = common_setup + """ N=10000 -index = [rands(10) for _ in xrange(N)] +index = tm.makeStringIndex(N) df = DataFrame({'float1' : randn(N), 'float2' : randn(N), 'string1' : ['foo'] * N, @@ -115,7 +115,7 @@ def remove(f): # write to a table (mixed) setup6 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000), 'string1' : ['foo'] * 25000, @@ -134,7 +134,7 @@ def remove(f): # select from a table setup7 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000) }, index=index) @@ -153,7 +153,7 @@ def remove(f): # write to a table setup8 = common_setup + """ -index = [rands(10) for _ in xrange(25000)] +index = tm.makeStringIndex(25000) df = DataFrame({'float1' : randn(25000), 'float2' : randn(25000) }, index=index) diff --git a/vb_suite/indexing.py b/vb_suite/indexing.py index 34cbadc2e042b..320f261050e07 100644 --- a/vb_suite/indexing.py +++ b/vb_suite/indexing.py @@ -20,7 +20,7 @@ name='series_getitem_scalar') setup = common_setup + """ -index = [tm.rands(10) for _ in xrange(1000)] +index = tm.makeStringIndex(1000) s = Series(np.random.rand(1000), index=index) idx = index[100] """ @@ -51,8 +51,8 @@ # DataFrame __getitem__ setup = common_setup + """ -index = [tm.rands(10) for _ in xrange(1000)] -columns = [tm.rands(10) for _ in xrange(30)] +index = tm.makeStringIndex(1000) +columns = tm.makeStringIndex(30) df = DataFrame(np.random.rand(1000, 30), index=index, columns=columns) idx = index[100] @@ -68,10 +68,9 @@ except: klass = DataFrame -index = [tm.rands(10) for _ in xrange(1000)] -columns = [tm.rands(10) for _ in xrange(30)] -df = klass(np.random.rand(1000, 30), index=index, - columns=columns) +index = tm.makeStringIndex(1000) +columns = tm.makeStringIndex(30) +df = klass(np.random.rand(1000, 30), index=index, columns=columns) idx = index[100] col = columns[10] """ @@ -84,10 +83,9 @@ # ix get scalar setup = common_setup + """ -index = [tm.rands(10) for _ in xrange(1000)] -columns = [tm.rands(10) for _ in xrange(30)] -df = DataFrame(np.random.randn(1000, 30), index=index, - columns=columns) +index = tm.makeStringIndex(1000) +columns = tm.makeStringIndex(30) +df = DataFrame(np.random.randn(1000, 30), index=index, columns=columns) idx = index[100] col = columns[10] """ diff --git a/vb_suite/io_bench.py b/vb_suite/io_bench.py index b70a060233dae..0b9f68f0e6ed5 100644 --- a/vb_suite/io_bench.py +++ b/vb_suite/io_bench.py @@ -8,7 +8,7 @@ # read_csv setup1 = common_setup + """ -index = [rands(10) for _ in xrange(10000)] +index = tm.makeStringIndex(10000) df = DataFrame({'float1' : randn(10000), 'float2' : randn(10000), 'string1' : ['foo'] * 10000, @@ -26,7 +26,7 @@ # write_csv setup2 = common_setup + """ -index = [rands(10) for _ in xrange(10000)] +index = tm.makeStringIndex(10000) df = DataFrame({'float1' : randn(10000), 'float2' : randn(10000), 'string1' : ['foo'] * 10000, diff --git a/vb_suite/io_sql.py b/vb_suite/io_sql.py index 1a60982c487d4..7f580165939bb 100644 --- a/vb_suite/io_sql.py +++ b/vb_suite/io_sql.py @@ -17,7 +17,7 @@ # to_sql setup = common_setup + """ -index = [rands(10) for _ in xrange(10000)] +index = tm.makeStringIndex(10000) df = DataFrame({'float1' : randn(10000), 'float2' : randn(10000), 'string1' : ['foo'] * 10000, @@ -37,7 +37,7 @@ # read_sql setup = common_setup + """ -index = [rands(10) for _ in xrange(10000)] +index = tm.makeStringIndex(10000) df = DataFrame({'float1' : randn(10000), 'float2' : randn(10000), 'string1' : ['foo'] * 10000, diff --git a/vb_suite/join_merge.py b/vb_suite/join_merge.py index eb0608f12a8cb..facec39559ed3 100644 --- a/vb_suite/join_merge.py +++ b/vb_suite/join_merge.py @@ -5,8 +5,8 @@ """ setup = common_setup + """ -level1 = np.array([rands(10) for _ in xrange(10)], dtype='O') -level2 = np.array([rands(10) for _ in xrange(1000)], dtype='O') +level1 = tm.makeStringIndex(10).values +level2 = tm.makeStringIndex(1000).values label1 = np.arange(10).repeat(1000) label2 = np.tile(np.arange(1000), 10) @@ -91,8 +91,8 @@ setup = common_setup + """ N = 10000 -indices = np.array([rands(10) for _ in xrange(N)], dtype='O') -indices2 = np.array([rands(10) for _ in xrange(N)], dtype='O') +indices = tm.makeStringIndex(N).values +indices2 = tm.makeStringIndex(N).values key = np.tile(indices[:8000], 10) key2 = np.tile(indices2[:8000], 10) @@ -141,7 +141,7 @@ # data alignment setup = common_setup + """n = 1000000 -# indices = Index([rands(10) for _ in xrange(n)]) +# indices = tm.makeStringIndex(n) def sample(values, k): sampler = np.random.permutation(len(values)) return values.take(sampler[:k]) @@ -170,7 +170,7 @@ def sample(values, k): setup = common_setup + """ n = 1000 -indices = Index([rands(10) for _ in xrange(1000)]) +indices = tm.makeStringIndex(1000) s = Series(n, index=indices) pieces = [s[i:-i] for i in range(1, 10)] pieces = pieces * 50 @@ -205,7 +205,7 @@ def sample(values, k): # Ordered merge setup = common_setup + """ -groups = np.array([rands(10) for _ in xrange(10)], dtype='O') +groups = tm.makeStringIndex(10).values left = DataFrame({'group': groups.repeat(5000), 'key' : np.tile(np.arange(0, 10000, 2), 10), diff --git a/vb_suite/miscellaneous.py b/vb_suite/miscellaneous.py index eeeaf01a8b4af..27efadc7acfe0 100644 --- a/vb_suite/miscellaneous.py +++ b/vb_suite/miscellaneous.py @@ -24,9 +24,7 @@ def prop(self): # match setup = common_setup + """ -from pandas.util.testing import rands - -uniques = np.array([rands(10) for _ in xrange(1000)], dtype='O') +uniques = tm.makeStringIndex(1000).values all = uniques.repeat(10) """ diff --git a/vb_suite/pandas_vb_common.py b/vb_suite/pandas_vb_common.py index 77d0e2e27260e..a599301bb53fe 100644 --- a/vb_suite/pandas_vb_common.py +++ b/vb_suite/pandas_vb_common.py @@ -1,5 +1,4 @@ from pandas import * -from pandas.util.testing import rands from datetime import timedelta from numpy.random import randn from numpy.random import randint diff --git a/vb_suite/reindex.py b/vb_suite/reindex.py index 5d3d07783c9a8..156382f1fb13a 100644 --- a/vb_suite/reindex.py +++ b/vb_suite/reindex.py @@ -34,9 +34,8 @@ N = 1000 K = 20 -level1 = np.array([tm.rands(10) for _ in xrange(N)], dtype='O').repeat(K) -level2 = np.tile(np.array([tm.rands(10) for _ in xrange(K)], dtype='O'), - N) +level1 = tm.makeStringIndex(N).values.repeat(K) +level2 = np.tile(tm.makeStringIndex(K).values, N) index = MultiIndex.from_arrays([level1, level2]) s1 = Series(np.random.randn(N * K), index=index) @@ -125,8 +124,8 @@ def backfill(): N = 10000 K = 10 -key1 = np.array([rands(10) for _ in xrange(N)], dtype='O').repeat(K) -key2 = np.array([rands(10) for _ in xrange(N)], dtype='O').repeat(K) +key1 = tm.makeStringIndex(N).values.repeat(K) +key2 = tm.makeStringIndex(N).values.repeat(K) df = DataFrame({'key1' : key1, 'key2' : key2, 'value' : np.random.randn(N * K)}) @@ -166,7 +165,7 @@ def backfill(): setup = common_setup + """ s = Series(np.random.randint(0, 1000, size=10000)) -s2 = Series(np.tile([rands(10) for i in xrange(1000)], 10)) +s2 = Series(np.tile(tm.makeStringIndex(1000).values, 10)) """ series_drop_duplicates_int = Benchmark('s.drop_duplicates()', setup, @@ -195,7 +194,7 @@ def backfill(): setup = common_setup + """ n = 50000 -indices = Index([rands(10) for _ in xrange(n)]) +indices = tm.makeStringIndex(n) def sample(values, k): from random import shuffle
As a performance junkie I benchmark a lot and that involves frequent creation of temporary arrays of significant sizes. I couldn't but notice that `tm.makeStringIndex` is somewhat slow in that regard and this PR should fix that. It also does: ``` * add tm.rands_array(nchars, size) function to generate string arrays * add tm.randu_array(nchars, size) function to generate unicode arrays * replace [rands(N) for _ in range(X)] idioms: - with makeStringIndex in benchmarks to maintain backward compatibility - with rands_array in tests to maintain 1-to-1 type correspondence ```
https://api.github.com/repos/pandas-dev/pandas/pulls/8575
2014-10-17T17:17:08Z
2014-10-20T00:58:25Z
2014-10-20T00:58:25Z
2018-07-17T21:28:49Z
PERF: improve perf of array_equivalent_object (GH8512)
diff --git a/pandas/core/common.py b/pandas/core/common.py index da512dc56eaef..1e3d789ce206b 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -411,12 +411,13 @@ def array_equivalent(left, right, strict_nan=False): if left.shape != right.shape: return False # Object arrays can contain None, NaN and NaT. - if issubclass(left.dtype.type, np.object_): + if issubclass(left.dtype.type, np.object_) or issubclass(right.dtype.type, np.object_): if not strict_nan: # pd.isnull considers NaN and None to be equivalent. - return lib.array_equivalent_object(left.ravel(), right.ravel()) - + return lib.array_equivalent_object(_ensure_object(left.ravel()), + _ensure_object(right.ravel())) + for left_value, right_value in zip(left, right): if left_value is tslib.NaT and right_value is not tslib.NaT: return False diff --git a/pandas/lib.pyx b/pandas/lib.pyx index a845b9c90865b..88c458ce95226 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -330,26 +330,6 @@ def list_to_object_array(list obj): return arr -@cython.wraparound(False) -@cython.boundscheck(False) -def array_equivalent_object(ndarray left, ndarray right): - cdef Py_ssize_t i, n - cdef object lobj, robj - - n = len(left) - for i from 0 <= i < n: - lobj = left[i] - robj = right[i] - - # we are either not equal or both nan - # I think None == None will be true here - if lobj != robj: - if checknull(lobj) and checknull(robj): - continue - return False - return True - - @cython.wraparound(False) @cython.boundscheck(False) def fast_unique(ndarray[object] values): @@ -692,6 +672,31 @@ def scalar_compare(ndarray[object] values, object val, object op): return result.view(bool) +@cython.wraparound(False) +@cython.boundscheck(False) +def array_equivalent_object(ndarray[object] left, ndarray[object] right): + """ perform an element by element comparion on 1-d object arrays + taking into account nan positions """ + cdef Py_ssize_t i, n + cdef object x, y + + n = len(left) + for i from 0 <= i < n: + x = left[i] + y = right[i] + + # we are either not equal or both nan + # I think None == None will be true here + if cpython.PyObject_RichCompareBool(x, y, cpython.Py_EQ): + continue + elif _checknull(x) and _checknull(y): + continue + else: + return False + + return True + + @cython.wraparound(False) @cython.boundscheck(False) def vec_compare(ndarray[object] left, ndarray[object] right, object op):
xref #8512
https://api.github.com/repos/pandas-dev/pandas/pulls/8570
2014-10-17T00:44:36Z
2014-10-17T01:19:54Z
2014-10-17T01:19:54Z
2014-10-17T01:19:54Z
DOC: ignore_na warning
diff --git a/doc/source/v0.15.0.txt b/doc/source/v0.15.0.txt index 5149be1afbd2f..f8a245f750068 100644 --- a/doc/source/v0.15.0.txt +++ b/doc/source/v0.15.0.txt @@ -534,7 +534,7 @@ Rolling/Expanding Moments API changes ``ddof`` argument (with a default value of ``1``) was previously undocumented. (:issue:`8064`) - :func:`ewma`, :func:`ewmstd`, :func:`ewmvol`, :func:`ewmvar`, :func:`ewmcov`, and :func:`ewmcorr` - now interpret ``min_periods`` in the same manner that the ``rolling_*`` and ``expanding_*`` functions do: + now interpret ``min_periods`` in the same manner that the :func:`rolling_*()` and :func:`expanding_*()` functions do: a given result entry will be ``NaN`` if the (expanding, in this case) window does not contain at least ``min_periods`` values. The previous behavior was to set to ``NaN`` the ``min_periods`` entries starting with the first non- ``NaN`` value. (:issue:`7977`) @@ -582,11 +582,18 @@ Rolling/Expanding Moments API changes ewma(Series([1., None, 8.]), com=2., ignore_na=True) # pre-0.15.0 behavior ewma(Series([1., None, 8.]), com=2., ignore_na=False) # new default + .. warning:: + + By default (``ignore_na=False``) the :func:`ewm*()` functions' weights calculation + in the presence of missing values is different than in pre-0.15.0 versions. + To reproduce the pre-0.15.0 calculation of weights in the presence of missing values + one must specify explicitly ``ignore_na=True``. + - Bug in :func:`expanding_cov`, :func:`expanding_corr`, :func:`rolling_cov`, :func:`rolling_cor`, :func:`ewmcov`, and :func:`ewmcorr` returning results with columns sorted by name and producing an error for non-unique columns; now handles non-unique columns and returns columns in original order (except for the case of two DataFrames with ``pairwise=False``, where behavior is unchanged) (:issue:`7542`) -- Bug in :func:`rolling_count` and ``expanding_*`` functions unnecessarily producing error message for zero-length data (:issue:`8056`) +- Bug in :func:`rolling_count` and :func:`expanding_*()` functions unnecessarily producing error message for zero-length data (:issue:`8056`) - Bug in :func:`rolling_apply` and :func:`expanding_apply` interpreting ``min_periods=0`` as ``min_periods=1`` (:issue:`8080`) - Bug in :func:`expanding_std` and :func:`expanding_var` for a single value producing a confusing error message (:issue:`7900`) - Bug in :func:`rolling_std` and :func:`rolling_var` for a single value producing ``0`` rather than ``NaN`` (:issue:`7900`) @@ -609,16 +616,16 @@ Rolling/Expanding Moments API changes .. code-block:: python - In [69]: ewmvar(s, com=2., bias=False) - Out[69]: + In [89]: ewmvar(s, com=2., bias=False) + Out[89]: 0 -2.775558e-16 1 3.000000e-01 2 9.556787e-01 3 3.585799e+00 dtype: float64 - In [70]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True) - Out[70]: + In [90]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True) + Out[90]: 0 1.25 1 1.25 2 1.25
Added a warning about the new default behavior of the `ewm*()` differing from prior versions. Also cleaned up some formatting.
https://api.github.com/repos/pandas-dev/pandas/pulls/8567
2014-10-16T01:59:17Z
2014-10-16T12:49:02Z
2014-10-16T12:49:02Z
2014-10-18T12:42:19Z
Switch default colormap for scatterplot to 'Greys'
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 1793d6806be83..1651c9f50e525 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1605,8 +1605,8 @@ def test_plot_scatter_with_c(self): axes = [df.plot(kind='scatter', x='x', y='y', c='z'), df.plot(kind='scatter', x=0, y=1, c=2)] for ax in axes: - # default to RdBu - self.assertEqual(ax.collections[0].cmap.name, 'RdBu') + # default to Greys + self.assertEqual(ax.collections[0].cmap.name, 'Greys') if self.mpl_ge_1_3_1: diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 1d47c3781a7d7..d2c0bcfa5f2a0 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -1405,7 +1405,7 @@ def _make_plot(self): cb = self.kwds.pop('colorbar', self.colormap or c in self.data.columns) # pandas uses colormap, matplotlib uses cmap. - cmap = self.colormap or 'RdBu' + cmap = self.colormap or 'Greys' cmap = plt.cm.get_cmap(cmap) if c is None:
In #7780, I added colormap support to scatter, but choose to override the default rainbow colormap (https://github.com/pydata/pandas/pull/7780#issuecomment-49533995). I'm now regreting choosing 'RdBu' as the default colormap. I think a simple black-white colormap is a more conservative and better default choice for coloring scatter plots -- binary colormaps really only make sense if the data is signed. 'Greys' is also the default colormap set by the seaborn package, and @mwaskom has done far more thinking about colormaps than I have. If possible, I would love to get this in before 0.15 is released, so we don't have to worry about changing how anyone's plots look. CC @sinhrks
https://api.github.com/repos/pandas-dev/pandas/pulls/8566
2014-10-16T00:58:38Z
2014-10-16T16:12:20Z
2014-10-16T16:12:20Z
2014-10-28T07:35:24Z
ENH: Add type conversion to read_stata and StataReader
diff --git a/doc/source/io.rst b/doc/source/io.rst index 70d5c195233c3..ae07e0af10c0e 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3654,10 +3654,15 @@ missing values are represented as ``np.nan``. If ``True``, missing values are represented using ``StataMissingValue`` objects, and columns containing missing values will have ``dtype`` set to ``object``. - The StataReader supports .dta Formats 104, 105, 108, 113-115 and 117. Alternatively, the function :func:`~pandas.io.stata.read_stata` can be used +.. note:: + + Setting ``preserve_dtypes=False`` will upcast all integer data types to + ``int64`` and all floating point data types to ``float64``. By default, + the Stata data types are preserved when importing. + .. ipython:: python :suppress: diff --git a/doc/source/v0.15.0.txt b/doc/source/v0.15.0.txt index f8a245f750068..a40e1d87776d9 100644 --- a/doc/source/v0.15.0.txt +++ b/doc/source/v0.15.0.txt @@ -864,6 +864,7 @@ Enhancements - Added support for writing datetime64 columns with ``to_sql`` for all database flavors (:issue:`7103`). - Added support for bool, uint8, uint16 and uint32 datatypes in ``to_stata`` (:issue:`7097`, :issue:`7365`) +- Added conversion option when importing Stata files (:issue:`8527`) - Added ``layout`` keyword to ``DataFrame.plot``. You can pass a tuple of ``(rows, columns)``, one of which can be ``-1`` to automatically infer (:issue:`6667`, :issue:`8071`). - Allow to pass multiple axes to ``DataFrame.plot``, ``hist`` and ``boxplot`` (:issue:`5353`, :issue:`6970`, :issue:`7069`) diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 246465153c611..8bf1c596b62cf 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -29,7 +29,7 @@ def read_stata(filepath_or_buffer, convert_dates=True, convert_categoricals=True, encoding=None, index=None, - convert_missing=False): + convert_missing=False, preserve_dtypes=True): """ Read Stata file into DataFrame @@ -52,13 +52,14 @@ def read_stata(filepath_or_buffer, convert_dates=True, If True, columns containing missing values are returned with object data types and missing values are represented by StataMissingValue objects. + preserve_dtypes : boolean, defaults to True + Preserve Stata datatypes. If False, numeric data are upcast to pandas + default types for foreign data (float64 or int64) """ reader = StataReader(filepath_or_buffer, encoding) - return reader.data(convert_dates, - convert_categoricals, - index, - convert_missing) + return reader.data(convert_dates, convert_categoricals, index, + convert_missing, preserve_dtypes) _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"] @@ -976,7 +977,7 @@ def _read_strls(self): self.path_or_buf.read(1) # zero-termination def data(self, convert_dates=True, convert_categoricals=True, index=None, - convert_missing=False): + convert_missing=False, preserve_dtypes=True): """ Reads observations from Stata file, converting them into a dataframe @@ -995,7 +996,9 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None, nans. If True, columns containing missing values are returned with object data types and missing values are represented by StataMissingValue objects. - + preserve_dtypes : boolean, defaults to True + Preserve Stata datatypes. If False, numeric data are upcast to + pandas default types for foreign data (float64 or int64) Returns ------- y : DataFrame instance @@ -1107,6 +1110,21 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None, labeled_data[(data[col] == k).values] = v data[col] = Categorical.from_array(labeled_data) + if not preserve_dtypes: + retyped_data = [] + convert = False + for col in data: + dtype = data[col].dtype + if dtype in (np.float16, np.float32): + dtype = np.float64 + convert = True + elif dtype in (np.int8, np.int16, np.int32): + dtype = np.int64 + convert = True + retyped_data.append((col, data[col].astype(dtype))) + if convert: + data = DataFrame.from_items(retyped_data) + return data def data_label(self): diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index c458688b3d2d2..c5727a5579b79 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -83,6 +83,7 @@ def setUp(self): def read_dta(self, file): + # Legacy default reader configuration return read_stata(file, convert_dates=True) def read_csv(self, file): @@ -694,6 +695,35 @@ def test_big_dates(self): tm.assert_frame_equal(written_and_read_again.set_index('index'), expected) + def test_dtype_conversion(self): + expected = self.read_csv(self.csv15) + expected['byte_'] = expected['byte_'].astype(np.int8) + expected['int_'] = expected['int_'].astype(np.int16) + expected['long_'] = expected['long_'].astype(np.int32) + expected['float_'] = expected['float_'].astype(np.float32) + expected['double_'] = expected['double_'].astype(np.float64) + expected['date_td'] = expected['date_td'].apply(datetime.strptime, + args=('%Y-%m-%d',)) + + no_conversion = read_stata(self.dta15_117, + convert_dates=True) + tm.assert_frame_equal(expected, no_conversion) + + conversion = read_stata(self.dta15_117, + convert_dates=True, + preserve_dtypes=False) + + # read_csv types are the same + expected = self.read_csv(self.csv15) + expected['date_td'] = expected['date_td'].apply(datetime.strptime, + args=('%Y-%m-%d',)) + + tm.assert_frame_equal(expected, conversion) + + + + +
Added upcasting of numeric types which will default numeric data to either int64 or float64. This option has been enabled by default. closes #8527
https://api.github.com/repos/pandas-dev/pandas/pulls/8564
2014-10-15T22:24:29Z
2014-10-17T07:22:32Z
2014-10-17T07:22:32Z
2014-11-12T15:51:49Z
ENH Change ExcelFile to accept a workbook for the path_or_buf argument.
diff --git a/doc/source/release.rst b/doc/source/release.rst index 34720c49b163b..659947cae1ea7 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -133,7 +133,10 @@ Improvements to existing features (0.4.3 and 0.5.0) (:issue:`4981`). - Better string representations of ``MultiIndex`` (including ability to roundtrip via ``repr``). (:issue:`3347`, :issue:`4935`) - + - Both ExcelFile and read_excel to accept an xlrd.Book for the io + (formerly path_or_buf) argument; this requires engine to be set. + (:issue:`4961`). + API Changes ~~~~~~~~~~~ diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 02dbc381a10be..6b83fada19001 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -45,11 +45,13 @@ def get_writer(engine_name): except KeyError: raise ValueError("No Excel writer '%s'" % engine_name) -def read_excel(path_or_buf, sheetname, **kwds): +def read_excel(io, sheetname, **kwds): """Read an Excel table into a pandas DataFrame Parameters ---------- + io : string, file-like object or xlrd workbook + If a string, expected to be a path to xls or xlsx file sheetname : string Name of Excel sheet header : int, default 0 @@ -74,7 +76,10 @@ def read_excel(path_or_buf, sheetname, **kwds): values are overridden, otherwise they're appended to verbose : boolean, default False Indicate number of NA values placed in non-numeric columns - + engine: string, default None + If io is not a buffer or path, this must be set to identify io. + Acceptable values are None or xlrd + Returns ------- parsed : DataFrame @@ -84,7 +89,10 @@ def read_excel(path_or_buf, sheetname, **kwds): kwds.pop('kind') warn("kind keyword is no longer supported in read_excel and may be " "removed in a future version", FutureWarning) - return ExcelFile(path_or_buf).parse(sheetname=sheetname, **kwds) + + engine = kwds.pop('engine', None) + + return ExcelFile(io, engine=engine).parse(sheetname=sheetname, **kwds) class ExcelFile(object): @@ -94,10 +102,13 @@ class ExcelFile(object): Parameters ---------- - path : string or file-like object - Path to xls or xlsx file + io : string, file-like object or xlrd workbook + If a string, expected to be a path to xls or xlsx file + engine: string, default None + If io is not a buffer or path, this must be set to identify io. + Acceptable values are None or xlrd """ - def __init__(self, path_or_buf, **kwds): + def __init__(self, io, **kwds): import xlrd # throw an ImportError if we need to @@ -106,14 +117,22 @@ def __init__(self, path_or_buf, **kwds): raise ImportError("pandas requires xlrd >= 0.9.0 for excel " "support, current version " + xlrd.__VERSION__) - self.path_or_buf = path_or_buf - self.tmpfile = None - - if isinstance(path_or_buf, compat.string_types): - self.book = xlrd.open_workbook(path_or_buf) - else: - data = path_or_buf.read() + self.io = io + + engine = kwds.pop('engine', None) + + if engine is not None and engine != 'xlrd': + raise ValueError("Unknown engine: %s" % engine) + + if isinstance(io, compat.string_types): + self.book = xlrd.open_workbook(io) + elif engine == "xlrd" and isinstance(io, xlrd.Book): + self.book = io + elif hasattr(io, "read"): + data = io.read() self.book = xlrd.open_workbook(file_contents=data) + else: + raise ValueError('Must explicitly set engine if not passing in buffer or path for io.') def parse(self, sheetname, header=0, skiprows=None, skip_footer=0, index_col=None, parse_cols=None, parse_dates=False, @@ -261,9 +280,9 @@ def sheet_names(self): return self.book.sheet_names() def close(self): - """close path_or_buf if necessary""" - if hasattr(self.path_or_buf, 'close'): - self.path_or_buf.close() + """close io if necessary""" + if hasattr(self.io, 'close'): + self.io.close() def __enter__(self): return self diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 2bcf4789412f6..cd101d325f21d 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -254,6 +254,26 @@ def test_excel_read_buffer(self): f = open(pth, 'rb') xl = ExcelFile(f) xl.parse('Sheet1', index_col=0, parse_dates=True) + + def test_read_xlrd_Book(self): + _skip_if_no_xlrd() + _skip_if_no_xlwt() + + import xlrd + + pth = '__tmp_excel_read_worksheet__.xls' + df = self.frame + + with ensure_clean(pth) as pth: + df.to_excel(pth, "SheetA") + book = xlrd.open_workbook(pth) + + with ExcelFile(book, engine="xlrd") as xl: + result = xl.parse("SheetA") + tm.assert_frame_equal(df, result) + + result = read_excel(book, sheetname="SheetA", engine="xlrd") + tm.assert_frame_equal(df, result) def test_xlsx_table(self): _skip_if_no_xlrd()
That will also mean that read_excel accepts workbooks too. (closes #4961 and #4959)
https://api.github.com/repos/pandas-dev/pandas/pulls/4962
2013-09-24T05:04:38Z
2013-09-27T04:39:58Z
2013-09-27T04:39:58Z
2014-06-26T19:32:20Z
BUG: fix incorrect TypeError raise
diff --git a/doc/source/release.rst b/doc/source/release.rst index 442dcd0305913..04908ee8c9e03 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -453,6 +453,8 @@ Bug Fixes - Fixed a bug with setting invalid or out-of-range values in indexing enlargement scenarios (:issue:`4940`) - Tests for fillna on empty Series (:issue:`4346`), thanks @immerrr + - Fixed a bug where ``ValueError`` wasn't correctly raised when column names + weren't strings (:issue:`4956`) pandas 0.12.0 ------------- diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 65edaed89c1f7..491e090cab4fe 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -3033,7 +3033,9 @@ def create_axes(self, axes, obj, validate=True, nan_rep=None, new_blocks.append(b) except: raise ValueError( - "cannot match existing table structure for [%s] on appending data" % ','.join(items)) + "cannot match existing table structure for [%s] on " + "appending data" % ','.join(com.pprint_thing(item) for + item in items)) blocks = new_blocks # add my values diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index ee438cb2cd45a..a79ac9d3f9e40 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -3696,6 +3696,21 @@ def test_store_datetime_mixed(self): # self.assertRaises(Exception, store.put, 'foo', df, format='table') + def test_append_with_diff_col_name_types_raises_value_error(self): + df = DataFrame(np.random.randn(10, 1)) + df2 = DataFrame({'a': np.random.randn(10)}) + df3 = DataFrame({(1, 2): np.random.randn(10)}) + 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: + name = 'df_%s' % tm.rands(10) + store.append(name, df) + + for d in (df2, df3, df4, df5): + with tm.assertRaises(ValueError): + store.append(name, d) + def _test_sort(obj): if isinstance(obj, DataFrame):
TypeError was being raised when a ValueError was raised because the ValueError's message wasn't being converted to a string. closes #4956
https://api.github.com/repos/pandas-dev/pandas/pulls/4957
2013-09-24T01:52:16Z
2013-09-24T02:20:40Z
2013-09-24T02:20:40Z
2014-06-12T14:21:17Z
ENH: Added colspecs detection to read_fwf
diff --git a/.gitignore b/.gitignore index df7002a79d974..da76a414865e5 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,4 @@ pandas/io/*.json .project .pydevproject +.settings diff --git a/doc/source/io.rst b/doc/source/io.rst index 01795f6a4a9bf..5e04fcff61539 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -742,10 +742,13 @@ function works with data files that have known and fixed column widths. The function parameters to ``read_fwf`` are largely the same as `read_csv` with two extra parameters: - - ``colspecs``: a list of pairs (tuples), giving the extents of the - fixed-width fields of each line as half-open intervals [from, to[ - - ``widths``: a list of field widths, which can be used instead of - ``colspecs`` if the intervals are contiguous + - ``colspecs``: A list of pairs (tuples) giving the extents of the + fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). + String value 'infer' can be used to instruct the parser to try detecting + the column specifications from the first 100 rows of the data. Default + behaviour, if not specified, is to infer. + - ``widths``: A list of field widths which can be used instead of 'colspecs' + if the intervals are contiguous. .. ipython:: python :suppress: @@ -789,6 +792,18 @@ column widths for contiguous columns: The parser will take care of extra white spaces around the columns so it's ok to have extra separation between the columns in the file. +.. versionadded:: 0.13.0 + +By default, ``read_fwf`` will try to infer the file's ``colspecs`` by using the +first 100 rows of the file. It can do it only in cases when the columns are +aligned and correctly separated by the provided ``delimiter`` (default delimiter +is whitespace). + +.. ipython:: python + + df = pd.read_fwf('bar.csv', header=None, index_col=0) + df + .. ipython:: python :suppress: diff --git a/doc/source/release.rst b/doc/source/release.rst index f3f86dec92502..177381346e2d1 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -59,6 +59,7 @@ New features - Added ``isin`` method to DataFrame (:issue:`4211`) - Clipboard functionality now works with PySide (:issue:`4282`) - New ``extract`` string method returns regex matches more conveniently (:issue:`4685`) + - Auto-detect field widths in read_fwf when unspecified (:issue:`4488`) Experimental Features ~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 0796f34ead839..0e3c3b50fcd85 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -421,6 +421,9 @@ Enhancements can also be used. - ``read_stata` now accepts Stata 13 format (:issue:`4291`) + - ``read_fwf`` now infers the column specifications from the first 100 rows of + the file if the data has correctly separated and properly aligned columns + using the delimiter provided to the function (:issue:`4488`). .. _whatsnew_0130.experimental: diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index e0b12277f4416..3ef3cbf856fef 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -160,11 +160,15 @@ """ % (_parser_params % _table_sep) _fwf_widths = """\ -colspecs : a list of pairs (tuples), giving the extents - of the fixed-width fields of each line as half-open internals - (i.e., [from, to[ ). -widths : a list of field widths, which can be used instead of - 'colspecs' if the intervals are contiguous. +colspecs : list of pairs (int, int) or 'infer'. optional + A list of pairs (tuples) giving the extents of the fixed-width + fields of each line as half-open intervals (i.e., [from, to[ ). + String value 'infer' can be used to instruct the parser to try + detecting the column specifications from the first 100 rows of + the data (default='infer'). +widths : list of ints. optional + A list of field widths which can be used instead of 'colspecs' if + the intervals are contiguous. """ _read_fwf_doc = """ @@ -184,7 +188,8 @@ def _read(filepath_or_buffer, kwds): if skipfooter is not None: kwds['skip_footer'] = skipfooter - filepath_or_buffer, _ = get_filepath_or_buffer(filepath_or_buffer) + filepath_or_buffer, _ = get_filepath_or_buffer(filepath_or_buffer, + encoding) if kwds.get('date_parser', None) is not None: if isinstance(kwds['parse_dates'], bool): @@ -267,8 +272,8 @@ def _read(filepath_or_buffer, kwds): } _fwf_defaults = { - 'colspecs': None, - 'widths': None + 'colspecs': 'infer', + 'widths': None, } _c_unsupported = set(['skip_footer']) @@ -412,15 +417,15 @@ def parser_f(filepath_or_buffer, @Appender(_read_fwf_doc) -def read_fwf(filepath_or_buffer, colspecs=None, widths=None, **kwds): +def read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds): # Check input arguments. if colspecs is None and widths is None: raise ValueError("Must specify either colspecs or widths") - elif colspecs is not None and widths is not None: + elif colspecs not in (None, 'infer') and widths is not None: raise ValueError("You must specify only one of 'widths' and " "'colspecs'") - # Compute 'colspec' from 'widths', if specified. + # Compute 'colspecs' from 'widths', if specified. if widths is not None: colspecs, col = [], 0 for w in widths: @@ -519,7 +524,8 @@ def _clean_options(self, options, engine): engine = 'python' elif sep is not None and len(sep) > 1: # wait until regex engine integrated - engine = 'python' + if engine not in ('python', 'python-fwf'): + engine = 'python' # C engine not supported yet if engine == 'c': @@ -2012,31 +2018,65 @@ class FixedWidthReader(object): """ A reader of fixed-width lines. """ - def __init__(self, f, colspecs, filler, thousands=None, encoding=None): + def __init__(self, f, colspecs, delimiter, comment): self.f = f - self.colspecs = colspecs - self.filler = filler # Empty characters between fields. - self.thousands = thousands - if encoding is None: - encoding = get_option('display.encoding') - self.encoding = encoding - - if not isinstance(colspecs, (tuple, list)): + self.buffer = None + self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t ' + self.comment = comment + if colspecs == 'infer': + self.colspecs = self.detect_colspecs() + else: + self.colspecs = colspecs + + if not isinstance(self.colspecs, (tuple, list)): raise TypeError("column specifications must be a list or tuple, " "input was a %r" % type(colspecs).__name__) - for colspec in colspecs: + for colspec in self.colspecs: if not (isinstance(colspec, (tuple, list)) and - len(colspec) == 2 and - isinstance(colspec[0], int) and - isinstance(colspec[1], int)): + len(colspec) == 2 and + isinstance(colspec[0], (int, np.integer)) and + isinstance(colspec[1], (int, np.integer))): raise TypeError('Each column specification must be ' '2 element tuple or list of integers') + def get_rows(self, n): + rows = [] + for i, row in enumerate(self.f, 1): + rows.append(row) + if i >= n: + break + self.buffer = iter(rows) + return rows + + def detect_colspecs(self, n=100): + # Regex escape the delimiters + delimiters = ''.join([r'\%s' % x for x in self.delimiter]) + pattern = re.compile('([^%s]+)' % delimiters) + rows = self.get_rows(n) + max_len = max(map(len, rows)) + mask = np.zeros(max_len + 1, dtype=int) + if self.comment is not None: + rows = [row.partition(self.comment)[0] for row in rows] + for row in rows: + for m in pattern.finditer(row): + mask[m.start():m.end()] = 1 + shifted = np.roll(mask, 1) + shifted[0] = 0 + edges = np.where((mask ^ shifted) == 1)[0] + return list(zip(edges[::2], edges[1::2])) + def next(self): - line = next(self.f) + if self.buffer is not None: + try: + line = next(self.buffer) + except StopIteration: + self.buffer = None + line = next(self.f) + else: + line = next(self.f) # Note: 'colspecs' is a sequence of half-open intervals. - return [line[fromm:to].strip(self.filler or ' ') + return [line[fromm:to].strip(self.delimiter) for (fromm, to) in self.colspecs] # Iterator protocol in Python 3 uses __next__() @@ -2050,10 +2090,10 @@ class FixedWidthFieldParser(PythonParser): """ def __init__(self, f, **kwds): # Support iterators, convert to a list. - self.colspecs = list(kwds.pop('colspecs')) + self.colspecs = kwds.pop('colspecs') PythonParser.__init__(self, f, **kwds) def _make_reader(self, f): self.data = FixedWidthReader(f, self.colspecs, self.delimiter, - encoding=self.encoding) + self.comment) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 4e0c00c8a31eb..44e40dc34ff25 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -1706,7 +1706,7 @@ def test_utf16_example(self): self.assertEquals(len(result), 50) def test_converters_corner_with_nas(self): - # skip aberration observed on Win64 Python 3.2.2 + # skip aberration observed on Win64 Python 3.2.2 if hash(np.int64(-1)) != -2: raise nose.SkipTest("skipping because of windows hash on Python" " 3.2.2") @@ -2078,19 +2078,19 @@ def test_fwf(self): read_fwf(StringIO(data3), colspecs=colspecs, widths=[6, 10, 10, 7]) with tm.assertRaisesRegexp(ValueError, "Must specify either"): - read_fwf(StringIO(data3)) + read_fwf(StringIO(data3), colspecs=None, widths=None) def test_fwf_colspecs_is_list_or_tuple(self): with tm.assertRaisesRegexp(TypeError, 'column specifications must be a list or ' 'tuple.+'): - fwr = pd.io.parsers.FixedWidthReader(StringIO(self.data1), - {'a': 1}, ',') + pd.io.parsers.FixedWidthReader(StringIO(self.data1), + {'a': 1}, ',', '#') def test_fwf_colspecs_is_list_or_tuple_of_two_element_tuples(self): with tm.assertRaisesRegexp(TypeError, 'Each column specification must be.+'): - read_fwf(StringIO(self.data1), {'a': 1}) + read_fwf(StringIO(self.data1), [('a', 1)]) def test_fwf_regression(self): # GH 3594 @@ -2223,6 +2223,107 @@ def test_iteration_open_handle(self): expected = Series(['DDD', 'EEE', 'FFF', 'GGG']) tm.assert_series_equal(result, expected) + +class TestFwfColspaceSniffing(unittest.TestCase): + def test_full_file(self): + # File with all values + test = '''index A B C +2000-01-03T00:00:00 0.980268513777 3 foo +2000-01-04T00:00:00 1.04791624281 -4 bar +2000-01-05T00:00:00 0.498580885705 73 baz +2000-01-06T00:00:00 1.12020151869 1 foo +2000-01-07T00:00:00 0.487094399463 0 bar +2000-01-10T00:00:00 0.836648671666 2 baz +2000-01-11T00:00:00 0.157160753327 34 foo''' + colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_missing(self): + # File with missing values + test = '''index A B C +2000-01-03T00:00:00 0.980268513777 3 foo +2000-01-04T00:00:00 1.04791624281 -4 bar + 0.498580885705 73 baz +2000-01-06T00:00:00 1.12020151869 1 foo +2000-01-07T00:00:00 0 bar +2000-01-10T00:00:00 0.836648671666 2 baz + 34''' + colspecs = ((0, 19), (21, 35), (38, 40), (42, 45)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_spaces(self): + # File with spaces in columns + test = ''' +Account Name Balance CreditLimit AccountCreated +101 Keanu Reeves 9315.45 10000.00 1/17/1998 +312 Gerard Butler 90.00 1000.00 8/6/2003 +868 Jennifer Love Hewitt 0 17000.00 5/25/1985 +761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 +317 Bill Murray 789.65 5000.00 2/5/2007 +'''.strip('\r\n') + colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_full_file_with_spaces_and_missing(self): + # File with spaces and missing values in columsn + test = ''' +Account Name Balance CreditLimit AccountCreated +101 10000.00 1/17/1998 +312 Gerard Butler 90.00 1000.00 8/6/2003 +868 5/25/1985 +761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 +317 Bill Murray 789.65 +'''.strip('\r\n') + colspecs = ((0, 7), (8, 28), (30, 38), (42, 53), (56, 70)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_messed_up_data(self): + # Completely messed up file + test = ''' + Account Name Balance Credit Limit Account Created + 101 10000.00 1/17/1998 + 312 Gerard Butler 90.00 1000.00 + + 761 Jada Pinkett-Smith 49654.87 100000.00 12/5/2006 + 317 Bill Murray 789.65 +'''.strip('\r\n') + colspecs = ((2, 10), (15, 33), (37, 45), (49, 61), (64, 79)) + expected = read_fwf(StringIO(test), colspecs=colspecs) + tm.assert_frame_equal(expected, read_fwf(StringIO(test))) + + def test_multiple_delimiters(self): + test = r''' +col1~~~~~col2 col3++++++++++++++++++col4 +~~22.....11.0+++foo~~~~~~~~~~Keanu Reeves + 33+++122.33\\\bar.........Gerard Butler +++44~~~~12.01 baz~~Jennifer Love Hewitt +~~55 11+++foo++++Jada Pinkett-Smith +..66++++++.03~~~bar Bill Murray +'''.strip('\r\n') + colspecs = ((0, 4), (7, 13), (15, 19), (21, 41)) + expected = read_fwf(StringIO(test), colspecs=colspecs, + delimiter=' +~.\\') + tm.assert_frame_equal(expected, read_fwf(StringIO(test), + delimiter=' +~.\\')) + + def test_variable_width_unicode(self): + if not compat.PY3: + raise nose.SkipTest('Bytes-related test - only needs to work on Python 3') + test = ''' +שלום שלום +ום שלל +של ום +'''.strip('\r\n') + expected = pd.read_fwf(BytesIO(test.encode('utf8')), + colspecs=[(0, 4), (5, 9)], header=None) + tm.assert_frame_equal(expected, read_fwf(BytesIO(test.encode('utf8')), + header=None)) + + class TestCParserHighMemory(ParserTests, unittest.TestCase): def read_csv(self, *args, **kwds):
closes #4488 Implemented an algorithm that uses a bitmask to detect the gaps between the columns. Also the reader buffers the lines used for detection in case it's input is not seek-able. Added tests.
https://api.github.com/repos/pandas-dev/pandas/pulls/4955
2013-09-23T22:52:12Z
2013-09-30T10:18:12Z
2013-09-30T10:18:12Z
2014-06-13T19:04:55Z
BUG: Fixed _format_labels in tile.py for np.inf.
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index ce42ee3b7bc88..7d2555e8cba81 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -505,6 +505,12 @@ normally distributed data into equal-size quartiles like so: factor value_counts(factor) +We can also pass infinite values to define the bins: +.. ipython:: python + + arr = np.random.randn(20) + factor = cut(arr, [-np.inf, 0, np.inf]) + factor .. _basics.apply: diff --git a/doc/source/release.rst b/doc/source/release.rst index 12787c5d04b45..eec2e91f0a755 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -462,6 +462,8 @@ Bug Fixes - Fixed ``copy()`` to shallow copy axes/indices as well and thereby keep separate metadata. (:issue:`4202`, :issue:`4830`) - Fixed skiprows option in Python parser for read_csv (:issue:`4382`) + - Fixed bug preventing ``cut`` from working with ``np.inf`` levels without + explicitly passing labels (:issue:`3415`) pandas 0.12.0 ------------- diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py index 53258864b1ab8..86a43f648526b 100644 --- a/pandas/tools/tests/test_tile.py +++ b/pandas/tools/tests/test_tile.py @@ -114,6 +114,22 @@ def test_na_handling(self): ex_result = np.where(com.isnull(arr), np.nan, result) tm.assert_almost_equal(result, ex_result) + def test_inf_handling(self): + data = np.arange(6) + data_ser = Series(data) + + 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]') + self.assertEquals(result[0], '(-inf, 2]') + self.assertEquals(result_ser[5], '(4, inf]') + self.assertEquals(result_ser[0], '(-inf, 2]') + def test_qcut(self): arr = np.random.randn(1000) diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index aa64b046c6891..0c1d6d1c1cbab 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -245,7 +245,7 @@ def _format_label(x, precision=3): else: # pragma: no cover return sgn + '.'.join(('%d' % whole, val)) else: - return sgn + '%d' % whole + return sgn + '%0.f' % whole else: return str(x)
Fixes issue https://github.com/pydata/pandas/issues/3415.
https://api.github.com/repos/pandas-dev/pandas/pulls/4954
2013-09-23T20:04:51Z
2013-09-25T15:10:31Z
2013-09-25T15:10:31Z
2014-06-19T23:59:23Z
BUG: Make sure series-series boolean comparions are label based (GH4947)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 65e6ca0e1d95c..026791438a905 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -374,6 +374,8 @@ Bug Fixes - appending a 0-len table will work correctly (:issue:`4273`) - ``to_hdf`` was raising when passing both arguments ``append`` and ``table`` (:issue:`4584`) - reading from a store with duplicate columns across dtypes would raise (:issue:`4767`) + - Fixed a bug where ``ValueError`` wasn't correctly raised when column names + weren't strings (:issue:`4956`) - 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 @@ -500,8 +502,6 @@ Bug Fixes - Fixed a bug with setting invalid or out-of-range values in indexing enlargement scenarios (:issue:`4940`) - Tests for fillna on empty Series (:issue:`4346`), thanks @immerrr - - Fixed a bug where ``ValueError`` wasn't correctly raised when column names - weren't strings (:issue:`4956`) - Fixed ``copy()`` to shallow copy axes/indices as well and thereby keep separate metadata. (:issue:`4202`, :issue:`4830`) - Fixed skiprows option in Python parser for read_csv (:issue:`4382`) @@ -521,6 +521,7 @@ Bug Fixes - Fix a bug where reshaping a ``Series`` to its own shape raised ``TypeError`` (:issue:`4554`) and other reshaping issues. - Bug in setting with ``ix/loc`` and a mixed int/string index (:issue:`4544`) + - Make sure series-series boolean comparions are label based (:issue:`4947`) pandas 0.12.0 ------------- diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 4ce2143fdd92c..c1c6e6e2f83d3 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -564,21 +564,31 @@ def na_op(x, y): y = com._ensure_object(y) result = lib.vec_binop(x, y, op) else: - result = lib.scalar_binop(x, y, op) + try: + + # let null fall thru + if not isnull(y): + y = bool(y) + result = lib.scalar_binop(x, y, op) + except: + raise TypeError("cannot compare a dtyped [{0}] array with " + "a scalar of type [{1}]".format(x.dtype,type(y).__name__)) return result def wrapper(self, other): if isinstance(other, pd.Series): name = _maybe_match_name(self, other) + + other = other.reindex_like(self).fillna(False).astype(bool) return self._constructor(na_op(self.values, other.values), - index=self.index, name=name) + index=self.index, name=name).fillna(False).astype(bool) elif isinstance(other, pd.DataFrame): return NotImplemented else: # scalars return self._constructor(na_op(self.values, other), - index=self.index, name=self.name) + index=self.index, name=self.name).fillna(False).astype(bool) return wrapper diff --git a/pandas/core/series.py b/pandas/core/series.py index 1bc35008cc341..79faad93ff1c1 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -21,7 +21,8 @@ _values_from_object, _possibly_cast_to_datetime, _possibly_castable, _possibly_convert_platform, - ABCSparseArray, _maybe_match_name) + ABCSparseArray, _maybe_match_name, _ensure_object) + from pandas.core.index import (Index, MultiIndex, InvalidIndexError, _ensure_index, _handle_legacy_indexes) from pandas.core.indexing import ( @@ -1170,7 +1171,7 @@ def duplicated(self, take_last=False): ------- duplicated : Series """ - keys = com._ensure_object(self.values) + keys = _ensure_object(self.values) duplicated = lib.duplicated(keys, take_last=take_last) return self._constructor(duplicated, index=self.index, name=self.name) diff --git a/pandas/lib.pyx b/pandas/lib.pyx index f5205ae0c3133..56ef9a4fcb160 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -672,6 +672,9 @@ def scalar_binop(ndarray[object] values, object val, object op): object x result = np.empty(n, dtype=object) + if util._checknull(val): + result.fill(val) + return result for i in range(n): x = values[i] diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index ce8d84840ed69..e8d9f3a7fc7cc 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4523,8 +4523,10 @@ def f(): def test_logical_with_nas(self): d = DataFrame({'a': [np.nan, False], 'b': [True, True]}) + # GH4947 + # bool comparisons should return bool result = d['a'] | d['b'] - expected = Series([np.nan, True]) + expected = Series([False, True]) assert_series_equal(result, expected) # GH4604, automatic casting here @@ -4533,10 +4535,6 @@ def test_logical_with_nas(self): assert_series_equal(result, expected) result = d['a'].fillna(False,downcast=False) | d['b'] - expected = Series([True, True],dtype=object) - assert_series_equal(result, expected) - - result = (d['a'].fillna(False,downcast=False) | d['b']).convert_objects() expected = Series([True, True]) assert_series_equal(result, expected) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index a70f2931e36fe..7f3ea130259dc 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -2757,6 +2757,93 @@ def test_comparison_different_length(self): b = Series([2, 3, 4]) self.assertRaises(ValueError, a.__eq__, b) + def test_comparison_label_based(self): + + # GH 4947 + # comparisons should be label based + + a = Series([True, False, True], list('bca')) + b = Series([False, True, False], list('abc')) + + expected = Series([True, False, False], list('bca')) + result = a & b + assert_series_equal(result,expected) + + expected = Series([True, False, True], list('bca')) + result = a | b + assert_series_equal(result,expected) + + expected = Series([False, False, True], list('bca')) + result = a ^ b + assert_series_equal(result,expected) + + # rhs is bigger + a = Series([True, False, True], list('bca')) + b = Series([False, True, False, True], list('abcd')) + + expected = Series([True, False, False], list('bca')) + result = a & b + assert_series_equal(result,expected) + + expected = Series([True, False, True], list('bca')) + result = a | b + assert_series_equal(result,expected) + + # filling + + # vs empty + result = a & Series([]) + expected = Series([False, False, False], list('bca')) + assert_series_equal(result,expected) + + result = a | Series([]) + expected = Series([True, False, True], list('bca')) + assert_series_equal(result,expected) + + # vs non-matching + result = a & Series([1],['z']) + expected = Series([False, False, False], list('bca')) + assert_series_equal(result,expected) + + result = a | Series([1],['z']) + expected = Series([True, False, True], list('bca')) + assert_series_equal(result,expected) + + # identity + # we would like s[s|e] == s to hold for any e, whether empty or not + for e in [Series([]),Series([1],['z']),Series(['z']),Series(np.nan,b.index),Series(np.nan,a.index)]: + result = a[a | e] + assert_series_equal(result,a[a]) + + # vs scalars + index = list('bca') + t = Series([True,False,True]) + + for v in [True,1,2]: + result = Series([True,False,True],index=index) | v + expected = Series([True,True,True],index=index) + assert_series_equal(result,expected) + + for v in [np.nan,'foo']: + self.assertRaises(TypeError, lambda : t | v) + + for v in [False,0]: + result = Series([True,False,True],index=index) | v + expected = Series([True,False,True],index=index) + assert_series_equal(result,expected) + + for v in [True,1]: + result = Series([True,False,True],index=index) & v + expected = Series([True,False,True],index=index) + assert_series_equal(result,expected) + + for v in [False,0]: + result = Series([True,False,True],index=index) & v + expected = Series([False,False,False],index=index) + assert_series_equal(result,expected) + for v in [np.nan]: + self.assertRaises(TypeError, lambda : t & v) + def test_between(self): s = Series(bdate_range('1/1/2000', periods=20).asobject) s[::2] = np.nan @@ -2793,12 +2880,14 @@ def test_scalar_na_cmp_corners(self): def tester(a, b): return a & b - self.assertRaises(ValueError, tester, s, datetime(2005, 1, 1)) + self.assertRaises(TypeError, tester, s, datetime(2005, 1, 1)) s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)]) s[::2] = np.nan - assert_series_equal(tester(s, list(s)), s) + expected = Series(True,index=s.index) + expected[::2] = False + assert_series_equal(tester(s, list(s)), expected) d = DataFrame({'A': s}) # TODO: Fix this exception - needs to be fixed! (see GH5035)
closes #4947 You ask for a boolean comparsion, that is what you get ``` In [1]: a = Series([True, False, True], list('bca')) In [2]: b = Series([False, True, False], list('abc')) In [19]: a Out[19]: b True c False a True dtype: bool In [20]: b Out[20]: a False b True c False dtype: bool In [21]: a & b Out[21]: b True c False a False dtype: bool In [22]: a | b Out[22]: b True c False a True dtype: bool In [23]: a ^ b Out[23]: b False c False a True dtype: bool In [24]: a & Series([]) Out[24]: b False c False a False dtype: bool In [25]: a | Series([]) Out[25]: b True c False a True dtype: bool ``` these ok? ``` In [9]: Series([np.nan,np.nan])&Series([np.nan,np.nan]) Out[9]: 0 False 1 False dtype: bool In [10]: Series([np.nan,np.nan])|Series([np.nan,np.nan]) Out[10]: 0 False 1 False dtype: bool ``` scalars ``` In [30]: a | True Out[30]: b True c True a True dtype: bool In [31]: a | False Out[31]: b True c False a True dtype: bool In [32]: a & True Out[32]: b True c False a True dtype: bool In [33]: a & False Out[33]: b False c False a False dtype: bool ``` Invalid scalars ``` In [28]: a | 'foo' TypeError: cannot compare a dtyped [bool] array with a scalar of type [bool] In [29]: a | np.nan TypeError: cannot compare a dtyped [bool] array with a scalar of type [float] ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4953
2013-09-23T18:15:50Z
2013-10-01T13:28:40Z
2013-10-01T13:28:40Z
2014-06-26T17:16:32Z
CLN: removed Panel.fillna, replacing within core/generic.py/fillna
diff --git a/doc/source/release.rst b/doc/source/release.rst index 33e421fa55960..efde15bc23d3c 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -254,7 +254,8 @@ See :ref:`Internal Refactoring<whatsnew_0130.refactoring>` - ``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 same api as original ``DataFrame`` filter + - ``fillna`` refactored to ``core/generic.py``, while > 3ndim is ``NotImplemented`` - Series now inherits from ``NDFrame`` rather than directly from ``ndarray``. There are several minor changes that affect the API. diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 36c3ff9085d87..4553e4804e98b 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1569,6 +1569,18 @@ def fillna(self, value=None, method=None, axis=0, inplace=False, return result + # > 3d + if self.ndim > 3: + raise NotImplementedError('cannot fillna with a method for > 3dims') + + # 3d + elif self.ndim == 3: + + # fill in 2d chunks + result = dict([ (col,s.fillna(method=method, value=value)) for col, s in compat.iteritems(self) ]) + return self._constructor.from_dict(result) + + # 2d or less method = com._clean_fill_method(method) new_data = self._data.interpolate(method=method, axis=axis, diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 45101b1e2afd5..5f90eb9fa31a7 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -750,54 +750,6 @@ def _combine_panel(self, other, func): return self._constructor(result_values, items, major, minor) - def fillna(self, value=None, method=None): - """ - Fill NaN values using the specified method. - - Member Series / TimeSeries are filled separately. - - Parameters - ---------- - value : any kind (should be same type as array) - Value to use to fill holes (e.g. 0) - - method : {'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad' - Method to use for filling holes in reindexed Series - - pad / ffill: propagate last valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill gap - - Returns - ------- - y : DataFrame - - See also - -------- - DataFrame.reindex, DataFrame.asfreq - """ - if isinstance(value, (list, tuple)): - raise TypeError('"value" parameter must be a scalar or dict, but ' - 'you passed a "{0}"'.format(type(value).__name__)) - if value is None: - if method is None: - raise ValueError('must specify a fill method or value') - result = {} - for col, s in compat.iteritems(self): - result[col] = s.fillna(method=method, value=value) - - return self._constructor.from_dict(result) - else: - if method is not None: - raise ValueError('cannot specify both a fill method and value') - new_data = self._data.fillna(value) - return self._constructor(new_data) - - def ffill(self): - return self.fillna(method='ffill') - - def bfill(self): - return self.fillna(method='bfill') - def major_xs(self, key, copy=True): """ Return slice of panel along major axis diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 9b34631ecc894..4f7e75b401216 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -849,23 +849,11 @@ def test_sort_index(self): # assert_panel_equal(sorted_panel, self.panel) def test_fillna(self): + self.assert_(not np.isfinite(self.panel4d.values).all()) filled = self.panel4d.fillna(0) self.assert_(np.isfinite(filled.values).all()) - filled = self.panel4d.fillna(method='backfill') - assert_panel_equal(filled['l1'], - self.panel4d['l1'].fillna(method='backfill')) - - panel4d = self.panel4d.copy() - panel4d['str'] = 'foo' - - filled = panel4d.fillna(method='backfill') - assert_panel_equal(filled['l1'], - panel4d['l1'].fillna(method='backfill')) - - empty = self.panel4d.reindex(labels=[]) - filled = empty.fillna(0) - assert_panel4d_equal(filled, empty) + self.assertRaises(NotImplementedError, self.panel4d.fillna, method='pad') def test_swapaxes(self): result = self.panel4d.swapaxes('labels', 'items')
https://api.github.com/repos/pandas-dev/pandas/pulls/4951
2013-09-23T14:20:55Z
2013-09-23T14:33:56Z
2013-09-23T14:33:56Z
2014-06-21T04:20:45Z
Pairwise versions for rolling_cov, ewmcov and expanding_cov
diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 66e0d457e33b6..7bd3c1aa03d90 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -59,6 +59,19 @@ The ``Series`` object has a method ``cov`` to compute covariance between series Analogously, ``DataFrame`` has a method ``cov`` to compute pairwise covariances among the series in the DataFrame, also excluding NA/null values. +.. _computation.covariance.caveats: + +.. note:: + + Assuming the missing data are missing at random this results in an estimate + for the covariance matrix which is unbiased. However, for many applications + this estimate may not be acceptable because the estimated covariance matrix + is not guaranteed to be positive semi-definite. This could lead to + estimated correlations having absolute values which are greater than one, + and/or a non-invertible covariance matrix. See `Estimation of covariance + matrices <http://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_matrices>`_ + for more details. + .. ipython:: python frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e']) @@ -99,6 +112,12 @@ correlation methods are provided: All of these are currently computed using pairwise complete observations. +.. note:: + + Please see the :ref:`caveats <computation.covariance.caveats>` associated + with this method of calculating correlation matrices in the + :ref:`covariance section <computation.covariance>`. + .. ipython:: python frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e']) @@ -325,11 +344,14 @@ Binary rolling moments two ``Series`` or any combination of ``DataFrame/Series`` or ``DataFrame/DataFrame``. Here is the behavior in each case: -- two ``Series``: compute the statistic for the pairing +- two ``Series``: compute the statistic for the pairing. - ``DataFrame/Series``: compute the statistics for each column of the DataFrame - with the passed Series, thus returning a DataFrame -- ``DataFrame/DataFrame``: compute statistic for matching column names, - returning a DataFrame + with the passed Series, thus returning a DataFrame. +- ``DataFrame/DataFrame``: by default compute the statistic for matching column + names, returning a DataFrame. If the keyword argument ``pairwise=True`` is + passed then computes the statistic for each pair of columns, returning a + ``Panel`` whose ``items`` are the dates in question (see :ref:`the next section + <stats.moments.corr_pairwise>`). For example: @@ -340,20 +362,42 @@ For example: .. _stats.moments.corr_pairwise: -Computing rolling pairwise correlations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Computing rolling pairwise covariances and correlations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In financial data analysis and other fields it's common to compute correlation -matrices for a collection of time series. More difficult is to compute a -moving-window correlation matrix. This can be done using the -``rolling_corr_pairwise`` function, which yields a ``Panel`` whose ``items`` -are the dates in question: +In financial data analysis and other fields it's common to compute covariance +and correlation matrices for a collection of time series. Often one is also +interested in moving-window covariance and correlation matrices. This can be +done by passing the ``pairwise`` keyword argument, which in the case of +``DataFrame`` inputs will yield a ``Panel`` whose ``items`` are the dates in +question. In the case of a single DataFrame argument the ``pairwise`` argument +can even be omitted: + +.. note:: + + Missing values are ignored and each entry is computed using the pairwise + complete observations. Please see the :ref:`covariance section + <computation.covariance>` for :ref:`caveats + <computation.covariance.caveats>` associated with this method of + calculating covariance and correlation matrices. .. ipython:: python - correls = rolling_corr_pairwise(df, 50) + covs = rolling_cov(df[['B','C','D']], df[['A','B','C']], 50, pairwise=True) + covs[df.index[-50]] + +.. ipython:: python + + correls = rolling_corr(df, 50) correls[df.index[-50]] +.. note:: + + Prior to version 0.14 this was available through ``rolling_corr_pairwise`` + which is now simply syntactic sugar for calling ``rolling_corr(..., + pairwise=True)`` and deprecated. This is likely to be removed in a future + release. + You can efficiently retrieve the time series of correlations between two columns using ``ix`` indexing: diff --git a/doc/source/v0.14.0.txt b/doc/source/v0.14.0.txt index 95537878871b1..344198d6e5ef1 100644 --- a/doc/source/v0.14.0.txt +++ b/doc/source/v0.14.0.txt @@ -183,6 +183,19 @@ These are out-of-bounds selections Because of the default `align` value changes, coordinates of bar plots are now located on integer values (0.0, 1.0, 2.0 ...). This is intended to make bar plot be located on the same coodinates as line plot. However, bar plot may differs unexpectedly when you manually adjust the bar location or drawing area, such as using `set_xlim`, `set_ylim`, etc. In this cases, please modify your script to meet with new coordinates. +- ``pairwise`` keyword was added to the statistical moment functions + ``rolling_cov``, ``rolling_corr``, ``ewmcov``, ``ewmcorr``, + ``expanding_cov``, ``expanding_corr`` to allow the calculation of moving + window covariance and correlation matrices (:issue:`4950`). See + :ref:`Computing rolling pairwise covariances and correlations + <stats.moments.corr_pairwise>` in the docs. + + .. ipython:: python + + df = DataFrame(np.random.randn(10,4),columns=list('ABCD')) + covs = rolling_cov(df[['A','B','C']], df[['B','C','D']], 5, pairwise=True) + covs[df.index[-1]] + MultiIndexing Using Slicers ~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index ec01113abc8f2..523f055eaf605 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -5,14 +5,14 @@ from __future__ import division from functools import wraps +from collections import defaultdict from numpy import NaN import numpy as np from pandas.core.api import DataFrame, Series, Panel, notnull import pandas.algos as algos -import pandas.core.common as com -from pandas.core.common import _values_from_object +import pandas.core.common as pdcom from pandas.util.decorators import Substitution, Appender @@ -31,13 +31,22 @@ #------------------------------------------------------------------------------ # Docs +# The order of arguments for the _doc_template is: +# (header, args, kwargs, returns, notes) + _doc_template = """ %s Parameters ---------- +%s%s +Returns +------- +%s %s -window : int +""" + +_roll_kw = """window : int Size of the moving window. This is the number of observations used for calculating the statistic. min_periods : int, default None @@ -49,11 +58,9 @@ for `freq`. center : boolean, default False Set the labels at the center of the window. - -Returns -------- -%s +""" +_roll_notes = r""" Notes ----- By default, the result is set to the right edge of the window. This can be @@ -65,12 +72,7 @@ """ -_ewm_doc = r"""%s - -Parameters ----------- -%s -com : float. optional +_ewm_kw = r"""com : float. optional Center of mass: :math:`\alpha = 1 / (1 + com)`, span : float, optional Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)` @@ -85,8 +87,9 @@ adjust : boolean, default True Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average) +""" -%s +_ewm_notes = """ Notes ----- Either center of mass or span must be specified @@ -99,55 +102,51 @@ :math:`c = (s - 1) / 2` So a "20-day EWMA" would have center 9.5. - -Returns -------- -y : type of input argument """ - -_expanding_doc = """ -%s - -Parameters ----------- -%s -min_periods : int, default None +_expanding_kw = """min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). freq : string or DateOffset object, optional (default None) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. `time_rule` is a legacy alias for `freq`. - -Returns -------- -%s - -Notes ------ -The `freq` keyword is used to conform time series data to a specified -frequency by resampling the data. This is done with the default parameters -of :meth:`~pandas.Series.resample` (i.e. using the `mean`). """ -_type_of_input = "y : type of input argument" +_type_of_input_retval = "y : type of input argument" _flex_retval = """y : type depends on inputs - DataFrame / DataFrame -> DataFrame (matches on columns) + DataFrame / DataFrame -> DataFrame (matches on columns) or Panel (pairwise) DataFrame / Series -> Computes result for each column Series / Series -> Series""" -_unary_arg = "arg : Series, DataFrame" +_pairwise_retval = "y : Panel whose items are df1.index values" + +_unary_arg = "arg : Series, DataFrame\n" _binary_arg_flex = """arg1 : Series, DataFrame, or ndarray -arg2 : Series, DataFrame, or ndarray""" +arg2 : Series, DataFrame, or ndarray, optional + if not supplied then will default to arg1 and produce pairwise output +""" _binary_arg = """arg1 : Series, DataFrame, or ndarray -arg2 : Series, DataFrame, or ndarray""" +arg2 : Series, DataFrame, or ndarray +""" + +_pairwise_arg = """df1 : DataFrame +df2 : DataFrame +""" + +_pairwise_kw = """pairwise : bool, default False + If False then only matching columns between arg1 and arg2 will be used and + the output will be a DataFrame. + If True then all pairwise combinations will be calculated and the output + will be a Panel in the case of DataFrame inputs. In the case of missing + elements, only complete pairwise observations will be used. +""" -_bias_doc = r"""bias : boolean, default False +_bias_kw = r"""bias : boolean, default False Use a standard estimation bias correction """ @@ -194,27 +193,47 @@ def rolling_count(arg, window, freq=None, center=False, time_rule=None): return return_hook(result) -@Substitution("Unbiased moving covariance.", _binary_arg_flex, _flex_retval) +@Substitution("Unbiased moving covariance.", _binary_arg_flex, + _roll_kw+_pairwise_kw, _flex_retval, _roll_notes) @Appender(_doc_template) -def rolling_cov(arg1, arg2, window, min_periods=None, freq=None, - center=False, time_rule=None): +def rolling_cov(arg1, arg2=None, window=None, min_periods=None, freq=None, + center=False, time_rule=None, pairwise=None): + if window is None and isinstance(arg2, (int, float)): + window = arg2 + arg2 = arg1 + pairwise = True if pairwise is None else pairwise # only default unset + elif arg2 is None: + arg2 = arg1 + pairwise = True if pairwise is None else pairwise # only default unset arg1 = _conv_timerule(arg1, freq, time_rule) arg2 = _conv_timerule(arg2, freq, time_rule) window = min(window, len(arg1), len(arg2)) def _get_cov(X, Y): - mean = lambda x: rolling_mean(x, window, min_periods,center=center) - count = rolling_count(X + Y, window,center=center) + mean = lambda x: rolling_mean(x, window, min_periods, center=center) + count = rolling_count(X + Y, window, center=center) bias_adj = count / (count - 1) return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj - rs = _flex_binary_moment(arg1, arg2, _get_cov) + rs = _flex_binary_moment(arg1, arg2, _get_cov, pairwise=bool(pairwise)) return rs -@Substitution("Moving sample correlation.", _binary_arg_flex, _flex_retval) +@Substitution("Moving sample correlation.", _binary_arg_flex, + _roll_kw+_pairwise_kw, _flex_retval, _roll_notes) @Appender(_doc_template) -def rolling_corr(arg1, arg2, window, min_periods=None, freq=None, - center=False, time_rule=None): +def rolling_corr(arg1, arg2=None, window=None, min_periods=None, freq=None, + center=False, time_rule=None, pairwise=None): + if window is None and isinstance(arg2, (int, float)): + window = arg2 + arg2 = arg1 + pairwise = True if pairwise is None else pairwise # only default unset + elif arg2 is None: + arg2 = arg1 + pairwise = True if pairwise is None else pairwise # only default unset + arg1 = _conv_timerule(arg1, freq, time_rule) + arg2 = _conv_timerule(arg2, freq, time_rule) + window = min(window, len(arg1), len(arg2)) + def _get_corr(a, b): num = rolling_cov(a, b, window, min_periods, freq=freq, center=center, time_rule=time_rule) @@ -223,16 +242,17 @@ def _get_corr(a, b): rolling_std(b, window, min_periods, freq=freq, center=center, time_rule=time_rule)) return num / den - return _flex_binary_moment(arg1, arg2, _get_corr) + return _flex_binary_moment(arg1, arg2, _get_corr, pairwise=bool(pairwise)) -def _flex_binary_moment(arg1, arg2, f): +def _flex_binary_moment(arg1, arg2, f, pairwise=False): if not (isinstance(arg1,(np.ndarray, Series, DataFrame)) and isinstance(arg2,(np.ndarray, Series, DataFrame))): raise TypeError("arguments to moment function must be of type " "np.ndarray/Series/DataFrame") - if isinstance(arg1, (np.ndarray,Series)) and isinstance(arg2, (np.ndarray,Series)): + if isinstance(arg1, (np.ndarray, Series)) and \ + isinstance(arg2, (np.ndarray,Series)): X, Y = _prep_binary(arg1, arg2) return f(X, Y) elif isinstance(arg1, DataFrame): @@ -241,10 +261,23 @@ def _flex_binary_moment(arg1, arg2, f): X, Y = arg1.align(arg2, join='outer') X = X + 0 * Y Y = Y + 0 * X - res_columns = arg1.columns.union(arg2.columns) - for col in res_columns: - if col in X and col in Y: - results[col] = f(X[col], Y[col]) + if pairwise is False: + res_columns = arg1.columns.union(arg2.columns) + for col in res_columns: + if col in X and col in Y: + results[col] = f(X[col], Y[col]) + elif pairwise is True: + results = defaultdict(dict) + for i, k1 in enumerate(arg1.columns): + for j, k2 in enumerate(arg2.columns): + if j<i and arg2 is arg1: + # Symmetric case + results[k1][k2] = results[k2][k1] + else: + results[k1][k2] = f(arg1[k1], arg2[k2]) + return Panel.from_dict(results).swapaxes('items', 'major') + else: + raise ValueError("'pairwise' is not True/False") else: res_columns = arg1.columns X, Y = arg1.align(arg2, axis=0, join='outer') @@ -258,38 +291,17 @@ def _flex_binary_moment(arg1, arg2, f): return _flex_binary_moment(arg2, arg1, f) -def rolling_corr_pairwise(df, window, min_periods=None): - """ - Computes pairwise rolling correlation matrices as Panel whose items are - dates. - - Parameters - ---------- - df : DataFrame - window : int - Size of the moving window. This is the number of observations used for - calculating the statistic. - min_periods : int, default None - Minimum number of observations in window required to have a value - (otherwise result is NA). - - Returns - ------- - correls : Panel - """ - from pandas import Panel - from collections import defaultdict - - all_results = defaultdict(dict) - - for i, k1 in enumerate(df.columns): - for k2 in df.columns[i:]: - corr = rolling_corr(df[k1], df[k2], window, - min_periods=min_periods) - all_results[k1][k2] = corr - all_results[k2][k1] = corr - - return Panel.from_dict(all_results).swapaxes('items', 'major') +@Substitution("Deprecated. Use rolling_corr(..., pairwise=True) instead.\n\n" + "Pairwise moving sample correlation", _pairwise_arg, + _roll_kw, _pairwise_retval, _roll_notes) +@Appender(_doc_template) +def rolling_corr_pairwise(df1, df2=None, window=None, min_periods=None, + freq=None, center=False, time_rule=None): + import warnings + warnings.warn("rolling_corr_pairwise is deprecated, use rolling_corr(..., pairwise=True)", FutureWarning) + return rolling_corr(df1, df2, window=window, min_periods=min_periods, + freq=freq, center=center, time_rule=time_rule, + pairwise=True) def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False, @@ -338,7 +350,8 @@ def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False, def _center_window(rs, window, axis): if axis > rs.ndim-1: - raise ValueError("Requested axis is larger then no. of argument dimensions") + raise ValueError("Requested axis is larger then no. of argument " + "dimensions") offset = int((window - 1) / 2.) if isinstance(rs, (Series, DataFrame, Panel)): @@ -401,8 +414,9 @@ def _get_center_of_mass(com, span, halflife): return float(com) -@Substitution("Exponentially-weighted moving average", _unary_arg, "") -@Appender(_ewm_doc) +@Substitution("Exponentially-weighted moving average", _unary_arg, _ewm_kw, + _type_of_input_retval, _ewm_notes) +@Appender(_doc_template) def ewma(arg, com=None, span=None, halflife=None, min_periods=0, freq=None, time_rule=None, adjust=True): com = _get_center_of_mass(com, span, halflife) @@ -424,8 +438,9 @@ def _first_valid_index(arr): return notnull(arr).argmax() if len(arr) else 0 -@Substitution("Exponentially-weighted moving variance", _unary_arg, _bias_doc) -@Appender(_ewm_doc) +@Substitution("Exponentially-weighted moving variance", _unary_arg, + _ewm_kw+_bias_kw, _type_of_input_retval, _ewm_notes) +@Appender(_doc_template) def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, freq=None, time_rule=None): com = _get_center_of_mass(com, span, halflife) @@ -440,8 +455,9 @@ def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, return result -@Substitution("Exponentially-weighted moving std", _unary_arg, _bias_doc) -@Appender(_ewm_doc) +@Substitution("Exponentially-weighted moving std", _unary_arg, + _ewm_kw+_bias_kw, _type_of_input_retval, _ewm_notes) +@Appender(_doc_template) def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, time_rule=None): result = ewmvar(arg, com=com, span=span, halflife=halflife, time_rule=time_rule, @@ -451,38 +467,56 @@ def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, ewmvol = ewmstd -@Substitution("Exponentially-weighted moving covariance", _binary_arg, "") -@Appender(_ewm_doc) -def ewmcov(arg1, arg2, com=None, span=None, halflife=None, min_periods=0, bias=False, - freq=None, time_rule=None): - X, Y = _prep_binary(arg1, arg2) - - X = _conv_timerule(X, freq, time_rule) - Y = _conv_timerule(Y, freq, time_rule) - - mean = lambda x: ewma(x, com=com, span=span, halflife=halflife, min_periods=min_periods) +@Substitution("Exponentially-weighted moving covariance", _binary_arg_flex, + _ewm_kw+_pairwise_kw, _type_of_input_retval, _ewm_notes) +@Appender(_doc_template) +def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, bias=False, + freq=None, time_rule=None, pairwise=None): + if arg2 is None: + arg2 = arg1 + pairwise = True if pairwise is None else pairwise + elif isinstance(arg2, (int, float)) and com is None: + com = arg2 + arg2 = arg1 + pairwise = True if pairwise is None else pairwise + arg1 = _conv_timerule(arg1, freq, time_rule) + arg2 = _conv_timerule(arg2, freq, time_rule) - result = (mean(X * Y) - mean(X) * mean(Y)) - com = _get_center_of_mass(com, span, halflife) + def _get_ewmcov(X, Y): + mean = lambda x: ewma(x, com=com, span=span, halflife=halflife, min_periods=min_periods) + return (mean(X * Y) - mean(X) * mean(Y)) + result = _flex_binary_moment(arg1, arg2, _get_ewmcov, + pairwise=bool(pairwise)) if not bias: + com = _get_center_of_mass(com, span, halflife) result *= (1.0 + 2.0 * com) / (2.0 * com) return result -@Substitution("Exponentially-weighted moving " "correlation", _binary_arg, "") -@Appender(_ewm_doc) -def ewmcorr(arg1, arg2, com=None, span=None, halflife=None, min_periods=0, - freq=None, time_rule=None): - X, Y = _prep_binary(arg1, arg2) - - X = _conv_timerule(X, freq, time_rule) - Y = _conv_timerule(Y, freq, time_rule) +@Substitution("Exponentially-weighted moving correlation", _binary_arg_flex, + _ewm_kw+_pairwise_kw, _type_of_input_retval, _ewm_notes) +@Appender(_doc_template) +def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, + freq=None, time_rule=None, pairwise=None): + if arg2 is None: + arg2 = arg1 + pairwise = True if pairwise is None else pairwise + elif isinstance(arg2, (int, float)) and com is None: + com = arg2 + arg2 = arg1 + pairwise = True if pairwise is None else pairwise + arg1 = _conv_timerule(arg1, freq, time_rule) + arg2 = _conv_timerule(arg2, freq, time_rule) - mean = lambda x: ewma(x, com=com, span=span, halflife=halflife, min_periods=min_periods) - var = lambda x: ewmvar(x, com=com, span=span, halflife=halflife, min_periods=min_periods, - bias=True) - return (mean(X * Y) - mean(X) * mean(Y)) / _zsqrt(var(X) * var(Y)) + def _get_ewmcorr(X, Y): + mean = lambda x: ewma(x, com=com, span=span, halflife=halflife, min_periods=min_periods) + var = lambda x: ewmvar(x, com=com, span=span, halflife=halflife, min_periods=min_periods, + bias=True) + return (mean(X * Y) - mean(X) * mean(Y)) / _zsqrt(var(X) * var(Y)) + result = _flex_binary_moment(arg1, arg2, _get_ewmcorr, + pairwise=bool(pairwise)) + return result def _zsqrt(x): @@ -546,7 +580,7 @@ def _use_window(minp, window): def _rolling_func(func, desc, check_minp=_use_window): - @Substitution(desc, _unary_arg, _type_of_input) + @Substitution(desc, _unary_arg, _roll_kw, _type_of_input_retval, _roll_notes) @Appender(_doc_template) @wraps(func) def f(arg, window, min_periods=None, freq=None, center=False, @@ -729,8 +763,8 @@ def rolling_window(arg, window=None, win_type=None, min_periods=None, if win_type is not None: raise ValueError(('Do not specify window type if using custom ' 'weights')) - window = com._asarray_tuplesafe(window).astype(float) - elif com.is_integer(window): # window size + window = pdcom._asarray_tuplesafe(window).astype(float) + elif pdcom.is_integer(window): # window size if win_type is None: raise ValueError('Must specify window type') try: @@ -779,8 +813,8 @@ def _pop_args(win_type, arg_names, kwargs): def _expanding_func(func, desc, check_minp=_use_window): - @Substitution(desc, _unary_arg, _type_of_input) - @Appender(_expanding_doc) + @Substitution(desc, _unary_arg, _expanding_kw, _type_of_input_retval, "") + @Appender(_doc_template) @wraps(func) def f(arg, min_periods=1, freq=None, center=False, time_rule=None, **kwargs): @@ -876,46 +910,54 @@ def expanding_quantile(arg, quantile, min_periods=1, freq=None, freq=freq, center=center, time_rule=time_rule) -@Substitution("Unbiased expanding covariance.", _binary_arg_flex, _flex_retval) -@Appender(_expanding_doc) -def expanding_cov(arg1, arg2, min_periods=1, freq=None, center=False, - time_rule=None): +@Substitution("Unbiased expanding covariance.", _binary_arg_flex, + _expanding_kw+_pairwise_kw, _flex_retval, "") +@Appender(_doc_template) +def expanding_cov(arg1, arg2=None, min_periods=1, freq=None, center=False, + time_rule=None, pairwise=None): + if arg2 is None: + arg2 = arg1 + pairwise = True if pairwise is None else pairwise + elif isinstance(arg2, (int, float)) and min_periods is None: + min_periods = arg2 + arg2 = arg1 + pairwise = True if pairwise is None else pairwise window = max(len(arg1), len(arg2)) return rolling_cov(arg1, arg2, window, min_periods=min_periods, freq=freq, - center=center, time_rule=time_rule) + center=center, time_rule=time_rule, pairwise=pairwise) -@Substitution("Expanding sample correlation.", _binary_arg_flex, _flex_retval) -@Appender(_expanding_doc) -def expanding_corr(arg1, arg2, min_periods=1, freq=None, center=False, - time_rule=None): +@Substitution("Expanding sample correlation.", _binary_arg_flex, + _expanding_kw+_pairwise_kw, _flex_retval, "") +@Appender(_doc_template) +def expanding_corr(arg1, arg2=None, min_periods=1, freq=None, center=False, + time_rule=None, pairwise=None): + if arg2 is None: + arg2 = arg1 + pairwise = True if pairwise is None else pairwise + elif isinstance(arg2, (int, float)) and min_periods is None: + min_periods = arg2 + arg2 = arg1 + pairwise = True if pairwise is None else pairwise window = max(len(arg1), len(arg2)) return rolling_corr(arg1, arg2, window, min_periods=min_periods, - freq=freq, center=center, time_rule=time_rule) - + freq=freq, center=center, time_rule=time_rule, + pairwise=pairwise) -def expanding_corr_pairwise(df, min_periods=1): - """ - Computes pairwise expanding correlation matrices as Panel whose items are - dates. - - Parameters - ---------- - df : DataFrame - min_periods : int, default 1 - Minimum number of observations in window required to have a value - (otherwise result is NA). - Returns - ------- - correls : Panel - """ - - window = len(df) - - return rolling_corr_pairwise(df, window, min_periods=min_periods) +@Substitution("Deprecated. Use expanding_corr(..., pairwise=True) instead.\n\n" + "Pairwise expanding sample correlation", _pairwise_arg, + _expanding_kw, _pairwise_retval, "") +@Appender(_doc_template) +def expanding_corr_pairwise(df1, df2=None, min_periods=1, freq=None, + center=False, time_rule=None): + import warnings + warnings.warn("expanding_corr_pairwise is deprecated, use expanding_corr(..., pairwise=True)", FutureWarning) + return expanding_corr(df1, df2, min_periods=min_periods, + freq=freq, center=center, time_rule=time_rule, + pairwise=True) def expanding_apply(arg, func, min_periods=1, freq=None, center=False, diff --git a/pandas/stats/tests/test_moments.py b/pandas/stats/tests/test_moments.py index a8359c102a902..97f08e7052c87 100644 --- a/pandas/stats/tests/test_moments.py +++ b/pandas/stats/tests/test_moments.py @@ -586,6 +586,9 @@ def test_rolling_cov(self): result = mom.rolling_cov(A, B, 50, min_periods=25) assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1]) + def test_rolling_cov_pairwise(self): + self._check_pairwise_moment(mom.rolling_cov, 10, min_periods=5) + def test_rolling_corr(self): A = self.series B = A + randn(len(A)) @@ -603,12 +606,14 @@ def test_rolling_corr(self): assert_almost_equal(result[-1], a.corr(b)) def test_rolling_corr_pairwise(self): - panel = mom.rolling_corr_pairwise(self.frame, 10, min_periods=5) + self._check_pairwise_moment(mom.rolling_corr, 10, min_periods=5) + + def _check_pairwise_moment(self, func, *args, **kwargs): + panel = func(self.frame, *args, **kwargs) - correl = panel.ix[:, 1, 5] - exp = mom.rolling_corr(self.frame[1], self.frame[5], - 10, min_periods=5) - tm.assert_series_equal(correl, exp) + actual = panel.ix[:, 1, 5] + expected = func(self.frame[1], self.frame[5], *args, **kwargs) + tm.assert_series_equal(actual, expected) def test_flex_binary_moment(self): # GH3155 @@ -666,9 +671,15 @@ def _check(method): def test_ewmcov(self): self._check_binary_ew(mom.ewmcov) + def test_ewmcov_pairwise(self): + self._check_pairwise_moment(mom.ewmcov, span=10, min_periods=5) + def test_ewmcorr(self): self._check_binary_ew(mom.ewmcorr) + def test_ewmcorr_pairwise(self): + self._check_pairwise_moment(mom.ewmcorr, span=10, min_periods=5) + def _check_binary_ew(self, func): A = Series(randn(50), index=np.arange(50)) B = A[2:] + randn(48) @@ -746,12 +757,20 @@ def test_expanding_cov(self): def test_expanding_max(self): self._check_expanding(mom.expanding_max, np.max, preserve_nan=False) + def test_expanding_cov_pairwise(self): + result = mom.expanding_cov(self.frame) + + rolling_result = mom.rolling_cov(self.frame, len(self.frame), + min_periods=1) + + for i in result.items: + assert_almost_equal(result[i], rolling_result[i]) + def test_expanding_corr_pairwise(self): - result = mom.expanding_corr_pairwise(self.frame) + result = mom.expanding_corr(self.frame) - rolling_result = mom.rolling_corr_pairwise(self.frame, - len(self.frame), - min_periods=1) + rolling_result = mom.rolling_corr(self.frame, len(self.frame), + min_periods=1) for i in result.items: assert_almost_equal(result[i], rolling_result[i])
I added the functions rolling_cov_pairwise(), expanding_cov_pairwise() and ewmcov_pairwise(). I also modified the behaviour of rolling_corr_pairwise(), expanding_corr_pairwise() and ewmcorr_pairwise() slightly so that now these can be computed between different DataFrames and the resulting matrices at each time slice will be rectangular rather than square. I think this is what was asked for in the original discussion that gave rise to issue #1853. Fixes #1853
https://api.github.com/repos/pandas-dev/pandas/pulls/4950
2013-09-23T12:23:36Z
2014-03-28T14:47:49Z
2014-03-28T14:47:49Z
2014-06-12T17:47:11Z
BUG: Conflict between thousands sep and date parser.
diff --git a/doc/source/release.rst b/doc/source/release.rst index 75097ee50e8c1..cd0a2ba6e3884 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -447,6 +447,7 @@ Bug Fixes - Fixed a bug in ``convert_objects`` for > 2 ndims (:issue:`4937`) - Fixed a bug in DataFrame/Panel cache insertion and subsequent indexing (:issue:`4939`) - Fixed string methods for ``FrozenNDArray`` and ``FrozenList`` (:issue:`4929`) + - Fixed conflict between thousands separator and date parser in csv_parser (:issue:`4678`) pandas 0.12.0 ------------- diff --git a/pandas/io/date_converters.py b/pandas/io/date_converters.py index 2be477f49e28b..5c99ab4d0a664 100644 --- a/pandas/io/date_converters.py +++ b/pandas/io/date_converters.py @@ -26,7 +26,7 @@ def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col, minute_col = _maybe_cast(minute_col) second_col = _maybe_cast(second_col) return lib.try_parse_datetime_components(year_col, month_col, day_col, - hour_col, minute_col, second_col) + hour_col, minute_col, second_col) def generic_parser(parse_func, *cols): diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 7b9347a821fad..c109508ea2c19 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -1020,6 +1020,14 @@ def _set(x): else: _set(val) + elif isinstance(self.parse_dates, dict): + for val in self.parse_dates.values(): + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + def set_error_bad_lines(self, status): self._reader.set_error_bad_lines(int(status)) @@ -1269,6 +1277,7 @@ def __init__(self, f, **kwds): self._make_reader(f) else: self.data = f + self.columns = self._infer_columns() # we are processing a multi index column @@ -1292,6 +1301,38 @@ def __init__(self, f, **kwds): self.index_names = index_names self._first_chunk = True + if self.parse_dates: + self._no_thousands_columns = self._set_no_thousands_columns() + else: + self._no_thousands_columns = None + + def _set_no_thousands_columns(self): + # Create a set of column ids that are not to be stripped of thousands operators. + noconvert_columns = set() + + def _set(x): + if com.is_integer(x): + noconvert_columns.add(x) + else: + noconvert_columns.add(self.columns.index(x)) + + if isinstance(self.parse_dates, list): + for val in self.parse_dates: + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + + elif isinstance(self.parse_dates, dict): + for val in self.parse_dates.values(): + if isinstance(val, list): + for k in val: + _set(k) + else: + _set(val) + return noconvert_columns + def _make_reader(self, f): sep = self.delimiter @@ -1500,7 +1541,6 @@ def _next_line(self): line = next(self.data) line = self._check_comments([line])[0] - line = self._check_thousands([line])[0] self.pos += 1 self.buf.append(line) @@ -1532,9 +1572,10 @@ def _check_thousands(self, lines): ret = [] for l in lines: rl = [] - for x in l: + for i, x in enumerate(l): if (not isinstance(x, compat.string_types) or self.thousands not in x or + (self._no_thousands_columns and i in self._no_thousands_columns) or nonnum.search(x.strip())): rl.append(x) else: @@ -1608,7 +1649,6 @@ def _rows_to_cols(self, content): raise AssertionError() if col_len != zip_len and self.index_col is not False: - row_num = -1 i = 0 for (i, l) in enumerate(content): if len(l) != col_len: diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index fb2b3fdd33bf1..6c32224dc7808 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -233,6 +233,18 @@ def test_1000_sep_with_decimal(self): df = self.read_table(StringIO(data_with_odd_sep), sep='|', thousands='.', decimal=',') tm.assert_frame_equal(df, expected) + def test_separator_date_conflict(self): + # Regression test for issue #4678: make sure thousands separator and + # date parsing do not conflict. + data = '06-02-2013;13:00;1-000.215' + expected = DataFrame( + [[datetime(2013, 6, 2, 13, 0, 0), 1000.215]], + columns=['Date', 2] + ) + + df = self.read_csv(StringIO(data), sep=';', thousands='-', parse_dates={'Date': [0, 1]}, header=None) + tm.assert_frame_equal(df, expected) + def test_squeeze(self): data = """\ a,1 diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index e0bbc1a4e64c1..2a3f85b550a7c 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -708,20 +708,22 @@ def try_parse_datetime_components(ndarray[object] years, Py_ssize_t i, n ndarray[object] result int secs + double float_secs double micros from datetime import datetime n = len(years) - if (len(months) != n and len(days) != n and len(hours) != n and - len(minutes) != n and len(seconds) != n): + if (len(months) != n or len(days) != n or len(hours) != n or + len(minutes) != n or len(seconds) != n): raise ValueError('Length of all datetime components must be equal') result = np.empty(n, dtype='O') for i from 0 <= i < n: - secs = int(seconds[i]) + float_secs = float(seconds[i]) + secs = int(float_secs) - micros = seconds[i] - secs + micros = float_secs - secs if micros > 0: micros = micros * 1000000
closes #4678 closes #4322 I've fixed the C parser portion. The issue there was that it did not handle the case where parse_dates is a dict. Python parser fix yet to come. That test still fails. Example: s = '06-02-2013;13:00;1-000,215;0,215;0,185;0,205;0,00' d = pd.read_csv(StringIO(s), header=None, parse_dates={'Date': [0, 1]}, thousands='-', sep=';', decimal=',') Then d should have a column 'Date' with value "2013-06-02 13:00:00"
https://api.github.com/repos/pandas-dev/pandas/pulls/4945
2013-09-23T03:43:26Z
2013-09-26T01:03:38Z
2013-09-26T01:03:38Z
2014-06-24T14:30:24Z
DOC: Added to docstrings in factorize function GH2916
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index f6b1131120aa6..5778a524a584a 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -109,9 +109,13 @@ def factorize(values, sort=False, order=None, na_sentinel=-1): Parameters ---------- - values : sequence - sort : + values : ndarray (1-d) + Sequence + sort : boolean, default False + Sort by values order : + na_sentinel: int, default -1 + Value to mark "not found" Returns -------
https://api.github.com/repos/pandas-dev/pandas/pulls/4944
2013-09-23T03:16:10Z
2013-09-26T00:51:38Z
2013-09-26T00:51:38Z
2014-06-27T14:49:45Z
BUG: Fixed a bug with setting invalid or out-of-range values in indexing enlargement scenarios (GH4940)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 444f25e8662bd..33e421fa55960 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -449,6 +449,8 @@ Bug Fixes - Fixed a bug in ``convert_objects`` for > 2 ndims (:issue:`4937`) - Fixed a bug in DataFrame/Panel cache insertion and subsequent indexing (:issue:`4939`) - Fixed string methods for ``FrozenNDArray`` and ``FrozenList`` (:issue:`4929`) + - Fixed a bug with setting invalid or out-of-range values in indexing + enlargement scenarios (:issue:`4940`) pandas 0.12.0 ------------- diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 3779e78599bde..cb738df6966da 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -750,6 +750,13 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): is_int_index = _is_integer_index(labels) if com.is_integer(obj) and not is_int_index: + + # if we are setting and its not a valid location + # its an insert which fails by definition + if is_setter: + if obj >= len(self.obj) and not isinstance(labels, MultiIndex): + raise ValueError("cannot set by positional indexing with enlargement") + return obj try: @@ -1340,7 +1347,12 @@ def _safe_append_to_index(index, key): try: return index.insert(len(index), key) except: - return Index(np.concatenate([index.asobject.values,np.array([key])])) + + # raise here as this is basically an unsafe operation and we want + # it to be obvious that you are doing something wrong + + raise ValueError("unsafe appending to index of " + "type {0} with a key {1}".format(index.__class__.__name__,key)) def _maybe_convert_indices(indices, n): """ if we have negative indicies, translate to postive here diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py index e0a9d2b937225..45543547fd64b 100644 --- a/pandas/sparse/tests/test_sparse.py +++ b/pandas/sparse/tests/test_sparse.py @@ -1078,6 +1078,12 @@ def test_icol(self): def test_set_value(self): + # this is invalid because it is not a valid type for this index + self.assertRaises(ValueError, self.frame.set_value, 'foobar', 'B', 1.5) + + res = self.frame + res.index = res.index.astype(object) + res = self.frame.set_value('foobar', 'B', 1.5) self.assert_(res is not self.frame) self.assert_(res.index[-1] == 'foobar') diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 267cc8605366c..ced4cbdc4dc36 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1480,6 +1480,30 @@ def test_series_partial_set(self): result = ser.iloc[[1,1,0,0]] assert_series_equal(result, expected) + def test_partial_set_invalid(self): + + # GH 4940 + # allow only setting of 'valid' values + + df = tm.makeTimeDataFrame() + + def f(): + df.loc[100.0, :] = df.ix[0] + self.assertRaises(ValueError, f) + def f(): + df.loc[100,:] = df.ix[0] + self.assertRaises(ValueError, f) + def f(): + df.loc['a',:] = df.ix[0] + self.assertRaises(ValueError, f) + + def f(): + df.ix[100.0, :] = df.ix[0] + self.assertRaises(ValueError, f) + def f(): + df.ix[100,:] = df.ix[0] + self.assertRaises(ValueError, f) + def test_cache_updating(self): # GH 4939, make sure to update the cache on setitem
closes #4940
https://api.github.com/repos/pandas-dev/pandas/pulls/4943
2013-09-23T01:08:07Z
2013-09-23T11:38:40Z
2013-09-23T11:38:40Z
2014-06-23T14:34:13Z
BUG: Fixed a bug in DataFrame/Panel cache insertion and subsequent indexing GH4939
diff --git a/doc/source/release.rst b/doc/source/release.rst index 789fbd0fe4ccc..b7b5c8835a265 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -438,6 +438,7 @@ Bug Fixes - Fixed a bug in ``Series.hist`` where two figures were being created when 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`) pandas 0.12.0 ------------- diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 53d3687854cac..36c3ff9085d87 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -825,14 +825,20 @@ def _maybe_cache_changed(self, item, value): maybe it has changed """ self._data.set(item, value) - def _maybe_update_cacher(self): - """ see if we need to update our parent cacher """ + def _maybe_update_cacher(self, clear=False): + """ see if we need to update our parent cacher + if clear, then clear our cache """ cacher = getattr(self,'_cacher',None) if cacher is not None: cacher[1]()._maybe_cache_changed(cacher[0],self) + if clear: + self._clear_item_cache() - def _clear_item_cache(self): - self._item_cache.clear() + def _clear_item_cache(self, i=None): + if i is not None: + self._item_cache.pop(i,None) + else: + self._item_cache.clear() def _set_item(self, key, value): self._data.set(key, value) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 4f5e6623e1512..3779e78599bde 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -190,12 +190,14 @@ def _setitem_with_indexer(self, indexer, value): new_values = np.concatenate([self.obj.values, [value]]) self.obj._data = self.obj._constructor(new_values, index=new_index, name=self.obj.name) + self.obj._maybe_update_cacher(clear=True) return self.obj elif self.ndim == 2: index = self.obj._get_axis(0) labels = _safe_append_to_index(index, indexer) self.obj._data = self.obj.reindex_axis(labels,0)._data + self.obj._maybe_update_cacher(clear=True) return getattr(self.obj,self.name).__setitem__(indexer,value) # set using setitem (Panel and > dims) @@ -255,6 +257,7 @@ def setter(item, v): # set the item, possibly having a dtype change s = s.copy() s._data = s._data.setitem(pi,v) + s._maybe_update_cacher(clear=True) self.obj[item] = s def can_do_equal_len(): @@ -327,6 +330,7 @@ def can_do_equal_len(): value = self._align_panel(indexer, value) self.obj._data = self.obj._data.setitem(indexer,value) + self.obj._maybe_update_cacher(clear=True) def _align_series(self, indexer, ser): # indexer to assign Series can be tuple or scalar diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index aad9fb2f95483..267cc8605366c 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1480,6 +1480,21 @@ def test_series_partial_set(self): result = ser.iloc[[1,1,0,0]] assert_series_equal(result, expected) + def test_cache_updating(self): + # GH 4939, make sure to update the cache on setitem + + df = tm.makeDataFrame() + df['A'] # cache series + df.ix["Hello Friend"] = df.ix[0] + self.assert_("Hello Friend" in df['A'].index) + self.assert_("Hello Friend" in df['B'].index) + + panel = tm.makePanel() + panel.ix[0] # get first item into cache + panel.ix[:, :, 'A+1'] = panel.ix[:, :, 'A'] + 1 + self.assert_("A+1" in panel.ix[0].columns) + self.assert_("A+1" in panel.ix[1].columns) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
closes #4939
https://api.github.com/repos/pandas-dev/pandas/pulls/4942
2013-09-22T23:59:54Z
2013-09-23T00:24:59Z
2013-09-23T00:24:59Z
2014-06-20T04:53:26Z
BUG: Fixed a bug in convert_objects for > 2 ndims GH4937
diff --git a/doc/source/release.rst b/doc/source/release.rst index 47e0cfd78271e..789fbd0fe4ccc 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -437,7 +437,7 @@ Bug Fixes - Fixed ``_ensure_numeric`` does not check for complex numbers (:issue:`4902`) - Fixed a bug in ``Series.hist`` where two figures were being created when the ``by`` argument was passed (:issue:`4112`, :issue:`4113`). - + - Fixed a bug in ``convert_objects`` for > 2 ndims (:issue:`4937`) pandas 0.12.0 ------------- diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 72b0212f77ac9..bc16f549f0cf1 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -331,7 +331,6 @@ Enhancements can also be used. - ``read_stata` now accepts Stata 13 format (:issue:`4291`) - .. _whatsnew_0130.experimental: Experimental diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 585b1c817ff19..3ab1bfb2c58ed 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1164,8 +1164,8 @@ def convert(self, convert_dates=True, convert_numeric=True, copy=True, by_item=T values = self.iget(i) values = com._possibly_convert_objects( - values, convert_dates=convert_dates, convert_numeric=convert_numeric) - values = _block_shape(values) + values.ravel(), convert_dates=convert_dates, convert_numeric=convert_numeric).reshape(values.shape) + values = _block_shape(values, ndim=self.ndim) items = self.items.take([i]) placement = None if is_unique else [i] newb = make_block( @@ -1175,7 +1175,7 @@ def convert(self, convert_dates=True, convert_numeric=True, copy=True, by_item=T else: values = com._possibly_convert_objects( - self.values, convert_dates=convert_dates, convert_numeric=convert_numeric) + self.values.ravel(), convert_dates=convert_dates, convert_numeric=convert_numeric).reshape(self.values.shape) blocks.append( make_block(values, self.items, self.ref_items, ndim=self.ndim)) @@ -3610,7 +3610,7 @@ def _merge_blocks(blocks, items, dtype=None, _can_consolidate=True): def _block_shape(values, ndim=1, shape=None): """ guarantee the shape of the values to be at least 1 d """ - if values.ndim == ndim: + if values.ndim <= ndim: if shape is None: shape = values.shape values = values.reshape(tuple((1,) + shape)) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index a498cca528043..0195fe30a05c8 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1010,6 +1010,14 @@ def test_conform(self): assert(conformed.index.equals(self.panel.major_axis)) assert(conformed.columns.equals(self.panel.minor_axis)) + def test_convert_objects(self): + + # GH 4937 + p = Panel(dict(A = dict(a = ['1','1.0']))) + expected = Panel(dict(A = dict(a = [1,1.0]))) + result = p.convert_objects(convert_numeric='force') + assert_panel_equal(result, expected) + def test_reindex(self): ref = self.panel['ItemB'] @@ -1287,9 +1295,6 @@ def test_to_panel_duplicates(self): def test_filter(self): pass - def test_apply(self): - pass - def test_compound(self): compounded = self.panel.compound() @@ -1350,7 +1355,7 @@ def test_tshift(self): assert_panel_equal(shifted, shifted2) inferred_ts = Panel(panel.values, - items=panel.items, + items=panel.items, major_axis=Index(np.asarray(panel.major_axis)), minor_axis=panel.minor_axis) shifted = inferred_ts.tshift(1)
closes #4937
https://api.github.com/repos/pandas-dev/pandas/pulls/4938
2013-09-22T18:46:31Z
2013-09-22T19:28:16Z
2013-09-22T19:28:16Z
2014-06-22T04:28:57Z
ENH: Add 'records' outtype to to_dict method.
diff --git a/doc/source/release.rst b/doc/source/release.rst index 769b47b18db08..f6afea583762b 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -47,6 +47,8 @@ pandas 0.13 - Added a more informative error message when plot arguments contain overlapping color and style arguments (:issue:`4402`) - Significant table writing performance improvements in ``HDFStore`` + - ``to_dict`` now takes ``records`` as a possible outtype. Returns an array + of column-keyed dictionaries. (:pullrequest:`4936`) **API Changes** diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 7da2f03ad4c74..b74de1c7be17b 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -81,6 +81,8 @@ Enhancements - Clipboard functionality now works with PySide (:issue:`4282`) - Added a more informative error message when plot arguments contain overlapping color and style arguments (:issue:`4402`) + - ``to_dict`` now takes ``records`` as a possible outtype. Returns an array + of column-keyed dictionaries. (:pullrequest:`4936`) Bug Fixes ~~~~~~~~~ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 0f3bcb32f7287..c0e868f27345c 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -962,11 +962,11 @@ def to_dict(self, outtype='dict'): Parameters ---------- - outtype : str {'dict', 'list', 'series'} + outtype : str {'dict', 'list', 'series', 'records'} Determines the type of the values of the dictionary. The default `dict` is a nested dictionary {column -> {index -> value}}. `list` returns {column -> list(values)}. `series` returns - {column -> Series(values)}. + {column -> Series(values)}. `records` returns [{columns -> value}]. Abbreviations are allowed. @@ -983,6 +983,9 @@ def to_dict(self, outtype='dict'): return dict((k, v.tolist()) for k, v in compat.iteritems(self)) elif outtype.lower().startswith('s'): return dict((k, v) for k, v in compat.iteritems(self)) + elif outtype.lower().startswith('r'): + return [dict((k, v) for k, v in zip(self.columns, row)) \ + for row in self.values] else: # pragma: no cover raise ValueError("outtype %s not understood" % outtype) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1b405eae08797..c5ecba3555784 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -3396,6 +3396,14 @@ def test_to_dict(self): for k2, v2 in compat.iteritems(v): self.assertEqual(v2, recons_data[k][k2]) + recons_data = DataFrame(test_data).to_dict("r") + + expected_records = [{'A': 1.0, 'B': '1'}, + {'A': 2.0, 'B': '2'}, + {'A': nan, 'B': '3'}] + + tm.assert_almost_equal(recons_data, expected_records) + def test_to_records_dt64(self): df = DataFrame([["one", "two", "three"], ["four", "five", "six"]],
`to_json` has a records type that turns a DataFrame into a ``` [{'a': 1, 'b': 3}, {'a': 2, 'b': 4}] ``` records json string. It'd be nice if you could do a similar thing but not have to go `json.loads`.
https://api.github.com/repos/pandas-dev/pandas/pulls/4936
2013-09-22T16:11:12Z
2013-09-30T13:44:54Z
2013-09-30T13:44:54Z
2014-06-12T14:29:29Z
ENH: Better string representation for MultiIndex
diff --git a/doc/source/release.rst b/doc/source/release.rst index 8ba0574df97cb..1d29e64860b2d 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -131,6 +131,8 @@ Improvements to existing features (:issue:`3441`, :issue:`4933`) - ``pandas`` is now tested with two different versions of ``statsmodels`` (0.4.3 and 0.5.0) (:issue:`4981`). + - Better string representations of ``MultiIndex`` (including ability to roundtrip + via ``repr``). (:issue:`3347`, :issue:`4935`) API Changes ~~~~~~~~~~~ diff --git a/pandas/core/index.py b/pandas/core/index.py index 7f136450daf6e..d488a29182a18 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -1,6 +1,6 @@ # pylint: disable=E1101,E1103,W0232 from functools import partial -from pandas.compat import range, zip, lrange, lzip +from pandas.compat import range, zip, lrange, lzip, u from pandas import compat import numpy as np @@ -17,6 +17,10 @@ from pandas.core.common import _values_from_object, is_float, is_integer from pandas.core.config import get_option +# simplify +default_pprint = lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n'), + quote_strings=True) + __all__ = ['Index'] @@ -2052,18 +2056,40 @@ def _array_values(self): def dtype(self): return np.dtype('O') + def __repr__(self): + encoding = get_option('display.encoding') + attrs = [('levels', default_pprint(self.levels)), + ('labels', default_pprint(self.labels))] + if not all(name is None for name in self.names): + attrs.append(('names', default_pprint(self.names))) + if self.sortorder is not None: + attrs.append(('sortorder', default_pprint(self.sortorder))) + + space = ' ' * (len(self.__class__.__name__) + 1) + prepr = (u("\n%s") % space).join([u("%s=%s") % (k, v) + for k, v in attrs]) + res = u("%s(%s)") % (self.__class__.__name__, prepr) + + if not compat.PY3: + # needs to be str in Python 2 + res = res.encode(encoding) + return res + + def __unicode__(self): """ Return a string representation for a particular Index Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ - output = 'MultiIndex\n%s' - - summary = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'), - quote_strings=True) - - return output % summary + rows = self.format(names=True) + max_rows = get_option('display.max_rows') + if len(rows) > max_rows: + spaces = (len(rows[0]) - 3) // 2 + centered = ' ' * spaces + half = max_rows // 2 + rows = rows[:half] + [centered + '...' + centered] + rows[-half:] + return "\n".join(rows) def __len__(self): return len(self.labels[0])
Fixes #3347. All interested parties are invited to submit test cases. cc @y-p @hayd --- Examples: ``` In [1]: import pandas as pd In [2]: pd.MultiIndex.from_arrays([[1, 1, 1, 1], [1, 3, 5, 7], [9, 9, 1, 1]]) Out[2]: MultiIndex(levels=[[1], [1, 3, 5, 7], [1, 9]] labels=[[0, 0, 0, 0], [0, 1, 2, 3], [1, 1, 0, 0]]) In [3]: mi = _ In [4]: mi.names = list('abc') In [5]: print mi a b c 1 1 9 3 9 5 1 7 1 ``` And with too many rows: ``` In [10]: pd.set_option('display.max_rows', 15) In [11]: lst1 = [1] * 3 + [2] * 5 + [3] * 2 In [12]: lst1 Out[12]: [1, 1, 1, 2, 2, 2, 2, 2, 3, 3] In [13]: lst2 = ['a'] * 6 + ['b'] * 3 + ['c'] * 1 In [14]: lst2 Out[14]: ['a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'c'] In [15]: mi = pd.MultiIndex.from_arrays([lst1 * 10, lst2 * 10, range(100)]); mi Out[15]: MultiIndex(levels=[[1, 2, 3], [u'a', u'b', u'c'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]] labels=[[0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2], [0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]) In [16]: mi = _ In [17]: print mi 1 a 0 1 2 2 a 3 4 5 ... 2 a 93 94 95 b 96 97 3 b 98 c 99 ``` Not sure how to handle too wide -- wrap? format() doesn't try to sparsify after a column with values everywhere. ``` In [19]: mi = pd.MultiIndex.from_arrays([lst1 * 10, lst2 * 10, range(100)] * 10) In [20]: print mi 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 ... 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 ``` ``` In [9]: labels = [('foo', '2012-07-26T00:00:00', 'b5c2700'), ...: ('foo', '2012-08-06T00:00:00', '900b2ca'), ...: ('foo', '2012-08-15T00:00:00', '07f1ce0'), ...: ('foo', '2012-09-25T00:00:00', '5c93e83'), ...: ('foo', '2012-09-25T00:00:00', '9345bba')] In [10]: print pd.MultiIndex.from_tuples(labels) foo 2012-07-26T00:00:00 b5c2700 2012-08-06T00:00:00 900b2ca 2012-08-15T00:00:00 07f1ce0 2012-09-25T00:00:00 5c93e83 9345bba ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4935
2013-09-22T05:28:08Z
2013-09-27T00:30:19Z
2013-09-27T00:30:19Z
2014-06-20T15:58:33Z
ENH: Make ExcelWriter & ExcelFile contextmanagers
diff --git a/doc/source/io.rst b/doc/source/io.rst index 951960493975e..a0e41a96181a2 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1672,14 +1672,13 @@ The Panel class also has a ``to_excel`` instance method, which writes each DataFrame in the Panel to a separate sheet. In order to write separate DataFrames to separate sheets in a single Excel file, -one can use the ExcelWriter class, as in the following example: +one can pass an :class:`~pandas.io.excel.ExcelWriter`. .. code-block:: python - writer = ExcelWriter('path_to_file.xlsx') - df1.to_excel(writer, sheet_name='Sheet1') - df2.to_excel(writer, sheet_name='Sheet2') - writer.save() + with ExcelWriter('path_to_file.xlsx') as writer: + df1.to_excel(writer, sheet_name='Sheet1') + df2.to_excel(writer, sheet_name='Sheet2') .. _io.excel.writers: @@ -1693,14 +1692,13 @@ Excel writer engines 1. the ``engine`` keyword argument 2. the filename extension (via the default specified in config options) -By default ``pandas`` only supports -`openpyxl <http://packages.python.org/openpyxl/>`__ as a writer for ``.xlsx`` -and ``.xlsm`` files and `xlwt <http://www.python-excel.org/>`__ as a writer for -``.xls`` files. If you have multiple engines installed, you can change the -default engine via the ``io.excel.xlsx.writer`` and ``io.excel.xls.writer`` -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``. -For example if the optional `XlsxWriter <http://xlsxwriter.readthedocs.org>`__ +For example if the `XlsxWriter <http://xlsxwriter.readthedocs.org>`__ module is installed you can use it as a xlsx writer engine as follows: .. code-block:: python @@ -1712,8 +1710,8 @@ module is installed you can use it as a xlsx writer engine as follows: writer = ExcelWriter('path_to_file.xlsx', engine='xlsxwriter') # Or via pandas configuration. - from pandas import set_option - set_option('io.excel.xlsx.writer', 'xlsxwriter') + from pandas import options + options.io.excel.xlsx.writer = 'xlsxwriter' df.to_excel('path_to_file.xlsx', sheet_name='Sheet1') diff --git a/doc/source/release.rst b/doc/source/release.rst index 75097ee50e8c1..444f25e8662bd 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -126,6 +126,8 @@ Improvements to existing features - ``read_json`` now raises a (more informative) ``ValueError`` when the dict contains a bad key and ``orient='split'`` (:issue:`4730`, :issue:`4838`) - ``read_stata`` now accepts Stata 13 format (:issue:`4291`) + - ``ExcelWriter`` and ``ExcelFile`` can be used as contextmanagers. + (:issue:`3441`, :issue:`4933`) API Changes ~~~~~~~~~~~ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 0482312a71547..799d96f46a15b 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3453,7 +3453,7 @@ def unstack(self, level=-1): See also -------- DataFrame.pivot : Pivot a table based on column values. - DataFrame.stack : Pivot a level of the column labels (inverse operation + DataFrame.stack : Pivot a level of the column labels (inverse operation from `unstack`). Examples diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 6ce8eb697268b..02dbc381a10be 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -14,7 +14,7 @@ from pandas import json from pandas.compat import map, zip, reduce, range, lrange, u, add_metaclass from pandas.core import config -from pandas.core.common import pprint_thing, PandasError +from pandas.core.common import pprint_thing import pandas.compat as compat from warnings import warn @@ -260,6 +260,17 @@ def _parse_excel(self, sheetname, header=0, skiprows=None, skip_footer=0, def sheet_names(self): return self.book.sheet_names() + def close(self): + """close path_or_buf if necessary""" + if hasattr(self.path_or_buf, 'close'): + self.path_or_buf.close() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.close() + def _trim_excel_header(row): # trim header row so auto-index inference works @@ -408,6 +419,17 @@ def check_extension(cls, ext): else: return True + # Allow use as a contextmanager + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.close() + + def close(self): + """synonym for save, to make it more file-like""" + return self.save() + class _OpenpyxlWriter(ExcelWriter): engine = 'openpyxl' diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 94f3e5a8cf746..2bcf4789412f6 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -275,6 +275,18 @@ def test_xlsx_table(self): tm.assert_frame_equal(df4, df.ix[:-1]) tm.assert_frame_equal(df4, df5) + def test_reader_closes_file(self): + _skip_if_no_xlrd() + _skip_if_no_openpyxl() + + pth = os.path.join(self.dirpath, 'test.xlsx') + f = open(pth, 'rb') + with ExcelFile(f) as xlsx: + # parses okay + df = xlsx.parse('Sheet1', index_col=0) + + self.assertTrue(f.closed) + class ExcelWriterBase(SharedItems): # Base class for test cases to run with different Excel writers. @@ -310,6 +322,21 @@ def test_excel_sheet_by_name_raise(self): self.assertRaises(xlrd.XLRDError, xl.parse, '0') + def test_excelwriter_contextmanager(self): + ext = self.ext + pth = os.path.join(self.dirpath, 'testit.{0}'.format(ext)) + + with ensure_clean(pth) as pth: + with ExcelWriter(pth) as writer: + self.frame.to_excel(writer, 'Data1') + self.frame2.to_excel(writer, 'Data2') + + with ExcelFile(pth) as reader: + found_df = reader.parse('Data1') + found_df2 = reader.parse('Data2') + tm.assert_frame_equal(found_df, self.frame) + tm.assert_frame_equal(found_df2, self.frame2) + def test_roundtrip(self): _skip_if_no_xlrd() ext = self.ext
Fixes #3441. both can be used in with statements now. Makes it easier to use with multiple writers, etc. Plus touch up the docs on writers and such.
https://api.github.com/repos/pandas-dev/pandas/pulls/4933
2013-09-22T02:47:02Z
2013-09-23T04:15:21Z
2013-09-23T04:15:21Z
2014-06-16T04:28:54Z
BUG: Fix FrozenNDArray & FrozenList string methods
diff --git a/doc/source/release.rst b/doc/source/release.rst index 555c79baf8584..75097ee50e8c1 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -446,6 +446,7 @@ 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`) + - Fixed string methods for ``FrozenNDArray`` and ``FrozenList`` (:issue:`4929`) pandas 0.12.0 ------------- diff --git a/pandas/core/base.py b/pandas/core/base.py index fb0d56113ede9..14070d8825393 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -122,9 +122,12 @@ def _disabled(self, *args, **kwargs): def __unicode__(self): from pandas.core.common import pprint_thing + return pprint_thing(self, quote_strings=True, + escape_chars=('\t', '\r', '\n')) + + def __repr__(self): return "%s(%s)" % (self.__class__.__name__, - pprint_thing(self, quote_strings=True, - escape_chars=('\t', '\r', '\n'))) + str(self)) __setitem__ = __setslice__ = __delitem__ = __delslice__ = _disabled pop = append = extend = remove = sort = insert = _disabled @@ -154,3 +157,12 @@ def values(self): """returns *copy* of underlying array""" arr = self.view(np.ndarray).copy() return arr + + def __unicode__(self): + """ + Return a string representation for this object. + + Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. + """ + prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True) + return '%s(%s, dtype=%s)' % (type(self).__name__, prepr, self.dtype) diff --git a/pandas/core/index.py b/pandas/core/index.py index 1d3f181b731e8..f2a22580f16b4 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -228,15 +228,6 @@ def copy(self, names=None, name=None, dtype=None, deep=False): new_index = new_index.astype(dtype) return new_index - def __unicode__(self): - """ - Return a string representation for a particular Index - - Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. - """ - prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True) - return '%s(%s, dtype=%s)' % (type(self).__name__, prepr, self.dtype) - def to_series(self): """ return a series with both index and values equal to the index keys diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index c6285bc95b855..5d5a269b90428 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -1,10 +1,30 @@ 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 +class CheckStringMixin(object): + def test_string_methods_dont_fail(self): + repr(self.container) + str(self.container) + bytes(self.container) + if not compat.PY3: + unicode(self.container) + + def test_tricky_container(self): + if not hasattr(self, 'unicode_container'): + raise nose.SkipTest('Need unicode_container to test with this') + repr(self.unicode_container) + str(self.unicode_container) + bytes(self.unicode_container) + if not compat.PY3: + unicode(self.unicode_container) + + class CheckImmutable(object): mutable_regex = re.compile('does not support mutable operations') @@ -43,8 +63,9 @@ def check_result(self, result, expected, klass=None): self.assertEqual(result, expected) -class TestFrozenList(CheckImmutable, unittest.TestCase): +class TestFrozenList(CheckImmutable, CheckStringMixin, unittest.TestCase): mutable_methods = ('extend', 'pop', 'remove', 'insert') + unicode_container = FrozenList([u("\u05d0"), u("\u05d1"), "c"]) def setUp(self): self.lst = [1, 2, 3, 4, 5] @@ -68,8 +89,9 @@ def test_inplace(self): self.check_result(r, self.lst) -class TestFrozenNDArray(CheckImmutable, unittest.TestCase): +class TestFrozenNDArray(CheckImmutable, CheckStringMixin, unittest.TestCase): mutable_methods = ('put', 'itemset', 'fill') + unicode_container = FrozenNDArray([u("\u05d0"), u("\u05d1"), "c"]) def setUp(self): self.lst = [3, 5, 7, -2]
Plus basic tests (i.e., "Hey! If you pass me unicode I don't fail - yay!") Previously were showing bad object reprs, now do this: ``` In [4]: mi = MultiIndex.from_arrays([range(10), range(10)]) In [5]: mi Out[5]: MultiIndex [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9)] In [6]: mi.levels Out[6]: FrozenList([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]) In [7]: mi.levels[0] Out[7]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64) In [8]: mi.labels[0] Out[8]: FrozenNDArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64) In [9]: mi.labels[1] Out[9]: FrozenNDArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64) In [10]: mi = MultiIndex.from MultiIndex.from_arrays MultiIndex.from_tuples In [10]: mi = MultiIndex.from_arrays([range(5), list('abcde')]) In [11]: mi.levels Out[11]: FrozenList([[0, 1, 2, 3, 4], [u'a', u'b', u'c', u'd', u'e']]) In [12]: mi.labels[0] Out[12]: FrozenNDArray([0, 1, 2, 3, 4], dtype=int64) In [13]: mi.labels[1] Out[13]: FrozenNDArray([0, 1, 2, 3, 4], dtype=int64) In [14]: mi.levels[0] Out[14]: Int64Index([0, 1, 2, 3, 4], dtype=int64) In [15]: mi.levels[1] Out[15]: Index([u'a', u'b', u'c', u'd', u'e'], dtype=object) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4929
2013-09-22T01:35:30Z
2013-09-23T01:24:51Z
2013-09-23T01:24:51Z
2014-06-13T23:41:37Z
BUG: Fix bound checking for Timestamp() with dt64 #4065
diff --git a/doc/source/release.rst b/doc/source/release.rst index ebba7444e82d8..3b7bd6544e569 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -555,7 +555,7 @@ Bug Fixes type of headers (:issue:`5048`). - Fixed a bug where ``DatetimeIndex`` joins with ``PeriodIndex`` caused a stack overflow (:issue:`3899`). - + - Fix bound checking for Timestamp() with datetime64 input (:issue:`4065`) pandas 0.12.0 ------------- diff --git a/pandas/core/common.py b/pandas/core/common.py index 2c5ca42c7be86..108b82eaf9056 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -348,6 +348,13 @@ def _pickle_array(arr): def _unpickle_array(bytes): arr = read_array(BytesIO(bytes)) + + # All datetimes should be stored as M8[ns]. When unpickling with + # numpy1.6, it will read these as M8[us]. So this ensures all + # datetime64 types are read as MS[ns] + if is_datetime64_dtype(arr): + arr = arr.view(_NS_DTYPE) + return arr @@ -1780,6 +1787,14 @@ def is_datetime64_dtype(arr_or_dtype): tipo = arr_or_dtype.dtype.type return issubclass(tipo, np.datetime64) +def is_datetime64_ns_dtype(arr_or_dtype): + if isinstance(arr_or_dtype, np.dtype): + tipo = arr_or_dtype + elif isinstance(arr_or_dtype, type): + tipo = np.dtype(arr_or_dtype) + else: + tipo = arr_or_dtype.dtype + return tipo == _NS_DTYPE def is_timedelta64_dtype(arr_or_dtype): if isinstance(arr_or_dtype, np.dtype): diff --git a/pandas/src/datetime.pxd b/pandas/src/datetime.pxd index 1a977aab48514..3e11c9d20fb0d 100644 --- a/pandas/src/datetime.pxd +++ b/pandas/src/datetime.pxd @@ -85,6 +85,9 @@ cdef extern from "datetime/np_datetime.h": npy_int64 year npy_int32 month, day, hour, min, sec, us, ps, as + int cmp_pandas_datetimestruct(pandas_datetimestruct *a, + pandas_datetimestruct *b) + int convert_pydatetime_to_datetimestruct(PyObject *obj, pandas_datetimestruct *out, PANDAS_DATETIMEUNIT *out_bestunit, diff --git a/pandas/src/datetime/np_datetime.c b/pandas/src/datetime/np_datetime.c index 527ce615917cf..c30b404d2b8b2 100644 --- a/pandas/src/datetime/np_datetime.c +++ b/pandas/src/datetime/np_datetime.c @@ -273,6 +273,69 @@ set_datetimestruct_days(npy_int64 days, pandas_datetimestruct *dts) } } +/* + * Compares two pandas_datetimestruct objects chronologically + */ +int +cmp_pandas_datetimestruct(pandas_datetimestruct *a, pandas_datetimestruct *b) +{ + if (a->year > b->year) { + return 1; + } else if (a->year < b->year) { + return -1; + } + + if (a->month > b->month) { + return 1; + } else if (a->month < b->month) { + return -1; + } + + if (a->day > b->day) { + return 1; + } else if (a->day < b->day) { + return -1; + } + + if (a->hour > b->hour) { + return 1; + } else if (a->hour < b->hour) { + return -1; + } + + if (a->min > b->min) { + return 1; + } else if (a->min < b->min) { + return -1; + } + + if (a->sec > b->sec) { + return 1; + } else if (a->sec < b->sec) { + return -1; + } + + if (a->us > b->us) { + return 1; + } else if (a->us < b->us) { + return -1; + } + + if (a->ps > b->ps) { + return 1; + } else if (a->ps < b->ps) { + return -1; + } + + if (a->as > b->as) { + return 1; + } else if (a->as < b->as) { + return -1; + } + + return 0; +} + /* * * Tests for and converts a Python datetime.datetime or datetime.date diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 281ac0cc8a35a..7d67f3b013b37 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -204,7 +204,7 @@ def __new__(cls, data=None, data = _str_to_dt_array(data, offset, dayfirst=dayfirst, yearfirst=yearfirst) else: - data = tools.to_datetime(data) + data = tools.to_datetime(data, errors='raise') data.offset = offset if isinstance(data, DatetimeIndex): if name is not None: @@ -243,14 +243,14 @@ def __new__(cls, data=None, subarr = data.view(_NS_DTYPE) else: try: - subarr = tools.to_datetime(data) + subarr = tools.to_datetime(data, box=False) except ValueError: # tz aware - subarr = tools.to_datetime(data, utc=True) + subarr = tools.to_datetime(data, box=False, utc=True) if not np.issubdtype(subarr.dtype, np.datetime64): - raise TypeError('Unable to convert %s to datetime dtype' - % str(data)) + raise ValueError('Unable to convert %s to datetime dtype' + % str(data)) if isinstance(subarr, DatetimeIndex): if tz is None: @@ -934,7 +934,7 @@ def join(self, other, how='left', level=None, return_indexers=False): 'mixed-integer-float', 'mixed')): try: other = DatetimeIndex(other) - except TypeError: + except (TypeError, ValueError): pass this, other = self._maybe_utc_convert(other) @@ -1051,7 +1051,7 @@ def intersection(self, other): if not isinstance(other, DatetimeIndex): try: other = DatetimeIndex(other) - except TypeError: + except (TypeError, ValueError): pass result = Index.intersection(self, other) if isinstance(result, DatetimeIndex): diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 5329f37095961..cda84a99a95db 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1,5 +1,5 @@ # pylint: disable-msg=E1101,W0612 -from datetime import datetime, time, timedelta +from datetime import datetime, time, timedelta, date import sys import os import unittest @@ -952,6 +952,81 @@ def test_to_datetime_list_of_integers(self): self.assert_(rng.equals(result)) + def test_to_datetime_dt64s(self): + in_bound_dts = [ + np.datetime64('2000-01-01'), + np.datetime64('2000-01-02'), + ] + + for dt in in_bound_dts: + self.assertEqual( + pd.to_datetime(dt), + Timestamp(dt) + ) + + oob_dts = [ + np.datetime64('1000-01-01'), + np.datetime64('5000-01-02'), + ] + + for dt in oob_dts: + self.assertRaises(ValueError, pd.to_datetime, dt, errors='raise') + self.assertRaises(ValueError, tslib.Timestamp, dt) + self.assert_(pd.to_datetime(dt, coerce=True) is NaT) + + def test_to_datetime_array_of_dt64s(self): + dts = [ + np.datetime64('2000-01-01'), + np.datetime64('2000-01-02'), + ] + + # Assuming all datetimes are in bounds, to_datetime() returns + # an array that is equal to Timestamp() parsing + self.assert_( + np.array_equal( + pd.to_datetime(dts, box=False), + np.array([Timestamp(x).asm8 for x in dts]) + ) + ) + + # A list of datetimes where the last one is out of bounds + dts_with_oob = dts + [np.datetime64('9999-01-01')] + + self.assertRaises( + ValueError, + pd.to_datetime, + dts_with_oob, + coerce=False, + errors='raise' + ) + + self.assert_( + np.array_equal( + pd.to_datetime(dts_with_oob, box=False, coerce=True), + np.array( + [ + Timestamp(dts_with_oob[0]).asm8, + Timestamp(dts_with_oob[1]).asm8, + iNaT, + ], + dtype='M8' + ) + ) + ) + + # With coerce=False and errors='ignore', out of bounds datetime64s + # are converted to their .item(), which depending on the version of + # numpy is either a python datetime.datetime or datetime.date + self.assert_( + np.array_equal( + pd.to_datetime(dts_with_oob, box=False, coerce=False), + np.array( + [dt.item() for dt in dts_with_oob], + dtype='O' + ) + ) + ) + def test_index_to_datetime(self): idx = Index(['1/1/2000', '1/2/2000', '1/3/2000']) diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 4e7daede03085..20138cb8b1eb8 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -4,7 +4,7 @@ import numpy as np from pandas import tslib -from datetime import datetime +import datetime from pandas.core.api import Timestamp @@ -15,19 +15,53 @@ from pandas import _np_version_under1p7 -class TestDatetimeParsingWrappers(unittest.TestCase): - def test_verify_datetime_bounds(self): - for year in (1, 1000, 1677, 2262, 5000): - dt = datetime(year, 1, 1) - self.assertRaises( - ValueError, - tslib.verify_datetime_bounds, - dt - ) +class TestTimestamp(unittest.TestCase): + def test_bounds_with_different_units(self): + out_of_bounds_dates = ( + '1677-09-21', + '2262-04-12', + ) + + time_units = ('D', 'h', 'm', 's', 'ms', 'us') - for year in (1678, 2000, 2261): - tslib.verify_datetime_bounds(datetime(year, 1, 1)) + for date_string in out_of_bounds_dates: + for unit in time_units: + self.assertRaises( + ValueError, + tslib.Timestamp, + np.datetime64(date_string, dtype='M8[%s]' % unit) + ) + + in_bounds_dates = ( + '1677-09-23', + '2262-04-11', + ) + for date_string in in_bounds_dates: + for unit in time_units: + tslib.Timestamp( + np.datetime64(date_string, dtype='M8[%s]' % unit) + ) + + def test_barely_oob_dts(self): + one_us = np.timedelta64(1) + + # By definition we can't go out of bounds in [ns], so we + # convert the datetime64s to [us] so we can go out of bounds + min_ts_us = np.datetime64(tslib.Timestamp.min).astype('M8[us]') + max_ts_us = np.datetime64(tslib.Timestamp.max).astype('M8[us]') + + # No error for the min/max datetimes + tslib.Timestamp(min_ts_us) + tslib.Timestamp(max_ts_us) + + # One us less than the minimum is an error + self.assertRaises(ValueError, tslib.Timestamp, min_ts_us - one_us) + + # One us more than the maximum is an error + self.assertRaises(ValueError, tslib.Timestamp, max_ts_us + one_us) + +class TestDatetimeParsingWrappers(unittest.TestCase): def test_does_not_convert_mixed_integer(self): bad_date_strings = ( '-50000', @@ -97,15 +131,45 @@ def test_number_looking_strings_not_into_datetime(self): arr = np.array(['1', '2', '3', '4', '5'], dtype=object) self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) - def test_dates_outside_of_datetime64_ns_bounds(self): - # These datetimes are outside of the bounds of the - # datetime64[ns] bounds, so they cannot be converted to - # datetimes - arr = np.array(['1/1/1676', '1/2/1676'], dtype=object) - self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + def test_coercing_dates_outside_of_datetime64_ns_bounds(self): + invalid_dates = [ + datetime.date(1000, 1, 1), + datetime.datetime(1000, 1, 1), + '1000-01-01', + 'Jan 1, 1000', + np.datetime64('1000-01-01'), + ] - arr = np.array(['1/1/2263', '1/2/2263'], dtype=object) - self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + for invalid_date in invalid_dates: + self.assertRaises( + ValueError, + tslib.array_to_datetime, + np.array([invalid_date], dtype='object'), + coerce=False, + raise_=True, + ) + self.assert_( + np.array_equal( + tslib.array_to_datetime( + np.array([invalid_date], dtype='object'), coerce=True + ), + np.array([tslib.iNaT], dtype='M8[ns]') + ) + ) + + arr = np.array(['1/1/1000', '1/1/2000'], dtype=object) + self.assert_( + np.array_equal( + tslib.array_to_datetime(arr, coerce=True), + np.array( + [ + tslib.iNaT, + '2000-01-01T00:00:00.000000000-0000' + ], + dtype='M8[ns]' + ) + ) + ) def test_coerce_of_invalid_datetimes(self): arr = np.array(['01-01-2013', 'not_a_date', '1'], dtype=object) @@ -130,11 +194,11 @@ def test_coerce_of_invalid_datetimes(self): ) -class TestTimestamp(unittest.TestCase): +class TestTimestampNsOperations(unittest.TestCase): def setUp(self): if _np_version_under1p7: raise nose.SkipTest('numpy >= 1.7 required') - self.timestamp = Timestamp(datetime.utcnow()) + self.timestamp = Timestamp(datetime.datetime.utcnow()) def assert_ns_timedelta(self, modified_timestamp, expected_value): value = self.timestamp.value diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 39364d21d4aa1..793d9409e662e 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -89,13 +89,12 @@ def _convert_listlike(arg, box): if isinstance(arg, (list,tuple)): arg = np.array(arg, dtype='O') - if com.is_datetime64_dtype(arg): + if com.is_datetime64_ns_dtype(arg): if box and not isinstance(arg, DatetimeIndex): try: return DatetimeIndex(arg, tz='utc' if utc else None) - except ValueError as e: - values, tz = tslib.datetime_to_datetime64(arg) - return DatetimeIndex._simple_new(values, None, tz=tz) + except ValueError: + pass return arg diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 5f81389f318f8..ff3284b72aecb 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -404,6 +404,11 @@ cpdef object get_value_box(ndarray arr, object loc): # wraparound behavior when using the true int64 lower boundary cdef int64_t _NS_LOWER_BOUND = -9223285636854775000LL cdef int64_t _NS_UPPER_BOUND = 9223372036854775807LL + +cdef pandas_datetimestruct _NS_MIN_DTS, _NS_MAX_DTS +pandas_datetime_to_datetimestruct(_NS_LOWER_BOUND, PANDAS_FR_ns, &_NS_MIN_DTS) +pandas_datetime_to_datetimestruct(_NS_UPPER_BOUND, PANDAS_FR_ns, &_NS_MAX_DTS) + Timestamp.min = Timestamp(_NS_LOWER_BOUND) Timestamp.max = Timestamp(_NS_UPPER_BOUND) @@ -759,7 +764,7 @@ cdef convert_to_tsobject(object ts, object tz, object unit): if is_timestamp(ts): obj.value += ts.nanosecond - _check_dts_bounds(obj.value, &obj.dts) + _check_dts_bounds(&obj.dts) return obj elif PyDate_Check(ts): # Keep the converter same as PyDateTime's @@ -770,7 +775,7 @@ cdef convert_to_tsobject(object ts, object tz, object unit): type(ts)) if obj.value != NPY_NAT: - _check_dts_bounds(obj.value, &obj.dts) + _check_dts_bounds(&obj.dts) if tz is not None: _localize_tso(obj, tz) @@ -825,16 +830,26 @@ cdef inline object _get_zone(object tz): return tz -cdef inline _check_dts_bounds(int64_t value, pandas_datetimestruct *dts): - cdef pandas_datetimestruct dts2 - if dts.year <= 1677 or dts.year >= 2262: - pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts2) - if dts2.year != dts.year: - fmt = '%d-%.2d-%.2d %.2d:%.2d:%.2d' % (dts.year, dts.month, - dts.day, dts.hour, - dts.min, dts.sec) +class OutOfBoundsDatetime(ValueError): + pass + +cdef inline _check_dts_bounds(pandas_datetimestruct *dts): + cdef: + bint error = False + + if dts.year <= 1677 and cmp_pandas_datetimestruct(dts, &_NS_MIN_DTS) == -1: + error = True + elif ( + dts.year >= 2262 and + cmp_pandas_datetimestruct(dts, &_NS_MAX_DTS) == 1): + error = True - raise ValueError('Out of bounds nanosecond timestamp: %s' % fmt) + if error: + fmt = '%d-%.2d-%.2d %.2d:%.2d:%.2d' % (dts.year, dts.month, + dts.day, dts.hour, + dts.min, dts.sec) + + raise OutOfBoundsDatetime('Out of bounds nanosecond timestamp: %s' % fmt) # elif isinstance(ts, _Timestamp): # tmp = ts @@ -869,12 +884,12 @@ def datetime_to_datetime64(ndarray[object] values): _ts = convert_to_tsobject(val, None, None) iresult[i] = _ts.value - _check_dts_bounds(iresult[i], &_ts.dts) + _check_dts_bounds(&_ts.dts) else: if inferred_tz is not None: raise ValueError('Cannot mix tz-aware with tz-naive values') iresult[i] = _pydatetime_to_dts(val, &dts) - _check_dts_bounds(iresult[i], &dts) + _check_dts_bounds(&dts) else: raise TypeError('Unrecognized value type: %s' % type(val)) @@ -882,14 +897,6 @@ def datetime_to_datetime64(ndarray[object] values): _not_datelike_strings = set(['a','A','m','M','p','P','t','T']) -def verify_datetime_bounds(dt): - """Verify datetime.datetime is within the datetime64[ns] bounds.""" - if dt.year <= 1677 or dt.year >= 2262: - raise ValueError( - 'Given datetime not within valid datetime64[ns] bounds' - ) - return dt - def _does_string_look_like_datetime(date_string): if date_string.startswith('0'): # Strings starting with 0 are more consistent with a @@ -907,15 +914,11 @@ def _does_string_look_like_datetime(date_string): return True -def parse_datetime_string(date_string, verify_bounds=True, **kwargs): +def parse_datetime_string(date_string, **kwargs): if not _does_string_look_like_datetime(date_string): raise ValueError('Given date string not likely a datetime.') dt = parse_date(date_string, **kwargs) - - if verify_bounds: - verify_datetime_bounds(dt) - return dt def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, @@ -942,7 +945,13 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, if utc_convert: _ts = convert_to_tsobject(val, None, unit) iresult[i] = _ts.value - _check_dts_bounds(iresult[i], &_ts.dts) + try: + _check_dts_bounds(&_ts.dts) + except ValueError: + if coerce: + iresult[i] = iNaT + continue + raise else: raise ValueError('Tz-aware datetime.datetime cannot ' 'be converted to datetime64 unless ' @@ -951,12 +960,30 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, iresult[i] = _pydatetime_to_dts(val, &dts) if is_timestamp(val): iresult[i] += (<_Timestamp>val).nanosecond - _check_dts_bounds(iresult[i], &dts) + try: + _check_dts_bounds(&dts) + except ValueError: + if coerce: + iresult[i] = iNaT + continue + raise elif PyDate_Check(val): iresult[i] = _date_to_datetime64(val, &dts) - _check_dts_bounds(iresult[i], &dts) + try: + _check_dts_bounds(&dts) + except ValueError: + if coerce: + iresult[i] = iNaT + continue + raise elif util.is_datetime64_object(val): - iresult[i] = _get_datetime64_nanos(val) + try: + iresult[i] = _get_datetime64_nanos(val) + except ValueError: + if coerce: + iresult[i] = iNaT + continue + raise # if we are coercing, dont' allow integers elif util.is_integer_object(val) and not coerce: @@ -982,17 +1009,26 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, _string_to_dts(val, &dts) iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts) - _check_dts_bounds(iresult[i], &dts) + _check_dts_bounds(&dts) except ValueError: try: - result[i] = parse_datetime_string( - val, dayfirst=dayfirst + iresult[i] = _pydatetime_to_dts( + parse_datetime_string(val, dayfirst=dayfirst), + &dts ) except Exception: if coerce: iresult[i] = iNaT continue raise TypeError + + try: + _check_dts_bounds(&dts) + except ValueError: + if coerce: + iresult[i] = iNaT + continue + raise except: if coerce: iresult[i] = iNaT @@ -1000,6 +1036,18 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, raise return result + except OutOfBoundsDatetime: + if raise_: + raise + + oresult = np.empty(n, dtype=object) + for i in range(n): + val = values[i] + if util.is_datetime64_object(val): + oresult[i] = val.item() + else: + oresult[i] = val + return oresult except TypeError: oresult = np.empty(n, dtype=object) @@ -1014,6 +1062,8 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, continue try: oresult[i] = parse_datetime_string(val, dayfirst=dayfirst) + _pydatetime_to_dts(oresult[i], &dts) + _check_dts_bounds(&dts) except Exception: if raise_: raise @@ -1320,7 +1370,7 @@ def array_strptime(ndarray[object] values, object fmt): dts.us = fraction iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts) - _check_dts_bounds(iresult[i], &dts) + _check_dts_bounds(&dts) return result @@ -1339,6 +1389,7 @@ cdef inline _get_datetime64_nanos(object val): if unit != PANDAS_FR_ns: pandas_datetime_to_datetimestruct(ival, unit, &dts) + _check_dts_bounds(&dts) return pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts) else: return ival @@ -1398,6 +1449,7 @@ def cast_to_nanoseconds(ndarray arr): for i in range(n): pandas_datetime_to_datetimestruct(ivalues[i], unit, &dts) iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts) + _check_dts_bounds(&dts) return result
closes #4065 To fix the bug, this change adds bounds checking to _get_datetime64_nanos() for numpy datetimes that aren't already in [ns] units. Additionally, it updates _check_dts_bounds() to do the bound check just based off the pandas_datetimestruct, by comparing to the minimum and maximum valid pandas_datetimestructs for datetime64[ns]. It is simpler and more accurate than the previous system.
https://api.github.com/repos/pandas-dev/pandas/pulls/4926
2013-09-21T21:57:03Z
2013-10-07T23:00:26Z
2013-10-07T23:00:26Z
2014-06-21T19:09:49Z
ENH: evaluate datetime ops in python with eval
diff --git a/doc/source/release.rst b/doc/source/release.rst index e49812b207921..a932eda55ff32 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -335,6 +335,9 @@ Experimental Features - A :meth:`~pandas.DataFrame.query` method has been added that allows you to select elements of a ``DataFrame`` using a natural query syntax nearly identical to Python syntax. +- ``pd.eval`` and friends now evaluate operations involving ``datetime64`` + objects in Python space because ``numexpr`` cannot handle ``NaT`` values + (:issue:`4897`). .. _release.bug_fixes-0.13.0: diff --git a/pandas/computation/align.py b/pandas/computation/align.py index 60975bdc8a5b4..f420d0dacf34c 100644 --- a/pandas/computation/align.py +++ b/pandas/computation/align.py @@ -111,14 +111,20 @@ def _align_core(terms): typ = biggest._constructor axes = biggest.axes naxes = len(axes) + gt_than_one_axis = naxes > 1 - for term in (terms[i] for i in term_index): - for axis, items in enumerate(term.value.axes): - if isinstance(term.value, pd.Series) and naxes > 1: - ax, itm = naxes - 1, term.value.index + for value in (terms[i].value for i in term_index): + is_series = isinstance(value, pd.Series) + is_series_and_gt_one_axis = is_series and gt_than_one_axis + + for axis, items in enumerate(value.axes): + if is_series_and_gt_one_axis: + ax, itm = naxes - 1, value.index else: ax, itm = axis, items - axes[ax] = axes[ax].join(itm, how='outer') + + if not axes[ax].is_(itm): + axes[ax] = axes[ax].join(itm, how='outer') for i, ndim in compat.iteritems(ndims): for axis, items in zip(range(ndim), axes): @@ -136,7 +142,7 @@ def _align_core(terms): warnings.warn("Alignment difference on axis {0} is larger" " than an order of magnitude on term {1!r}, " "by more than {2:.4g}; performance may suffer" - "".format(axis, term.name, ordm), + "".format(axis, terms[i].name, ordm), category=pd.io.common.PerformanceWarning) if transpose: diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index ff9adc26b8201..ba2dffa9e71b8 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -493,8 +493,15 @@ def _possibly_evaluate_binop(self, op, op_class, lhs, rhs, maybe_eval_in_python=('==', '!=')): res = op(lhs, rhs) - # "in"/"not in" ops are always evaluated in python + 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 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'): diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index debc79e33968c..fd5ee159fe2b4 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -5,6 +5,7 @@ import operator as op from functools import partial from itertools import product, islice, chain +from datetime import datetime import numpy as np @@ -161,24 +162,16 @@ def raw(self): self.type)) @property - def kind(self): + def is_datetime(self): try: - return self.type.__name__ + t = self.type.type except AttributeError: - return self.type.type.__name__ + t = self.type + + return issubclass(t, (datetime, np.datetime64)) @property def value(self): - kind = self.kind.lower() - if kind == 'datetime64': - try: - return self._value.asi8 - except AttributeError: - return self._value.view('i8') - elif kind == 'datetime': - return pd.Timestamp(self._value) - elif kind == 'timestamp': - return self._value.asm8.view('i8') return self._value @value.setter @@ -248,6 +241,15 @@ def return_type(self): def isscalar(self): return all(operand.isscalar for operand in self.operands) + @property + def is_datetime(self): + try: + t = self.return_type.type + except AttributeError: + t = self.return_type + + return issubclass(t, (datetime, np.datetime64)) + def _in(x, y): """Compute the vectorized membership of ``x in y`` if possible, otherwise @@ -424,24 +426,20 @@ def stringify(value): lhs, rhs = self.lhs, self.rhs - if (is_term(lhs) and lhs.kind.startswith('datetime') and is_term(rhs) - and rhs.isscalar): + if is_term(lhs) and lhs.is_datetime and is_term(rhs) and rhs.isscalar: v = rhs.value if isinstance(v, (int, float)): v = stringify(v) - v = _ensure_decoded(v) - v = pd.Timestamp(v) + v = pd.Timestamp(_ensure_decoded(v)) if v.tz is not None: v = v.tz_convert('UTC') self.rhs.update(v) - if (is_term(rhs) and rhs.kind.startswith('datetime') and - is_term(lhs) and lhs.isscalar): + if is_term(rhs) and rhs.is_datetime and is_term(lhs) and lhs.isscalar: v = lhs.value if isinstance(v, (int, float)): v = stringify(v) - v = _ensure_decoded(v) - v = pd.Timestamp(v) + v = pd.Timestamp(_ensure_decoded(v)) if v.tz is not None: v = v.tz_convert('UTC') self.lhs.update(v) diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 3554b8a3f81e1..e9201c233753f 100755 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1003,7 +1003,7 @@ def check_performance_warning_for_poor_alignment(self, engine, parser): expected = ("Alignment difference on axis {0} is larger" " than an order of magnitude on term {1!r}, " "by more than {2:.4g}; performance may suffer" - "".format(1, 's', np.log10(s.size - df.shape[1]))) + "".format(1, 'df', np.log10(s.size - df.shape[1]))) assert_equal(msg, expected) def test_performance_warning_for_poor_alignment(self): diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 7b9a75753136e..01e0d74ef8ce6 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1894,29 +1894,6 @@ def _getitem_frame(self, key): raise ValueError('Must pass DataFrame with boolean values only') return self.where(key) - def _get_index_resolvers(self, axis): - # index or columns - axis_index = getattr(self, axis) - d = dict() - - for i, name in enumerate(axis_index.names): - if name is not None: - key = level = name - else: - # prefix with 'i' or 'c' depending on the input axis - # e.g., you must do ilevel_0 for the 0th level of an unnamed - # multiiindex - level_string = '{prefix}level_{i}'.format(prefix=axis[0], i=i) - key = level_string - level = i - - d[key] = Series(axis_index.get_level_values(level).values, - index=axis_index, name=level) - - # put the index/columns itself in the dict - d[axis] = axis_index - return d - def query(self, expr, **kwargs): """Query the columns of a frame with a boolean expression. @@ -2037,8 +2014,7 @@ def eval(self, expr, **kwargs): """ resolvers = kwargs.pop('resolvers', None) if resolvers is None: - index_resolvers = self._get_index_resolvers('index') - index_resolvers.update(self._get_index_resolvers('columns')) + index_resolvers = self._get_resolvers() resolvers = [self, index_resolvers] kwargs['local_dict'] = _ensure_scope(resolvers=resolvers, **kwargs) return _eval(expr, **kwargs) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 4553e4804e98b..705679136c3d2 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -272,6 +272,42 @@ def _get_block_manager_axis(self, axis): return m - axis return axis + def _get_axis_resolvers(self, axis): + # index or columns + axis_index = getattr(self, axis) + d = dict() + prefix = axis[0] + + for i, name in enumerate(axis_index.names): + if name is not None: + key = level = name + else: + # prefix with 'i' or 'c' depending on the input axis + # e.g., you must do ilevel_0 for the 0th level of an unnamed + # multiiindex + key = '{prefix}level_{i}'.format(prefix=prefix, i=i) + level = i + + level_values = axis_index.get_level_values(level) + s = level_values.to_series() + s.index = axis_index + d[key] = s + + # put the index/columns itself in the dict + if isinstance(axis_index, MultiIndex): + dindex = axis_index + else: + dindex = axis_index.to_series() + + d[axis] = dindex + return d + + def _get_resolvers(self): + d = {} + for axis_name in self._AXIS_ORDERS: + d.update(self._get_axis_resolvers(axis_name)) + return d + @property def _info_axis(self): return getattr(self, self._info_axis_name) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index a6f806d5ce097..e5d2bb17ec7a8 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -11423,6 +11423,57 @@ def test_query_with_partially_named_multiindex(self): for parser, engine in product(['pandas'], ENGINES): yield self.check_query_with_partially_named_multiindex, parser, engine + def test_query_multiindex_get_index_resolvers(self): + for parser, engine in product(['pandas'], ENGINES): + yield self.check_query_multiindex_get_index_resolvers, parser, engine + + def check_query_multiindex_get_index_resolvers(self, parser, engine): + df = mkdf(10, 3, r_idx_nlevels=2, r_idx_names=['spam', 'eggs']) + resolvers = df._get_resolvers() + + def to_series(mi, level): + level_values = mi.get_level_values(level) + s = level_values.to_series() + s.index = mi + return s + + col_series = df.columns.to_series() + expected = {'index': df.index, + 'columns': col_series, + 'spam': to_series(df.index, 'spam'), + 'eggs': to_series(df.index, 'eggs'), + 'C0': col_series} + for k, v in resolvers.items(): + if isinstance(v, Index): + assert v.is_(expected[k]) + elif isinstance(v, Series): + print(k) + tm.assert_series_equal(v, expected[k]) + else: + raise AssertionError("object must be a Series or Index") + + def test_raise_on_panel_with_multiindex(self): + for parser, engine in product(PARSERS, ENGINES): + yield self.check_raise_on_panel_with_multiindex, parser, engine + + def check_raise_on_panel_with_multiindex(self, parser, engine): + skip_if_no_ne() + p = tm.makePanel(7) + p.items = tm.makeCustomIndex(len(p.items), nlevels=2) + with tm.assertRaises(NotImplementedError): + pd.eval('p + 1', parser=parser, engine=engine) + + def test_raise_on_panel4d_with_multiindex(self): + for parser, engine in product(PARSERS, ENGINES): + yield self.check_raise_on_panel4d_with_multiindex, parser, engine + + def check_raise_on_panel4d_with_multiindex(self, parser, engine): + skip_if_no_ne() + p4d = tm.makePanel4D(7) + p4d.items = tm.makeCustomIndex(len(p4d.items), nlevels=2) + with tm.assertRaises(NotImplementedError): + pd.eval('p4d + 1', parser=parser, engine=engine) + class TestDataFrameQueryNumExprPandas(unittest.TestCase): @classmethod @@ -11446,6 +11497,71 @@ def test_date_query_method(self): expec = df[(df.dates1 < '20130101') & ('20130101' < df.dates3)] assert_frame_equal(res, expec) + def test_date_query_with_NaT(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates2'] = date_range('1/1/2013', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.loc[np.random.rand(n) > 0.5, 'dates1'] = pd.NaT + df.loc[np.random.rand(n) > 0.5, 'dates3'] = pd.NaT + res = df.query('dates1 < 20130101 < dates3', engine=engine, + parser=parser) + expec = df[(df.dates1 < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_index_query(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.set_index('dates1', inplace=True, drop=True) + res = df.query('index < 20130101 < dates3', engine=engine, + parser=parser) + expec = df[(df.index < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.iloc[0, 0] = pd.NaT + df.set_index('dates1', inplace=True, drop=True) + res = df.query('index < 20130101 < dates3', engine=engine, + parser=parser) + expec = df[(df.index < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT_duplicates(self): + engine, parser = self.engine, self.parser + n = 10 + d = {} + d['dates1'] = date_range('1/1/2012', periods=n) + d['dates3'] = date_range('1/1/2014', periods=n) + df = DataFrame(d) + df.loc[np.random.rand(n) > 0.5, 'dates1'] = pd.NaT + df.set_index('dates1', inplace=True, drop=True) + res = df.query('index < 20130101 < dates3', engine=engine, parser=parser) + expec = df[(df.index.to_series() < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_query_with_non_date(self): + engine, parser = self.engine, self.parser + + n = 10 + df = DataFrame({'dates': date_range('1/1/2012', periods=n), + 'nondate': np.arange(n)}) + + ops = '==', '!=', '<', '>', '<=', '>=' + + for op in ops: + with tm.assertRaises(TypeError): + df.query('dates %s nondate' % op, parser=parser, engine=engine) + def test_query_scope(self): engine, parser = self.engine, self.parser from pandas.computation.common import NameResolutionError @@ -11608,6 +11724,57 @@ def test_date_query_method(self): expec = df[(df.dates1 < '20130101') & ('20130101' < df.dates3)] assert_frame_equal(res, expec) + def test_date_query_with_NaT(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates2'] = date_range('1/1/2013', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.loc[np.random.rand(n) > 0.5, 'dates1'] = pd.NaT + df.loc[np.random.rand(n) > 0.5, 'dates3'] = pd.NaT + res = df.query('(dates1 < 20130101) & (20130101 < dates3)', + engine=engine, parser=parser) + expec = df[(df.dates1 < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_index_query(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.set_index('dates1', inplace=True, drop=True) + res = df.query('(index < 20130101) & (20130101 < dates3)', + engine=engine, parser=parser) + expec = df[(df.index < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.iloc[0, 0] = pd.NaT + df.set_index('dates1', inplace=True, drop=True) + res = df.query('(index < 20130101) & (20130101 < dates3)', + engine=engine, parser=parser) + expec = df[(df.index < '20130101') & ('20130101' < df.dates3)] + assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT_duplicates(self): + engine, parser = self.engine, self.parser + n = 10 + df = DataFrame(randn(n, 3)) + df['dates1'] = date_range('1/1/2012', periods=n) + df['dates3'] = date_range('1/1/2014', periods=n) + df.loc[np.random.rand(n) > 0.5, 'dates1'] = pd.NaT + df.set_index('dates1', inplace=True, drop=True) + with tm.assertRaises(NotImplementedError): + res = df.query('index < 20130101 < dates3', engine=engine, + parser=parser) + def test_nested_scope(self): engine = self.engine parser = self.parser diff --git a/vb_suite/binary_ops.py b/vb_suite/binary_ops.py index 8293f650425e3..fc84dd8bcdb81 100644 --- a/vb_suite/binary_ops.py +++ b/vb_suite/binary_ops.py @@ -106,7 +106,7 @@ setup = common_setup + """ N = 1000000 halfway = N // 2 - 1 -s = Series(date_range('20010101', periods=N, freq='D')) +s = Series(date_range('20010101', periods=N, freq='T')) ts = s[halfway] """ diff --git a/vb_suite/eval.py b/vb_suite/eval.py index c666cd431cbb4..506d00b8bf9f9 100644 --- a/vb_suite/eval.py +++ b/vb_suite/eval.py @@ -47,12 +47,12 @@ eval_frame_mult_all_threads = \ Benchmark("pd.eval('df * df2 * df3 * df4')", common_setup, name='eval_frame_mult_all_threads', - start_date=datetime(2012, 7, 21)) + start_date=datetime(2013, 7, 21)) eval_frame_mult_one_thread = \ Benchmark("pd.eval('df * df2 * df3 * df4')", setup, name='eval_frame_mult_one_thread', - start_date=datetime(2012, 7, 26)) + start_date=datetime(2013, 7, 26)) eval_frame_mult_python = \ Benchmark("pdl.eval('df * df2 * df3 * df4', engine='python')", @@ -62,7 +62,7 @@ eval_frame_mult_python_one_thread = \ Benchmark("pd.eval('df * df2 * df3 * df4', engine='python')", setup, name='eval_frame_mult_python_one_thread', - start_date=datetime(2012, 7, 26)) + start_date=datetime(2013, 7, 26)) #---------------------------------------------------------------------- # multi and @@ -71,12 +71,12 @@ Benchmark("pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)')", common_setup, name='eval_frame_and_all_threads', - start_date=datetime(2012, 7, 21)) + start_date=datetime(2013, 7, 21)) eval_frame_and_one_thread = \ Benchmark("pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)')", setup, name='eval_frame_and_one_thread', - start_date=datetime(2012, 7, 26)) + start_date=datetime(2013, 7, 26)) setup = common_setup eval_frame_and_python = \ @@ -88,19 +88,19 @@ Benchmark("pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)', engine='python')", setup, name='eval_frame_and_python_one_thread', - start_date=datetime(2012, 7, 26)) + start_date=datetime(2013, 7, 26)) #-------------------------------------------------------------------- # chained comp eval_frame_chained_cmp_all_threads = \ Benchmark("pd.eval('df < df2 < df3 < df4')", common_setup, name='eval_frame_chained_cmp_all_threads', - start_date=datetime(2012, 7, 21)) + start_date=datetime(2013, 7, 21)) eval_frame_chained_cmp_one_thread = \ Benchmark("pd.eval('df < df2 < df3 < df4')", setup, name='eval_frame_chained_cmp_one_thread', - start_date=datetime(2012, 7, 26)) + start_date=datetime(2013, 7, 26)) setup = common_setup eval_frame_chained_cmp_python = \ @@ -111,4 +111,31 @@ eval_frame_chained_cmp_one_thread = \ Benchmark("pd.eval('df < df2 < df3 < df4', engine='python')", setup, name='eval_frame_chained_cmp_python_one_thread', - start_date=datetime(2012, 7, 26)) + start_date=datetime(2013, 7, 26)) + + +common_setup = """from pandas_vb_common import * +""" + +setup = common_setup + """ +N = 1000000 +halfway = N // 2 - 1 +index = date_range('20010101', periods=N, freq='T') +s = Series(index) +ts = s.iloc[halfway] +""" + +series_setup = setup + """ +df = DataFrame({'dates': s.values}) +""" + +query_datetime_series = Benchmark("df.query('dates < ts')", + series_setup, + start_date=datetime(2013, 9, 27)) + +index_setup = setup + """ +df = DataFrame({'a': np.random.randn(N)}, index=index) +""" + +query_datetime_index = Benchmark("df.query('index < ts')", + index_setup, start_date=datetime(2013, 9, 27))
closes #4897 Also adds `DatetimeIndex` comparisons in query expressions and a corresponding vbench for a simple `Timestamp` vs either `Series` or `DatetimeIndex`.
https://api.github.com/repos/pandas-dev/pandas/pulls/4924
2013-09-21T21:47:41Z
2013-09-27T19:34:46Z
2013-09-27T19:34:46Z
2014-06-20T02:58:26Z
CLN: Remove py3 next method from FixedWidthReader
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index c4ea76585df83..7b9347a821fad 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -1964,20 +1964,11 @@ def __init__(self, f, colspecs, filler, thousands=None, encoding=None): isinstance(colspec[1], int) ): raise AssertionError() - if compat.PY3: - def next(self): - line = next(self.f) - if isinstance(line, bytes): - line = line.decode(self.encoding) - # Note: 'colspecs' is a sequence of half-open intervals. - return [line[fromm:to].strip(self.filler or ' ') - for (fromm, to) in self.colspecs] - else: - def next(self): - line = next(self.f) - # Note: 'colspecs' is a sequence of half-open intervals. - return [line[fromm:to].strip(self.filler or ' ') - for (fromm, to) in self.colspecs] + def next(self): + line = next(self.f) + # Note: 'colspecs' is a sequence of half-open intervals. + return [line[fromm:to].strip(self.filler or ' ') + for (fromm, to) in self.colspecs] # Iterator protocol in Python 3 uses __next__() __next__ = next
It's unnecessary - get file handle wraps this instead.
https://api.github.com/repos/pandas-dev/pandas/pulls/4923
2013-09-21T20:53:20Z
2013-09-21T21:29:28Z
2013-09-21T21:29:28Z
2014-06-24T23:41:01Z
DOC: show disallowed eval syntax
diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index bade382f03c59..e59cb6ac30964 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -330,6 +330,42 @@ engine in addition to some extensions available only in pandas. The larger the frame and the larger the expression the more speedup you will see from using :func:`~pandas.eval`. +Supported Syntax +~~~~~~~~~~~~~~~~ + +These operations are supported by :func:`~pandas.eval`: + +- Arithmetic operations except for the left shift (``<<``) and right shift + (``>>``) operators, e.g., ``df + 2 * pi / s ** 4 % 42 - the_golden_ratio`` +- Comparison operations, e.g., ``2 < df < df2`` +- Boolean operations, e.g., ``df < df2 and df3 < df4 or not df_bool`` +- ``list`` and ``tuple`` literals, e.g., ``[1, 2]`` or ``(1, 2)`` +- Attribute access, e.g., ``df.a`` +- Subscript expressions, e.g., ``df[0]`` +- Simple variable evaluation, e.g., ``pd.eval('df')`` (this is not very useful) + +This Python syntax is **not** allowed: + +* Expressions + + - Function calls + - ``is``/``is not`` operations + - ``if`` expressions + - ``lambda`` expressions + - ``list``/``set``/``dict`` comprehensions + - Literal ``dict`` and ``set`` expressions + - ``yield`` expressions + - Generator expressions + - Boolean expressions consisting of only scalar values + +* Statements + + - Neither `simple <http://docs.python.org/2/reference/simple_stmts.html>`__ + nor `compound <http://docs.python.org/2/reference/compound_stmts.html>`__ + statements are allowed. This includes things like ``for``, ``while``, and + ``if``. + + :func:`~pandas.eval` Examples ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
closes #4898
https://api.github.com/repos/pandas-dev/pandas/pulls/4922
2013-09-21T20:44:17Z
2013-09-22T13:55:27Z
2013-09-22T13:55:27Z
2014-06-19T07:17:07Z
DOC: expand the documentation on the xs method
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index e7af0e325a1b2..a8b9a4be01ae8 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -1590,6 +1590,41 @@ selecting data at a particular level of a MultiIndex easier. df.xs('one', level='second') +You can also select on the columns with :meth:`~pandas.MultiIndex.xs`, by +providing the axis argument + +.. ipython:: python + + df = df.T + df.xs('one', level='second', axis=1) + +:meth:`~pandas.MultiIndex.xs` also allows selection with multiple keys + +.. ipython:: python + + df.xs(('one', 'bar'), level=('second', 'first'), axis=1) + + +.. versionadded:: 0.13.0 + +You can pass ``drop_level=False`` to :meth:`~pandas.MultiIndex.xs` to retain +the level that was selected + +.. ipython:: + + df.xs('one', level='second', axis=1, drop_level=False) + +versus the result with ``drop_level=True`` (the default value) + +.. ipython:: + + df.xs('one', level='second', axis=1, drop_level=True) + +.. ipython:: + :suppress: + + df = df.T + .. _indexing.advanced_reindex: Advanced reindexing and alignment with hierarchical index
closes #4900
https://api.github.com/repos/pandas-dev/pandas/pulls/4921
2013-09-21T20:27:41Z
2013-09-22T02:52:23Z
2013-09-22T02:52:23Z
2014-07-16T08:29:31Z
CLN: more plotting test cleanups
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index cb6ec3d648afa..558bf17b0cd5c 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -29,17 +29,11 @@ def _skip_if_no_scipy(): raise nose.SkipTest +@tm.mplskip class TestSeriesPlots(unittest.TestCase): - @classmethod - def setUpClass(cls): - try: - import matplotlib as mpl - mpl.use('Agg', warn=False) - cls.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1') - except ImportError: - raise nose.SkipTest("matplotlib not installed") - def setUp(self): + import matplotlib as mpl + self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1') self.ts = tm.makeTimeSeries() self.ts.name = 'ts' @@ -50,8 +44,7 @@ def setUp(self): self.iseries.name = 'iseries' def tearDown(self): - import matplotlib.pyplot as plt - plt.close('all') + tm.close() @slow def test_plot(self): @@ -352,24 +345,14 @@ def test_dup_datetime_index_plot(self): _check_plot_works(s.plot) +@tm.mplskip class TestDataFramePlots(unittest.TestCase): - - @classmethod - def setUpClass(cls): - # import sys - # if 'IPython' in sys.modules: - # raise nose.SkipTest - - try: - import matplotlib as mpl - mpl.use('Agg', warn=False) - cls.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1') - except ImportError: - raise nose.SkipTest("matplotlib not installed") + def setUp(self): + import matplotlib as mpl + self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1') def tearDown(self): - import matplotlib.pyplot as plt - plt.close('all') + tm.close() @slow def test_plot(self): @@ -949,19 +932,10 @@ def test_invalid_kind(self): df.plot(kind='aasdf') +@tm.mplskip class TestDataFrameGroupByPlots(unittest.TestCase): - @classmethod - def setUpClass(cls): - try: - import matplotlib as mpl - mpl.use('Agg', warn=False) - except ImportError: - raise nose.SkipTest - def tearDown(self): - import matplotlib.pyplot as plt - for fignum in plt.get_fignums(): - plt.close(fignum) + tm.close() @slow def test_boxplot(self): @@ -999,13 +973,16 @@ def test_time_series_plot_color_with_empty_kwargs(self): import matplotlib as mpl def_colors = mpl.rcParams['axes.color_cycle'] + index = date_range('1/1/2000', periods=12) + s = Series(np.arange(1, 13), index=index) + + ncolors = 3 - for i in range(3): - ax = Series(np.arange(12) + 1, index=date_range('1/1/2000', - periods=12)).plot() + for i in range(ncolors): + ax = s.plot() line_colors = [l.get_color() for l in ax.get_lines()] - self.assertEqual(line_colors, def_colors[:3]) + self.assertEqual(line_colors, def_colors[:ncolors]) @slow def test_grouped_hist(self): @@ -1155,27 +1132,30 @@ def _check_plot_works(f, *args, **kwargs): import matplotlib.pyplot as plt try: - fig = kwargs['figure'] - except KeyError: - fig = plt.gcf() - plt.clf() - ax = kwargs.get('ax', fig.add_subplot(211)) - ret = f(*args, **kwargs) + try: + fig = kwargs['figure'] + except KeyError: + fig = plt.gcf() - assert ret is not None - assert_is_valid_plot_return_object(ret) + plt.clf() - try: - kwargs['ax'] = fig.add_subplot(212) + ax = kwargs.get('ax', fig.add_subplot(211)) ret = f(*args, **kwargs) - except Exception: - pass - else: + assert_is_valid_plot_return_object(ret) - with ensure_clean() as path: - plt.savefig(path) - plt.close(fig) + try: + kwargs['ax'] = fig.add_subplot(212) + ret = f(*args, **kwargs) + except Exception: + pass + else: + assert_is_valid_plot_return_object(ret) + + with ensure_clean() as path: + plt.savefig(path) + finally: + tm.close(fig) def curpath(): diff --git a/pandas/tests/test_rplot.py b/pandas/tests/test_rplot.py index e7faa8f25deb3..d59b182b77d4c 100644 --- a/pandas/tests/test_rplot.py +++ b/pandas/tests/test_rplot.py @@ -8,16 +8,11 @@ import nose -try: - import matplotlib.pyplot as plt -except: - raise nose.SkipTest - - def curpath(): pth, _ = os.path.split(os.path.abspath(__file__)) return pth + def between(a, b, x): """Check if x is in the somewhere between a and b. @@ -36,6 +31,8 @@ def between(a, b, x): else: return x <= a and x >= b + +@tm.mplskip class TestUtilityFunctions(unittest.TestCase): """ Tests for RPlot utility functions. @@ -74,9 +71,9 @@ def test_dictionary_union(self): self.assertTrue(2 in keys) self.assertTrue(3 in keys) self.assertTrue(4 in keys) - self.assertTrue(rplot.dictionary_union(dict1, {}) == dict1) - self.assertTrue(rplot.dictionary_union({}, dict1) == dict1) - self.assertTrue(rplot.dictionary_union({}, {}) == {}) + self.assertEqual(rplot.dictionary_union(dict1, {}), dict1) + self.assertEqual(rplot.dictionary_union({}, dict1), dict1) + self.assertEqual(rplot.dictionary_union({}, {}), {}) def test_merge_aes(self): layer1 = rplot.Layer(size=rplot.ScaleSize('test')) @@ -84,14 +81,15 @@ def test_merge_aes(self): rplot.merge_aes(layer1, layer2) self.assertTrue(isinstance(layer2.aes['size'], rplot.ScaleSize)) self.assertTrue(isinstance(layer2.aes['shape'], rplot.ScaleShape)) - self.assertTrue(layer2.aes['size'] == layer1.aes['size']) + self.assertEqual(layer2.aes['size'], layer1.aes['size']) for key in layer2.aes.keys(): if key != 'size' and key != 'shape': self.assertTrue(layer2.aes[key] is None) def test_sequence_layers(self): layer1 = rplot.Layer(self.data) - layer2 = rplot.GeomPoint(x='SepalLength', y='SepalWidth', size=rplot.ScaleSize('PetalLength')) + layer2 = rplot.GeomPoint(x='SepalLength', y='SepalWidth', + size=rplot.ScaleSize('PetalLength')) layer3 = rplot.GeomPolyFit(2) result = rplot.sequence_layers([layer1, layer2, layer3]) self.assertEqual(len(result), 3) @@ -102,6 +100,8 @@ def test_sequence_layers(self): self.assertTrue(self.data is last.data) self.assertTrue(rplot.sequence_layers([layer1])[0] is layer1) + +@tm.mplskip class TestTrellis(unittest.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/tips.csv') @@ -148,11 +148,15 @@ def test_trellis_cols_rows(self): self.assertEqual(self.trellis3.cols, 2) self.assertEqual(self.trellis3.rows, 1) + +@tm.mplskip class TestScaleGradient(unittest.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') self.data = read_csv(path, sep=',') - self.gradient = rplot.ScaleGradient("SepalLength", colour1=(0.2, 0.3, 0.4), colour2=(0.8, 0.7, 0.6)) + self.gradient = rplot.ScaleGradient("SepalLength", colour1=(0.2, 0.3, + 0.4), + colour2=(0.8, 0.7, 0.6)) def test_gradient(self): for index in range(len(self.data)): @@ -164,6 +168,8 @@ def test_gradient(self): self.assertTrue(between(g1, g2, g)) self.assertTrue(between(b1, b2, b)) + +@tm.mplskip class TestScaleGradient2(unittest.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') @@ -190,6 +196,8 @@ def test_gradient2(self): self.assertTrue(between(g2, g3, g)) self.assertTrue(between(b2, b3, b)) + +@tm.mplskip class TestScaleRandomColour(unittest.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') @@ -208,6 +216,8 @@ def test_random_colour(self): self.assertTrue(g <= 1.0) self.assertTrue(b <= 1.0) + +@tm.mplskip class TestScaleConstant(unittest.TestCase): def test_scale_constant(self): scale = rplot.ScaleConstant(1.0) @@ -215,6 +225,7 @@ def test_scale_constant(self): scale = rplot.ScaleConstant("test") self.assertEqual(scale(None, None), "test") + class TestScaleSize(unittest.TestCase): def setUp(self): path = os.path.join(curpath(), 'data/iris.csv') @@ -235,8 +246,10 @@ def f(): self.assertRaises(ValueError, f) +@tm.mplskip class TestRPlot(unittest.TestCase): def test_rplot1(self): + import matplotlib.pyplot as plt path = os.path.join(curpath(), 'data/tips.csv') plt.figure() self.data = read_csv(path, sep=',') @@ -247,6 +260,7 @@ def test_rplot1(self): self.plot.render(self.fig) def test_rplot2(self): + import matplotlib.pyplot as plt path = os.path.join(curpath(), 'data/tips.csv') plt.figure() self.data = read_csv(path, sep=',') @@ -257,6 +271,7 @@ def test_rplot2(self): self.plot.render(self.fig) def test_rplot3(self): + import matplotlib.pyplot as plt path = os.path.join(curpath(), 'data/tips.csv') plt.figure() self.data = read_csv(path, sep=',') @@ -267,6 +282,7 @@ def test_rplot3(self): self.plot.render(self.fig) def test_rplot_iris(self): + import matplotlib.pyplot as plt path = os.path.join(curpath(), 'data/iris.csv') plt.figure() self.data = read_csv(path, sep=',') @@ -277,5 +293,6 @@ def test_rplot_iris(self): self.fig = plt.gcf() plot.render(self.fig) + if __name__ == '__main__': unittest.main() diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index a22d2a65248a9..96888df114950 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -26,16 +26,8 @@ def _skip_if_no_scipy(): raise nose.SkipTest +@tm.mplskip class TestTSPlot(unittest.TestCase): - - @classmethod - def setUpClass(cls): - try: - import matplotlib as mpl - mpl.use('Agg', warn=False) - except ImportError: - raise nose.SkipTest - 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] @@ -52,9 +44,7 @@ def setUp(self): for x in idx] def tearDown(self): - import matplotlib.pyplot as plt - for fignum in plt.get_fignums(): - plt.close(fignum) + tm.close() @slow def test_ts_plot_with_tz(self): diff --git a/pandas/util/testing.py b/pandas/util/testing.py index e7e930320116b..a5a96d3e03cac 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -18,6 +18,8 @@ from numpy.random import randn, rand import numpy as np +import nose + from pandas.core.common import isnull, _is_sequence import pandas.core.index as index import pandas.core.series as series @@ -70,6 +72,31 @@ def choice(x, size=10): except AttributeError: return np.random.randint(len(x), size=size).choose(x) + +def close(fignum=None): + from matplotlib.pyplot import get_fignums, close as _close + + if fignum is None: + for fignum in get_fignums(): + _close(fignum) + else: + _close(fignum) + + +def mplskip(cls): + """Skip a TestCase instance if matplotlib isn't installed""" + @classmethod + def setUpClass(cls): + try: + import matplotlib as mpl + mpl.use("Agg", warn=False) + except ImportError: + raise nose.SkipTest("matplotlib not installed") + + cls.setUpClass = setUpClass + return cls + + #------------------------------------------------------------------------------ # Console debugging tools
Just a couple of cleanups here and there Adds a new decorator to skip matplotlib in a class method
https://api.github.com/repos/pandas-dev/pandas/pulls/4912
2013-09-21T02:28:14Z
2013-09-21T18:26:06Z
2013-09-21T18:26:06Z
2014-07-16T08:29:28Z
BUG/TST: Allow generators in DataFrame.from_records
diff --git a/doc/source/release.rst b/doc/source/release.rst index 78236bbf821dd..0026e8c27d176 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -169,6 +169,7 @@ Improvements to existing features high-dimensional arrays). - :func:`~pandas.read_html` now supports the ``parse_dates``, ``tupleize_cols`` and ``thousands`` parameters (:issue:`4770`). + - ``DataFrame.from_records()`` accept generators (:issue:`4910`) API Changes ~~~~~~~~~~~ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index d778fa096f589..1bbaeffff77bc 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -724,12 +724,17 @@ def from_records(cls, data, index=None, exclude=None, columns=None, values = [first_row] - i = 1 - for row in data: - values.append(row) - i += 1 - if i >= nrows: - break + #if unknown length iterable (generator) + if nrows == None: + #consume whole generator + values += list(data) + else: + i = 1 + for row in data: + values.append(row) + i += 1 + if i >= nrows: + break if dtype is not None: data = np.array(values, dtype=dtype) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1e4e988431f43..4c31961cbf8fb 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -3739,6 +3739,36 @@ def test_from_records_iterator(self): nrows=2) assert_frame_equal(df, xp.reindex(columns=['x','y']), check_dtype=False) + def test_from_records_tuples_generator(self): + def tuple_generator(length): + for i in range(length): + letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' + yield (i, letters[i % len(letters)], i/length) + + columns_names = ['Integer', 'String', 'Float'] + columns = [[i[j] for i in tuple_generator(10)] for j in range(len(columns_names))] + data = {'Integer': columns[0], 'String': columns[1], 'Float': columns[2]} + expected = DataFrame(data, columns=columns_names) + + generator = tuple_generator(10) + result = DataFrame.from_records(generator, columns=columns_names) + assert_frame_equal(result, expected) + + def test_from_records_lists_generator(self): + def list_generator(length): + for i in range(length): + letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' + yield [i, letters[i % len(letters)], i/length] + + columns_names = ['Integer', 'String', 'Float'] + columns = [[i[j] for i in list_generator(10)] for j in range(len(columns_names))] + data = {'Integer': columns[0], 'String': columns[1], 'Float': columns[2]} + expected = DataFrame(data, columns=columns_names) + + generator = list_generator(10) + result = DataFrame.from_records(generator, columns=columns_names) + assert_frame_equal(result, expected) + def test_from_records_columns_not_modified(self): tuples = [(1, 2, 3), (1, 2, 3),
closes #4910 - nrows implementation doesn't allow unknown size iterator like generators, if nrows = none ends with a TypeError. - To allow generators if nrows=None consume it into a list. - Add two tests of generators input.
https://api.github.com/repos/pandas-dev/pandas/pulls/4911
2013-09-21T00:56:24Z
2013-10-04T20:19:07Z
2013-10-04T20:19:07Z
2014-06-20T19:30:09Z
ENH: Add 'is_' method to Index for identity checks
diff --git a/doc/source/release.rst b/doc/source/release.rst index 789fbd0fe4ccc..70981df6b3187 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -198,6 +198,10 @@ API Changes data - allowing metadata changes. - ``MultiIndex.astype()`` now only allows ``np.object_``-like dtypes and now returns a ``MultiIndex`` rather than an ``Index``. (:issue:`4039`) + - Added ``is_`` method to ``Index`` that allows fast equality comparison of + views (similar to ``np.may_share_memory`` but no false positives, and + changes on ``levels`` and ``labels`` setting on ``MultiIndex``). + (:issue:`4859`, :issue:`4909`) - 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`) diff --git a/pandas/core/index.py b/pandas/core/index.py index eea09138a0638..1d3f181b731e8 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -50,6 +50,9 @@ def _shouldbe_timestamp(obj): or tslib.is_timestamp_array(obj)) +_Identity = object + + class Index(FrozenNDArray): """ Immutable ndarray implementing an ordered, sliceable set. The basic object @@ -87,6 +90,35 @@ class Index(FrozenNDArray): _engine_type = _index.ObjectEngine + def is_(self, other): + """ + More flexible, faster check like ``is`` but that works through views + + Note: this is *not* the same as ``Index.identical()``, which checks + that metadata is also the same. + + Parameters + ---------- + other : object + other object to compare against. + + Returns + ------- + True if both have same underlying data, False otherwise : bool + """ + # use something other than None to be clearer + return self._id is getattr(other, '_id', Ellipsis) + + def _reset_identity(self): + "Initializes or resets ``_id`` attribute with new object" + self._id = _Identity() + + def view(self, *args, **kwargs): + result = super(Index, self).view(*args, **kwargs) + if isinstance(result, Index): + result._id = self._id + return result + def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False, **kwargs): @@ -151,6 +183,7 @@ def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False, return subarr def __array_finalize__(self, obj): + self._reset_identity() if not isinstance(obj, type(self)): # Only relevant if array being created from an Index instance return @@ -279,6 +312,7 @@ def set_names(self, names, inplace=False): raise TypeError("Must pass list-like as `names`.") if inplace: idx = self + idx._reset_identity() else: idx = self._shallow_copy() idx._set_names(names) @@ -554,7 +588,7 @@ def equals(self, other): """ Determines if two Index objects contain the same elements. """ - if self is other: + if self.is_(other): return True if not isinstance(other, Index): @@ -1536,7 +1570,7 @@ def equals(self, other): """ Determines if two Index objects contain the same elements. """ - if self is other: + if self.is_(other): return True # if not isinstance(other, Int64Index): @@ -1645,6 +1679,7 @@ def set_levels(self, levels, inplace=False): idx = self else: idx = self._shallow_copy() + idx._reset_identity() idx._set_levels(levels) return idx @@ -1683,6 +1718,7 @@ def set_labels(self, labels, inplace=False): idx = self else: idx = self._shallow_copy() + idx._reset_identity() idx._set_labels(labels) return idx @@ -1736,6 +1772,8 @@ def __array_finalize__(self, obj): Update custom MultiIndex attributes when a new array is created by numpy, e.g. when calling ndarray.view() """ + # overriden if a view + self._reset_identity() if not isinstance(obj, type(self)): # Only relevant if this array is being created from an Index # instance. @@ -2754,7 +2792,7 @@ def equals(self, other): -------- equal_levels """ - if self is other: + if self.is_(other): return True if not isinstance(other, MultiIndex): diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 5b91f011c98f8..3e7ec5c3a3c12 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -192,6 +192,28 @@ def test_identical(self): i2 = i2.rename('foo') self.assert_(i1.identical(i2)) + def test_is_(self): + ind = Index(range(10)) + self.assertTrue(ind.is_(ind)) + self.assertTrue(ind.is_(ind.view().view().view().view())) + self.assertFalse(ind.is_(Index(range(10)))) + self.assertFalse(ind.is_(ind.copy())) + self.assertFalse(ind.is_(ind.copy(deep=False))) + self.assertFalse(ind.is_(ind[:])) + self.assertFalse(ind.is_(ind.view(np.ndarray).view(Index))) + self.assertFalse(ind.is_(np.array(range(10)))) + self.assertTrue(ind.is_(ind.view().base)) # quasi-implementation dependent + ind2 = ind.view() + ind2.name = 'bob' + self.assertTrue(ind.is_(ind2)) + self.assertTrue(ind2.is_(ind)) + # doesn't matter if Indices are *actually* views of underlying data, + self.assertFalse(ind.is_(Index(ind.values))) + arr = np.array(range(1, 11)) + ind1 = Index(arr, copy=False) + ind2 = Index(arr, copy=False) + self.assertFalse(ind1.is_(ind2)) + def test_asof(self): d = self.dateIndex[0] self.assert_(self.dateIndex.asof(d) is d) @@ -1719,6 +1741,29 @@ def test_identical(self): mi2 = mi2.set_names(['new1','new2']) self.assert_(mi.identical(mi2)) + def test_is_(self): + mi = MultiIndex.from_tuples(lzip(range(10), range(10))) + self.assertTrue(mi.is_(mi)) + self.assertTrue(mi.is_(mi.view())) + self.assertTrue(mi.is_(mi.view().view().view().view())) + mi2 = mi.view() + # names are metadata, they don't change id + mi2.names = ["A", "B"] + self.assertTrue(mi2.is_(mi)) + self.assertTrue(mi.is_(mi2)) + self.assertTrue(mi.is_(mi.set_names(["C", "D"]))) + # levels are inherent properties, they change identity + mi3 = mi2.set_levels([lrange(10), lrange(10)]) + self.assertFalse(mi3.is_(mi2)) + # shouldn't change + self.assertTrue(mi2.is_(mi)) + mi4 = mi3.view() + mi4.set_levels([[1 for _ in range(10)], lrange(10)], inplace=True) + self.assertFalse(mi4.is_(mi3)) + mi5 = mi.view() + mi5.set_levels(mi5.levels, inplace=True) + self.assertFalse(mi5.is_(mi)) + def test_union(self): piece1 = self.index[:5][::-1] piece2 = self.index[3:] diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 8646d261306ca..b9e8684dfa856 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -8,7 +8,7 @@ from pandas.core.common import (isnull, _NS_DTYPE, _INT64_DTYPE, is_list_like,_values_from_object, _maybe_box) -from pandas.core.index import Index, Int64Index +from pandas.core.index import Index, Int64Index, _Identity import pandas.compat as compat from pandas.compat import u from pandas.tseries.frequencies import ( @@ -1029,6 +1029,7 @@ def __array_finalize__(self, obj): self.offset = getattr(obj, 'offset', None) self.tz = getattr(obj, 'tz', None) self.name = getattr(obj, 'name', None) + self._reset_identity() def intersection(self, other): """ @@ -1446,7 +1447,7 @@ def equals(self, other): """ Determines if two Index objects contain the same elements. """ - if self is other: + if self.is_(other): return True if (not hasattr(other, 'inferred_type') or diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 45894eb419489..afa267ed5b4e4 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -812,7 +812,7 @@ def equals(self, other): """ Determines if two Index objects contain the same elements. """ - if self is other: + if self.is_(other): return True return np.array_equal(self.asi8, other.asi8) @@ -1076,6 +1076,7 @@ def __array_finalize__(self, obj): self.freq = getattr(obj, 'freq', None) self.name = getattr(obj, 'name', None) + self._reset_identity() def __repr__(self): output = com.pprint_thing(self.__class__) + '\n' diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index b95ea2cacda55..96e96607ad9de 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -1054,9 +1054,6 @@ def test_conv_secondly(self): class TestPeriodIndex(TestCase): - def __init__(self, *args, **kwds): - TestCase.__init__(self, *args, **kwds) - def setUp(self): pass @@ -1168,6 +1165,25 @@ def test_constructor_datetime64arr(self): self.assertRaises(ValueError, PeriodIndex, vals, freq='D') + def test_is_(self): + create_index = lambda: PeriodIndex(freq='A', start='1/1/2001', + end='12/1/2009') + index = create_index() + self.assertTrue(index.is_(index)) + self.assertFalse(index.is_(create_index())) + self.assertTrue(index.is_(index.view())) + self.assertTrue(index.is_(index.view().view().view().view().view())) + self.assertTrue(index.view().is_(index)) + ind2 = index.view() + index.name = "Apple" + self.assertTrue(ind2.is_(index)) + self.assertFalse(index.is_(index[:])) + self.assertFalse(index.is_(index.asfreq('M'))) + self.assertFalse(index.is_(index.asfreq('A'))) + self.assertFalse(index.is_(index - 2)) + self.assertFalse(index.is_(index - 0)) + + def test_comp_period(self): idx = period_range('2007-01', periods=20, freq='M') diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index a6a9bc0211bd9..2b2513b37bab6 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -274,6 +274,12 @@ def assert_range_equal(left, right): class TestTimeSeries(unittest.TestCase): _multiprocess_can_split_ = True + def test_is_(self): + dti = DatetimeIndex(start='1/1/2005', end='12/1/2005', freq='M') + self.assertTrue(dti.is_(dti)) + self.assertTrue(dti.is_(dti.view())) + self.assertFalse(dti.is_(dti.copy())) + def test_dti_slicing(self): dti = DatetimeIndex(start='1/1/2005', end='12/1/2005', freq='M') dti2 = dti[[1, 3, 5]] @@ -655,7 +661,7 @@ def test_index_astype_datetime64(self): idx = Index([datetime(2012, 1, 1)], dtype=object) if np.__version__ >= '1.7': - raise nose.SkipTest + raise nose.SkipTest("Test requires numpy < 1.7") casted = idx.astype(np.dtype('M8[D]')) expected = DatetimeIndex(idx.values)
Closes #4859. Adds a method that can quickly tell whether something is a view or not (in the way that actually matters). Also changes checks in `equals()` to use `is_` rather than `is`.
https://api.github.com/repos/pandas-dev/pandas/pulls/4909
2013-09-21T00:23:39Z
2013-09-22T22:17:09Z
2013-09-22T22:17:09Z
2014-06-13T23:57:44Z
Fixing issue #4902.
diff --git a/.gitignore b/.gitignore index 3da165e07c77c..df7002a79d974 100644 --- a/.gitignore +++ b/.gitignore @@ -38,3 +38,6 @@ pandas/io/*.json .vagrant *.whl **/wheelhouse/* + +.project +.pydevproject diff --git a/doc/source/release.rst b/doc/source/release.rst index ffb792ca98da5..1e1b665a478a1 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -433,6 +433,7 @@ Bug Fixes - Fix an issue in TextFileReader w/ Python engine (i.e. PythonParser) with thousands != "," (:issue:`4596`) - Bug in getitem with a duplicate index when using where (:issue:`4879`) + - Fixed ``_ensure_numeric`` does not check for complex numbers (:issue:`4902`) pandas 0.12.0 diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 3a185ca83604d..247f429d4b331 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -546,12 +546,14 @@ def _ensure_numeric(x): if isinstance(x, np.ndarray): if x.dtype == np.object_: x = x.astype(np.float64) - elif not (com.is_float(x) or com.is_integer(x)): + elif not (com.is_float(x) or com.is_integer(x) or com.is_complex(x)): try: x = float(x) except Exception: - raise TypeError('Could not convert %s to numeric' % str(x)) - + try: + x = complex(x) + except Exception: + raise TypeError('Could not convert %s to numeric' % str(x)) return x # NA-friendly array comparisons diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 8c5764a3f59a6..740e3d0821cd7 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -14,6 +14,7 @@ import pandas.core.common as com import pandas.util.testing as tm import pandas.core.config as cf +from pandas.core import nanops _multiprocess_can_split_ = True @@ -311,6 +312,54 @@ def test_ensure_int32(): assert(result.dtype == np.int32) +class TestEnsureNumeric(unittest.TestCase): + def test_numeric_values(self): + # Test integer + self.assertEqual(nanops._ensure_numeric(1), 1, 'Failed for int') + # Test float + self.assertEqual(nanops._ensure_numeric(1.1), 1.1, 'Failed for float') + # Test complex + self.assertEqual(nanops._ensure_numeric(1 + 2j), 1 + 2j, + 'Failed for complex') + + def test_ndarray(self): + # Test numeric ndarray + values = np.array([1, 2, 3]) + self.assertTrue(np.allclose(nanops._ensure_numeric(values), values), + 'Failed for numeric ndarray') + + # Test object ndarray + o_values = values.astype(object) + self.assertTrue(np.allclose(nanops._ensure_numeric(o_values), values), + 'Failed for object ndarray') + + # Test convertible string ndarray + s_values = np.array(['1', '2', '3'], dtype=object) + self.assertTrue(np.allclose(nanops._ensure_numeric(s_values), values), + 'Failed for convertible string ndarray') + + # Test non-convertible string ndarray + s_values = np.array(['foo', 'bar', 'baz'], dtype=object) + self.assertRaises(ValueError, + lambda: nanops._ensure_numeric(s_values)) + + def test_convertable_values(self): + self.assertTrue(np.allclose(nanops._ensure_numeric('1'), 1.0), + 'Failed for convertible integer string') + self.assertTrue(np.allclose(nanops._ensure_numeric('1.1'), 1.1), + 'Failed for convertible float string') + self.assertTrue(np.allclose(nanops._ensure_numeric('1+1j'), 1 + 1j), + 'Failed for convertible complex string') + + def test_non_convertable_values(self): + self.assertRaises(TypeError, + lambda: nanops._ensure_numeric('foo')) + self.assertRaises(TypeError, + lambda: nanops._ensure_numeric({})) + self.assertRaises(TypeError, + lambda: nanops._ensure_numeric([])) + + def test_ensure_platform_int(): # verify that when we create certain types of indices
closes #4902, Added a check for complex numbers.
https://api.github.com/repos/pandas-dev/pandas/pulls/4904
2013-09-20T18:58:30Z
2013-09-21T12:03:26Z
2013-09-21T12:03:26Z
2014-08-15T20:01:48Z
CI: allow print_versions to work without pandas install
diff --git a/ci/print_versions.py b/ci/print_versions.py index f2549fe73be24..16418de3c73be 100755 --- a/ci/print_versions.py +++ b/ci/print_versions.py @@ -1,5 +1,22 @@ #!/usr/bin/env python -from pandas.util.print_versions import show_versions -show_versions() +try: + from pandas.util.print_versions import show_versions +except Exception as e: + + print("Failed to import pandas: %s" % e) + + def show_versions(): + import subprocess + 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]) + + +if __name__ == '__main__': + show_versions() diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index b7b4a936a1e90..f67cbb4b80f56 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -1,20 +1,29 @@ import os import sys + def show_versions(): print("\nINSTALLED VERSIONS") print("------------------") print("Python: %d.%d.%d.%s.%s" % sys.version_info[:]) + try: - (sysname, nodename, release, version, machine) = os.uname() - print("OS: %s %s %s %s" % (sysname, release, version,machine)) + sysname, nodename, release, version, machine = os.uname() + print("OS: %s %s %s %s" % (sysname, release, version, machine)) print("byteorder: %s" % sys.byteorder) - print("LC_ALL: %s" % os.environ.get('LC_ALL',"None")) - print("LANG: %s" % os.environ.get('LANG',"None")) + print("LC_ALL: %s" % os.environ.get('LC_ALL', "None")) + print("LANG: %s" % os.environ.get('LANG', "None")) except: pass print("") + + try: + import pandas + print("pandas: %s" % pandas.__version__) + except: + print("pandas: Not installed") + try: import Cython print("Cython: %s" % Cython.__version__) @@ -129,7 +138,7 @@ def show_versions(): except: print("html5lib: Not installed") - print("\n") + if __name__ == "__main__": show_versions()
https://api.github.com/repos/pandas-dev/pandas/pulls/4901
2013-09-20T18:10:43Z
2013-09-20T22:20:28Z
2013-09-20T22:20:28Z
2014-07-16T08:29:21Z
BUG/TST: Fix failing FRED comparison tests and remove skips.
diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index 4b2d99dfd176f..091e149ebb1c0 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -369,7 +369,6 @@ def test_fred(self): FRED. """ - raise nose.SkipTest('Skip as this is unstable #4427 ') start = datetime(2010, 1, 1) end = datetime(2013, 1, 27) @@ -381,19 +380,17 @@ def test_fred(self): @network def test_fred_nan(self): - raise nose.SkipTest("Unstable test case - needs to be fixed.") start = datetime(2010, 1, 1) end = datetime(2013, 1, 27) df = web.DataReader("DFII5", "fred", start, end) - assert pd.isnull(df.ix['2010-01-01']) + assert pd.isnull(df.ix['2010-01-01'][0]) @network def test_fred_parts(self): - raise nose.SkipTest("Unstable test case - needs to be fixed.") start = datetime(2010, 1, 1) end = datetime(2013, 1, 27) df = web.get_data_fred("CPIAUCSL", start, end) - self.assertEqual(df.ix['2010-05-01'], 217.23) + self.assertEqual(df.ix['2010-05-01'][0], 217.23) t = df.CPIAUCSL.values assert np.issubdtype(t.dtype, np.floating)
Replacing my mess of a PR in #4890. Fixes #4827 Assertions were being made about arrays (of one item) equaling a scaler.
https://api.github.com/repos/pandas-dev/pandas/pulls/4891
2013-09-20T04:28:00Z
2013-09-20T08:01:33Z
2013-09-20T08:01:33Z
2017-04-05T02:05:40Z
CLN: move README.rst to markdown
diff --git a/README.md b/README.md new file mode 100644 index 0000000000000..e7a139ac26f0c --- /dev/null +++ b/README.md @@ -0,0 +1,213 @@ +# pandas: powerful Python data analysis toolkit + +![Travis-CI Build Status](https://travis-ci.org/pydata/pandas.png) + +## What is it +**pandas** is a Python package providing fast, flexible, and expressive data +structures designed to make working with "relational" or "labeled" data both +easy and intuitive. It aims to be the fundamental high-level building block for +doing practical, **real world** data analysis in Python. Additionally, it has +the broader goal of becoming **the most powerful and flexible open source data +analysis / manipulation tool available in any language**. It is already well on +its way toward this goal. + +## Main Features +Here are just a few of the things that pandas does well: + + - Easy handling of [**missing data**][missing-data] (represented as + `NaN`) in floating point as well as non-floating point data + - Size mutability: columns can be [**inserted and + deleted**][insertion-deletion] from DataFrame and higher dimensional + objects + - Automatic and explicit [**data alignment**][alignment]: objects can + be explicitly aligned to a set of labels, or the user can simply + ignore the labels and let `Series`, `DataFrame`, etc. automatically + align the data for you in computations + - Powerful, flexible [**group by**][groupby] functionality to perform + split-apply-combine operations on data sets, for both aggregating + and transforming data + - Make it [**easy to convert**][conversion] ragged, + differently-indexed data in other Python and NumPy data structures + into DataFrame objects + - Intelligent label-based [**slicing**][slicing], [**fancy + indexing**][fancy-indexing], and [**subsetting**][subsetting] of + large data sets + - Intuitive [**merging**][merging] and [**joining**][joining] data + sets + - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of + data sets + - [**Hierarchical**][mi] labeling of axes (possible to have multiple + labels per tick) + - Robust IO tools for loading data from [**flat files**][flat-files] + (CSV and delimited), [**Excel files**][excel], [**databases**][db], + and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] + - [**Time series**][timeseries]-specific functionality: date range + generation and frequency conversion, moving window statistics, + moving window linear regressions, date shifting and lagging, etc. + + + [missing-data]: http://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data + [insertion-deletion]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion + [alignment]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures + [groupby]: http://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine + [conversion]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe + [slicing]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges + [fancy-indexing]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix + [subsetting]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing + [merging]: http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging + [joining]: http://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index + [reshape]: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables + [pivot-table]: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations + [mi]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex + [flat-files]: http://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files + [excel]: http://pandas.pydata.org/pandas-docs/stable/io.html#excel-files + [db]: http://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries + [hdfstore]: http://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables + [timeseries]: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality + +## Where to get it +The source code is currently hosted on GitHub at: +http://github.com/pydata/pandas + +Binary installers for the latest released version are available at the Python +package index + + http://pypi.python.org/pypi/pandas/ + +And via `easy_install`: + +```sh +easy_install pandas +``` + +or `pip`: + +```sh +pip install pandas +``` + +## Dependencies +- [NumPy](http://www.numpy.org): 1.6.1 or higher +- [python-dateutil](http://labix.org/python-dateutil): 1.5 or higher +- [pytz](http://pytz.sourceforge.net) + - Needed for time zone support with ``pandas.date_range`` + +### Highly Recommended Dependencies +- [numexpr](http://code.google.com/p/numexpr/) + - Needed to accelerate some expression evaluation operations + - Required by PyTables +- [bottleneck](http://berkeleyanalytics.com/bottleneck) + - Needed to accelerate certain numerical operations + +### Optional dependencies +- [Cython](http://www.cython.org): Only necessary to build development version. Version 0.17.1 or higher. +- [SciPy](http://www.scipy.org): miscellaneous statistical functions +- [PyTables](http://www.pytables.org): necessary for HDF5-based storage +- [matplotlib](http://matplotlib.sourceforge.net/): for plotting +- [statsmodels](http://statsmodels.sourceforge.net/) + - Needed for parts of `pandas.stats` +- [openpyxl](http://packages.python.org/openpyxl/), [xlrd/xlwt](http://www.python-excel.org/) + - openpyxl version 1.6.1 or higher, for writing .xlsx files + - xlrd >= 0.9.0 + - Needed for Excel I/O +- [boto](https://pypi.python.org/pypi/boto): necessary for Amazon S3 access. +- One of the following combinations of libraries is needed to use the + top-level [`pandas.read_html`][read-html-docs] function: + - [BeautifulSoup4][BeautifulSoup4] and [html5lib][html5lib] (Any + recent version of [html5lib][html5lib] is okay.) + - [BeautifulSoup4][BeautifulSoup4] and [lxml][lxml] + - [BeautifulSoup4][BeautifulSoup4] and [html5lib][html5lib] and [lxml][lxml] + - Only [lxml][lxml], although see [HTML reading gotchas][html-gotchas] + for reasons as to why you should probably **not** take this approach. + +#### Notes about HTML parsing libraries +- If you install [BeautifulSoup4][BeautifulSoup4] you must install + either [lxml][lxml] or [html5lib][html5lib] or both. + `pandas.read_html` will **not** work with *only* `BeautifulSoup4` + installed. +- You are strongly encouraged to read [HTML reading + gotchas][html-gotchas]. It explains issues surrounding the + installation and usage of the above three libraries. +- You may need to install an older version of + [BeautifulSoup4][BeautifulSoup4]: + - Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and + 32-bit Ubuntu/Debian +- Additionally, if you're using [Anaconda][Anaconda] you should + definitely read [the gotchas about HTML parsing][html-gotchas] + libraries +- If you're on a system with `apt-get` you can do + + ```sh + sudo apt-get build-dep python-lxml + ``` + + to get the necessary dependencies for installation of [lxml][lxml]. + This will prevent further headaches down the line. + + [html5lib]: https://github.com/html5lib/html5lib-python "html5lib" + [BeautifulSoup4]: http://www.crummy.com/software/BeautifulSoup "BeautifulSoup4" + [lxml]: http://lxml.de + [Anaconda]: https://store.continuum.io/cshop/anaconda + [NumPy]: http://numpy.scipy.org/ + [html-gotchas]: http://pandas.pydata.org/pandas-docs/stable/gotchas.html#html-table-parsing + [read-html-docs]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.html.read_html.html#pandas.io.html.read_html + +## Installation from sources +To install pandas from source you need Cython in addition to the normal +dependencies above. Cython can be installed from pypi: + +```sh +pip install cython +``` + +In the `pandas` directory (same one where you found this file after +cloning the git repo), execute: + +```sh +python setup.py install +``` + +or for installing in [development mode](http://www.pip-installer.org/en/latest/usage.html): + +```sh +python setup.py develop +``` + +Alternatively, you can use `pip` if you want all the dependencies pulled +in automatically (the `-e` option is for installing it in [development +mode](http://www.pip-installer.org/en/latest/usage.html)): + +```sh +pip install -e . +``` + +On Windows, you will need to install MinGW and execute: + +```sh +python setup.py build --compiler=mingw32 +python setup.py install +``` + +See http://pandas.pydata.org/ for more information. + +## License +BSD + +## Documentation +The official documentation is hosted on PyData.org: http://pandas.pydata.org/ + +The Sphinx documentation should provide a good starting point for learning how +to use the library. Expect the docs to continue to expand as time goes on. + +## Background +Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and +has been under active development since then. + +## Discussion and Development +Since pandas development is related to a number of other scientific +Python projects, questions are welcome on the scipy-user mailing +list. Specialized discussions or design issues should take place on +the pystatsmodels mailing list / Google group, where +``scikits.statsmodels`` and other libraries will also be discussed: + +http://groups.google.com/group/pystatsmodels diff --git a/README.rst b/README.rst deleted file mode 100644 index da789e704ebad..0000000000000 --- a/README.rst +++ /dev/null @@ -1,199 +0,0 @@ -============================================= -pandas: powerful Python data analysis toolkit -============================================= - -.. image:: https://travis-ci.org/pydata/pandas.png - :target: https://travis-ci.org/pydata/pandas - -What is it -========== - -**pandas** is a Python package providing fast, flexible, and expressive data -structures designed to make working with "relational" or "labeled" data both -easy and intuitive. It aims to be the fundamental high-level building block for -doing practical, **real world** data analysis in Python. Additionally, it has -the broader goal of becoming **the most powerful and flexible open source data -analysis / manipulation tool available in any language**. It is already well on -its way toward this goal. - -Main Features -============= - -Here are just a few of the things that pandas does well: - - - Easy handling of **missing data** (represented as NaN) in floating point as - well as non-floating point data - - Size mutability: columns can be **inserted and deleted** from DataFrame and - higher dimensional objects - - Automatic and explicit **data alignment**: objects can be explicitly - aligned to a set of labels, or the user can simply ignore the labels and - let `Series`, `DataFrame`, etc. automatically align the data for you in - computations - - Powerful, flexible **group by** functionality to perform - split-apply-combine operations on data sets, for both aggregating and - transforming data - - Make it **easy to convert** ragged, differently-indexed data in other - Python and NumPy data structures into DataFrame objects - - Intelligent label-based **slicing**, **fancy indexing**, and **subsetting** - of large data sets - - Intuitive **merging** and **joining** data sets - - Flexible **reshaping** and pivoting of data sets - - **Hierarchical** labeling of axes (possible to have multiple labels per - tick) - - Robust IO tools for loading data from **flat files** (CSV and delimited), - Excel files, databases, and saving / loading data from the ultrafast **HDF5 - format** - - **Time series**-specific functionality: date range generation and frequency - conversion, moving window statistics, moving window linear regressions, - date shifting and lagging, etc. - -Where to get it -=============== - -The source code is currently hosted on GitHub at: http://github.com/pydata/pandas - -Binary installers for the latest released version are available at the Python -package index:: - - http://pypi.python.org/pypi/pandas/ - -And via ``easy_install`` or ``pip``:: - - easy_install pandas - pip install pandas - -Dependencies -============ - - - `NumPy <http://www.numpy.org>`__: 1.6.1 or higher - - `python-dateutil <http://labix.org/python-dateutil>`__ 1.5 or higher - - `pytz <http://pytz.sourceforge.net/>`__ - - Needed for time zone support with ``date_range`` - -Highly Recommended Dependencies -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - - `numexpr <http://code.google.com/p/numexpr/>`__ - - Needed to accelerate some expression evaluation operations - - Required by `PyTables` - - `bottleneck <http://berkeleyanalytics.com/bottleneck>`__ - - Needed to accelerate certain numerical operations - -Optional dependencies -~~~~~~~~~~~~~~~~~~~~~ - - - `Cython <http://www.cython.org>`__: Only necessary to build development version. Version 0.17.1 or higher. - - `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions - - `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage - - `matplotlib <http://matplotlib.sourceforge.net/>`__: for plotting - - `statsmodels <http://statsmodels.sourceforge.net/>`__ - - Needed for parts of :mod:`pandas.stats` - - `openpyxl <http://packages.python.org/openpyxl/>`__, `xlrd/xlwt <http://www.python-excel.org/>`__ - - openpyxl version 1.6.1 or higher, for writing .xlsx files - - xlrd >= 0.9.0 - - Needed for Excel I/O - - `boto <https://pypi.python.org/pypi/boto>`__: necessary for Amazon S3 - access. - - One of the following combinations of libraries is needed to use the - top-level :func:`~pandas.io.html.read_html` function: - - - `BeautifulSoup4`_ and `html5lib`_ (Any recent version of `html5lib`_ is - okay.) - - `BeautifulSoup4`_ and `lxml`_ - - `BeautifulSoup4`_ and `html5lib`_ and `lxml`_ - - Only `lxml`_, although see :ref:`HTML reading gotchas <html-gotchas>` - for reasons as to why you should probably **not** take this approach. - - .. warning:: - - - if you install `BeautifulSoup4`_ you must install either - `lxml`_ or `html5lib`_ or both. - :func:`~pandas.io.html.read_html` will **not** work with *only* - `BeautifulSoup4`_ installed. - - You are highly encouraged to read :ref:`HTML reading gotchas - <html-gotchas>`. It explains issues surrounding the installation and - usage of the above three libraries - - You may need to install an older version of `BeautifulSoup4`_: - - Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and - 32-bit Ubuntu/Debian - - Additionally, if you're using `Anaconda`_ you should definitely - read :ref:`the gotchas about HTML parsing libraries <html-gotchas>` - - .. note:: - - - if you're on a system with ``apt-get`` you can do - - .. code-block:: sh - - sudo apt-get build-dep python-lxml - - to get the necessary dependencies for installation of `lxml`_. This - will prevent further headaches down the line. - - -.. _html5lib: https://github.com/html5lib/html5lib-python -.. _BeautifulSoup4: http://www.crummy.com/software/BeautifulSoup -.. _lxml: http://lxml.de -.. _Anaconda: https://store.continuum.io/cshop/anaconda - - -Installation from sources -========================= - -To install pandas from source you need ``cython`` in addition to the normal dependencies above, -which can be installed from pypi:: - - pip install cython - -In the ``pandas`` directory (same one where you found this file after cloning the git repo), execute:: - - python setup.py install - -or for installing in `development mode <http://www.pip-installer.org/en/latest/usage.html>`__:: - - python setup.py develop - -Alternatively, you can use `pip` if you want all the dependencies pulled in automatically -(the optional ``-e`` option is for installing it in -`development mode <http://www.pip-installer.org/en/latest/usage.html>`__):: - - pip install -e . - -On Windows, you will need to install MinGW and execute:: - - python setup.py build --compiler=mingw32 - python setup.py install - -See http://pandas.pydata.org/ for more information. - -License -======= - -BSD - -Documentation -============= - -The official documentation is hosted on PyData.org: http://pandas.pydata.org/ - -The Sphinx documentation should provide a good starting point for learning how -to use the library. Expect the docs to continue to expand as time goes on. - -Background -========== - -Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and -has been under active development since then. - -Discussion and Development -========================== - -Since ``pandas`` development is related to a number of other scientific -Python projects, questions are welcome on the scipy-user mailing -list. Specialized discussions or design issues should take place on -the pystatsmodels mailing list / Google group, where -``scikits.statsmodels`` and other libraries will also be discussed: - -http://groups.google.com/group/pystatsmodels - - .. _NumPy: http://numpy.scipy.org/
Because GitHub's rst is ugly
https://api.github.com/repos/pandas-dev/pandas/pulls/4888
2013-09-19T19:00:25Z
2013-09-19T19:37:58Z
2013-09-19T19:37:58Z
2014-07-16T08:29:15Z
API: disable to_csv and friends on GroupBy objects
diff --git a/doc/source/release.rst b/doc/source/release.rst index 74e54526cfe9a..e49812b207921 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -243,6 +243,8 @@ API Changes - Remove deprecated ``Factor`` (:issue:`3650`) - Remove deprecated ``set_printoptions/reset_printoptions`` (:issue:``3046``) - Remove deprecated ``_verbose_info`` (:issue:`3215`) + - Begin removing methods that don't make sense on ``GroupBy`` objects + (:issue:`4887`). Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 186277777abe8..2e07662bffbfe 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -45,6 +45,16 @@ """ +_apply_whitelist = frozenset(['last', 'first', + 'mean', 'sum', 'min', 'max', + 'head', 'tail', + 'cumsum', 'cumprod', 'cummin', 'cummax', + 'resample', + 'describe', + 'rank', 'quantile', 'count', + 'fillna', 'dtype']) + + class GroupByError(Exception): pass @@ -241,13 +251,21 @@ def __getattr__(self, attr): if hasattr(self.obj, attr) and attr != '_cache': return self._make_wrapper(attr) - raise AttributeError("'%s' object has no attribute '%s'" % + raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attr)) def __getitem__(self, key): raise NotImplementedError def _make_wrapper(self, name): + if name not in _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 " + "using the 'apply' method".format(kind, name, + type(self).__name__)) + raise AttributeError(msg) + f = getattr(self.obj, name) if not isinstance(f, types.MethodType): return self.apply(lambda self: getattr(self, name)) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 02eb4015c133f..46ab0fe022e78 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -9,7 +9,8 @@ from pandas.core.index import Index, MultiIndex from pandas.core.common import rands from pandas.core.api import Categorical, DataFrame -from pandas.core.groupby import GroupByError, SpecificationError, DataError +from pandas.core.groupby import (GroupByError, SpecificationError, DataError, + _apply_whitelist) from pandas.core.series import Series from pandas.util.testing import (assert_panel_equal, assert_frame_equal, assert_series_equal, assert_almost_equal, @@ -2696,8 +2697,40 @@ def test_filter_against_workaround(self): new_way = grouped.filter(lambda x: x['ints'].mean() > N/20) assert_frame_equal(new_way.sort_index(), old_way.sort_index()) + def test_groupby_whitelist(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', 'shift', 'tshift', 'where', + 'mask', 'align', 'groupby', 'clip', 'astype', + 'at', 'combine', 'consolidate', 'convert_objects', + 'corr', 'corr_with', 'cov'] + to_methods = [method for method in dir(df) if method.startswith('to_')] + + blacklist.extend(to_methods) + + # e.g., to_csv + defined_but_not_allowed = ("(?:^Cannot.+{0!r}.+{1!r}.+try using the " + "'apply' method$)") + + # e.g., query, eval + not_defined = "(?:^{1!r} object has no attribute {0!r}$)" + fmt = defined_but_not_allowed + '|' + not_defined + for bl in blacklist: + for obj in (df, s): + gb = obj.groupby(df.letters) + msg = fmt.format(bl, type(gb).__name__) + with tm.assertRaisesRegexp(AttributeError, msg): + getattr(gb, bl) + + def assert_fp_equal(a, b): - assert((np.abs(a - b) < 1e-12).all()) + assert (np.abs(a - b) < 1e-12).all() def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
https://api.github.com/repos/pandas-dev/pandas/pulls/4887
2013-09-19T18:59:23Z
2013-09-27T17:02:55Z
2013-09-27T17:02:55Z
2014-06-19T00:53:18Z
BUG: bug in getitem with a duplicate index when using where (GH4879)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 4224880d3fde0..5a49f13cd8409 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -429,9 +429,11 @@ Bug Fixes ``ascending`` was being interpreted as ``True`` (:issue:`4839`, :issue:`4846`) - Fixed ``Panel.tshift`` not working. Added `freq` support to ``Panel.shift`` (:issue:`4853`) - - Fix an issue in TextFileReader w/ Python engine (i.e. PythonParser) + - Fix an issue in TextFileReader w/ Python engine (i.e. PythonParser) with thousands != "," (:issue:`4596`) - + - Bug in getitem with a duplicate index when using where (:issue:`4879`) + + pandas 0.12.0 ------------- diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 70fcc2c9d9c0a..cf0afe4267f69 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1840,8 +1840,12 @@ def _getitem_column(self, key): if self.columns.is_unique: return self._get_item_cache(key) - # duplicate columns - return self._constructor(self._data.get(key)) + # duplicate columns & possible reduce dimensionaility + result = self._constructor(self._data.get(key)) + if result.columns.is_unique: + result = result[key] + + return result def _getitem_slice(self, key): return self._slice(key, axis=0) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 4b9fdb0422526..585b1c817ff19 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -401,7 +401,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, if values is None: values = com._astype_nansafe(self.values, dtype, copy=True) newb = make_block( - values, self.items, self.ref_items, ndim=self.ndim, + values, self.items, self.ref_items, ndim=self.ndim, placement=self._ref_locs, fastpath=True, dtype=dtype, klass=klass) except: if raise_on_error is True: @@ -716,7 +716,7 @@ def create_block(v, m, n, item, reshape=True): if inplace: return [self] - return [make_block(new_values, self.items, self.ref_items, fastpath=True)] + return [make_block(new_values, self.items, self.ref_items, placement=self._ref_locs, fastpath=True)] def interpolate(self, method='pad', axis=0, inplace=False, limit=None, fill_value=None, coerce=False, @@ -2853,12 +2853,13 @@ def _reindex_indexer_items(self, new_items, indexer, fill_value): # TODO: less efficient than I'd like item_order = com.take_1d(self.items.values, indexer) + new_axes = [new_items] + self.axes[1:] + new_blocks = [] + is_unique = new_items.is_unique # keep track of what items aren't found anywhere + l = np.arange(len(item_order)) mask = np.zeros(len(item_order), dtype=bool) - new_axes = [new_items] + self.axes[1:] - - new_blocks = [] for blk in self.blocks: blk_indexer = blk.items.get_indexer(item_order) selector = blk_indexer != -1 @@ -2872,12 +2873,19 @@ def _reindex_indexer_items(self, new_items, indexer, fill_value): new_block_items = new_items.take(selector.nonzero()[0]) new_values = com.take_nd(blk.values, blk_indexer[selector], axis=0, allow_fill=False) - new_blocks.append(make_block(new_values, new_block_items, - new_items, fastpath=True)) + placement = l[selector] if not is_unique else None + new_blocks.append(make_block(new_values, + new_block_items, + new_items, + placement=placement, + fastpath=True)) if not mask.all(): na_items = new_items[-mask] - na_block = self._make_na_block(na_items, new_items, + placement = l[-mask] if not is_unique else None + na_block = self._make_na_block(na_items, + new_items, + placement=placement, fill_value=fill_value) new_blocks.append(na_block) new_blocks = _consolidate(new_blocks, new_items) @@ -2943,7 +2951,7 @@ def reindex_items(self, new_items, indexer=None, copy=True, fill_value=None): return self.__class__(new_blocks, new_axes) - def _make_na_block(self, items, ref_items, fill_value=None): + def _make_na_block(self, items, ref_items, placement=None, fill_value=None): # TODO: infer dtypes other than float64 from fill_value if fill_value is None: @@ -2954,8 +2962,7 @@ def _make_na_block(self, items, ref_items, fill_value=None): dtype, fill_value = com._infer_dtype_from_scalar(fill_value) block_values = np.empty(block_shape, dtype=dtype) block_values.fill(fill_value) - na_block = make_block(block_values, items, ref_items) - return na_block + return make_block(block_values, items, ref_items, placement=placement) def take(self, indexer, new_index=None, axis=1, verify=True): if axis < 1: diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index d216cebc1abf3..0bc454d6ef2bc 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -3150,6 +3150,36 @@ def check(result, expected=None): expected = DataFrame([[1],[1],[1]],columns=['bar']) check(df,expected) + def test_column_dups_indexing(self): + + def check(result, expected=None): + if expected is not None: + assert_frame_equal(result,expected) + result.dtypes + str(result) + + # boolean indexing + # GH 4879 + dups = ['A', 'A', 'C', 'D'] + df = DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'],dtype='float64') + expected = df[df.C > 6] + expected.columns = dups + df = DataFrame(np.arange(12).reshape(3,4), columns=dups,dtype='float64') + result = df[df.C > 6] + check(result,expected) + + # where + df = DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'],dtype='float64') + expected = df[df > 6] + expected.columns = dups + df = DataFrame(np.arange(12).reshape(3,4), columns=dups,dtype='float64') + result = df[df > 6] + check(result,expected) + + # boolean with the duplicate raises + df = DataFrame(np.arange(12).reshape(3,4), columns=dups,dtype='float64') + self.assertRaises(ValueError, lambda : df[df.A > 6]) + def test_insert_benchmark(self): # from the vb_suite/frame_methods/frame_insert_columns N = 10 diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 3453e69ed72b6..aad9fb2f95483 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1233,9 +1233,10 @@ def test_mi_access(self): # GH 4146, not returning a block manager when selecting a unique index # from a duplicate index - expected = DataFrame([['a',1,1]],index=['A1'],columns=['h1','h3','h5'],).T + # as of 4879, this returns a Series (which is similar to what happens with a non-unique) + expected = Series(['a',1,1],index=['h1','h3','h5']) result = df2['A']['A1'] - assert_frame_equal(result,expected) + assert_series_equal(result,expected) # selecting a non_unique from the 2nd level expected = DataFrame([['d',4,4],['e',5,5]],index=Index(['B2','B2'],name='sub'),columns=['h1','h3','h5'],).T
raising previously as this was not handled properly closes #4879 ``` In [1]: df = DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'],dtype='float64') In [2]: df Out[2]: A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 In [3]: df[df.C>6] Out[3]: A B C D 2 8 9 10 11 In [4]: df = DataFrame(np.arange(12).reshape(3,4), columns=['A', 'A', 'C', 'D'],dtype='float64') In [5]: df Out[5]: A A C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 In [6]: df[df.C>6] Out[6]: A A C D 2 8 9 10 11 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4881
2013-09-19T12:23:50Z
2013-09-19T12:50:15Z
2013-09-19T12:50:15Z
2014-06-20T21:01:35Z
CLN: clean up test plotting
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 45289dac44254..cb6ec3d648afa 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -67,17 +67,16 @@ def test_plot(self): _check_plot_works(self.series[:5].plot, kind='line') _check_plot_works(self.series[:5].plot, kind='barh') _check_plot_works(self.series[:10].plot, kind='barh') - - Series(randn(10)).plot(kind='bar', color='black') + _check_plot_works(Series(randn(10)).plot, kind='bar', color='black') # figsize and title import matplotlib.pyplot as plt plt.close('all') ax = self.series.plot(title='Test', figsize=(16, 8)) - self.assert_(ax.title.get_text() == 'Test') - self.assert_((np.round(ax.figure.get_size_inches()) - == np.array((16., 8.))).all()) + self.assertEqual(ax.title.get_text(), 'Test') + assert_array_equal(np.round(ax.figure.get_size_inches()), + np.array((16., 8.))) @slow def test_bar_colors(self): @@ -97,7 +96,7 @@ def test_bar_colors(self): for i, rect in enumerate(rects[::5]): xp = conv.to_rgba(default_colors[i % len(default_colors)]) rs = rect.get_facecolor() - self.assert_(xp == rs) + self.assertEqual(xp, rs) plt.close('all') @@ -109,7 +108,7 @@ def test_bar_colors(self): for i, rect in enumerate(rects[::5]): xp = conv.to_rgba(custom_colors[i]) rs = rect.get_facecolor() - self.assert_(xp == rs) + self.assertEqual(xp, rs) plt.close('all') @@ -124,7 +123,7 @@ def test_bar_colors(self): for i, rect in enumerate(rects[::5]): xp = rgba_colors[i] rs = rect.get_facecolor() - self.assert_(xp == rs) + self.assertEqual(xp, rs) plt.close('all') @@ -137,7 +136,7 @@ def test_bar_colors(self): for i, rect in enumerate(rects[::5]): xp = rgba_colors[i] rs = rect.get_facecolor() - self.assert_(xp == rs) + self.assertEqual(xp, rs) plt.close('all') @@ -150,18 +149,18 @@ def test_bar_linewidth(self): # regular ax = df.plot(kind='bar', linewidth=2) for r in ax.patches: - self.assert_(r.get_linewidth() == 2) + self.assertEqual(r.get_linewidth(), 2) # stacked ax = df.plot(kind='bar', stacked=True, linewidth=2) for r in ax.patches: - self.assert_(r.get_linewidth() == 2) + self.assertEqual(r.get_linewidth(), 2) # subplots axes = df.plot(kind='bar', linewidth=2, subplots=True) for ax in axes: for r in ax.patches: - self.assert_(r.get_linewidth() == 2) + self.assertEqual(r.get_linewidth(), 2) @slow def test_bar_log(self): @@ -177,7 +176,7 @@ def test_rotation(self): df = DataFrame(randn(5, 5)) ax = df.plot(rot=30) for l in ax.get_xticklabels(): - self.assert_(l.get_rotation() == 30) + self.assertEqual(l.get_rotation(), 30) def test_irregular_datetime(self): rng = date_range('1/1/2000', '3/1/2000') @@ -186,7 +185,7 @@ def test_irregular_datetime(self): ax = ser.plot() xp = datetime(1999, 1, 1).toordinal() ax.set_xlim('1/1/1999', '1/1/2001') - self.assert_(xp == ax.get_xlim()[0]) + self.assertEqual(xp, ax.get_xlim()[0]) @slow def test_hist(self): @@ -205,8 +204,9 @@ def test_hist(self): fig, (ax1, ax2) = plt.subplots(1, 2) _check_plot_works(self.ts.hist, figure=fig, ax=ax1) _check_plot_works(self.ts.hist, figure=fig, ax=ax2) - self.assertRaises(ValueError, self.ts.hist, by=self.ts.index, - figure=fig) + + with tm.assertRaises(ValueError): + self.ts.hist(by=self.ts.index, figure=fig) @slow def test_hist_layout(self): @@ -216,8 +216,11 @@ def test_hist_layout(self): size=n)], 'height': random.normal(66, 4, size=n), 'weight': random.normal(161, 32, size=n)}) - self.assertRaises(ValueError, df.height.hist, layout=(1, 1)) - self.assertRaises(ValueError, df.height.hist, layout=[1, 1]) + with tm.assertRaises(ValueError): + df.height.hist(layout=(1, 1)) + + with tm.assertRaises(ValueError): + df.height.hist(layout=[1, 1]) @slow def test_hist_layout_with_by(self): @@ -231,10 +234,13 @@ def test_hist_layout_with_by(self): 'category': random.randint(4, size=n)}) _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1)) plt.close('all') + _check_plot_works(df.height.hist, by=df.gender, layout=(1, 2)) plt.close('all') + _check_plot_works(df.weight.hist, by=df.category, layout=(1, 4)) plt.close('all') + _check_plot_works(df.weight.hist, by=df.category, layout=(4, 1)) plt.close('all') @@ -250,19 +256,20 @@ def test_hist_no_overlap(self): fig = gcf() axes = fig.get_axes() self.assertEqual(len(axes), 2) - close('all') @slow def test_plot_fails_with_dupe_color_and_style(self): x = Series(randn(2)) - self.assertRaises(ValueError, x.plot, style='k--', color='k') + with tm.assertRaises(ValueError): + x.plot(style='k--', color='k') def test_plot_fails_when_ax_differs_from_figure(self): - from pylab import figure + from pylab import figure, close fig1 = figure() fig2 = figure() ax1 = fig1.add_subplot(111) - self.assertRaises(AssertionError, self.ts.hist, ax=ax1, figure=fig2) + with tm.assertRaises(AssertionError): + self.ts.hist(ax=ax1, figure=fig2) @slow def test_kde(self): @@ -311,7 +318,8 @@ def test_invalid_plot_data(self): kinds = 'line', 'bar', 'barh', 'kde', 'density' for kind in kinds: - self.assertRaises(TypeError, s.plot, kind=kind) + with tm.assertRaises(TypeError): + s.plot(kind=kind) @slow def test_valid_object_plot(self): @@ -326,11 +334,13 @@ def test_partially_invalid_plot_data(self): kinds = 'line', 'bar', 'barh', 'kde', 'density' for kind in kinds: - self.assertRaises(TypeError, s.plot, kind=kind) + with tm.assertRaises(TypeError): + s.plot(kind=kind) def test_invalid_kind(self): s = Series([1, 2]) - self.assertRaises(ValueError, s.plot, kind='aasdf') + with tm.assertRaises(ValueError): + s.plot(kind='aasdf') @slow def test_dup_datetime_index_plot(self): @@ -342,7 +352,6 @@ def test_dup_datetime_index_plot(self): _check_plot_works(s.plot) - class TestDataFramePlots(unittest.TestCase): @classmethod @@ -406,23 +415,21 @@ def test_plot(self): def test_nonnumeric_exclude(self): import matplotlib.pyplot as plt - plt.close('all') - df = DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]}) ax = df.plot() - self.assert_(len(ax.get_lines()) == 1) # B was plotted + self.assertEqual(len(ax.get_lines()), 1) # B was plotted @slow - def test_label(self): - import matplotlib.pyplot as plt - plt.close('all') + def test_implicit_label(self): df = DataFrame(randn(10, 3), columns=['a', 'b', 'c']) ax = df.plot(x='a', y='b') - self.assert_(ax.xaxis.get_label().get_text() == 'a') + self.assertEqual(ax.xaxis.get_label().get_text(), 'a') - plt.close('all') + @slow + def test_explicit_label(self): + df = DataFrame(randn(10, 3), columns=['a', 'b', 'c']) ax = df.plot(x='a', y='b', label='LABEL') - self.assert_(ax.xaxis.get_label().get_text() == 'LABEL') + self.assertEqual(ax.xaxis.get_label().get_text(), 'LABEL') @slow def test_plot_xy(self): @@ -449,9 +456,9 @@ def test_plot_xy(self): plt.close('all') ax = df.plot(x=1, y=2, title='Test', figsize=(16, 8)) - self.assert_(ax.title.get_text() == 'Test') - self.assert_((np.round(ax.figure.get_size_inches()) - == np.array((16., 8.))).all()) + self.assertEqual(ax.title.get_text(), 'Test') + assert_array_equal(np.round(ax.figure.get_size_inches()), + np.array((16., 8.))) # columns.inferred_type == 'mixed' # TODO add MultiIndex test @@ -541,19 +548,25 @@ def test_subplots(self): @slow def test_plot_bar(self): + from matplotlib.pylab import close df = DataFrame(randn(6, 4), index=list(string.ascii_letters[:6]), columns=['one', 'two', 'three', 'four']) _check_plot_works(df.plot, kind='bar') + close('all') _check_plot_works(df.plot, kind='bar', legend=False) + close('all') _check_plot_works(df.plot, kind='bar', subplots=True) + close('all') _check_plot_works(df.plot, kind='bar', stacked=True) + close('all') df = DataFrame(randn(10, 15), index=list(string.ascii_letters[:10]), columns=lrange(15)) _check_plot_works(df.plot, kind='bar') + close('all') df = DataFrame({'a': [0, 1], 'b': [1, 0]}) _check_plot_works(df.plot, kind='bar') @@ -608,14 +621,11 @@ def test_boxplot(self): _check_plot_works(df.boxplot) _check_plot_works(df.boxplot, column=['one', 'two']) - _check_plot_works(df.boxplot, column=['one', 'two'], - by='indic') + _check_plot_works(df.boxplot, column=['one', 'two'], by='indic') _check_plot_works(df.boxplot, column='one', by=['indic', 'indic2']) _check_plot_works(df.boxplot, by='indic') _check_plot_works(df.boxplot, by=['indic', 'indic2']) - - _check_plot_works(lambda x: plotting.boxplot(x), df['one']) - + _check_plot_works(plotting.boxplot, df['one']) _check_plot_works(df.boxplot, notch=1) _check_plot_works(df.boxplot, by='indic', notch=1) @@ -633,7 +643,7 @@ def test_kde(self): self.assert_(ax.get_legend() is not None) axes = df.plot(kind='kde', logy=True, subplots=True) for ax in axes: - self.assert_(ax.get_yscale() == 'log') + self.assertEqual(ax.get_yscale(), 'log') @slow def test_hist(self): @@ -694,11 +704,13 @@ def test_hist(self): plt.close('all') ax = ser.hist(log=True) # scale of y must be 'log' - self.assert_(ax.get_yscale() == 'log') + self.assertEqual(ax.get_yscale(), 'log') plt.close('all') + # propagate attr exception from matplotlib.Axes.hist - self.assertRaises(AttributeError, ser.hist, foo='bar') + with tm.assertRaises(AttributeError): + ser.hist(foo='bar') @slow def test_hist_layout(self): @@ -716,14 +728,16 @@ def test_hist_layout(self): for layout_test in layout_to_expected_size: ax = df.hist(layout=layout_test['layout']) - self.assert_(len(ax) == layout_test['expected_size'][0]) - self.assert_(len(ax[0]) == layout_test['expected_size'][1]) + self.assertEqual(len(ax), layout_test['expected_size'][0]) + self.assertEqual(len(ax[0]), layout_test['expected_size'][1]) # layout too small for all 4 plots - self.assertRaises(ValueError, df.hist, layout=(1, 1)) + with tm.assertRaises(ValueError): + df.hist(layout=(1, 1)) # invalid format for layout - self.assertRaises(ValueError, df.hist, layout=(1,)) + with tm.assertRaises(ValueError): + df.hist(layout=(1,)) @slow def test_scatter(self): @@ -734,6 +748,7 @@ def test_scatter(self): def scat(**kwds): return plt.scatter_matrix(df, **kwds) + _check_plot_works(scat) _check_plot_works(scat, marker='+') _check_plot_works(scat, vmin=0) @@ -752,8 +767,10 @@ def scat2(x, y, by=None, ax=None, figsize=None): def test_andrews_curves(self): from pandas import read_csv from pandas.tools.plotting import andrews_curves - path = os.path.join(curpath(), 'data/iris.csv') + + path = os.path.join(curpath(), 'data', 'iris.csv') df = read_csv(path) + _check_plot_works(andrews_curves, df, 'Name') @slow @@ -761,7 +778,7 @@ def test_parallel_coordinates(self): from pandas import read_csv from pandas.tools.plotting import parallel_coordinates from matplotlib import cm - path = os.path.join(curpath(), 'data/iris.csv') + path = os.path.join(curpath(), 'data', 'iris.csv') df = read_csv(path) _check_plot_works(parallel_coordinates, df, 'Name') _check_plot_works(parallel_coordinates, df, 'Name', @@ -774,8 +791,8 @@ def test_parallel_coordinates(self): colors=['dodgerblue', 'aquamarine', 'seagreen']) _check_plot_works(parallel_coordinates, df, 'Name', colormap=cm.jet) - df = read_csv( - path, header=None, skiprows=1, names=[1, 2, 4, 8, 'Name']) + df = read_csv(path, header=None, skiprows=1, names=[1, 2, 4, 8, + 'Name']) _check_plot_works(parallel_coordinates, df, 'Name', use_columns=True) _check_plot_works(parallel_coordinates, df, 'Name', xticks=[1, 5, 25, 125]) @@ -785,7 +802,8 @@ def test_radviz(self): from pandas import read_csv from pandas.tools.plotting import radviz from matplotlib import cm - path = os.path.join(curpath(), 'data/iris.csv') + + path = os.path.join(curpath(), 'data', 'iris.csv') df = read_csv(path) _check_plot_works(radviz, df, 'Name') _check_plot_works(radviz, df, 'Name', colormap=cm.jet) @@ -803,10 +821,11 @@ def test_legend_name(self): ax = multi.plot() leg_title = ax.legend_.get_title() - self.assert_(leg_title.get_text(), 'group,individual') + self.assertEqual(leg_title.get_text(), 'group,individual') def _check_plot_fails(self, f, *args, **kwargs): - self.assertRaises(Exception, f, *args, **kwargs) + with tm.assertRaises(Exception): + f(*args, **kwargs) @slow def test_style_by_column(self): @@ -832,7 +851,6 @@ def test_line_colors(self): custom_colors = 'rgcby' - plt.close('all') df = DataFrame(randn(5, 5)) ax = df.plot(color=custom_colors) @@ -841,7 +859,7 @@ def test_line_colors(self): for i, l in enumerate(lines): xp = custom_colors[i] rs = l.get_color() - self.assert_(xp == rs) + self.assertEqual(xp, rs) tmp = sys.stderr sys.stderr = StringIO() @@ -850,7 +868,7 @@ def test_line_colors(self): ax2 = df.plot(colors=custom_colors) lines2 = ax2.get_lines() for l1, l2 in zip(lines, lines2): - self.assert_(l1.get_color(), l2.get_color()) + self.assertEqual(l1.get_color(), l2.get_color()) finally: sys.stderr = tmp @@ -864,7 +882,7 @@ def test_line_colors(self): for i, l in enumerate(lines): xp = rgba_colors[i] rs = l.get_color() - self.assert_(xp == rs) + self.assertEqual(xp, rs) plt.close('all') @@ -876,7 +894,7 @@ def test_line_colors(self): for i, l in enumerate(lines): xp = rgba_colors[i] rs = l.get_color() - self.assert_(xp == rs) + self.assertEqual(xp, rs) # make color a list if plotting one column frame # handles cases like df.plot(color='DodgerBlue') @@ -895,7 +913,7 @@ def test_default_color_cycle(self): for i, l in enumerate(lines): xp = plt.rcParams['axes.color_cycle'][i] rs = l.get_color() - self.assert_(xp == rs) + self.assertEqual(xp, rs) def test_unordered_ts(self): df = DataFrame(np.array([3.0, 2.0, 1.0]), @@ -907,13 +925,14 @@ def test_unordered_ts(self): xticks = ax.lines[0].get_xdata() self.assert_(xticks[0] < xticks[1]) ydata = ax.lines[0].get_ydata() - self.assert_(np.all(ydata == np.array([1.0, 2.0, 3.0]))) + assert_array_equal(ydata, np.array([1.0, 2.0, 3.0])) def test_all_invalid_plot_data(self): kinds = 'line', 'bar', 'barh', 'kde', 'density' df = DataFrame(list('abcd')) for kind in kinds: - self.assertRaises(TypeError, df.plot, kind=kind) + with tm.assertRaises(TypeError): + df.plot(kind=kind) @slow def test_partially_invalid_plot_data(self): @@ -921,11 +940,13 @@ def test_partially_invalid_plot_data(self): df = DataFrame(randn(10, 2), dtype=object) df[np.random.rand(df.shape[0]) > 0.5] = 'a' for kind in kinds: - self.assertRaises(TypeError, df.plot, kind=kind) + with tm.assertRaises(TypeError): + df.plot(kind=kind) def test_invalid_kind(self): df = DataFrame(randn(10, 2)) - self.assertRaises(ValueError, df.plot, kind='aasdf') + with tm.assertRaises(ValueError): + df.plot(kind='aasdf') class TestDataFrameGroupByPlots(unittest.TestCase): @@ -939,7 +960,8 @@ def setUpClass(cls): def tearDown(self): import matplotlib.pyplot as plt - plt.close('all') + for fignum in plt.get_fignums(): + plt.close(fignum) @slow def test_boxplot(self): @@ -955,36 +977,29 @@ def test_boxplot(self): grouped = df.groupby(level=1) _check_plot_works(grouped.boxplot) _check_plot_works(grouped.boxplot, subplots=False) + grouped = df.unstack(level=1).groupby(level=0, axis=1) _check_plot_works(grouped.boxplot) _check_plot_works(grouped.boxplot, subplots=False) def test_series_plot_color_kwargs(self): - # #1890 - import matplotlib.pyplot as plt - - plt.close('all') + # GH1890 ax = Series(np.arange(12) + 1).plot(color='green') line = ax.get_lines()[0] - self.assert_(line.get_color() == 'green') + self.assertEqual(line.get_color(), 'green') def test_time_series_plot_color_kwargs(self): # #1890 - import matplotlib.pyplot as plt - - plt.close('all') ax = Series(np.arange(12) + 1, index=date_range( '1/1/2000', periods=12)).plot(color='green') line = ax.get_lines()[0] - self.assert_(line.get_color() == 'green') + self.assertEqual(line.get_color(), 'green') def test_time_series_plot_color_with_empty_kwargs(self): import matplotlib as mpl - import matplotlib.pyplot as plt def_colors = mpl.rcParams['axes.color_cycle'] - plt.close('all') for i in range(3): ax = Series(np.arange(12) + 1, index=date_range('1/1/2000', periods=12)).plot() @@ -998,12 +1013,12 @@ def test_grouped_hist(self): df = DataFrame(randn(500, 2), columns=['A', 'B']) df['C'] = np.random.randint(0, 4, 500) axes = plotting.grouped_hist(df.A, by=df.C) - self.assert_(len(axes.ravel()) == 4) + self.assertEqual(len(axes.ravel()), 4) plt.close('all') axes = df.hist(by=df.C) - self.assert_(axes.ndim == 2) - self.assert_(len(axes.ravel()) == 4) + self.assertEqual(axes.ndim, 2) + self.assertEqual(len(axes.ravel()), 4) for ax in axes.ravel(): self.assert_(len(ax.patches) > 0) @@ -1022,12 +1037,13 @@ def test_grouped_hist(self): axes = plotting.grouped_hist(df.A, by=df.C, log=True) # scale of y must be 'log' for ax in axes.ravel(): - self.assert_(ax.get_yscale() == 'log') + self.assertEqual(ax.get_yscale(), 'log') plt.close('all') + # propagate attr exception from matplotlib.Axes.hist - self.assertRaises(AttributeError, plotting.grouped_hist, df.A, - by=df.C, foo='bar') + with tm.assertRaises(AttributeError): + plotting.grouped_hist(df.A, by=df.C, foo='bar') @slow def test_grouped_hist_layout(self): @@ -1057,49 +1073,67 @@ def test_grouped_hist_layout(self): layout=(4, 2)).shape, (4, 2)) @slow - def test_axis_shared(self): + def test_axis_share_x(self): # GH4089 - import matplotlib.pyplot as plt - def tick_text(tl): - return [x.get_text() for x in tl] - n = 100 - df = DataFrame({'gender': np.array(['Male', 'Female'])[random.randint(2, size=n)], + df = DataFrame({'gender': tm.choice(['Male', 'Female'], size=n), 'height': random.normal(66, 4, size=n), 'weight': random.normal(161, 32, size=n)}) ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True) - self.assert_(ax1._shared_x_axes.joined(ax1, ax2)) + + # share x + self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) + + # don't share y self.assertFalse(ax1._shared_y_axes.joined(ax1, ax2)) - self.assert_(ax2._shared_x_axes.joined(ax1, ax2)) self.assertFalse(ax2._shared_y_axes.joined(ax1, ax2)) - plt.close('all') + @slow + def test_axis_share_y(self): + n = 100 + df = DataFrame({'gender': tm.choice(['Male', 'Female'], size=n), + 'height': random.normal(66, 4, size=n), + 'weight': random.normal(161, 32, size=n)}) ax1, ax2 = df.hist(column='height', by=df.gender, sharey=True) + + # share y + self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) + + # don't share x self.assertFalse(ax1._shared_x_axes.joined(ax1, ax2)) - self.assert_(ax1._shared_y_axes.joined(ax1, ax2)) self.assertFalse(ax2._shared_x_axes.joined(ax1, ax2)) - self.assert_(ax2._shared_y_axes.joined(ax1, ax2)) - plt.close('all') + @slow + def test_axis_share_xy(self): + n = 100 + df = DataFrame({'gender': tm.choice(['Male', 'Female'], size=n), + 'height': random.normal(66, 4, size=n), + 'weight': random.normal(161, 32, size=n)}) ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True, sharey=True) - self.assert_(ax1._shared_x_axes.joined(ax1, ax2)) - self.assert_(ax1._shared_y_axes.joined(ax1, ax2)) - self.assert_(ax2._shared_x_axes.joined(ax1, ax2)) - self.assert_(ax2._shared_y_axes.joined(ax1, ax2)) + + # share both x and y + self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) + + self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) def test_option_mpl_style(self): set_option('display.mpl_style', 'default') set_option('display.mpl_style', None) set_option('display.mpl_style', False) - try: + + with tm.assertRaises(ValueError): set_option('display.mpl_style', 'default2') - except ValueError: - pass def test_invalid_colormap(self): df = DataFrame(randn(3, 2), columns=['A', 'B']) - self.assertRaises(ValueError, df.plot, colormap='invalid_colormap') + + with tm.assertRaises(ValueError): + df.plot(colormap='invalid_colormap') def assert_is_valid_plot_return_object(objs): @@ -1141,6 +1175,7 @@ def _check_plot_works(f, *args, **kwargs): with ensure_clean() as path: plt.savefig(path) + plt.close(fig) def curpath(): diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index 87cb65601bdd9..a22d2a65248a9 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -1,9 +1,8 @@ -import os from datetime import datetime, timedelta, date, time import unittest import nose -from pandas.compat import range, lrange, zip +from pandas.compat import lrange, zip import numpy as np from numpy.testing.decorators import slow @@ -52,48 +51,49 @@ def setUp(self): columns=['A', 'B', 'C']) for x in idx] + def tearDown(self): + import matplotlib.pyplot as plt + for fignum in plt.get_fignums(): + plt.close(fignum) + @slow def test_ts_plot_with_tz(self): # GH2877 - index = date_range('1/1/2011', periods=2, freq='H', tz='Europe/Brussels') + index = date_range('1/1/2011', periods=2, freq='H', + tz='Europe/Brussels') ts = Series([188.5, 328.25], index=index) - ts.plot() + _check_plot_works(ts.plot) @slow def test_frame_inferred(self): # inferred freq import matplotlib.pyplot as plt - plt.close('all') idx = date_range('1/1/1987', freq='MS', periods=100) idx = DatetimeIndex(idx.values, freq=None) + df = DataFrame(np.random.randn(len(idx), 3), index=idx) - df.plot() + _check_plot_works(df.plot) # axes freq idx = idx[0:40] + idx[45:99] df2 = DataFrame(np.random.randn(len(idx), 3), index=idx) - df2.plot() - plt.close('all') + _check_plot_works(df2.plot) # N > 1 idx = date_range('2008-1-1 00:15:00', freq='15T', periods=10) idx = DatetimeIndex(idx.values, freq=None) df = DataFrame(np.random.randn(len(idx), 3), index=idx) - df.plot() + _check_plot_works(df.plot) - @slow def test_nonnumeric_exclude(self): import matplotlib.pyplot as plt - plt.close('all') idx = date_range('1/1/1987', freq='A', periods=3) df = DataFrame({'A': ["x", "y", "z"], 'B': [1,2,3]}, idx) - plt.close('all') ax = df.plot() # it works self.assert_(len(ax.get_lines()) == 1) #B was plotted - - plt.close('all') + plt.close(plt.gcf()) self.assertRaises(TypeError, df['A'].plot) @@ -101,30 +101,23 @@ def test_nonnumeric_exclude(self): def test_tsplot(self): from pandas.tseries.plotting import tsplot import matplotlib.pyplot as plt - plt.close('all') ax = plt.gca() ts = tm.makeTimeSeries() - tsplot(ts, plt.Axes.plot) f = lambda *args, **kwds: tsplot(s, plt.Axes.plot, *args, **kwds) - plt.close('all') for s in self.period_ser: _check_plot_works(f, s.index.freq, ax=ax, series=s) - plt.close('all') + for s in self.datetime_ser: _check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s) - plt.close('all') - plt.close('all') ax = ts.plot(style='k') - self.assert_((0., 0., 0.) == ax.get_lines()[0].get_color()) + self.assertEqual((0., 0., 0.), ax.get_lines()[0].get_color()) - @slow def test_both_style_and_color(self): import matplotlib.pyplot as plt - plt.close('all') ts = tm.makeTimeSeries() self.assertRaises(ValueError, ts.plot, style='b-', color='#000099') @@ -143,11 +136,11 @@ def test_high_freq(self): def test_get_datevalue(self): from pandas.tseries.converter import get_datevalue self.assert_(get_datevalue(None, 'D') is None) - self.assert_(get_datevalue(1987, 'A') == 1987) - self.assert_(get_datevalue(Period(1987, 'A'), 'M') == - Period('1987-12', 'M').ordinal) - self.assert_(get_datevalue('1/1/1987', 'D') == - Period('1987-1-1', 'D').ordinal) + self.assertEqual(get_datevalue(1987, 'A'), 1987) + self.assertEqual(get_datevalue(Period(1987, 'A'), 'M'), + Period('1987-12', 'M').ordinal) + self.assertEqual(get_datevalue('1/1/1987', 'D'), + Period('1987-1-1', 'D').ordinal) @slow def test_line_plot_period_series(self): @@ -179,7 +172,6 @@ def test_line_plot_inferred_freq(self): ser = ser[[0, 3, 5, 6]] _check_plot_works(ser.plot) - @slow def test_fake_inferred_business(self): import matplotlib.pyplot as plt fig = plt.gcf() @@ -189,7 +181,7 @@ def test_fake_inferred_business(self): ts = Series(lrange(len(rng)), rng) ts = ts[:3].append(ts[5:]) ax = ts.plot() - self.assert_(not hasattr(ax, 'freq')) + self.assertFalse(hasattr(ax, 'freq')) @slow def test_plot_offset_freq(self): @@ -227,8 +219,8 @@ def test_uhf(self): for loc, label in zip(tlocs, tlabels): xp = conv._from_ordinal(loc).strftime('%H:%M:%S.%f') rs = str(label.get_text()) - if len(rs) != 0: - self.assert_(xp == rs) + if len(rs): + self.assertEqual(xp, rs) @slow def test_irreg_hf(self): @@ -255,7 +247,6 @@ def test_irreg_hf(self): diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff() self.assert_((np.fabs(diffs[1:] - sec) < 1e-8).all()) - @slow def test_irregular_datetime64_repr_bug(self): import matplotlib.pyplot as plt ser = tm.makeTimeSeries() @@ -265,56 +256,52 @@ def test_irregular_datetime64_repr_bug(self): plt.clf() ax = fig.add_subplot(211) ret = ser.plot() - assert(ret is not None) + self.assert_(ret is not None) for rs, xp in zip(ax.get_lines()[0].get_xdata(), ser.index): - assert(rs == xp) + self.assertEqual(rs, xp) - @slow def test_business_freq(self): import matplotlib.pyplot as plt - plt.close('all') bts = tm.makePeriodSeries() ax = bts.plot() - self.assert_(ax.get_lines()[0].get_xydata()[0, 0], - bts.index[0].ordinal) + self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0], + bts.index[0].ordinal) idx = ax.get_lines()[0].get_xdata() - self.assert_(PeriodIndex(data=idx).freqstr == 'B') + self.assertEqual(PeriodIndex(data=idx).freqstr, 'B') @slow def test_business_freq_convert(self): - import matplotlib.pyplot as plt - plt.close('all') n = tm.N tm.N = 300 bts = tm.makeTimeSeries().asfreq('BM') tm.N = n ts = bts.to_period('M') ax = bts.plot() - self.assert_(ax.get_lines()[0].get_xydata()[0, 0], ts.index[0].ordinal) + self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0], + ts.index[0].ordinal) idx = ax.get_lines()[0].get_xdata() - self.assert_(PeriodIndex(data=idx).freqstr == 'M') + self.assertEqual(PeriodIndex(data=idx).freqstr, 'M') - @slow def test_nonzero_base(self): - import matplotlib.pyplot as plt - plt.close('all') - #GH2571 + # GH2571 idx = (date_range('2012-12-20', periods=24, freq='H') + timedelta(minutes=30)) df = DataFrame(np.arange(24), index=idx) ax = df.plot() rs = ax.get_lines()[0].get_xdata() - self.assert_(not Index(rs).is_normalized) + self.assertFalse(Index(rs).is_normalized) - @slow def test_dataframe(self): bts = DataFrame({'a': tm.makeTimeSeries()}) ax = bts.plot() idx = ax.get_lines()[0].get_xdata() + assert_array_equal(bts.index.to_period(), idx) @slow def test_axis_limits(self): + import matplotlib.pyplot as plt + def _test(ax): xlim = ax.get_xlim() ax.set_xlim(xlim[0] - 5, xlim[1] + 10) @@ -340,9 +327,7 @@ def _test(ax): result = ax.get_xlim() self.assertEqual(int(result[0]), expected[0].ordinal) self.assertEqual(int(result[1]), expected[1].ordinal) - - import matplotlib.pyplot as plt - plt.close('all') + plt.close(ax.get_figure()) ser = tm.makeTimeSeries() ax = ser.plot() @@ -354,7 +339,9 @@ def _test(ax): df = DataFrame({'a': ser, 'b': ser + 1}) axes = df.plot(subplots=True) - [_test(ax) for ax in axes] + + for ax in axes: + _test(ax) def test_get_finder(self): import pandas.tseries.converter as conv @@ -368,6 +355,7 @@ def test_get_finder(self): @slow def test_finder_daily(self): + import matplotlib.pyplot as plt xp = Period('1999-1-1', freq='B').ordinal day_lst = [10, 40, 252, 400, 950, 2750, 10000] for n in day_lst: @@ -377,35 +365,35 @@ def test_finder_daily(self): xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] self.assertEqual(xp, rs) - (vmin, vmax) = ax.get_xlim() + vmin, vmax = ax.get_xlim() ax.set_xlim(vmin + 0.9, vmax) rs = xaxis.get_majorticklocs()[0] self.assertEqual(xp, rs) + plt.close(ax.get_figure()) @slow def test_finder_quarterly(self): import matplotlib.pyplot as plt xp = Period('1988Q1').ordinal yrs = [3.5, 11] - plt.close('all') for n in yrs: rng = period_range('1987Q2', periods=int(n * 4), freq='Q') ser = Series(np.random.randn(len(rng)), rng) ax = ser.plot() xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] - self.assert_(rs == xp) + self.assertEqual(rs, xp) (vmin, vmax) = ax.get_xlim() ax.set_xlim(vmin + 0.9, vmax) rs = xaxis.get_majorticklocs()[0] self.assertEqual(xp, rs) + plt.close(ax.get_figure()) @slow def test_finder_monthly(self): import matplotlib.pyplot as plt xp = Period('Jan 1988').ordinal yrs = [1.15, 2.5, 4, 11] - plt.close('all') for n in yrs: rng = period_range('1987Q2', periods=int(n * 12), freq='M') ser = Series(np.random.randn(len(rng)), rng) @@ -413,28 +401,24 @@ def test_finder_monthly(self): xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] self.assert_(rs == xp) - (vmin, vmax) = ax.get_xlim() + vmin, vmax = ax.get_xlim() ax.set_xlim(vmin + 0.9, vmax) rs = xaxis.get_majorticklocs()[0] self.assertEqual(xp, rs) - plt.close('all') + plt.close(ax.get_figure()) - @slow def test_finder_monthly_long(self): - import matplotlib.pyplot as plt - plt.close('all') rng = period_range('1988Q1', periods=24 * 12, freq='M') ser = Series(np.random.randn(len(rng)), rng) ax = ser.plot() xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] xp = Period('1989Q1', 'M').ordinal - self.assert_(rs == xp) + self.assertEqual(rs, xp) @slow def test_finder_annual(self): import matplotlib.pyplot as plt - plt.close('all') xp = [1987, 1988, 1990, 1990, 1995, 2020, 2070, 2170] for i, nyears in enumerate([5, 10, 19, 49, 99, 199, 599, 1001]): rng = period_range('1987', periods=nyears, freq='A') @@ -442,13 +426,11 @@ def test_finder_annual(self): ax = ser.plot() xaxis = ax.get_xaxis() rs = xaxis.get_majorticklocs()[0] - self.assert_(rs == Period(xp[i], freq='A').ordinal) - plt.close('all') + self.assertEqual(rs, Period(xp[i], freq='A').ordinal) + plt.close(ax.get_figure()) @slow def test_finder_minutely(self): - import matplotlib.pyplot as plt - plt.close('all') nminutes = 50 * 24 * 60 rng = date_range('1/1/1999', freq='Min', periods=nminutes) ser = Series(np.random.randn(len(rng)), rng) @@ -458,10 +440,7 @@ def test_finder_minutely(self): xp = Period('1/1/1999', freq='Min').ordinal self.assertEqual(rs, xp) - @slow def test_finder_hourly(self): - import matplotlib.pyplot as plt - plt.close('all') nhours = 23 rng = date_range('1/1/1999', freq='H', periods=nhours) ser = Series(np.random.randn(len(rng)), rng) @@ -474,40 +453,40 @@ def test_finder_hourly(self): @slow def test_gaps(self): import matplotlib.pyplot as plt - plt.close('all') + ts = tm.makeTimeSeries() ts[5:25] = np.nan ax = ts.plot() lines = ax.get_lines() - self.assert_(len(lines) == 1) + self.assertEqual(len(lines), 1) l = lines[0] data = l.get_xydata() tm.assert_isinstance(data, np.ma.core.MaskedArray) mask = data.mask self.assert_(mask[5:25, 1].all()) + plt.close(ax.get_figure()) # irregular - plt.close('all') ts = tm.makeTimeSeries() ts = ts[[0, 1, 2, 5, 7, 9, 12, 15, 20]] ts[2:5] = np.nan ax = ts.plot() lines = ax.get_lines() - self.assert_(len(lines) == 1) + self.assertEqual(len(lines), 1) l = lines[0] data = l.get_xydata() tm.assert_isinstance(data, np.ma.core.MaskedArray) mask = data.mask self.assert_(mask[2:5, 1].all()) + plt.close(ax.get_figure()) # non-ts - plt.close('all') idx = [0, 1, 2, 5, 7, 9, 12, 15, 20] ser = Series(np.random.randn(len(idx)), idx) ser[2:5] = np.nan ax = ser.plot() lines = ax.get_lines() - self.assert_(len(lines) == 1) + self.assertEqual(len(lines), 1) l = lines[0] data = l.get_xydata() tm.assert_isinstance(data, np.ma.core.MaskedArray) @@ -516,8 +495,6 @@ def test_gaps(self): @slow def test_gap_upsample(self): - import matplotlib.pyplot as plt - plt.close('all') low = tm.makeTimeSeries() low[5:25] = np.nan ax = low.plot() @@ -526,8 +503,8 @@ def test_gap_upsample(self): s = Series(np.random.randn(len(idxh)), idxh) s.plot(secondary_y=True) lines = ax.get_lines() - self.assert_(len(lines) == 1) - self.assert_(len(ax.right_ax.get_lines()) == 1) + self.assertEqual(len(lines), 1) + self.assertEqual(len(ax.right_ax.get_lines()), 1) l = lines[0] data = l.get_xydata() tm.assert_isinstance(data, np.ma.core.MaskedArray) @@ -537,7 +514,7 @@ def test_gap_upsample(self): @slow def test_secondary_y(self): import matplotlib.pyplot as plt - plt.close('all') + ser = Series(np.random.randn(10)) ser2 = Series(np.random.randn(10)) ax = ser.plot(secondary_y=True).right_ax @@ -546,23 +523,21 @@ def test_secondary_y(self): l = ax.get_lines()[0] xp = Series(l.get_ydata(), l.get_xdata()) assert_series_equal(ser, xp) - self.assert_(ax.get_yaxis().get_ticks_position() == 'right') - self.assert_(not axes[0].get_yaxis().get_visible()) + self.assertEqual(ax.get_yaxis().get_ticks_position(), 'right') + self.assertFalse(axes[0].get_yaxis().get_visible()) + plt.close(fig) ax2 = ser2.plot() - self.assert_(ax2.get_yaxis().get_ticks_position() == 'left') + self.assertEqual(ax2.get_yaxis().get_ticks_position(), 'default') + plt.close(ax2.get_figure()) - plt.close('all') ax = ser2.plot() ax2 = ser.plot(secondary_y=True).right_ax self.assert_(ax.get_yaxis().get_visible()) - plt.close('all') - @slow def test_secondary_y_ts(self): import matplotlib.pyplot as plt - plt.close('all') idx = date_range('1/1/2000', periods=10) ser = Series(np.random.randn(10), idx) ser2 = Series(np.random.randn(10), idx) @@ -572,13 +547,14 @@ def test_secondary_y_ts(self): l = ax.get_lines()[0] xp = Series(l.get_ydata(), l.get_xdata()).to_timestamp() assert_series_equal(ser, xp) - self.assert_(ax.get_yaxis().get_ticks_position() == 'right') - self.assert_(not axes[0].get_yaxis().get_visible()) + self.assertEqual(ax.get_yaxis().get_ticks_position(), 'right') + self.assertFalse(axes[0].get_yaxis().get_visible()) + plt.close(fig) ax2 = ser2.plot() - self.assert_(ax2.get_yaxis().get_ticks_position() == 'left') + self.assertEqual(ax2.get_yaxis().get_ticks_position(), 'default') + plt.close(ax2.get_figure()) - plt.close('all') ax = ser2.plot() ax2 = ser.plot(secondary_y=True) self.assert_(ax.get_yaxis().get_visible()) @@ -588,50 +564,41 @@ def test_secondary_kde(self): _skip_if_no_scipy() import matplotlib.pyplot as plt - plt.close('all') ser = Series(np.random.randn(10)) ax = ser.plot(secondary_y=True, kind='density').right_ax fig = ax.get_figure() axes = fig.get_axes() - self.assert_(axes[1].get_yaxis().get_ticks_position() == 'right') + self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right') @slow def test_secondary_bar(self): - import matplotlib.pyplot as plt - plt.close('all') ser = Series(np.random.randn(10)) ax = ser.plot(secondary_y=True, kind='bar') fig = ax.get_figure() axes = fig.get_axes() - self.assert_(axes[1].get_yaxis().get_ticks_position() == 'right') + self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right') @slow def test_secondary_frame(self): - import matplotlib.pyplot as plt - plt.close('all') df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c']) axes = df.plot(secondary_y=['a', 'c'], subplots=True) - self.assert_(axes[0].get_yaxis().get_ticks_position() == 'right') - self.assert_(axes[1].get_yaxis().get_ticks_position() == 'default') - self.assert_(axes[2].get_yaxis().get_ticks_position() == 'right') + self.assertEqual(axes[0].get_yaxis().get_ticks_position(), 'right') + self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'default') + self.assertEqual(axes[2].get_yaxis().get_ticks_position(), 'right') @slow def test_secondary_bar_frame(self): - import matplotlib.pyplot as plt - plt.close('all') df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c']) axes = df.plot(kind='bar', secondary_y=['a', 'c'], subplots=True) - self.assert_(axes[0].get_yaxis().get_ticks_position() == 'right') - self.assert_(axes[1].get_yaxis().get_ticks_position() == 'default') - self.assert_(axes[2].get_yaxis().get_ticks_position() == 'right') + self.assertEqual(axes[0].get_yaxis().get_ticks_position(), 'right') + self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'default') + self.assertEqual(axes[2].get_yaxis().get_ticks_position(), 'right') - @slow def test_mixed_freq_regular_first(self): import matplotlib.pyplot as plt - plt.close('all') s1 = tm.makeTimeSeries() s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]] - s1.plot() + ax = s1.plot() ax2 = s2.plot(style='g') lines = ax2.get_lines() idx1 = lines[0].get_xdata() @@ -640,30 +607,24 @@ def test_mixed_freq_regular_first(self): self.assert_(idx2.equals(s2.index.to_period('B'))) left, right = ax2.get_xlim() pidx = s1.index.to_period() - self.assert_(left == pidx[0].ordinal) - self.assert_(right == pidx[-1].ordinal) - plt.close('all') + self.assertEqual(left, pidx[0].ordinal) + self.assertEqual(right, pidx[-1].ordinal) @slow def test_mixed_freq_irregular_first(self): import matplotlib.pyplot as plt - plt.close('all') s1 = tm.makeTimeSeries() s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]] s2.plot(style='g') ax = s1.plot() - self.assert_(not hasattr(ax, 'freq')) + self.assertFalse(hasattr(ax, 'freq')) lines = ax.get_lines() x1 = lines[0].get_xdata() assert_array_equal(x1, s2.index.asobject.values) x2 = lines[1].get_xdata() assert_array_equal(x2, s1.index.asobject.values) - plt.close('all') - @slow def test_mixed_freq_hf_first(self): - import matplotlib.pyplot as plt - plt.close('all') idxh = date_range('1/1/1999', periods=365, freq='D') idxl = date_range('1/1/1999', periods=12, freq='M') high = Series(np.random.randn(len(idxh)), idxh) @@ -671,27 +632,26 @@ def test_mixed_freq_hf_first(self): high.plot() ax = low.plot() for l in ax.get_lines(): - self.assert_(PeriodIndex(data=l.get_xdata()).freq == 'D') + self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'D') @slow def test_mixed_freq_alignment(self): - import matplotlib.pyplot as plt ts_ind = date_range('2012-01-01 13:00', '2012-01-02', freq='H') ts_data = np.random.randn(12) ts = Series(ts_data, index=ts_ind) ts2 = ts.asfreq('T').interpolate() - plt.close('all') ax = ts.plot() ts2.plot(style='r') - self.assert_(ax.lines[0].get_xdata()[0] == ax.lines[1].get_xdata()[0]) + self.assertEqual(ax.lines[0].get_xdata()[0], + ax.lines[1].get_xdata()[0]) @slow def test_mixed_freq_lf_first(self): import matplotlib.pyplot as plt - plt.close('all') + idxh = date_range('1/1/1999', periods=365, freq='D') idxl = date_range('1/1/1999', periods=12, freq='M') high = Series(np.random.randn(len(idxh)), idxh) @@ -699,11 +659,11 @@ def test_mixed_freq_lf_first(self): low.plot(legend=True) ax = high.plot(legend=True) for l in ax.get_lines(): - self.assert_(PeriodIndex(data=l.get_xdata()).freq == 'D') + self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'D') leg = ax.get_legend() - self.assert_(len(leg.texts) == 2) + self.assertEqual(len(leg.texts), 2) + plt.close(ax.get_figure()) - plt.close('all') idxh = date_range('1/1/1999', periods=240, freq='T') idxl = date_range('1/1/1999', periods=4, freq='H') high = Series(np.random.randn(len(idxh)), idxh) @@ -711,9 +671,8 @@ def test_mixed_freq_lf_first(self): low.plot() ax = high.plot() for l in ax.get_lines(): - self.assert_(PeriodIndex(data=l.get_xdata()).freq == 'T') + self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'T') - @slow def test_mixed_freq_irreg_period(self): ts = tm.makeTimeSeries() irreg = ts[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 29]] @@ -724,8 +683,6 @@ def test_mixed_freq_irreg_period(self): @slow def test_to_weekly_resampling(self): - import matplotlib.pyplot as plt - plt.close('all') idxh = date_range('1/1/1999', periods=52, freq='W') idxl = date_range('1/1/1999', periods=12, freq='M') high = Series(np.random.randn(len(idxh)), idxh) @@ -737,8 +694,6 @@ def test_to_weekly_resampling(self): @slow def test_from_weekly_resampling(self): - import matplotlib.pyplot as plt - plt.close('all') idxh = date_range('1/1/1999', periods=52, freq='W') idxl = date_range('1/1/1999', periods=12, freq='M') high = Series(np.random.randn(len(idxh)), idxh) @@ -763,9 +718,6 @@ def test_irreg_dtypes(self): @slow def test_time(self): - import matplotlib.pyplot as plt - plt.close('all') - t = datetime(1, 1, 1, 3, 30, 0) deltas = np.random.randint(1, 20, 3).cumsum() ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas]) @@ -783,7 +735,7 @@ def test_time(self): xp = l.get_text() if len(xp) > 0: rs = time(h, m, s).strftime('%H:%M:%S') - self.assert_(xp, rs) + self.assertEqual(xp, rs) # change xlim ax.set_xlim('1:30', '5:00') @@ -797,13 +749,10 @@ def test_time(self): xp = l.get_text() if len(xp) > 0: rs = time(h, m, s).strftime('%H:%M:%S') - self.assert_(xp, rs) + self.assertEqual(xp, rs) @slow def test_time_musec(self): - import matplotlib.pyplot as plt - plt.close('all') - t = datetime(1, 1, 1, 3, 30, 0) deltas = np.random.randint(1, 20, 3).cumsum() ts = np.array([(t + timedelta(microseconds=int(x))).time() @@ -823,12 +772,10 @@ def test_time_musec(self): xp = l.get_text() if len(xp) > 0: rs = time(h, m, s).strftime('%H:%M:%S.%f') - self.assert_(xp, rs) + self.assertEqual(xp, rs) @slow def test_secondary_upsample(self): - import matplotlib.pyplot as plt - plt.close('all') idxh = date_range('1/1/1999', periods=365, freq='D') idxl = date_range('1/1/1999', periods=12, freq='M') high = Series(np.random.randn(len(idxh)), idxh) @@ -836,9 +783,9 @@ def test_secondary_upsample(self): low.plot() ax = high.plot(secondary_y=True) for l in ax.get_lines(): - self.assert_(l.get_xdata().freq == 'D') + self.assertEqual(l.get_xdata().freq, 'D') for l in ax.right_ax.get_lines(): - self.assert_(l.get_xdata().freq == 'D') + self.assertEqual(l.get_xdata().freq, 'D') @slow def test_secondary_legend(self): @@ -851,54 +798,54 @@ def test_secondary_legend(self): df = tm.makeTimeDataFrame() ax = df.plot(secondary_y=['A', 'B']) leg = ax.get_legend() - self.assert_(len(leg.get_lines()) == 4) - self.assert_(leg.get_texts()[0].get_text() == 'A (right)') - self.assert_(leg.get_texts()[1].get_text() == 'B (right)') - self.assert_(leg.get_texts()[2].get_text() == 'C') - self.assert_(leg.get_texts()[3].get_text() == 'D') + self.assertEqual(len(leg.get_lines()), 4) + self.assertEqual(leg.get_texts()[0].get_text(), 'A (right)') + self.assertEqual(leg.get_texts()[1].get_text(), 'B (right)') + self.assertEqual(leg.get_texts()[2].get_text(), 'C') + self.assertEqual(leg.get_texts()[3].get_text(), 'D') self.assert_(ax.right_ax.get_legend() is None) colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems - self.assert_(len(colors) == 4) + self.assertEqual(len(colors), 4) plt.clf() ax = fig.add_subplot(211) ax = df.plot(secondary_y=['A', 'C'], mark_right=False) leg = ax.get_legend() - self.assert_(len(leg.get_lines()) == 4) - self.assert_(leg.get_texts()[0].get_text() == 'A') - self.assert_(leg.get_texts()[1].get_text() == 'B') - self.assert_(leg.get_texts()[2].get_text() == 'C') - self.assert_(leg.get_texts()[3].get_text() == 'D') + self.assertEqual(len(leg.get_lines()), 4) + self.assertEqual(leg.get_texts()[0].get_text(), 'A') + self.assertEqual(leg.get_texts()[1].get_text(), 'B') + self.assertEqual(leg.get_texts()[2].get_text(), 'C') + self.assertEqual(leg.get_texts()[3].get_text(), 'D') plt.clf() ax = df.plot(kind='bar', secondary_y=['A']) leg = ax.get_legend() - self.assert_(leg.get_texts()[0].get_text() == 'A (right)') - self.assert_(leg.get_texts()[1].get_text() == 'B') + self.assertEqual(leg.get_texts()[0].get_text(), 'A (right)') + self.assertEqual(leg.get_texts()[1].get_text(), 'B') plt.clf() ax = df.plot(kind='bar', secondary_y=['A'], mark_right=False) leg = ax.get_legend() - self.assert_(leg.get_texts()[0].get_text() == 'A') - self.assert_(leg.get_texts()[1].get_text() == 'B') + self.assertEqual(leg.get_texts()[0].get_text(), 'A') + self.assertEqual(leg.get_texts()[1].get_text(), 'B') plt.clf() ax = fig.add_subplot(211) df = tm.makeTimeDataFrame() ax = df.plot(secondary_y=['C', 'D']) leg = ax.get_legend() - self.assert_(len(leg.get_lines()) == 4) + self.assertEqual(len(leg.get_lines()), 4) self.assert_(ax.right_ax.get_legend() is None) colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems - self.assert_(len(colors) == 4) + self.assertEqual(len(colors), 4) # non-ts df = tm.makeDataFrame() @@ -906,29 +853,28 @@ def test_secondary_legend(self): ax = fig.add_subplot(211) ax = df.plot(secondary_y=['A', 'B']) leg = ax.get_legend() - self.assert_(len(leg.get_lines()) == 4) + self.assertEqual(len(leg.get_lines()), 4) self.assert_(ax.right_ax.get_legend() is None) colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems - self.assert_(len(colors) == 4) + self.assertEqual(len(colors), 4) plt.clf() ax = fig.add_subplot(211) ax = df.plot(secondary_y=['C', 'D']) leg = ax.get_legend() - self.assert_(len(leg.get_lines()) == 4) + self.assertEqual(len(leg.get_lines()), 4) self.assert_(ax.right_ax.get_legend() is None) colors = set() for line in leg.get_lines(): colors.add(line.get_color()) # TODO: color cycle problems - self.assert_(len(colors) == 4) + self.assertEqual(len(colors), 4) - @slow def test_format_date_axis(self): rng = date_range('1/1/2012', periods=12, freq='M') df = DataFrame(np.random.randn(len(rng), 3), rng) @@ -936,14 +882,15 @@ def test_format_date_axis(self): xaxis = ax.get_xaxis() for l in xaxis.get_ticklabels(): if len(l.get_text()) > 0: - self.assert_(l.get_rotation() == 30) + self.assertEqual(l.get_rotation(), 30) @slow def test_ax_plot(self): + import matplotlib.pyplot as plt + x = DatetimeIndex(start='2012-01-02', periods=10, freq='D') y = lrange(len(x)) - import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) lines = ax.plot(x, y, label='Y') @@ -971,39 +918,45 @@ def test_mpl_nopandas(self): assert_array_equal(np.array([x.toordinal() for x in dates]), line2.get_xydata()[:, 0]) + def _check_plot_works(f, freq=None, series=None, *args, **kwargs): import matplotlib.pyplot as plt fig = plt.gcf() - plt.clf() - ax = fig.add_subplot(211) - orig_ax = kwargs.pop('ax', plt.gca()) - orig_axfreq = getattr(orig_ax, 'freq', None) - - ret = f(*args, **kwargs) - assert(ret is not None) # do something more intelligent - - ax = kwargs.pop('ax', plt.gca()) - if series is not None: - dfreq = series.index.freq - if isinstance(dfreq, DateOffset): - dfreq = dfreq.rule_code - if orig_axfreq is None: - assert(ax.freq == dfreq) - - if freq is not None and orig_axfreq is None: - assert(ax.freq == freq) - - ax = fig.add_subplot(212) + try: - kwargs['ax'] = ax + plt.clf() + ax = fig.add_subplot(211) + orig_ax = kwargs.pop('ax', plt.gca()) + orig_axfreq = getattr(orig_ax, 'freq', None) + ret = f(*args, **kwargs) - assert(ret is not None) # do something more intelligent - except Exception: - pass + assert ret is not None # do something more intelligent + + ax = kwargs.pop('ax', plt.gca()) + if series is not None: + dfreq = series.index.freq + if isinstance(dfreq, DateOffset): + dfreq = dfreq.rule_code + if orig_axfreq is None: + assert ax.freq == dfreq + + if freq is not None and orig_axfreq is None: + assert ax.freq == freq + + ax = fig.add_subplot(212) + try: + kwargs['ax'] = ax + ret = f(*args, **kwargs) + assert ret is not None # do something more intelligent + except Exception: + pass + + with ensure_clean() as path: + plt.savefig(path) + finally: + plt.close(fig) - with ensure_clean() as path: - plt.savefig(path) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
Corrects tests of the form `self.assert_(x, y)` to `self.assertEqual(x, y)`. Also changes tests of the form `self.assert_(x == y)` to `self.assertEqual(x, y)`.
https://api.github.com/repos/pandas-dev/pandas/pulls/4876
2013-09-18T19:25:31Z
2013-09-18T20:41:31Z
2013-09-18T20:41:31Z
2014-06-13T05:16:05Z
FIX: iso date encoding year overflow
diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 77ad8533f2831..9c78e995f4fe3 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -1148,7 +1148,7 @@ make_iso_8601_datetime(pandas_datetimestruct *dts, char *outstr, int outlen, #ifdef _WIN32 tmplen = _snprintf(substr, sublen, "%04" NPY_INT64_FMT, dts->year); #else - tmplen = snprintf(substr, sublen, "%04" NPY_INT64_FMT, (long int)dts->year); + tmplen = snprintf(substr, sublen, "%04" NPY_INT64_FMT, (long long)dts->year); #endif /* If it ran out of space or there isn't space for the NULL terminator */ if (tmplen < 0 || tmplen > sublen) {
Fixes #4869
https://api.github.com/repos/pandas-dev/pandas/pulls/4871
2013-09-18T07:01:18Z
2013-09-18T12:03:41Z
2013-09-18T12:03:41Z
2014-07-16T08:29:00Z
TST: windows dtype 32-bit fixes
diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py index 12c696f7076a4..f5b5ba745d83c 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/io/tests/test_clipboard.py @@ -43,7 +43,7 @@ def check_round_trip_frame(self, data_type): data = self.data[data_type] data.to_clipboard() result = read_clipboard() - tm.assert_frame_equal(data, result) + tm.assert_frame_equal(data, result, check_dtype=False) def test_round_trip_frame(self): for dt in self.data_types: diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index aa989e9d785f8..45289dac44254 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -492,7 +492,7 @@ def test_xcompat(self): def test_unsorted_index(self): df = DataFrame({'y': np.arange(100)}, - index=np.arange(99, -1, -1)) + index=np.arange(99, -1, -1), dtype=np.int64) ax = df.plot() l = ax.get_lines()[0] rs = l.get_xydata()
Fix test failures related to incoorrect dtype, detail #4866.
https://api.github.com/repos/pandas-dev/pandas/pulls/4870
2013-09-18T06:00:10Z
2013-09-18T12:03:08Z
2013-09-18T12:03:08Z
2014-07-16T08:28:59Z
BUG: Fix for DateOffset's reprs. (GH4638)
diff --git a/doc/source/release.rst b/doc/source/release.rst index ce08a1ca0a175..34720c49b163b 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -480,6 +480,8 @@ Bug Fixes - Fixed wrong check for overlapping in ``DatetimeIndex.union`` (:issue:`4564`) - Fixed conflict between thousands separator and date parser in csv_parser (:issue:`4678`) - Fix appending when dtypes are not the same (error showing mixing float/np.datetime64) (:issue:`4993`) + - Fix repr for DateOffset. No longer show duplicate entries in kwds. + Removed unused offset fields. (:issue:`4638`) pandas 0.12.0 ------------- diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index bef54a0b37f21..92ed1e415d11a 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -117,19 +117,31 @@ def __repr__(self): className = getattr(self, '_outputName', type(self).__name__) exclude = set(['n', 'inc']) attrs = [] - for attr in self.__dict__: + for attr in sorted(self.__dict__): if ((attr == 'kwds' and len(self.kwds) == 0) or attr.startswith('_')): continue - if attr not in exclude: - attrs.append('='.join((attr, repr(getattr(self, attr))))) + elif attr == 'kwds': + kwds_new = {} + for key in self.kwds: + if not hasattr(self, key): + kwds_new[key] = self.kwds[key] + if len(kwds_new) > 0: + attrs.append('='.join((attr, repr(kwds_new)))) + else: + if attr not in exclude: + attrs.append('='.join((attr, repr(getattr(self, attr))))) if abs(self.n) != 1: plural = 's' else: plural = '' + + n_str = "" + if self.n != 1: + n_str = "%s * " % self.n - out = '<%s ' % self.n + className + plural + out = '<%s' % n_str + className + plural if attrs: out += ': ' + ', '.join(attrs) out += '>' @@ -247,7 +259,7 @@ def __init__(self, n=1, **kwds): def rule_code(self): return 'B' - def __repr__(self): + def __repr__(self): #TODO: Figure out if this should be merged into DateOffset if hasattr(self, 'name') and len(self.name): return self.name @@ -261,8 +273,12 @@ def __repr__(self): plural = 's' else: plural = '' + + n_str = "" + if self.n != 1: + n_str = "%s * " % self.n - out = '<%s ' % self.n + className + plural + out = '<%s' % n_str + className + plural if attrs: out += ': ' + ', '.join(attrs) out += '>' @@ -741,7 +757,6 @@ def __init__(self, n=1, **kwds): self.n = n self.startingMonth = kwds.get('startingMonth', 3) - self.offset = BMonthEnd(3) self.kwds = kwds def isAnchored(self): @@ -803,7 +818,6 @@ def __init__(self, n=1, **kwds): self.n = n self.startingMonth = kwds.get('startingMonth', 3) - self.offset = BMonthBegin(3) self.kwds = kwds def isAnchored(self): @@ -855,7 +869,6 @@ def __init__(self, n=1, **kwds): self.n = n self.startingMonth = kwds.get('startingMonth', 3) - self.offset = MonthEnd(3) self.kwds = kwds def isAnchored(self): @@ -894,7 +907,6 @@ def __init__(self, n=1, **kwds): self.n = n self.startingMonth = kwds.get('startingMonth', 3) - self.offset = MonthBegin(3) self.kwds = kwds def isAnchored(self): diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index c248e0a5e0de3..5b4e3251683bb 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -150,10 +150,10 @@ def test_different_normalize_equals(self): self.assertEqual(offset, offset2) def test_repr(self): - assert repr(self.offset) == '<1 BusinessDay>' - assert repr(self.offset2) == '<2 BusinessDays>' + self.assertEqual(repr(self.offset), '<BusinessDay>') + assert repr(self.offset2) == '<2 * BusinessDays>' - expected = '<1 BusinessDay: offset=datetime.timedelta(1)>' + expected = '<BusinessDay: offset=datetime.timedelta(1)>' assert repr(self.offset + timedelta(1)) == expected def test_with_offset(self): @@ -324,10 +324,10 @@ def test_different_normalize_equals(self): self.assertEqual(offset, offset2) def test_repr(self): - assert repr(self.offset) == '<1 CustomBusinessDay>' - assert repr(self.offset2) == '<2 CustomBusinessDays>' + assert repr(self.offset) == '<CustomBusinessDay>' + assert repr(self.offset2) == '<2 * CustomBusinessDays>' - expected = '<1 BusinessDay: offset=datetime.timedelta(1)>' + expected = '<BusinessDay: offset=datetime.timedelta(1)>' assert repr(self.offset + timedelta(1)) == expected def test_with_offset(self): @@ -526,6 +526,11 @@ def assertOnOffset(offset, date, expected): class TestWeek(unittest.TestCase): + def test_repr(self): + self.assertEqual(repr(Week(weekday=0)), "<Week: weekday=0>") + self.assertEqual(repr(Week(n=-1, weekday=0)), "<-1 * Week: weekday=0>") + self.assertEqual(repr(Week(n=-2, weekday=0)), "<-2 * Weeks: weekday=0>") + def test_corner(self): self.assertRaises(ValueError, Week, weekday=7) assertRaisesRegexp(ValueError, "Day must be", Week, weekday=-1) @@ -598,6 +603,9 @@ def test_constructor(self): assertRaisesRegexp(ValueError, "^Day", WeekOfMonth, n=1, week=0, weekday=-1) assertRaisesRegexp(ValueError, "^Day", WeekOfMonth, n=1, week=0, weekday=7) + def test_repr(self): + self.assertEqual(repr(WeekOfMonth(weekday=1,week=2)), "<WeekOfMonth: week=2, weekday=1>") + def test_offset(self): date1 = datetime(2011, 1, 4) # 1st Tuesday of Month date2 = datetime(2011, 1, 11) # 2nd Tuesday of Month @@ -895,6 +903,11 @@ def test_onOffset(self): class TestBQuarterBegin(unittest.TestCase): + + def test_repr(self): + self.assertEqual(repr(BQuarterBegin()),"<BusinessQuarterBegin: startingMonth=3>") + self.assertEqual(repr(BQuarterBegin(startingMonth=3)), "<BusinessQuarterBegin: startingMonth=3>") + self.assertEqual(repr(BQuarterBegin(startingMonth=1)), "<BusinessQuarterBegin: startingMonth=1>") def test_isAnchored(self): self.assert_(BQuarterBegin(startingMonth=1).isAnchored()) @@ -981,6 +994,11 @@ def test_offset(self): class TestBQuarterEnd(unittest.TestCase): + def test_repr(self): + self.assertEqual(repr(BQuarterEnd()),"<BusinessQuarterEnd: startingMonth=3>") + self.assertEqual(repr(BQuarterEnd(startingMonth=3)), "<BusinessQuarterEnd: startingMonth=3>") + self.assertEqual(repr(BQuarterEnd(startingMonth=1)), "<BusinessQuarterEnd: startingMonth=1>") + def test_isAnchored(self): self.assert_(BQuarterEnd(startingMonth=1).isAnchored()) self.assert_(BQuarterEnd().isAnchored()) @@ -1083,6 +1101,11 @@ def test_onOffset(self): class TestQuarterBegin(unittest.TestCase): + def test_repr(self): + self.assertEqual(repr(QuarterBegin()), "<QuarterBegin: startingMonth=3>") + self.assertEqual(repr(QuarterBegin(startingMonth=3)), "<QuarterBegin: startingMonth=3>") + self.assertEqual(repr(QuarterBegin(startingMonth=1)),"<QuarterBegin: startingMonth=1>") + def test_isAnchored(self): self.assert_(QuarterBegin(startingMonth=1).isAnchored()) self.assert_(QuarterBegin().isAnchored()) @@ -1152,7 +1175,11 @@ def test_offset(self): class TestQuarterEnd(unittest.TestCase): - + def test_repr(self): + self.assertEqual(repr(QuarterEnd()), "<QuarterEnd: startingMonth=3>") + self.assertEqual(repr(QuarterEnd(startingMonth=3)), "<QuarterEnd: startingMonth=3>") + self.assertEqual(repr(QuarterEnd(startingMonth=1)), "<QuarterEnd: startingMonth=1>") + def test_isAnchored(self): self.assert_(QuarterEnd(startingMonth=1).isAnchored()) self.assert_(QuarterEnd().isAnchored())
closes #4638 Before: ``` In [22]: WeekOfMonth(weekday=1,week=2) Out[22]: <1 WeekOfMonth: week=2, kwds={'week': 2, 'weekday': 1}, weekday=1> In [32]: QuarterEnd() Out[32]: <1 QuarterEnd: startingMonth=3, offset=<3 MonthEnds>> In [40]: BQuarterBegin() Out[40]: <1 BusinessQuarterBegin: startingMonth=3, offset=<3 BusinessMonthBegins>> In [41]: BQuarterBegin(startingMonth=3) Out[41]: <1 BusinessQuarterBegin: startingMonth=3, kwds={'startingMonth': 3}, offset=<3 BusinessMonthBegins>> ``` after: ``` In [2]: WeekOfMonth(weekday=1,week=2) Out[2]: <1 WeekOfMonth: week=2, weekday=1> In [3]: QuarterEnd() Out[3]: <1 QuarterEnd: startingMonth=3> In [4]: BQuarterBegin() Out[4]: <1 BusinessQuarterBegin: startingMonth=3> In [5]: BQuarterBegin(startingMonth=3) Out[5]: <1 BusinessQuarterBegin: startingMonth=3> ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4868
2013-09-18T04:10:11Z
2013-09-27T03:50:38Z
2013-09-27T03:50:38Z
2014-07-16T08:28:56Z
Panel tshift 4853
diff --git a/doc/source/release.rst b/doc/source/release.rst index 793d52223b6f5..d747505593c94 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -428,6 +428,7 @@ Bug Fixes single column and passing a list for ``ascending``, the argument for ``ascending`` was being interpreted as ``True`` (:issue:`4839`, :issue:`4846`) + - Fixed ``Panel.tshift`` not working. Added `freq` support to ``Panel.shift`` (:issue:`4853`) pandas 0.12.0 ------------- diff --git a/pandas/core/datetools.py b/pandas/core/datetools.py index 228dc7574f8f3..91a29259d8f2f 100644 --- a/pandas/core/datetools.py +++ b/pandas/core/datetools.py @@ -35,3 +35,23 @@ isBusinessDay = BDay().onOffset isMonthEnd = MonthEnd().onOffset isBMonthEnd = BMonthEnd().onOffset + +def _resolve_offset(freq, kwds): + if 'timeRule' in kwds or 'offset' in kwds: + offset = kwds.get('offset', None) + offset = kwds.get('timeRule', offset) + if isinstance(offset, compat.string_types): + offset = getOffset(offset) + warn = True + else: + offset = freq + warn = False + + if warn: + import warnings + warnings.warn("'timeRule' and 'offset' parameters are deprecated," + " please use 'freq' instead", + FutureWarning) + + return offset + diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 78ef806a45dcb..70fcc2c9d9c0a 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3497,55 +3497,6 @@ def diff(self, periods=1): new_data = self._data.diff(periods) return self._constructor(new_data) - def shift(self, periods=1, freq=None, **kwds): - """ - Shift the index of the DataFrame by desired number of periods with an - optional time freq - - Parameters - ---------- - periods : int - Number of periods to move, can be positive or negative - freq : DateOffset, timedelta, or time rule string, optional - Increment to use from datetools module or time rule (e.g. 'EOM') - - Notes - ----- - If freq is specified then the index values are shifted but the data - if not realigned - - Returns - ------- - shifted : DataFrame - """ - from pandas.core.series import _resolve_offset - - if periods == 0: - return self - - offset = _resolve_offset(freq, kwds) - - if isinstance(offset, compat.string_types): - offset = datetools.to_offset(offset) - - if offset is None: - indexer = com._shift_indexer(len(self), periods) - new_data = self._data.shift(indexer, periods) - elif isinstance(self.index, PeriodIndex): - orig_offset = datetools.to_offset(self.index.freq) - if offset == orig_offset: - new_data = self._data.copy() - new_data.axes[1] = self.index.shift(periods) - else: - msg = ('Given freq %s does not match PeriodIndex freq %s' % - (offset.rule_code, orig_offset.rule_code)) - raise ValueError(msg) - else: - new_data = self._data.copy() - new_data.axes[1] = self.index.shift(periods, offset) - - return self._constructor(new_data) - #---------------------------------------------------------------------- # Function application diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 2f6bc13983f93..53d3687854cac 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -11,8 +11,10 @@ import pandas.core.indexing as indexing from pandas.core.indexing import _maybe_convert_indices from pandas.tseries.index import DatetimeIndex +from pandas.tseries.period import PeriodIndex from pandas.core.internals import BlockManager import pandas.core.common as com +import pandas.core.datetools as datetools from pandas import compat, _np_version_under1p7 from pandas.compat import map, zip, lrange from pandas.core.common import (isnull, notnull, is_list_like, @@ -2667,7 +2669,40 @@ def cummin(self, axis=None, skipna=True): result = np.minimum.accumulate(y, axis) return self._wrap_array(result, self.axes, copy=False) - def tshift(self, periods=1, freq=None, **kwds): + def shift(self, periods=1, freq=None, axis=0, **kwds): + """ + Shift the index of the DataFrame by desired number of periods with an + optional time freq + + Parameters + ---------- + periods : int + Number of periods to move, can be positive or negative + freq : DateOffset, timedelta, or time rule string, optional + Increment to use from datetools module or time rule (e.g. 'EOM') + + Notes + ----- + If freq is specified then the index values are shifted but the data + if not realigned + + Returns + ------- + shifted : DataFrame + """ + if periods == 0: + return self + + if freq is None and not len(kwds): + block_axis = self._get_block_manager_axis(axis) + indexer = com._shift_indexer(len(self), periods) + new_data = self._data.shift(indexer, periods, axis=block_axis) + else: + return self.tshift(periods, freq, **kwds) + + return self._constructor(new_data) + + def tshift(self, periods=1, freq=None, axis=0, **kwds): """ Shift the time index, using the index's frequency if available @@ -2677,6 +2712,8 @@ def tshift(self, periods=1, freq=None, **kwds): Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, default None Increment to use from datetools module or time rule (e.g. 'EOM') + axis : int or basestring + Corresponds to the axis that contains the Index Notes ----- @@ -2686,19 +2723,45 @@ def tshift(self, periods=1, freq=None, **kwds): Returns ------- - shifted : Series + shifted : NDFrame """ + from pandas.core.datetools import _resolve_offset + + index = self._get_axis(axis) if freq is None: - freq = getattr(self.index, 'freq', None) + freq = getattr(index, 'freq', None) if freq is None: - freq = getattr(self.index, 'inferred_freq', None) + freq = getattr(index, 'inferred_freq', None) if freq is None: msg = 'Freq was not given and was not set in the index' raise ValueError(msg) - return self.shift(periods, freq, **kwds) + + if periods == 0: + return self + + offset = _resolve_offset(freq, kwds) + + if isinstance(offset, compat.string_types): + offset = datetools.to_offset(offset) + + block_axis = self._get_block_manager_axis(axis) + if isinstance(index, PeriodIndex): + orig_offset = datetools.to_offset(index.freq) + if offset == orig_offset: + new_data = self._data.copy() + new_data.axes[block_axis] = index.shift(periods) + else: + msg = ('Given freq %s does not match PeriodIndex freq %s' % + (offset.rule_code, orig_offset.rule_code)) + raise ValueError(msg) + else: + new_data = self._data.copy() + new_data.axes[block_axis] = index.shift(periods, offset) + + return self._constructor(new_data) def truncate(self, before=None, after=None, copy=True): """Function truncate a sorted DataFrame / Series before and/or after diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 11ce27b078b18..4b9fdb0422526 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -758,17 +758,27 @@ def diff(self, n): new_values = com.diff(self.values, n, axis=1) return [make_block(new_values, self.items, self.ref_items, ndim=self.ndim, fastpath=True)] - def shift(self, indexer, periods): + def shift(self, indexer, periods, axis=0): """ shift the block by periods, possibly upcast """ - new_values = self.values.take(indexer, axis=1) + new_values = self.values.take(indexer, axis=axis) # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also new_values, fill_value = com._maybe_upcast(new_values) - if periods > 0: - new_values[:, :periods] = fill_value + + # 1-d + if self.ndim == 1: + if periods > 0: + new_values[:periods] = fill_value + else: + new_values[periods:] = fill_value + + # 2-d else: - new_values[:, periods:] = fill_value + if periods > 0: + new_values[:, :periods] = fill_value + else: + new_values[:, periods:] = fill_value return [make_block(new_values, self.items, self.ref_items, ndim=self.ndim, fastpath=True)] def eval(self, func, other, raise_on_error=True, try_cast=False): @@ -1547,7 +1557,7 @@ def fillna(self, value, inplace=False, downcast=None): values = self.values if inplace else self.values.copy() return [ self.make_block(values.get_values(value), fill_value=value) ] - def shift(self, indexer, periods): + def shift(self, indexer, periods, axis=0): """ shift the block by periods """ new_values = self.values.to_dense().take(indexer) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 6f02b49326e4d..45101b1e2afd5 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -1017,7 +1017,7 @@ def count(self, axis='major'): return self._wrap_result(result, axis) - def shift(self, lags, axis='major'): + def shift(self, lags, freq=None, axis='major'): """ Shift major or minor axis by specified number of leads/lags. Drops periods right now compared with DataFrame.shift @@ -1036,6 +1036,9 @@ def shift(self, lags, axis='major'): major_axis = self.major_axis minor_axis = self.minor_axis + if freq: + return self.tshift(lags, freq, axis=axis) + if lags > 0: vslicer = slice(None, -lags) islicer = slice(lags, None) @@ -1058,6 +1061,9 @@ def shift(self, lags, axis='major'): return self._constructor(values, items=items, major_axis=major_axis, minor_axis=minor_axis) + def tshift(self, periods=1, freq=None, axis='major', **kwds): + return super(Panel, self).tshift(periods, freq, axis, **kwds) + def truncate(self, before=None, after=None, axis='major'): """Function truncates a sorted Panel before and/or after some particular values on the requested axis diff --git a/pandas/core/series.py b/pandas/core/series.py index beb398dfe6fd0..9f7ab0cb0346b 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -59,8 +59,6 @@ _np_version_under1p6 = LooseVersion(_np_version) < '1.6' _np_version_under1p7 = LooseVersion(_np_version) < '1.7' -_SHOW_WARNINGS = True - class _TimeOp(object): """ Wrapper around Series datetime/time/timedelta arithmetic operations. @@ -2917,62 +2915,6 @@ def last_valid_index(self): #---------------------------------------------------------------------- # Time series-oriented methods - def shift(self, periods=1, freq=None, copy=True, **kwds): - """ - Shift the index of the Series by desired number of periods with an - optional time offset - - Parameters - ---------- - periods : int - Number of periods to move, can be positive or negative - freq : DateOffset, timedelta, or offset alias string, optional - Increment to use from datetools module or time rule (e.g. 'EOM') - - Returns - ------- - shifted : Series - """ - if periods == 0: - return self.copy() - - offset = _resolve_offset(freq, kwds) - - if isinstance(offset, compat.string_types): - offset = datetools.to_offset(offset) - - def _get_values(): - values = self.values - if copy: - values = values.copy() - return values - - if offset is None: - dtype, fill_value = _maybe_promote(self.dtype) - new_values = pa.empty(len(self), dtype=dtype) - - if periods > 0: - new_values[periods:] = self.values[:-periods] - new_values[:periods] = fill_value - elif periods < 0: - new_values[:periods] = self.values[-periods:] - new_values[periods:] = fill_value - - return self._constructor(new_values, index=self.index, name=self.name) - elif isinstance(self.index, PeriodIndex): - orig_offset = datetools.to_offset(self.index.freq) - if orig_offset == offset: - return self._constructor( - _get_values(), self.index.shift(periods), - name=self.name) - msg = ('Given freq %s does not match PeriodIndex freq %s' % - (offset.rule_code, orig_offset.rule_code)) - raise ValueError(msg) - else: - return self._constructor(_get_values(), - index=self.index.shift(periods, offset), - name=self.name) - def asof(self, where): """ Return last good (non-NaN) value in TimeSeries if value is NaN for @@ -3317,26 +3259,6 @@ def _try_cast(arr, take_fast_path): return subarr -def _resolve_offset(freq, kwds): - if 'timeRule' in kwds or 'offset' in kwds: - offset = kwds.get('offset', None) - offset = kwds.get('timeRule', offset) - if isinstance(offset, compat.string_types): - offset = datetools.getOffset(offset) - warn = True - else: - offset = freq - warn = False - - if warn and _SHOW_WARNINGS: # pragma: no cover - import warnings - warnings.warn("'timeRule' and 'offset' parameters are deprecated," - " please use 'freq' instead", - FutureWarning) - - return offset - - # backwards compatiblity TimeSeries = Series diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 537b88db3c1f0..5cb29d717235d 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -605,7 +605,7 @@ def shift(self, periods, freq=None, **kwds): """ Analogous to Series.shift """ - from pandas.core.series import _resolve_offset + from pandas.core.datetools import _resolve_offset offset = _resolve_offset(freq, kwds) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index fc86a78ea684b..a498cca528043 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1323,6 +1323,44 @@ def test_shift(self): for i, f in compat.iteritems(self.panel))) assert_panel_equal(result, expected) + def test_tshift(self): + # PeriodIndex + ps = tm.makePeriodPanel() + shifted = ps.tshift(1) + unshifted = shifted.tshift(-1) + + assert_panel_equal(unshifted, ps) + + shifted2 = ps.tshift(freq='B') + assert_panel_equal(shifted, shifted2) + + shifted3 = ps.tshift(freq=bday) + assert_panel_equal(shifted, shifted3) + + assertRaisesRegexp(ValueError, 'does not match', ps.tshift, freq='M') + + # DatetimeIndex + panel = _panel + shifted = panel.tshift(1) + unshifted = shifted.tshift(-1) + + assert_panel_equal(panel, unshifted) + + shifted2 = panel.tshift(freq=panel.major_axis.freq) + assert_panel_equal(shifted, shifted2) + + inferred_ts = Panel(panel.values, + items=panel.items, + major_axis=Index(np.asarray(panel.major_axis)), + minor_axis=panel.minor_axis) + shifted = inferred_ts.tshift(1) + unshifted = shifted.tshift(-1) + assert_panel_equal(shifted, panel.tshift(1)) + assert_panel_equal(unshifted, inferred_ts) + + no_freq = panel.ix[:, [0, 5, 7], :] + self.assertRaises(ValueError, no_freq.tshift) + def test_multiindex_get(self): ind = MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)], names=['first', 'second']) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 686df18999850..b142adbd5b949 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -3550,13 +3550,11 @@ def test_shift(self): self.assertRaises(ValueError, ps.shift, freq='D') # legacy support - smod._SHOW_WARNINGS = False shifted4 = ps.shift(1, timeRule='B') assert_series_equal(shifted2, shifted4) shifted5 = ps.shift(1, offset=datetools.bday) assert_series_equal(shifted5, shifted4) - smod._SHOW_WARNINGS = True def test_tshift(self): # PeriodIndex diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 0718dc8926011..0481a522dabe8 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -460,12 +460,12 @@ def makeTimeDataFrame(nper=None): return DataFrame(data) -def getPeriodData(): - return dict((c, makePeriodSeries()) for c in getCols(K)) +def getPeriodData(nper=None): + return dict((c, makePeriodSeries(nper)) for c in getCols(K)) -def makePeriodFrame(): - data = getPeriodData() +def makePeriodFrame(nper=None): + data = getPeriodData(nper) return DataFrame(data) @@ -474,6 +474,10 @@ def makePanel(nper=None): data = dict((c, makeTimeDataFrame(nper)) for c in cols) return Panel.fromDict(data) +def makePeriodPanel(nper=None): + cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]] + data = dict((c, makePeriodFrame(nper)) for c in cols) + return Panel.fromDict(data) def makePanel4D(nper=None): return Panel4D(dict(l1=makePanel(nper), l2=makePanel(nper),
closes https://github.com/pydata/pandas/issues/4853 I think I'm doing this right. I moved the `tshift` into generic and had `shift` offload to `tshift` if `freq` is found.
https://api.github.com/repos/pandas-dev/pandas/pulls/4864
2013-09-17T20:43:41Z
2013-09-18T13:40:29Z
2013-09-18T13:40:29Z
2014-07-16T08:28:52Z
BUG: Constrain date parsing from strings a little bit more #4601
diff --git a/doc/source/release.rst b/doc/source/release.rst index ffb792ca98da5..b71285758d53b 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -433,6 +433,7 @@ Bug Fixes - Fix an issue in TextFileReader w/ Python engine (i.e. PythonParser) with thousands != "," (:issue:`4596`) - Bug in getitem with a duplicate index when using where (:issue:`4879`) + - Fix Type inference code coerces float column into datetime (:issue:`4601`) pandas 0.12.0 diff --git a/pandas/tests/test_tslib.py b/pandas/tests/test_tslib.py new file mode 100644 index 0000000000000..b9a7356412a10 --- /dev/null +++ b/pandas/tests/test_tslib.py @@ -0,0 +1,123 @@ +import unittest + +import numpy as np + +from pandas import tslib +from datetime import datetime + +class TestDatetimeParsingWrappers(unittest.TestCase): + def test_verify_datetime_bounds(self): + for year in (1, 1000, 1677, 2262, 5000): + dt = datetime(year, 1, 1) + self.assertRaises( + ValueError, + tslib.verify_datetime_bounds, + dt + ) + + for year in (1678, 2000, 2261): + tslib.verify_datetime_bounds(datetime(year, 1, 1)) + + def test_does_not_convert_mixed_integer(self): + bad_date_strings = ( + '-50000', + '999', + '123.1234', + 'm', + 'T' + ) + + for bad_date_string in bad_date_strings: + self.assertFalse( + tslib._does_string_look_like_datetime(bad_date_string) + ) + + good_date_strings = ( + '2012-01-01', + '01/01/2012', + 'Mon Sep 16, 2013', + '01012012', + '0101', + '1-1', + ) + + for good_date_string in good_date_strings: + self.assertTrue( + tslib._does_string_look_like_datetime(good_date_string) + ) + +class TestArrayToDatetime(unittest.TestCase): + def test_parsing_valid_dates(self): + arr = np.array(['01-01-2013', '01-02-2013'], dtype=object) + self.assert_( + np.array_equal( + tslib.array_to_datetime(arr), + np.array( + [ + '2013-01-01T00:00:00.000000000-0000', + '2013-01-02T00:00:00.000000000-0000' + ], + dtype='M8[ns]' + ) + ) + ) + + arr = np.array(['Mon Sep 16 2013', 'Tue Sep 17 2013'], dtype=object) + self.assert_( + np.array_equal( + tslib.array_to_datetime(arr), + np.array( + [ + '2013-09-16T00:00:00.000000000-0000', + '2013-09-17T00:00:00.000000000-0000' + ], + dtype='M8[ns]' + ) + ) + ) + + def test_number_looking_strings_not_into_datetime(self): + # #4601 + # These strings don't look like datetimes so they shouldn't be + # attempted to be converted + arr = np.array(['-352.737091', '183.575577'], dtype=object) + self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + + arr = np.array(['1', '2', '3', '4', '5'], dtype=object) + self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + + def test_dates_outside_of_datetime64_ns_bounds(self): + # These datetimes are outside of the bounds of the + # datetime64[ns] bounds, so they cannot be converted to + # datetimes + arr = np.array(['1/1/1676', '1/2/1676'], dtype=object) + self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + + arr = np.array(['1/1/2263', '1/2/2263'], dtype=object) + self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + + def test_coerce_of_invalid_datetimes(self): + arr = np.array(['01-01-2013', 'not_a_date', '1'], dtype=object) + + # Without coercing, the presence of any invalid dates prevents + # any values from being converted + self.assert_(np.array_equal(tslib.array_to_datetime(arr), arr)) + + # With coercing, the invalid dates becomes iNaT + self.assert_( + np.array_equal( + tslib.array_to_datetime(arr, coerce=True), + np.array( + [ + '2013-01-01T00:00:00.000000000-0000', + tslib.iNaT, + tslib.iNaT + ], + dtype='M8[ns]' + ) + ) + ) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index fd97512b0528b..075102dd63100 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -317,7 +317,6 @@ class Timestamp(_Timestamp): _nat_strings = set(['NaT','nat','NAT','nan','NaN','NAN']) -_not_datelike_strings = set(['a','A','m','M','p','P','t','T']) class NaTType(_NaT): """(N)ot-(A)-(T)ime, the time equivalent of NaN""" @@ -841,6 +840,43 @@ def datetime_to_datetime64(ndarray[object] values): return result, inferred_tz +_not_datelike_strings = set(['a','A','m','M','p','P','t','T']) + +def verify_datetime_bounds(dt): + """Verify datetime.datetime is within the datetime64[ns] bounds.""" + if dt.year <= 1677 or dt.year >= 2262: + raise ValueError( + 'Given datetime not within valid datetime64[ns] bounds' + ) + return dt + +def _does_string_look_like_datetime(date_string): + if date_string.startswith('0'): + # Strings starting with 0 are more consistent with a + # date-like string than a number + return True + + try: + if float(date_string) < 1000: + return False + except ValueError: + pass + + if date_string in _not_datelike_strings: + return False + + return True + +def parse_datetime_string(date_string, verify_bounds=True, **kwargs): + if not _does_string_look_like_datetime(date_string): + raise ValueError('Given date string not likely a datetime.') + + dt = parse_date(date_string, **kwargs) + + if verify_bounds: + verify_datetime_bounds(dt) + + return dt def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, format=None, utc=None, coerce=False, unit=None): @@ -908,24 +944,15 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, &dts) _check_dts_bounds(iresult[i], &dts) except ValueError: - - # for some reason, dateutil parses some single letter len-1 strings into today's date - if len(val) == 1 and val in _not_datelike_strings: - if coerce: - iresult[i] = iNaT - continue - elif raise_: - raise try: - result[i] = parse_date(val, dayfirst=dayfirst) + result[i] = parse_datetime_string( + val, dayfirst=dayfirst + ) except Exception: if coerce: iresult[i] = iNaT continue raise TypeError - pandas_datetime_to_datetimestruct(iresult[i], PANDAS_FR_ns, - &dts) - _check_dts_bounds(iresult[i], &dts) except: if coerce: iresult[i] = iNaT @@ -946,7 +973,7 @@ def array_to_datetime(ndarray[object] values, raise_=False, dayfirst=False, oresult[i] = 'NaT' continue try: - oresult[i] = parse_date(val, dayfirst=dayfirst) + oresult[i] = parse_datetime_string(val, dayfirst=dayfirst) except Exception: if raise_: raise
closes #4601 Currently dateutil will parse almost any string into a datetime. This change adds a filter in front of dateutil that will prevent it from parsing certain strings that don't look like datetimes: 1) Strings that parse to float values that are less than 1000 2) Certain special one character strings (this was already in there, this just moves that code) Additionally, this filters out datetimes that are out of range for the datetime64[ns] type. Currently any out-of-range datetimes will just overflow and be mapped to some random time within the bounds of datetime64[ns].
https://api.github.com/repos/pandas-dev/pandas/pulls/4863
2013-09-17T19:23:39Z
2013-09-20T22:34:04Z
2013-09-20T22:34:04Z
2014-06-22T11:10:12Z
TST: dtypes tests fix for 32-bit
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 6f6b3bf71c759..d216cebc1abf3 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -2743,7 +2743,7 @@ def test_constructor_generator(self): gen = ([ i, 'a'] for i in range(10)) result = DataFrame(gen) expected = DataFrame({ 0 : range(10), 1 : 'a' }) - assert_frame_equal(result, expected) + assert_frame_equal(result, expected, check_dtype=False) def test_constructor_list_of_dicts(self): data = [OrderedDict([['a', 1.5], ['b', 3], ['c', 4], ['d', 6]]), diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 0c862576b09a1..3453e69ed72b6 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1362,7 +1362,7 @@ def f(): ### frame ### - df_orig = DataFrame(np.arange(6).reshape(3,2),columns=['A','B']) + df_orig = DataFrame(np.arange(6).reshape(3,2),columns=['A','B'],dtype='int64') # iloc/iat raise df = df_orig.copy() diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index c52fcad3d5111..686df18999850 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -355,8 +355,8 @@ def test_constructor_series(self): def test_constructor_iterator(self): - expected = Series(list(range(10))) - result = Series(range(10)) + expected = Series(list(range(10)),dtype='int64') + result = Series(range(10),dtype='int64') assert_series_equal(result, expected) def test_constructor_generator(self):
https://api.github.com/repos/pandas-dev/pandas/pulls/4860
2013-09-17T12:53:19Z
2013-09-17T13:06:25Z
2013-09-17T13:06:25Z
2014-07-16T08:28:50Z
TST: reproducing tests for subtle PyTables bug (disabled for now)
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 322b626acc0ad..ee438cb2cd45a 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -2695,6 +2695,46 @@ 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: + + # floats w/o NaN + df = DataFrame(dict(cols = range(11), values = range(11)),dtype='float64') + df['cols'] = (df['cols']+10).apply(str) + + store.append('df1',df,data_columns=True) + result = store.select( + 'df1', where='values>2.0') + expected = df[df['values']>2.0] + tm.assert_frame_equal(expected, result) + + # floats with NaN + df.iloc[0] = np.nan + expected = df[df['values']>2.0] + + store.append('df2',df,data_columns=True,index=False) + result = store.select( + 'df2', where='values>2.0') + tm.assert_frame_equal(expected, result) + + # https://github.com/PyTables/PyTables/issues/282 + # bug in selection when 0th row has a np.nan and an index + #store.append('df3',df,data_columns=True) + #result = store.select( + # 'df3', where='values>2.0') + #tm.assert_frame_equal(expected, result) + + # not in first position float with NaN ok too + df = DataFrame(dict(cols = range(11), values = range(11)),dtype='float64') + df['cols'] = (df['cols']+10).apply(str) + + df.iloc[1] = np.nan + expected = df[df['values']>2.0] + + store.append('df4',df,data_columns=True) + result = store.select( + 'df4', where='values>2.0') + tm.assert_frame_equal(expected, result) + def test_select_with_many_inputs(self): with ensure_clean(self.path) as store:
https://github.com/PyTables/PyTables/issues/282
https://api.github.com/repos/pandas-dev/pandas/pulls/4858
2013-09-17T01:02:48Z
2013-09-17T01:24:10Z
2013-09-17T01:24:10Z
2014-07-09T10:41:36Z
ENH: Added xlsxwriter as an ExcelWriter option.
diff --git a/ci/requirements-2.7.txt b/ci/requirements-2.7.txt index 6a94d48ad7a5f..2e903102de7b1 100644 --- a/ci/requirements-2.7.txt +++ b/ci/requirements-2.7.txt @@ -8,6 +8,7 @@ numexpr==2.1 tables==2.3.1 matplotlib==1.1.1 openpyxl==1.6.2 +xlsxwriter==0.4.3 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 a7e9d62e3549b..056b63bbb8591 100644 --- a/ci/requirements-2.7_LOCALE.txt +++ b/ci/requirements-2.7_LOCALE.txt @@ -2,6 +2,7 @@ python-dateutil pytz==2013b xlwt==0.7.5 openpyxl==1.6.2 +xlsxwriter==0.4.3 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 e907a2fa828f1..b689047019ed7 100644 --- a/ci/requirements-3.2.txt +++ b/ci/requirements-3.2.txt @@ -1,6 +1,7 @@ python-dateutil==2.1 pytz==2013b openpyxl==1.6.2 +xlsxwriter==0.4.3 xlrd==0.9.2 numpy==1.6.2 cython==0.19.1 diff --git a/ci/requirements-3.3.txt b/ci/requirements-3.3.txt index eb1e725d98040..326098be5f7f4 100644 --- a/ci/requirements-3.3.txt +++ b/ci/requirements-3.3.txt @@ -1,6 +1,7 @@ python-dateutil==2.1 pytz==2013b openpyxl==1.6.2 +xlsxwriter==0.4.3 xlrd==0.9.2 html5lib==1.0b2 numpy==1.7.1 diff --git a/doc/source/10min.rst b/doc/source/10min.rst index 58c5b54968614..705514ac0c364 100644 --- a/doc/source/10min.rst +++ b/doc/source/10min.rst @@ -695,13 +695,13 @@ Writing to an excel file .. ipython:: python - df.to_excel('foo.xlsx', sheet_name='sheet1') + df.to_excel('foo.xlsx', sheet_name='Sheet1') Reading from an excel file .. ipython:: python - pd.read_excel('foo.xlsx', 'sheet1', index_col=None, na_values=['NA']) + pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']) .. ipython:: python :suppress: diff --git a/doc/source/install.rst b/doc/source/install.rst index 4472d844c1871..b1dcad9448cfd 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -100,6 +100,8 @@ Optional Dependencies * `openpyxl <http://packages.python.org/openpyxl/>`__, `xlrd/xlwt <http://www.python-excel.org/>`__ * openpyxl version 1.6.1 or higher * Needed for Excel I/O + * `XlsxWriter <https://pypi.python.org/pypi/XlsxWriter>`__ + * Alternative Excel writer. * `boto <https://pypi.python.org/pypi/boto>`__: necessary for Amazon S3 access. * One of `PyQt4 diff --git a/doc/source/io.rst b/doc/source/io.rst index b9581c37082bf..67492eddbac12 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1654,7 +1654,7 @@ indices to be parsed. .. code-block:: python - read_excel('path_to_file.xls', Sheet1', parse_cols=[0, 2, 3], index_col=None, na_values=['NA']) + read_excel('path_to_file.xls', 'Sheet1', parse_cols=[0, 2, 3], index_col=None, na_values=['NA']) To write a DataFrame object to a sheet of an Excel file, you can use the ``to_excel`` instance method. The arguments are largely the same as ``to_csv`` @@ -1664,7 +1664,7 @@ written. For example: .. code-block:: python - df.to_excel('path_to_file.xlsx', sheet_name='sheet1') + 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``. @@ -1677,8 +1677,8 @@ one can use the ExcelWriter class, as in the following example: .. code-block:: python writer = ExcelWriter('path_to_file.xlsx') - df1.to_excel(writer, sheet_name='sheet1') - df2.to_excel(writer, sheet_name='sheet2') + df1.to_excel(writer, sheet_name='Sheet1') + df2.to_excel(writer, sheet_name='Sheet2') writer.save() .. _io.excel.writers: @@ -1693,11 +1693,29 @@ Excel writer engines 1. the ``engine`` keyword argument 2. the filename extension (via the default specified in config options) -``pandas`` only supports ``openpyxl`` for ``.xlsx`` and ``.xlsm`` files and -``xlwt`` for ``.xls`` files. If you have multiple engines installed, you can choose the -engine to use by default via the options ``io.excel.xlsx.writer`` and -``io.excel.xls.writer``. +By default ``pandas`` only supports +`openpyxl <http://packages.python.org/openpyxl/>`__ as a writer for ``.xlsx`` +and ``.xlsm`` files and `xlwt <http://www.python-excel.org/>`__ as a writer for +``.xls`` files. If you have multiple engines installed, you can change the +default engine via the ``io.excel.xlsx.writer`` and ``io.excel.xls.writer`` +options. +For example if the optional `XlsxWriter <http://xlsxwriter.readthedocs.org>`__ +module is installed you can use it as a xlsx writer engine as follows: + +.. code-block:: python + + # By setting the 'engine' in the DataFrame and Panel 'to_excel()' methods. + df.to_excel('path_to_file.xlsx', sheet_name='Sheet1', engine='xlsxwriter') + + # By setting the 'engine' in the ExcelWriter constructor. + writer = ExcelWriter('path_to_file.xlsx', engine='xlsxwriter') + + # Or via pandas configuration. + from pandas import set_option + set_option('io.excel.xlsx.writer', 'xlsxwriter') + + df.to_excel('path_to_file.xlsx', sheet_name='Sheet1') .. _io.hdf5: diff --git a/doc/source/release.rst b/doc/source/release.rst index f7755afe8caae..b71410a8f7e59 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -113,6 +113,9 @@ Improvements to existing features ``io.excel.xls.writer``. (:issue:`4745`, :issue:`4750`) - ``Panel.to_excel()`` now accepts keyword arguments that will be passed to its ``DataFrame``'s ``to_excel()`` methods. (:issue:`4750`) + - Added XlsxWriter as an optional ``ExcelWriter`` engine. This is about 5x + faster than the default openpyxl xlsx writer and is equivalent in speed + to the xlwt xls writer module. (:issue:`4542`) - allow DataFrame constructor to accept more list-like objects, e.g. list of ``collections.Sequence`` and ``array.Array`` objects (:issue:`3783`,:issue:`4297`, :issue:`4851`), thanks @lgautier diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 75f81d20926a1..20fe33226d7ca 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1356,7 +1356,7 @@ def to_csv(self, path_or_buf, sep=",", na_rep='', float_format=None, tupleize_cols=tupleize_cols) formatter.save() - def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='', + 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): """ @@ -1366,7 +1366,7 @@ def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='', ---------- excel_writer : string or ExcelWriter object File path or existing ExcelWriter - sheet_name : string, default 'sheet1' + sheet_name : string, default 'Sheet1' Name of sheet which will contain DataFrame na_rep : string, default '' Missing data representation @@ -1397,8 +1397,8 @@ def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='', 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') + >>> df1.to_excel(writer,'Sheet1') + >>> df2.to_excel(writer,'Sheet2') >>> writer.save() """ from pandas.io.excel import ExcelWriter diff --git a/pandas/io/excel.py b/pandas/io/excel.py index f34c4f99a856d..6ce8eb697268b 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -596,6 +596,7 @@ def _convert_to_style(cls, style_dict, num_format_str=None): Parameters ---------- style_dict: style dictionary to convert + num_format_str: optional number format string """ import xlwt @@ -611,3 +612,95 @@ def _convert_to_style(cls, style_dict, num_format_str=None): register_writer(_XlwtWriter) + +class _XlsxWriter(ExcelWriter): + engine = 'xlsxwriter' + supported_extensions = ('.xlsx',) + + def __init__(self, path, **engine_kwargs): + # Use the xlsxwriter module as the Excel writer. + import xlsxwriter + + super(_XlsxWriter, self).__init__(path, **engine_kwargs) + + self.book = xlsxwriter.Workbook(path, **engine_kwargs) + + def save(self): + """ + Save workbook to disk. + """ + return self.book.close() + + def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0): + # Write the frame cells using xlsxwriter. + + sheet_name = self._get_sheet_name(sheet_name) + + if sheet_name in self.sheets: + wks = self.sheets[sheet_name] + else: + wks = self.book.add_worksheet(sheet_name) + self.sheets[sheet_name] = wks + + style_dict = {} + + for cell in cells: + val = _conv_value(cell.val) + + num_format_str = None + if isinstance(cell.val, datetime.datetime): + num_format_str = "YYYY-MM-DD HH:MM:SS" + if isinstance(cell.val, datetime.date): + num_format_str = "YYYY-MM-DD" + + stylekey = json.dumps(cell.style) + if num_format_str: + stylekey += num_format_str + + if stylekey in style_dict: + style = style_dict[stylekey] + else: + style = self._convert_to_style(cell.style, num_format_str) + style_dict[stylekey] = style + + if cell.mergestart is not None and cell.mergeend is not None: + wks.merge_range(startrow + cell.row, + startrow + cell.mergestart, + startcol + cell.col, + startcol + cell.mergeend, + val, style) + else: + wks.write(startrow + cell.row, + startcol + cell.col, + val, style) + + def _convert_to_style(self, style_dict, num_format_str=None): + """ + converts a style_dict to an xlsxwriter format object + Parameters + ---------- + style_dict: style dictionary to convert + num_format_str: optional number format string + """ + if style_dict is None: + return None + + # Create a XlsxWriter format object. + xl_format = self.book.add_format() + + # Map the cell font to XlsxWriter font properties. + if style_dict.get('font'): + font = style_dict['font'] + if font.get('bold'): + xl_format.set_bold() + + # Map the cell borders to XlsxWriter border properties. + if style_dict.get('borders'): + xl_format.set_border() + + if num_format_str is not None: + xl_format.set_num_format(num_format_str) + + return xl_format + +register_writer(_XlsxWriter) diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 00536026994c5..94f3e5a8cf746 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -16,9 +16,11 @@ register_writer ) from pandas.util.testing import ensure_clean +from pandas.core.config import set_option, get_option import pandas.util.testing as tm import pandas as pd + def _skip_if_no_xlrd(): try: import xlrd @@ -31,18 +33,25 @@ def _skip_if_no_xlrd(): def _skip_if_no_xlwt(): try: - import xlwt # NOQA + import xlwt # NOQA except ImportError: raise nose.SkipTest('xlwt not installed, skipping') def _skip_if_no_openpyxl(): try: - import openpyxl # NOQA + import openpyxl # NOQA except ImportError: raise nose.SkipTest('openpyxl not installed, skipping') +def _skip_if_no_xlsxwriter(): + try: + import xlsxwriter # NOQA + except ImportError: + raise nose.SkipTest('xlsxwriter not installed, skipping') + + def _skip_if_no_excelsuite(): _skip_if_no_xlrd() _skip_if_no_xlwt() @@ -268,15 +277,22 @@ def test_xlsx_table(self): class ExcelWriterBase(SharedItems): - # test cases to run with different extensions - # for each writer - # to add a writer test, define two things: - # 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 + # Base class for test cases to run with different Excel writers. + # To add a writer test, define the following: + # 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. + # 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.prev_engine = get_option(self.option_name) + set_option(self.option_name, self.engine_name) + + def tearDown(self): + set_option(self.option_name, self.prev_engine) def test_excel_sheet_by_name_raise(self): _skip_if_no_xlrd() @@ -790,6 +806,7 @@ def roundtrip(df, header=True, parser_hdr=0): class OpenpyxlTests(ExcelWriterBase, unittest.TestCase): ext = 'xlsx' + engine_name = 'openpyxl' check_skip = staticmethod(_skip_if_no_openpyxl) def test_to_excel_styleconverter(self): @@ -820,6 +837,7 @@ def test_to_excel_styleconverter(self): class XlwtTests(ExcelWriterBase, unittest.TestCase): ext = 'xls' + engine_name = 'xlwt' check_skip = staticmethod(_skip_if_no_xlwt) def test_to_excel_styleconverter(self): @@ -841,6 +859,52 @@ def test_to_excel_styleconverter(self): self.assertEquals(xlwt.Borders.THIN, xls_style.borders.left) self.assertEquals(xlwt.Alignment.HORZ_CENTER, xls_style.alignment.horz) + +class XlsxWriterTests(ExcelWriterBase, unittest.TestCase): + ext = 'xlsx' + 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() + ext = self.ext + path = '__tmp_to_excel_from_excel_indexlabels__.' + ext + + with ensure_clean(path) 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) + + # 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) + + 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) + + 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 ExcelWriterEngineTests(unittest.TestCase): def test_ExcelWriter_dispatch(self): with tm.assertRaisesRegexp(ValueError, 'No engine'): diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index fc86a78ea684b..d725a3ff6f135 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1429,6 +1429,26 @@ def test_to_excel(self): recdf = reader.parse(str(item), index_col=0) assert_frame_equal(df, recdf) + def test_to_excel_xlsxwriter(self): + try: + import xlrd + import xlsxwriter + from pandas.io.excel import ExcelFile + except ImportError: + raise nose.SkipTest("Requires xlrd and xlsxwriter. Skipping test.") + + path = '__tmp__.xlsx' + with ensure_clean(path) as path: + self.panel.to_excel(path, engine='xlsxwriter') + try: + reader = ExcelFile(path) + except ImportError: + raise nose.SkipTest + + for item, df in compat.iteritems(self.panel): + recdf = reader.parse(str(item), index_col=0) + assert_frame_equal(df, recdf) + def test_dropna(self): p = Panel(np.random.randn(4, 5, 6), major_axis=list('abcde')) p.ix[:, ['b', 'd'], 0] = np.nan diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index b7b4a936a1e90..d9c642372a9bb 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -104,6 +104,12 @@ def show_versions(): except: print("xlwt: Not installed") + try: + import xlsxwriter + print("xlsxwriter: %s" % xlsxwriter.__version__) + except: + print("xlsxwriter: Not installed") + try: import sqlalchemy print("sqlalchemy: %s" % sqlalchemy.__version__)
Added xlsxwriter as an optional writer engine. closes #4542.
https://api.github.com/repos/pandas-dev/pandas/pulls/4857
2013-09-16T21:35:00Z
2013-09-22T23:14:58Z
2013-09-22T23:14:58Z
2017-02-16T10:32:15Z
COMPAT: unicode compat issue fix (GH4854)
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index 10e1464739203..a2531ebd43c82 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -180,6 +180,8 @@ class to receive bound method def u(s): return s + def u_safe(s): + return s else: string_types = basestring, integer_types = (int, long) @@ -190,6 +192,12 @@ def u(s): def u(s): return unicode(s, "unicode_escape") + def u_safe(s): + try: + return unicode(s, "unicode_escape") + except: + return s + string_and_binary_types = string_types + (binary_type,) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index b79408a1bf8d2..5c1dd408f696c 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -29,7 +29,7 @@ import pandas.core.common as com from pandas.tools.merge import concat from pandas import compat -from pandas.compat import u, PY3, range, lrange +from pandas.compat import u_safe as u, PY3, range, lrange from pandas.io.common import PerformanceWarning from pandas.core.config import get_option from pandas.computation.pytables import Expr, maybe_expression
closes #4854
https://api.github.com/repos/pandas-dev/pandas/pulls/4856
2013-09-16T20:52:30Z
2013-09-16T22:49:08Z
2013-09-16T22:49:08Z
2014-07-07T14:19:37Z
CLN: _axis_len checking cleanup and better message
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index b9ffe788d183d..2f6bc13983f93 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -244,7 +244,7 @@ def _get_axis_number(self, axis): return self._AXIS_NUMBERS[axis] except: pass - raise ValueError('No axis named %s' % axis) + raise ValueError('No axis named {0} for object type {1}'.format(axis,type(self))) def _get_axis_name(self, axis): axis = self._AXIS_ALIASES.get(axis, axis) @@ -256,7 +256,7 @@ def _get_axis_name(self, axis): return self._AXIS_NAMES[axis] except: pass - raise ValueError('No axis named %s' % axis) + raise ValueError('No axis named {0} for object type {1}'.format(axis,type(self))) def _get_axis(self, axis): name = self._get_axis_name(axis) @@ -496,7 +496,7 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): ------- renamed : type of caller """ - axis = self._AXIS_NAMES[axis] + axis = self._get_axis_name(axis) d = { 'copy' : copy, 'inplace' : inplace } d[axis] = mapper return self.rename(**d) @@ -1546,9 +1546,6 @@ def fillna(self, value=None, method=None, axis=0, inplace=False, self._consolidate_inplace() axis = self._get_axis_number(axis) - if axis + 1 > self._AXIS_LEN: - raise ValueError( - "invalid axis passed for object type {0}".format(type(self))) method = com._clean_fill_method(method) if value is None:
https://api.github.com/repos/pandas-dev/pandas/pulls/4855
2013-09-16T20:30:07Z
2013-09-16T21:08:12Z
2013-09-16T21:08:12Z
2014-07-16T08:28:42Z
BUG: (GH4851) path for 0-dim arrays in DataFrame construction were incorrect
diff --git a/doc/source/release.rst b/doc/source/release.rst index 0ed1f39d72cb5..5c6af024f1663 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -114,7 +114,7 @@ Improvements to existing features - ``Panel.to_excel()`` now accepts keyword arguments that will be passed to its ``DataFrame``'s ``to_excel()`` methods. (:issue:`4750`) - allow DataFrame constructor to accept more list-like objects, e.g. list of - ``collections.Sequence`` and ``array.Array`` objects (:issue:`3783`,:issue:`42971`), + ``collections.Sequence`` and ``array.Array`` objects (:issue:`3783`,:issue:`4297`, :issue:`4851`), thanks @lgautier - DataFrame constructor now accepts a numpy masked record array (:issue:`3478`), thanks @jnothman diff --git a/pandas/core/frame.py b/pandas/core/frame.py index f56b6bc00cf15..a51bfee371b89 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -429,7 +429,7 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, if index is None and isinstance(data[0], Series): index = _get_names_from_index(data) - if is_list_like(data[0]) and getattr(data[0],'ndim',0) <= 1: + if is_list_like(data[0]) and getattr(data[0],'ndim',1) == 1: arrays, columns = _to_arrays(data, columns, dtype=dtype) columns = _ensure_index(columns) @@ -4710,7 +4710,7 @@ def extract_index(data): elif isinstance(v, dict): have_dicts = True indexes.append(list(v.keys())) - elif is_list_like(v) and getattr(v,'ndim',0) <= 1: + elif is_list_like(v) and getattr(v,'ndim',1) == 1: have_raw_arrays = True raw_lengths.append(len(v)) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index a5c1941a7f2d3..ff4cf1ca821ce 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -2675,6 +2675,13 @@ def test_constructor_list_of_lists(self): self.assert_(com.is_integer_dtype(df['num'])) self.assert_(df['str'].dtype == np.object_) + # GH 4851 + # list of 0-dim ndarrays + expected = DataFrame({ 0: range(10) }) + data = [np.array(x) for x in range(10)] + result = DataFrame(data) + assert_frame_equal(result, expected) + def test_constructor_sequence_like(self): # GH 3783 # collections.Squence like
closes #4851
https://api.github.com/repos/pandas-dev/pandas/pulls/4852
2013-09-16T17:15:05Z
2013-09-16T17:36:54Z
2013-09-16T17:36:54Z
2014-07-16T08:28:38Z